26th of June — 2026

When the Buyer Becomes a Bot: Agentic Commerce and What It Means for eCommerce SaaS

Honey Olesen

T he quick answer: Agentic commerce is online buying where an AI agent (not a person) handles discovery, comparison, checkout, and follow-up.

During the 2025 holiday season, AI influenced 20% of global online orders, worth $262 billion (Salesforce). For eCommerce SaaS platforms and the merchants on them, the shift mirrors the move from UX to APIs: the storefront still matters, but the real competition is happening at the infrastructure layer.

Something quiet happened over the 2025 holidays. While shoppers thought they were browsing, a growing share weren’t clicking through storefronts at all. They were asking an agent to do it.

Salesforce’s Shopping Index reported that AI influenced 20% of all retail sales during the holiday window, driving $262 billion in revenue. By March 2026, AI-referred traffic was converting 42% better than other channels a complete reversal from March 2025, when that same traffic converted 38% worse (Adobe Digital Insights).

AI shopping queries grew 4,700% year-over-year as of July 2025, with holiday season AI traffic up 693% year-over-year and Q1 2026 traffic up 393% year-over-year. Visa documented a 1,200% increase in AI-driven traffic from generative AI sites to merchant websites during 2024.

These aren’t rounding errors. They’re the early signal of a buying interface changing under our feet.

This post is for the people who have to respond: eCommerce SaaS stakeholders, agencies, and merchants weighing which platform can carry them through it. We’ll cover the numbers, the parallel to the headless transition many of you already lived through, the protocol landscape, and why B2B agentic commerce is a different animal entirely.

What Is Agentic Commerce?

Agentic commerce describes a transaction where an AI agent (not a human clicking buttons) executes the buying steps on someone’s behalf. The agent browses, compares SKUs, completes checkout, and can handle returns. It might live inside ChatGPT, Google’s AI Mode, Perplexity’s Comet browser, Amazon’s Alexa for Shopping, or a retailer’s own app.

What it is not: a chatbot answering FAQs, a recommendation widget, or a search box with autocomplete. Those are conversational features. Agentic commerce is software that holds a budget, calls APIs, parses inventory, picks products, executes payment, and resolves disputes. The infrastructure to do that safely arrived in the last twelve months.

The buyer, in other words, is becoming a bot. And bots don’t shop the way people do.

The Numbers That Should Get Your Attention

Metric	Figure	Source
AI-influenced share of global holiday orders, 2025	20% ($262B)	Salesforce Shopping Index
AI traffic conversion premium, March 2026	+42% vs. non-AI channels	Adobe Digital Insights
AI shopping query growth (July 2025 YoY)	4,700%	Adobe Digital Insights
AI retail traffic growth, 2025 holiday season	693% YoY	Adobe
AI retail traffic growth, Q1 2026	393% YoY	Adobe
AI-driven traffic to merchant sites (2024)	+1,200% from GenAI sources	Visa
Shopify AI-driven order growth, Q1 2026	~13x YoY	Shopify Q1 2026 Earnings
US agentic commerce spend by 2030	$190B–$385B	Morgan Stanley
Global agent-orchestrated retail revenue by 2030	$3T–$5T	McKinsey

Count it however you like. The direction holds. Every credible estimate points up and to the right.

One note on speed: Andreessen Horowitz has described agent workloads as “recursive, bursty and massive.” A single agent goal can fire thousands of sub-tasks in milliseconds. Backends built for a one-human-to-one-system rhythm aren’t ready for that.

From Dashboards to APIs: The Parallel You’ve Seen Before

If this feels familiar, it should. Many of you watched the same arc play out when commerce went headless. The pretty admin dashboard stopped being the product. The API became the product.

Agentic commerce repeats that pattern, just faster. When a human shops, your storefront UX does the heavy lifting: the hero image, the size selector, the trust badges near checkout. When an agent shops, none of that matters. The agent reads your product feed through an API and makes a decision in 600 milliseconds. It never sees your homepage.

The competitive surface moves. It shifts from how your store looks to how cleanly your data flows. The platforms that win this round aren’t the ones with the slickest themes. They’re the ones with the cleanest catalog APIs, real-time inventory webhooks, and tokenized checkout endpoints an agent can call without a human in the loop.

The part most teams underestimate: this is an infrastructure refresh cycle, not a redesign. Different budget, different team, different timeline.

Why Product Data Just Became Your Front Door

When an agent can’t see your storefront, your product data is your storefront.

Think about what an agent needs to transact: SKU-level inventory, accurate pricing, shipping cost, tax, lead time, dimensions, and clear variant logic. If an agent has to normalize your data before it can act, you’ve already lost the comparison to a competitor whose feed answers cleanly in one call.

This is where many merchants are quietly exposed. Years of patched-together PIM systems, inconsistent attributes across categories, and tribal knowledge about which SKUs are “really” in stock. A human shopper forgives some of that. An agent doesn’t. It routes to the cleaner feed.

BigCommerce leaned into this early, positioning its API-first stack (REST and GraphQL) as agent-readable infrastructure. Its Feedonomics acquisition handles the messy work of getting product data clean and syndicated. The lesson generalizes: product data quality is no longer a back-office hygiene task. It’s a front-door competitive variable.

Shopify has formalized this into a product. Shopify Catalog (confirmed in both Q1 2026 earnings and Spring ’26 Editions) automatically standardizes and enriches product data across over 1 billion products. Data syndicated through Shopify Catalog drives 2x more conversion in AI chats than traffic from general AI searches that rely on scraped or outdated data.

How Are Shopify and BigCommerce Responding?

The two platforms our clients ask about most are moving fast, in different lanes.

Shopify has reorganized its stack around agents. CEO Tobi Lütke told analysts that “AI is now Shopify’s native language.” In Q1 2026, the company reported $3.2 billion in revenue (up 34% YoY), GMV of $101 billion (up 35%), and AI-driven orders up nearly 13x year-over-year. Shopify activated Agentic Storefronts for all merchants in March 2026, and shipped an open-source AI Toolkit on April 9, 2026, letting developers build apps and manage stores using Claude Code, OpenAI Codex, Cursor, and Gemini CLI.

BigCommerce is playing the composable card. Its API-first, headless-friendly architecture was built for this moment. Add the Feedonomics data layer and support for both major agentic commerce protocols, and you get a platform positioned as agent-ready infrastructure rather than a storefront with AI bolted on.

Both stories point the same direction: clean APIs, machine-readable feeds, tokenized checkout. The difference is emphasis. Shopify is building the agent experience into its native rails. BigCommerce is betting that composable openness wins when merchants want to plug into whichever agent ecosystem matters most to them.

The Protocol Landscape: ACP, UCP, and Where Things Stand

Until recently, there were two competing standards for how AI agents transact with merchants. As of April 2026, the picture has largely resolved — though both protocols remain active.

ACP (Agentic Commerce Protocol) was co-developed by OpenAI and Stripe, launched September 2025. It originally powered ChatGPT’s Instant Checkout a feature that let users complete purchases without leaving the chat window. OpenAI shut that down in March 2026, roughly five months in, after fewer than 15 of Shopify’s millions of merchants ever went live.

The reasons were structural: product data synchronization at scale proved difficult, tax collection infrastructure wasn’t built out, and users preferred completing purchases on familiar sites where they had saved accounts. ACP itself survived the shutdown. ChatGPT now uses it as a discovery-and-handoff layer surfacing product recommendations and routing shoppers to the merchant’s own checkout, not completing the transaction in-chat.

Seven major retailers are currently live via ACP (Target, Sephora, Nordstrom, Lowe’s, Best Buy, The Home Depot, and Wayfair), and all Shopify merchants are automatically included via Shopify Catalog.

UCP (Universal Commerce Protocol) was co-developed by Shopify and Google, announced at NRF in January 2026, with backing from Etsy, Wayfair, Target, and Walmart, and endorsement from over 20 organizations including Visa, Mastercard, Adyen, Best Buy, and Flipkart. UCP covers the entire commerce journey (discovery, checkout, post-purchase support) and is designed so merchants declare their own capabilities for agents to negotiate against.

As of Spring ’26 Editions, Microsoft Copilot is live as a UCP-powered checkout surface: Shopify merchants’ products are purchasable directly in Copilot chat, paid via Shop Pay. Meta ads are next, listed as coming soon.

In April 2026, Amazon, Meta, Microsoft, Salesforce, and Stripe joined the UCP Tech Council, consolidating broad industry support. UCP is now the dominant standard for agent-driven commerce infrastructure.

For merchants and the SaaS teams serving them, the practical guidance is straightforward. Think of ACP as your ChatGPT discovery channel — it gets your products surfaced in conversation and routes interested shoppers to your storefront. UCP is the broader infrastructure play, connecting your catalog to Google AI Mode, Gemini, and any agent that adopts the open standard. On Shopify, Agentic Storefronts handles both. Off Shopify, prioritize UCP for Google surface reach and make sure your catalog is clean enough to be found. Tokenize your payments either way — Mastercard Agent Pay and Visa’s Trusted Agent Protocol both assume you’ve moved past passing raw card numbers through your stack.

Why B2B Agentic Commerce Is a Different Animal

Most headline numbers describe B2C. B2B is different, and pretending otherwise is how good projects go sideways.

Forrester found that 89% of B2B buyers now use generative AI as part of their procurement process. But only roughly 7–10% of retailers have fully scaled agentic commerce. That gap is the whole story.

Why the lag? B2B transactions carry complexity that consumer carts don’t:

Account-specific pricing. Contract rates, volume tiers, and negotiated discounts that vary by customer.
Approval workflows. Purchases above a threshold need sign-off, not one-click checkout.
Contracts and terms. Net-30 payment, catalog restrictions, and legal terms baked into the relationship.
Multi-party buying. A procurement agent might assemble a cart that three people approve and a fourth pays for.

An agent built for a B2C impulse buy chokes on all of that. B2B agentic commerce has to respect governance, not bypass it. The agent’s job isn’t to skip the approval step. It’s to navigate it faster.

The B2B Platform Landscape

Salesforce Agentforce Commerce brings agent capabilities into an ecosystem already rich in CRM data, account hierarchies, and approval logic. Strong fit when the buying relationship already lives in Salesforce.

commercetools offers the composable, API-first depth that complex B2B catalogs and pricing rules demand, and has integrated directly with Stripe’s Agentic Commerce Suite.

BigCommerce B2B Edition pairs agent-ready architecture with B2B-specific features like quoting, customer groups, and tiered pricing.

OroCommerce remains a B2B-native option built around the workflow, quote, and account-management needs that consumer platforms treat as afterthoughts.

One gap worth naming: Shopify’s B2B story still trails its B2C one. It’s excellent for direct-to-consumer agentic flows, but deeper B2B governance complex approval chains, account-specific contract pricing at scale is where it’s still catching up.

Spring ’26 Editions does move the needle somewhat: company profiles, volume pricing, and up to three B2B catalogs are now available on Basic, Grow, and Advanced plans at no extra cost, lowering the barrier to entry. But that’s table-stakes access, not a substitute for the governance depth that commercetools, OroCommerce, or BigCommerce B2B Edition provide.

If your business is heavily B2B, platform selection remains a real criterion, not a footnote.

The Hidden Bottleneck: Fulfillment and Delivery Data

There’s a piece of this almost nobody puts on the slide. An agent can find the product, compare it, and pay for it in under a second. Then what? Someone still has to ship it.

Fulfillment readiness is the quiet bottleneck. If an agent is going to commit to a purchase, it needs accurate delivery data at the moment of checkout: real shipping options, real lead times, real cutoffs. An agent’s promise is only as good as the shipping logic behind it.

We’ve lived this firsthand. When we worked with Godiva on Shopify, the hard part wasn’t the storefront it was the shipping and payment logic behind it. Godiva sells chocolate that melts and strawberries that spoil. Their system had to account for gel-season dates, warehouse and carrier closures, ZIP code and PO Box ineligibility, and time-of-day cutoffs, all to calculate an honest delivery date.

Then there was the payment timing problem: Shopify and card processors authorize charges for roughly 10 days, but customers wanted to schedule gifts months ahead. We solved it with Downpay, using Shopify’s Subscription API to tokenize payment and re-authorize the card about 7 days before shipment, capturing the full charge only once the order shipped.

That kind of API-driven checkout and delivery logic is exactly what agentic commerce demands at scale. The brands that win agent traffic will be the ones whose back end can answer “when will this actually arrive?” through an API, instantly and honestly.

Key Takeaways for eCommerce SaaS

Treat product data as front-door infrastructure. Audit whether an agent can pull SKU-level inventory, pricing, shipping, and tax in a single clean API call. If the answer is “after some normalization,” fix that first.

Favor composable, API-first architecture. Whether you choose Shopify, BigCommerce, commercetools, or another platform, the deciding factor is how cleanly agents can read and transact against your stack.

Get clear on protocol support. UCP now has the broadest industry backing. ACP still matters for ChatGPT-driven discovery. On Shopify, you get both by default. Off Shopify, prioritize based on where your buyers live. Tokenize payments either way.

Respect B2B governance. If you sell B2B, account-specific pricing, approval workflows, and contract terms aren’t optional. Pick a platform that navigates them.

Don’t forget fulfillment. API-driven checkout and accurate delivery data are part of the agentic stack, not an afterthought.

Mobile was a ten-year tailwind for eCommerce. Agentic commerce looks shorter and steeper. Three years out, a platform that can’t serve agent traffic natively will feel like a 2014 store without mobile checkout. The work to avoid that is measured in months, not years.

If you’re weighing which platform actually fits your catalog, your buyers, and your fulfillment reality, that’s the kind of complex commerce problem we like. Proof over promises. Start with the data, then build.

Honey Olesen

Seasoned digital and operations leader with 15+ years of experience driving strategic growth across operations, project management, budgeting, and organizational development; now working in AI Optimization. I help organizations adapt to the evolving search landscape by aligning content, technical infrastructure, and data strategy with how AI platforms like ChatGPT, Perplexity, and Google AI Overviews discover, interpret, and surface information

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8th of May — 2026

Agentic Commerce: Revolution or Liability for B2B eCommerce Teams?

Honey Olesen

A gentic commerce is here, and it's moving faster than most B2B teams expect.

The short answer: it’s both a revenue unlock and a control risk. Teams that treat it like a channel win early. Teams that ignore governance pay for it later.

You’re not deciding whether it will happen. You’re deciding how much control you keep.

What is Agentic Commerce?

Agentic commerce means AI agents can browse, evaluate, and complete purchases on behalf of users or businesses. These agents don’t just recommend products — they execute tasks like vendor selection, price comparison, and checkout.

This is no longer theoretical. OpenAI launched its Agentic Commerce Protocol (ACP), built with Stripe, in September 2025, enabling purchases directly inside ChatGPT without the buyer ever visiting a merchant’s site. In January 2026, Google launched the Universal Commerce Protocol (UCP) with partners including Walmart, Target, Shopify, and Etsy. On the enterprise side, procurement bots integrated into SAP Ariba and Coupa are already running structured purchasing workflows.

That means your buyer may not visit your site at all. The agent might.

Why Does This Matter for B2B eCommerce?

B2B buying is already structured, repeatable, and rules-driven. That’s exactly what agents handle well.

Gartner’s shift-to-digital forecast proved out: by 2025, 80% of B2B sales interactions between suppliers and buyers were occurring in digital channels. That’s now the baseline. But Gartner’s 2025 follow-up research adds a wrinkle worth noting; for complex, high-stakes deals, buyers are trending back toward wanting human involvement. The appetite for rep-free experiences is softening at the top of the deal spectrum.

For B2B agentic commerce, that’s actually clarifying rather than discouraging. It means agents will dominate routine, structured, repeat purchasing (exactly the workflow they’re built for) while humans remain in the loop for strategic or complex buys. Know which bucket your transactions fall into.

McKinsey’s 2024 B2B Pulse Survey confirms the momentum: 39% of B2B buyers are now willing to place orders of $500,000 or more through digital self-service or remote channels, up from 28% just two years earlier.

The behavior is there. Agents compress the journey further.

Here’s the key shift: you’re no longer optimizing for a human buyer alone. You’re optimizing for a decision system.

How Does Agentic Commerce Actually Work?

At a basic level, an agent follows a loop:

Understand the goal (e.g., reorder 500 units of industrial valves under $20 each)
Search suppliers and compare options
Evaluate constraints like price, delivery time, and contract terms
Execute the purchase through an API or web interface

In B2B, this often connects to procurement platforms like SAP Ariba, Oracle NetSuite, or Coupa. Many teams are already exposing product catalogs via APIs or structured feeds to make this easier.

Here’s the part most teams miss: agents rely heavily on structured data. If your catalog is messy, you’re invisible.

Where Agentic Commerce Drives Real Upside

It reduces friction in repeat purchases. That sounds simple, but it compounds fast.

If a procurement agent can reorder from you without human input, you become the default vendor. That increases retention and lifetime value.

It also rewards clarity. Clean pricing tiers, clear shipping terms, and consistent product data improve your chances of being selected.

Amazon Business pushes in this direction with its API-driven purchasing and bulk pricing models, making it an easy target for agent-based buying. Smaller suppliers can compete if they match that machine-readable clarity.

Agentic commerce also expands your reach. Your product can surface in decision flows you don’t control, including AI assistants embedded in ERP systems and procurement platforms.

That’s new distribution.

Where the risks show up

Agents optimize for the buyer, not for you.

If your pricing is inconsistent, agents find cheaper alternatives. If your delivery estimates are vague, you lose to vendors with precise SLAs.

Margin pressure is the first hit. Agents compare faster than any human buyer ever could.

Brand also takes a hit. If the agent makes the decision, your differentiation must be explicit in data, not just in messaging.

There’s also a governance risk. If an agent places an order incorrectly, who owns the error? In B2B, a wrong order can mean thousands of dollars in losses.

Security matters too. Granting agents access to accounts, pricing, and checkout flows opens new attack surfaces. Early rollouts have already surfaced real friction: OpenAI’s Instant Checkout launch faced merchant onboarding difficulties and order errors, leading the company to evolve its approach as of early 2026. The technology works, but it isn’t frictionless yet, and the gaps tend to show up in governance and data quality.

The Part Most Teams Miss

Agent readiness is not a feature. It’s an operating model.

Many teams think adding an API or chatbot solves this. It doesn’t. The real work is upstream: product data normalization, contract and pricing logic in machine-readable formats, near-real-time inventory accuracy, and clear rules for approvals and exceptions.

Without this, agents either fail or bypass you.

Agent behavior also isn’t static. It learns. That means your performance today affects future selection. Miss SLAs consistently, and agents will downgrade you.

How to Prepare Without Overcommitting

Start by treating agents as a new buyer segment.

Audit your catalog. Are your SKUs consistent? Do you expose attributes like dimensions, compliance standards, and lead times clearly?

Next, expose structured access through APIs, EDI, or clean product feeds. Shopify Plus, Adobe Commerce, and BigCommerce B2B Edition all support API-first catalog access. If you’re targeting enterprise procurement, prioritize integration with SAP Ariba, Coupa, or Jaggaer these are where enterprise agents are already operating.

Then define guardrails. Set rules for pricing exposure, order limits, and authentication. Don’t let agents transact without controls.

Test agent flows directly. Use tools like ChatGPT or Gemini to simulate how an agent interprets your product data. You’ll find gaps fast.

Revolution or Liability?

It’s both. The difference comes down to control.

Structure your data, expose clean access, and set rules: agentic commerce becomes a growth channel. Ignore it: it turns into a margin drain and a visibility problem.

The shift mirrors early marketplace adoption. The winners weren’t the first to join. They were the first to operate well inside the system.

Frequently Asked Questions

What’s the difference between agentic commerce and traditional eCommerce automation?

Traditional eCommerce automation handles rules-based tasks you define in advance — auto-reordering when stock hits a threshold, for example. Agentic commerce goes further: the AI agent interprets goals, searches and compares options across suppliers, evaluates constraints like price and delivery time, and executes the purchase — all without a human in the loop. It’s the difference between a workflow trigger and an autonomous decision-maker.

Does agentic commerce only apply to large enterprise B2B buyers?

No, but enterprise is where it’s moving fastest right now, given existing integrations with procurement platforms like SAP Ariba, Coupa, and Jaggaer. That said, the infrastructure is broadening quickly. OpenAI’s Agentic Commerce Protocol and Google’s Universal Commerce Protocol are open standards, meaning mid-market buyers using tools like Shopify or even ChatGPT can already run agent-assisted purchasing flows. Smaller suppliers who get their data clean early will be better positioned than larger ones who move slowly.

How do I know if my product catalog is “agent-ready”?

A quick test: open ChatGPT or Gemini, describe your product category and a set of buying constraints (price, lead time, compliance standard), and see if your business surfaces — and how accurately it’s represented. If key attributes like dimensions, certifications, or shipping terms are missing or inconsistent, agents will either skip you or return wrong information. SKU consistency, structured data feeds, and accurate inventory are the three most common gaps.

Who’s liable if an AI agent places a wrong order?

This is still largely unsettled, and it’s one of the most important governance questions B2B teams need to answer before scaling agent access. In practice, liability tends to fall on whoever granted the agent authorization — which means the buyer’s organization. For suppliers, the risk is fulfilling an order that later gets disputed. The safeguard is setting clear order limits, authentication requirements, and approval thresholds before any agent can transact against your catalog.

Will agentic commerce reduce the role of our sales team?

For repeat, structured purchasing, probably yes, over time. That’s the workflow agents are best suited for, and it’s also where your sales team adds the least unique value. For complex, high-value, or strategic deals, the answer is different: Gartner’s 2025 research shows buyers are actually trending back toward wanting human involvement for high-stakes transactions. The smarter framing isn’t “will agents replace sales?” but “which parts of the funnel should agents own so your sales team can focus where humans still win?”

What to do Next

Audit your product and pricing data within the next 30 days. Pilot one agent-friendly integration next quarter. Define governance rules before scaling access.

That’s enough to move from reactive to prepared. The window to shape how agents interact with your business is still open, but it’s closing faster than most teams realize.

Honey Olesen

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17th of April — 2026

Agentic Commerce: How to Prepare Your Backend for AI Buyers

Honey Olesen

A gents of AI are beginning to research, compare, and complete purchases on behalf of human buyers.

This shift (called agentic commerce) will favor merchants with clean data, fast APIs, and real-time inventory accuracy. Here is what it means and how to get ready.

What is agentic commerce?

Agentic commerce is the use of autonomous AI software (agents) to carry out purchasing decisions on behalf of a person or organization. Rather than a human browsing a site, adding items to a cart, and checking out, an AI agent receives a goal, evaluates options across multiple suppliers, and executes the transaction.

This is distinct from chatbot-assisted shopping. An agent does not need a human in the loop for routine decisions. It acts on pre-set parameters: budget, preferred suppliers, delivery requirements, and compliance rules.

Salesforce data shows 39% of consumers already use AI for product discovery. The purchasing step is next.

Why is this happening now?

Several converging developments have made agentic commerce viable in 2024 and 2025:

Large language models can now reliably parse product specifications, compare structured data, and execute multi-step tasks without human supervision.
Major platforms are shipping purpose-built shopping agents. Amazon deployed its Rufus assistant for multi-step purchasing tasks. Google Gemini added shopping agents capable of planning and sourcing in a single session. OpenAI has embedded decision-making models across its platform ecosystem.
B2B procurement software is beginning to integrate agentic capabilities, targeting the high-volume, repeat-purchase workflows where automation delivers the clearest ROI.

$5 Trillion

Projected global agentic commerce volume by 2030 (McKinsey)

805%

Year-over-year increase in AI traffic to US retail sites, Black Friday 2025 (Adobe)

42%

Customers who abandon purchases due to insufficient product data (Mirakl)

How do AI agents actually make purchasing decisions?

Agents do not read web pages the way humans do. They query structured data endpoints, evaluate responses against their parameters, and either proceed or move on; typically in milliseconds.

Consider a concrete B2B example: a dental clinic network runs low on surgical masks. Its inventory software triggers a purchasing agent. That agent queries connected suppliers, checks real-time stock levels, confirms negotiated pricing tiers, calculates estimated delivery windows, and places the order; all without a human opening a browser tab.

Three things determine whether your business is selected or skipped:

Data clarity: complete, structured, consistently formatted product information that an agent can parse without ambiguity.
API reliability: fast, well-documented endpoints that return accurate responses under automated load.
Real-time accuracy: stock levels, pricing, and availability that reflect actual state, not a batch update from last night.

If any of these fail, the agent does not file a support ticket. It queries your competitor.

What is the visibility gap, and why does it matter for attribution?

In conventional ecommerce, merchants see the full funnel: impressions, click-throughs, product page views, cart additions, and drop-offs. In agent-mediated commerce, that visibility collapses. The discovery, comparison, and shortlisting phases happen inside an AI interface. Merchants only see the transaction if it completes.

This has direct consequences for retail media, personalization strategies, and any analytics model built on behavioral data. Adobe’s Black Friday 2025 report documented the scale of the problem: AI-driven traffic to retail sites surged 805% year-on-year, but conversion rates on that traffic lagged significantly because most merchant infrastructure was not built to serve agents cleanly.

Attribution collapses in agent-mediated commerce. You only see the transaction — not the consideration phase that led to it.

What technical requirements do AI agents have?

Structured product data

Agents do not infer meaning from design or marketing copy. They read schema.org/Product markup, Global Trade Item Numbers (GTINs), standardized attribute sets, and clean product feeds. Incomplete GTINs, inconsistent attributes, or thin descriptions result in your products being excluded from agent shortlists. The Mirakl benchmark puts the cost of this at a 42% purchase abandonment rate a threshold agents apply programmatically, at scale.

Real-time inventory and pricing APIs

Batch-updating inventory once daily is no longer sufficient. When an agent builds a multi-item order, and one product appears available but is actually out of stock, the transaction fails, and your reliability score drops. Agents and the platforms running them maintain supplier quality signals. Repeated inaccuracies move you down the preference stack.

ERP and PIM integration

The most common failure point we see in B2B and mid-market merchants is the gap between front-end commerce platforms and back-end systems like NetSuite, Microsoft Dynamics, or SAP. When product information lives in a PIM like InRiver or Akeneo but does not sync instantly to the commerce layer, agents receive stale data. The fix is not a new platform; it is a proper integration architecture between the systems you already have.

Fast, documented API gateways

Your API is now a primary sales channel. It needs to be secure, rate-limit-tolerant, versioned, and capable of handling concurrent automated requests without degrading response times. Poorly documented or unstable APIs are filtered out by agent platforms that maintain supplier quality registries.

How to prepare your commerce infrastructure for AI agents

You do not need to rebuild your stack. You need to audit it honestly and fix the integration gaps that currently exist. Here are the four areas that move the metric:

1. Audit your structured data

Run your product catalog through Google’s Rich Results Test and the Schema Markup Validator. Every product needs a complete schema.org/Product entry: name, description, GTIN, brand, offers (with price and availability), and relevant category attributes. Products missing these fields will not surface in agent-driven shortlists or AI-generated shopping responses.

2. Examine your integration layer

Map the data path from your PIM or ERP to your commerce platform. Where does data wait in a queue? Where is it transformed manually? Every manual step is a latency risk. Automated, event-driven sync (where a stock update in your warehouse system triggers an immediate update in your product feed) is the target state.

3. Move to real-time inventory sync

Replace batch inventory jobs with event-driven updates. Most modern ERP and WMS platforms support webhooks or change-data-capture. If yours does not, a middleware integration layer can bridge the gap without a full platform replacement.

4. Upgrade your API gateway

Review your API documentation, rate limits, and uptime SLAs. Agents query at high concurrency during demand spikes. If your API degrades under load or returns inconsistent responses, you will be removed from agent preference lists. Implement caching, circuit breakers, and structured error responses that agents can parse and act on.

Frequently asked questions

Is agentic commerce only relevant for large enterprises?

No. B2B wholesale, mid-market manufacturing suppliers, and specialist retailers are early targets precisely because their purchasing patterns are repeat-heavy and rule-based, exactly the workflows agents handle best. The infrastructure requirements are the same regardless of company size.

Will human shoppers disappear?

No. High-consideration, emotionally complex, or first-time purchases will remain human-driven. Routine replenishment, specification-matched B2B procurement, and commodity purchasing are where agents will dominate first.

How quickly is this happening?

The foundational technology is already deployed. Walmart, Target, and Home Depot are investing in agent-ready infrastructure now. The brands that have clean data and reliable APIs in 2025 will be the default selections for AI agents in 2026 and beyond. Merchants who wait will face a steeper remediation curve.

What is the biggest mistake merchants are making right now?

Investing in front-end AI features (chatbots, recommendation widgets, visual search) while ignoring the data and integration quality that determines whether agents can transact with them at all. The visible layer is not the bottleneck. The plumbing is.

Honey Olesen

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3rd of April — 2026

Why Your B2B PDPs Must Speak AI in 2026

Aaron Shapiro

B uying expectations have shifted permanently. B2B buyers no longer want to dig through static spec sheets or wait days for a basic quote.

They expect consumer-grade ease combined with enterprise-grade accuracy. Your product detail page (PDP) is no longer just a digital catalog entry. It is a system that must answer complex questions, reduce purchasing risk, and accelerate decisions across procurement, engineering, and operations.

For business leaders driving digital commerce strategy, this reality is both an opportunity and a strict design requirement. Buyer expectations demand self-serve clarity, personalized guidance, and the ability to seamlessly compare options within complex catalogs. You need PDPs structured so AI and search engines can interpret them, rich enough to answer real questions, and integrated enough to reflect live pricing and inventory.

This guide explores what it means to build AI-ready PDPs. We will cover the foundational data requirements, practical design patterns, platform considerations, and the metrics you need to track success.

The Shift: PDPs are Now Decision Engines

Most B2B platforms fail the buyer because they treat the PDP as a passive information repository. When buyers land on your site, they arrive with specific, complex problems. They need to know if a part fits their specific machine model, if a component meets regulatory compliance, or if an item ships fast enough to prevent factory downtime.

When your PDP acts as a decision engine, it actively helps the buyer navigate these hurdles. AI tools, including generative and agentic AI, are completely reshaping B2B workflows by automating complex purchasing tasks. However, AI cannot function on a foundation of fragmented data. If your product truth is buried in PDFs or siloed in legacy systems, AI cannot surface it.

Transforming a PDP into a decision engine requires cleaning up technical debt and structuring your data so that artificial intelligence can read, understand, and serve it directly to the buyer precisely when they need it.

What “Speaking AI” Actually Means for B2B

Many teams hear “AI” and immediately picture a basic customer service chatbot. In reality, an AI-ready PDP is about making your core product data usable across multiple channels and systems. When your PDP speaks AI, it powers highly functional, revenue-driving features.

First, it enables on-site search and recommendations that actually understand user intent. Instead of requiring an exact part number, an AI-powered search can understand queries like “fits a 2018 compressor” or “meets FDA compliance.”

Second, it allows for guided selling flows that drastically reduce wrong orders and costly returns. AI can analyze compatibility, compliance rules, and performance thresholds to ensure the buyer selects the exact right configuration.

Third, it provides massive support deflection. By automatically answering common questions with product-specific data, you free up your sales and support teams to handle high-value interactions. Speaking AI ultimately means speaking structure, context, and confidence.

The Baseline: AI-Ready Product Content and Data

The biggest roadblock to AI integration is not the software you choose. It is the underlying product data model. For AI-supported experiences to work, your baseline must include strong product information management, clear taxonomy, and consistent metadata.

An AI-ready PDP requires clear product naming conventions, including both internal and customer-facing part numbers. It needs normalized attributes, meaning units, ranges, and materials are formatted consistently across the entire catalog. Variant logic must match how your customers actually buy, rather than how your ERP stores SKUs.

This is where backend plumbing becomes critical. Integrated solutions built to perform require seamless connections between your ERP for pricing and availability, your PIM for product attributes, and your digital asset manager for CAD files and safety data sheets. If your PDP content cannot be reliably reused for site search, dynamic filters, and comparison tables, your site simply is not AI-ready. We specialize in the complex technical plumbing required to solve these backend problems and drive real ROI.

Practical PDP Patterns to Improve the Buyer Experience

You do not necessarily need a total site rebuild to start seeing results. Many of the highest-impact improvements come from practical patterns you can apply through targeted optimization. Here are several ways to streamline your operations and enhance engagement right now.

“Choose Your Path” Purchasing Clarity

B2B buyers arrive with entirely different intents. A procurement officer might know the exact part number, while an engineer might need documentation before they can approve a purchase. Your PDP should support multiple entry points. A clear, simple navigation block near the top of the page can route buyers instantly to configuration tools, compatibility charts, or technical documentation.

Specification Tables That Answer Questions

Specs should be highly scannable, comparable, and ready for filtering. Instead of a massive, unorganized list, group your data by buyer concerns. Categorize specs into performance metrics, materials, compliance certifications, and environmental ratings. Structuring specs this way drastically improves the user experience and makes downstream AI integration much easier to implement.

Trustworthy Compatibility Modules

For manufacturers with complex catalogs, a compatibility module is often the most critical element for conversion. Make this tool trustworthy by allowing buyers to search by equipment model and clearly displaying known compatible accessories. Providing “verified by manufacturer” language builds trust, and including edge-case notes about fit constraints prevents frustrating returns.

Guided Selling for Complex Configurations

If your product requires selecting specific voltages, mounting types, or finishes, use a guided flow. Explain each choice in plain language, prevent invalid hardware combinations, and update lead times dynamically based on the configuration. This reduces friction and supports procurement teams that prioritize correctness above all else.

Operational Truth Blocks

Buyers care deeply about how a product arrives and how it is maintained over its lifecycle. Include clear operational details like palletization notes, warranty intervals, and recommended spare parts. Placing these details front and center reduces back-and-forth emails and shortens the overall sales cycle.

A professional buyer shopping online. Computer screen shows a table form of products.

Choosing the Right Platform

Scalable solutions require a robust foundation. Platforms like Shopify Plus and Optimizely are excellent choices for supporting B2B manufacturing outcomes. The right fit depends heavily on your specific catalog complexity, account-level pricing rules, and dealer network structure.

Shopify Plus offers a highly flexible architecture for custom attributes and account experiences, which is vital when procurement workflows require multi-level approvals. Optimizely is highly valued by manufacturers for its strengths in structured content and enterprise-grade commerce patterns.

Regardless of the platform, the goal is strategy aligned to execution. You need a platform that handles your specific technical debt while providing a scalable foundation for future growth. We help evaluate, curate, and implement these platforms, ensuring your digital strategy is built on execution you can trust.

A Phased Approach to Modernization

You do not have to endure the chaos of a massive, overnight replatforming project. Often, a phased approach reduces risk and delivers a faster return on investment.

Start by defining your exact outcomes. Clarify whether the PDP must enable self-serve purchasing, RFQ generation, or complex dealer ordering. Next, audit your product data. Identify the missing attributes and inconsistent documentation that block discovery.

From there, design a scalable PDP template system using modular blocks for specs and compatibility. Implement the data foundations and integrations first to unlock live pricing and inventory. Finally, launch in waves. Start with a high-impact product family, measure the results, and optimize continuously. This step-by-step strategy minimizes downtime and ensures the new system actually serves your business goals.

Metrics That Matter

To ensure your AI-ready PDP efforts connect directly to revenue and efficiency, you must track the right data. Avoid vanity metrics and focus on indicators of true business impact.

Track your PDP-to-cart and PDP-to-RFQ rates by product family to understand baseline conversion. Monitor search success rates and filter usage to gauge discoverability. Look closely at conversion rates separated by buyer type, comparing logged-in dealers against guest procurement users.

Pay attention to documentation engagement. If buyers are downloading CAD files and data sheets, your content is doing its job. Finally, track return rates and indicators of wrong orders. A successful AI-driven PDP will actively reduce the number of support tickets tied to incorrect configurations.

Moving Forward with Confidence

AI is already influencing how B2B buyers research and purchase. The companies that win will be the ones with PDPs built to support this reality. Upgrading your digital presence starts with structured data, modular design, and integrations that reflect operational truth.

For over two decades, BlueBolt has translated ambitious business goals into powerful, reliable digital platforms. We are an extension of your team, providing the strategic guidance and deep technical expertise to architect, build, and optimize the systems that power your growth. If you need a partner who provides expert-built custom solutions, we know the best path forward.

Aaron Shapiro

Aaron Shapiro is Director of the Project Management Office at BlueBolt, with 17+ years leading digital projects across UX, information architecture, and delivery operations. He focuses on building scalable systems, reporting frameworks, and structured digital experiences that support GEO, AIO, and AEO initiatives, enabling AI-driven discovery and decision-making.

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11th of March — 2026

Is This the End of Click-Based Commerce?

Aaron Shapiro

F or years, digital commerce growth meant improving how buyers click through category pages, search results, filters, and product detail pages.

That structural foundation still matters deeply. In complex B2B eCommerce, specifications, compatibility, and procurement rules shape every purchase. So, how can B2B companies prepare for chat-led discovery?

What is changing right now is where discovery starts. More buyers begin with a question rather than a click. They ask chat interfaces to help them shortlist products, understand complex options, compare tradeoffs, and decide what to do next. Sometimes that chat is a general assistant. Sometimes it is built directly into your site experience. Either way, the buyer expectation remains the same: “Help me find the right fit quickly.”

For B2B leaders, this represents a crucial strategic moment. The winners will not be the brands with the loudest artificial intelligence story. They will be the brands with the cleanest product data, clearest content, and best-connected workflows. When your data is clean, chat-led discovery routes buyers directly into a confident path to purchase.

The Data Driving Conversational Discovery

We are seeing a rapid evolution in how buyers research and select vendors. This shift is not anecdotal; the numbers paint a clear picture of changing buyer behavior.

Currently, 33% of companies in the U.S. B2B eCommerce sector have fully implemented AI into their operations, and nearly half are actively considering it. Buyers are driving this demand. Research shows that 56% of tech buyers now rely on chatbots as a top source for vendor discovery. They prefer to ask specific questions rather than dig through menus and PDFs.

We also see the rise of agentic commerce, where AI agents handle tasks like quoting and ordering autonomously. These tools do not just answer questions. They take action based on what the buyer is trying to do, checking inventory and applying negotiated pricing. But these tools only work when they have a solid data foundation to pull from.

What Do B2B Buyers Actually Want?

In B2B, discovery rarely means browsing for inspiration. It usually means reducing risk. Buyers want to validate fit, availability, compliance, and total cost while moving fast. They need answers they can trust.

Common discovery goals we hear include:

Finding the exact right product by specification, use-case, or equipment model
Confirming compatibility, substitutes, and approved alternates
Understanding pricing, lead times, and minimum order quantities
Following procurement workflows like purchase orders, credit terms, tax rules, and approval chains
Navigating dealer networks and territory rules without friction
Getting to a quote or assisted checkout with the correct context attached

Chat interfaces can help buyers reach those outcomes significantly faster. However, this only happens when strong product information, clear policies, and reliable eCommerce integration support the underlying B2B buyer experience.

a woman on a laptop asking questions about her orders

Practical Discovery Examples in Complex Catalogs

Here are a few practical scenarios where chat-led discovery perfectly complements traditional navigation and search.

Complex Catalog and Spec-Driven Buying

A buyer types: “I need a stainless sanitary pump for CIP, 3 inch tri-clamp, 20 GPM, food-grade compliance.”
A strong conversational experience guides them to the right product family. It filters down to qualifying options and clearly explains why each item fits the required specifications.

Replacement Parts and Compatibility

A technician asks: “Which gasket fits Model X, revision 3, built after 2021?”
A chat-led flow routes them to the exact part immediately. It shows viable alternatives and attaches the required documentation, ultimately reducing customer service calls and product returns.

Dealer Network and Service Coverage

A buyer asks: “Who can supply this in Ontario with installation support?”
A conversational path combines a dealer locator with inventory visibility. It then provides a clean handoff to the right sales channel without making the buyer jump through hoops.

Complex Procurement Workflows

A purchasing manager asks: “Can I reorder the last PO, apply net terms, and split ship to two sites?”
A well-designed flow takes them directly to a saved list. It confirms account rules and guides the user through checkout or quote creation.

These scenarios are not about replacing your eCommerce website with a simple chat box. They are about making discovery easier and routing the buyer to the right next step.

Preparing Your Commerce Foundation

If discovery is moving into chat interfaces, you must ask a core question: Is your commerce foundation readable, trustworthy, and actionable when a buyer asks for help?

A practical eCommerce optimization plan usually includes a few critical steps.

Clarify High-Intent Questions

Start by collecting the top 25 to 50 questions from your sales team, customer support, and site search logs. Map each of these questions to a best next step. This could be a product detail page, a comparison chart, a specification table, a quote request, or a contact form.

Strengthen Product Data and Taxonomy

Your data must be impeccable. Normalize your attributes, units of measure, and naming conventions. Define strict compatibility rules, authorized substitutes, and constraints. Ensure all technical documentation is complete, updated, and easy for both humans and machines to find.

Design Realistic Conversational Paths

Build flows that match reality. Some use cases require guided “choose your application” flows. Others just need fast SKU resolution and instant reordering. Always plan for assisted selling alongside self-serve options. Make sure the handoff to a human representative is seamless and retains the context of the chat.

Connect Key Systems

Prioritize eCommerce integration where it matters most. Connect systems for real-time pricing, inventory signals, customer-specific catalogs, and account rules. In many B2B use cases, teams need flexibility around ERP-driven pricing logic or specific contract terms. We always plan this architecture up front to avoid roadblocks later.

Build Governance and Measurement

Define exactly who owns product content, attribute quality, and policy updates within your organization. Track ticket deflection, conversion rates, and quote velocity. Do not guess; use your analytics to measure how effectively these new tools serve your buyers.

How Can You Get Ready?

BlueBolt builds and supports B2B brands across major platforms like Shopify Plus and Optimizely. Each of these platforms can support powerful chat-led discovery when paired with the right strategy and delivery.

During eCommerce replatforming or optimization, we evaluate several key considerations:

Structured product content: We ensure attributes, metafields, and content models can power precise filters and guided flows.
Search and merchandising: We make sure buyers can narrow choices quickly and confidently.
B2B experiences: We implement account-based pricing, custom catalogs, quoting, and role-based access.
Integration patterns: We select the APIs and middleware choices that actually fit your operational model.
Extensibility: We select applications and build custom components based on long-term maintainability.

The best-fit platform decision is rarely about a single feature. It is about aligning your requirements, timelines, and operating model to a foundation your team can run confidently. We have built these systems before, and we know the best path forward.

Metrics That Matter for Success

As discovery becomes more conversational, we still measure success in hard business outcomes. The difference is you will also track whether buyers can get answers without friction.

A simple scorecard for B2B eCommerce should include:

Discovery efficiency: You want to see fewer broad searches per successful product view and higher engagement with specification content.
Conversion health: Track the add-to-cart or quote-request rate by segment, paying special attention to new visitors.
Revenue quality: Look for higher reorder rates and fewer returns tied to fit or compatibility issues.
Sales productivity: Measure the volume of qualified inbound requests that arrive with a complete context attached.
Service impact: Watch for fewer basic “what fits” support tickets and faster resolution times for complex issues.

Next Steps for Your Digital Strategy

Click-based journeys are not going away, but the scope of product discovery is expanding rapidly. More buyers will begin with a question in a chat interface. They expect your brand to guide them accurately to the right product, the right workflow, and the right next step.

Start by auditing your product data and identifying the most common questions your buyers ask. Clean data and connected systems are the prerequisites for this new era of commerce. When you are ready to build a more intelligent, scalable foundation for your buyers, we are here to help you architect that future.

Aaron Shapiro

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20th of February — 2026

AI Agents Are Coming for Your Checkout: What Retailers Need to Know About Universal Commerce

Aaron Shapiro

Y our next biggest customer might not be a person at all; it might be an algorithm with a purchase order.

What is Universal Commerce, and Why it Matters Now

“Universal commerce” is the idea that buying does not happen in one place anymore. Your customers might start in a portal, an email, a product data sheet, a procurement system, a marketplace, or a chatbot. The expectation is consistent: accurate product information, correct pricing, reliable availability, and a smooth path to order confirmation.

Now add AI agents to the mix. Instead of a buyer clicking through your eCommerce website design, an agent can research, compare, assemble a cart, request approvals, and execute checkout. For B2B manufacturing eCommerce teams, this is not a futuristic edge case. It is a natural next step for time-starved buyers and procurement workflows that already prioritize speed, accuracy, and repeatability.

The opportunity is not about replacing your sales team or relationships. It is about making your digital commerce strategy resilient, so you can serve humans and agents with the same trusted data and rules

How AI agents Change the B2B Buying Journey

In manufacturing, buyers rarely want a simple add-to-cart experience. They want confidence:

The right SKU, pack size, and compatible accessories
The right price for their account, contract, or tier
Clear lead times and substitutions when needed
The right purchasing path (credit card, PO, terms, or invoicing)
Documentation (spec sheets, compliance, warranty, SDS, certificates)

AI agents can help buyers navigate this complexity. But agents also surface a hard truth: if your commerce stack relies on manual steps, hidden rules, and one-off exceptions, an agent will struggle to complete an order reliably. That does not mean your stack is “wrong.” It means the next wave of eCommerce optimization will focus on clarity, consistency, and integration.

In other words, the checkout is becoming an interface for systems, not just a page for people.

What “Agent-Ready Checkout” Means in Practice

Agent-ready does not mean you need a fully automated robot buyer. It means your commerce experience is structured so that an assistant, a procurement integration, or a customer service rep can all achieve the same outcome with fewer back-and-forth steps.

Here is a practical definition: an agent-ready checkout is one where pricing, eligibility, and fulfillment rules are explicit, accessible, and enforceable across channels.

Key capabilities to plan for:

Clean product data: normalized attributes, variants, units of measure, and compatibility rules
Account-aware pricing: customer-specific price lists, contract pricing, and promotions applied consistently
Inventory and lead time clarity: availability, backorders, and realistic shipping promises
Identity and permissions: who can see what, who can buy, who can approve
Integrations that behave predictably: ERP, PIM, OMS, CRM, tax, shipping, and payments
A consistent API surface: so tools can query catalog, pricing, and cart rules without scraping pages

This is not only about AI. These are also the fundamentals of strong eCommerce implementation in B2B.

Practical Examples for Complex B2B Manufacturing Workflows

Universal commerce becomes real when it meets the scenarios your team handles every day. Here are a few examples where AI agents can add value, and what your platform and integrations need to support.

Example 1: Dealer networks and channel visibility

Scenario: A dealer should see dealer pricing, limited assortments, and specific freight options.

What to support:

Segmented catalogs and customer groups
Price lists by account or group
Shipping rules tied to region, warehouse, or dealer tier
Clear role-based access for reps and dealer users

Result: An agent can assemble the right cart without accidentally selecting restricted SKUs or incorrect terms, protecting the dealer relationship and the b2b buyer experience.

Example 2: Procurement workflows (POs, approvals, and controls)

Scenario: A buyer needs to build a cart, submit for approval, then place the order on terms.

What to support:

Quote-to-order workflows, or draft orders with approvals
Purchase order capture and reference fields
Payment terms and invoicing paths where appropriate
Audit trails and user permissions

Result: Your checkout becomes a controlled process, not just a payment page.

Example 3: Configurable products and compatibility

Scenario: A buyer needs a motor, mounting kit, and controller that must be compatible.

What to support:

Structured product attributes and compatibility mapping (often PIM-led)
Guided selling or bundled recommendations
Clear documentation and constraints surfaced on product pages and in cart rules

Result: An agent can recommend and validate combinations, reducing returns and support burden.

Example 4: ERP-driven availability and lead times

Scenario: Availability changes by warehouse and customer priority, and lead times vary.

What to support:

Real-time or near-real-time inventory signals (with sensible caching)
Backorder rules and split shipment options
Clear messaging and order confirmation behavior

Result: Fewer surprises after checkout and fewer manual interventions, which is a major lever for eCommerce re-platforming ROI.

AI Agents for B2B. Governance, Success and Buyer Experience.

Platform-Positive Implementation Considerations

For B2B manufacturing brands, platforms like Shopify Plus and Optimizely can all be strong foundations. The right fit depends on priorities like speed-to-market, cost of ownership, required customization, and the complexity of your integrations.

A few platform-positive considerations to evaluate:

Data model alignment: how your catalog structure, pricing, and account hierarchy map to the platform
Extensibility: the best approach for custom workflows (apps, APIs, middleware, or platform features)
Integration strategy: where to put business logic so it is reusable across channels
Operational ownership: who maintains what after launch, and how change requests are handled

In some use cases, teams may want additional flexibility around pricing logic, quoting, or procurement integrations. This is where a complementary tool or a different architecture can be a better fit, while still keeping the commerce platform as the system of experience.

At BlueBolt, we stay platform-positive and requirements-led. If Shopify Plus is the best fit, we lean into its ecosystem and speed. If Optimizely aligns better with your content, experimentation, or commerce roadmap, we design around that. The goal is a maintainable, scalable foundation that supports universal commerce patterns.

A Roadmap: How to Prepare Your Universal Commerce Stack in 60 to 120 days

You do not need to boil the ocean. Start by making your current buyer journey more explicit and more integrated.

Step 1: Map your “checkout truth table”

Document the rules that determine whether an order is valid:

Who can buy which products
How pricing is determined
What minimums apply (MOQs, pack sizes, pallet quantities)
What shipping and payment options apply by account
What approvals are required

This becomes the blueprint for agent-ready commerce.

Step 2: Fix the data foundations

Prioritize:

Product attributes and variants that reflect how buyers actually specify products
Units of measure and packaging clarity
Documentation that is easy to find and consistently structured
A plan for PIM (if needed) to reduce platform-side workarounds

Step 3: Design your integration layer for reuse

Many brands benefit from integration patterns that centralize rules and reduce point-to-point fragility:

ERP and OMS for availability and order creation
PIM for product content and attributes
Tax and shipping services for accurate totals
Middleware or iPaaS for orchestration where it improves reliability

This is the heart of eCommerce integration and a key lever for long-term eCommerce optimization.

Step 4: Upgrade the buyer experience with clarity, not complexity

Focus on high-impact improvements:

Account-aware navigation and merchandising
Saved lists, reorders, and quick order by SKU
Clear lead time messaging and backorder behavior
Quote request paths for products that require it

Step 5: Add governance for agent-driven interactions

Define:

What an agent can do (and cannot do) on behalf of a user
Approval thresholds and audit logging
Rate limits and monitoring for automated activity
Support workflows when an order fails validation

This is operational readiness, not just technology.

What Success Looks Like (Metrics and Governance)

Universal commerce readiness should show up in measurable outcomes:

Higher conversion rate for authenticated B2B users
Faster time to reorder and fewer support tickets per order
Fewer order exceptions caused by pricing or availability mismatches
Improved on-time fulfillment expectations (because promises are accurate)
Lower cost of ownership for ongoing changes and enhancements

Equally important: internal confidence. Your sales, service, and operations teams should trust that the eCommerce experience enforces the same rules they do.

Conclusion and Next Step

AI agents will not replace your relationships, your expertise, or your channel strategy. But they will raise the bar for how clearly your commerce systems express pricing, eligibility, and fulfillment logic. For B2B manufacturing brands with complex catalogs, dealer networks, or procurement workflows, this is a practical moment to modernize your foundation and make every channel easier to buy from.

Aaron Shapiro

Do you need a platform-positive, end-to-end partner for website design and implementation on Shopify or Optimizely?

Talk with BlueBolt

23rd of January — 2026

Beyond Search: Is Your Store Ready for Agentic Commerce?

Chris Risner

T he fundamental rhythm of eCommerce has been the same for twenty years: a person searches, browses, clicks, and buys. But that rhythm is changing.

We’re at the beginning of a major architectural shift, moving from an era of human-driven search to one of AI-delegated action. This is the dawn of agentic commerce, and it represents a change as profound as the invention of the search engine itself.

For business leaders and technical teams, this isn’t a “wait and see” trend. With AI-driven traffic to retail sites surging 805% year-over-year, the platforms that are ready today will be the ones that lead tomorrow. The core question is no longer just about driving traffic; it’s about whether your infrastructure is built to speak the language of AI.

What is Agentic Commerce?

Agentic commerce describes transactions that are partially or fully handled by an autonomous AI agent acting on a consumer’s behalf. It’s the difference between a person searching for “best waterproof running shoes” and simply telling their AI assistant, “Find and buy the best-rated, waterproof trail running shoes in my size for under $150, and get them here by Friday.”

In this scenario, the AI agent performs the discovery, comparison, verification, and purchase. It interacts directly with a store’s data, not its visual interface. This transition from “searching” to “doing” is already underway, and its momentum is building fast.

Market Projections: McKinsey forecasts that agentic commerce will drive $5 trillion in global volume by 2030.
Traffic Transformation: Adobe has already tracked an 805% increase in AI-driven traffic to retail sites.

This is a clear signal that a new, high-volume channel is opening up. However, access to this channel depends entirely on your store’s technical readiness.

Agents need structured, machine-readable data to understand product attributes like size, material, and compatibility.

The Visibility Gap: Why Legacy Systems Are Invisible to AI

AI agents don’t browse. They don’t get swayed by beautiful design or clever marketing copy. They parse data. They query APIs. Their decision-making is ruthlessly efficient, and if your platform can’t answer their questions in milliseconds, you are functionally invisible.

This creates a “visibility gap” for many businesses running on traditional or monolithic platforms. These systems often fail at two critical tasks:

Providing Real-Time Data: An AI agent must verify product availability instantly. If your inventory data is updated on a delay, the agent can’t trust it. It will move on to a competitor whose system can provide immediate confirmation.
Structuring Product Information: Agents need structured, machine-readable data to understand product attributes like size, material, and compatibility. Without proper schema markup, an agent has to guess, and AI agents are programmed to avoid ambiguity. They will simply ignore products with messy or incomplete data.

In the age of agentic commerce, poor data quality isn’t just a backend headache—it’s a direct barrier to revenue.

Shopify’s Open Ecosystem vs. The Walled Garden

As this new landscape takes shape, two competing philosophies are emerging.

On one side are the “walled gardens” like Amazon and Walmart. These giants are integrating agentic AI to keep customers within their closed ecosystems. While effective for them, this strategy turns third-party brands into commodities and keeps valuable customer data away from the merchants themselves. The AI serves the marketplace, not your business.

On the other side is Shopify’s open ecosystem model. Instead of building higher walls, Shopify is helping build the universal standards for the future of retail. Through the co-developed Universal Commerce Protocol (UCP) with Google, Shopify is standardizing the language of agentic shopping.

This approach gives merchants a powerful advantage. It ensures your product catalog is a structured, validated data feed ready for AI assistants like Google Gemini. By choosing an open standard, you make your products discoverable by any agent, on any platform that adopts the protocol. You retain control over your brand, your data, and your customer relationships.

The Technical Foundation for an AI-Driven Future

Preparing for agentic commerce isn’t about adding a chatbot. It’s about ensuring your core infrastructure is built for data-first interactions. Shopify’s platform, especially its Agentic Plan, is engineered to provide these foundational capabilities.

Real-Time Inventory Sync: Shopify’s APIs are built for high-velocity synchronization, giving agents the instant stock validation they require to complete a purchase confidently.
Complex Cart Building: Agents fulfill complex goals, not just single-item purchases. Shopify’s architecture supports multi-item, cross-category cart building via API, handling requests that would cause friction on legacy platforms.
Structured Data Readiness: Through its support for UCP, Shopify helps ensure your product data is structured correctly, allowing AI agents to understand your catalog with near-zero error.

Future-Proof Your Business Today

The shift to agentic commerce is happening now. The businesses that will thrive are those that invest in the right data infrastructure today. Waiting to see what competitors do means you’ll already be behind.

The first step is to assess your current capabilities. Can your platform provide real-time inventory data? Is your product information structured and machine-readable? For many retailers, the honest answer is no.

Shopify provides a direct path to agentic readiness. We believe in replatforming for strategic advantage, and this is a moment of profound strategic importance.

Chris Risner

Chris Risner is Chief Strategy Officer at BlueBolt with over 20 years of experience leading digital strategy, marketing, and business operations across the internet and software industries. He drives the development of scalable digital strategies and platforms that support SEO, GEO, AIO, and AEO initiatives, aligning marketing, product, and data systems for AI-driven discovery and growth.

Ready to see how your infrastructure stacks up?

16th of January — 2026

The Impact of AI on eCommerce in 2026: A Structural Reckoning

Honey Olesen

I t wasn’t long ago that artificial intelligence felt like a futuristic concept—something reserved for sci-fi movies or the R&D labs of tech giants.

Today, it’s the engine running the world’s most sophisticated commerce operations. As we move into 2026, the eCommerce industry is undergoing a structural reset. It is no longer just about incremental upgrades or adding a chatbot to your homepage. We are witnessing a fundamental shift in how products are discovered, how supply chains operate, and how businesses compete.

In 2026, the competitive edge belongs to those who prioritize operational speed, data quality, and system reliability. The era of “digital transformation” as a buzzword is over; we have entered the age of connected intelligence, where people, data, and digital workers operate in unison. For mid-to-enterprise businesses in retail, wholesale, and manufacturing, understanding these shifts isn’t just about staying ahead; it’s about staying relevant.

This structural reckoning is driven by three massive forces: the maturity of AI-powered shopping, the rise of autonomous network operations, and significant global tariff changes reshaping supply chains. Let’s explore how these forces are rewriting the playbook for 2026.

AI-Powered Product Discovery

For decades, the “search bar” was the front door to the internet. If a customer wanted a product, they typed a keyword into Google or Amazon and sifted through pages of results. That dynamic has changed. Discovery has moved upstream, shifting away from keyword matching and toward conversational intent.

The Rise of Conversational Commerce

In 2026, customers don’t just search; they converse. Tools like Amazon’s Rufus, a conversational assistant that analyzes catalog data, reviews, and user context, have set a new standard. These generative shopping assistants provide tailored product guidance, answering complex questions like, “What’s the best running shoe for flat feet training for a marathon in rainy weather?”

This shift means your product data must do more than just exist; it must be “machine-readable” in a way that AI agents can understand and serve up in conversation. If your product attributes, naming conventions, and compliance documentation aren’t structured for these machines, you lose the sale before a human ever sees your brand. The battleground for visibility has moved from the search results page to the AI’s training data.

Recommendation Engines Evolve

Marketplace recommendation engines have also evolved from simple “customers who bought this also bought that” logic to complex predictive models. These engines now factor in operational performance metrics like delivery reliability, return rates, and regional availability directly into search rankings. A great product with poor backend logistics will effectively become invisible.

Generative AI’s Personalization

Personalization has been a goal for marketers for years, but in 2026, generative AI has finally delivered on the promise of “segments of one.” We aren’t just talking about inserting a customer’s first name into an email subject line. We are seeing the real-time generation of entire storefront experiences.

Dynamic Product Detail Pages (PDPs)

Generative AI is now capable of rewriting product detail pages (PDPs) on the fly to match the specific intent of the buyer.

For a technical buyer: The PDP might highlight specifications, compatibility charts, and warranty information.
For a lifestyle buyer: The same URL might display benefits-focused copy, user-generated content, and emotional imagery.

Platforms like Shopify have paved the way with tools like Shopify Magic, which allows merchants to generate high-quality product descriptions in seconds. In 2026, this technology has matured to the point where technical summaries and buyer-specific content are produced autonomously, reducing decision fatigue for customers and support volume for merchants.

The End of Static Content

This shift signals the end of static content. Companies that rely on rigid, one-size-fits-all product descriptions are finding themselves outpaced by competitors who use AI to speak directly to the customer’s immediate need. This level of fluidity requires a content management system (CMS) that is not just a repository, but an active participant in the sales process, capable of delivering flexible, modular content at scale.

Autonomous Network Operations

As AI takes a more active role in the front-end customer experience, the back-end infrastructure supporting it has had to evolve. We are moving from “AI operations” to agentic operations where digital workers autonomously manage the network stack.

From Firefighting to Supervision

In 2026, IT teams are no longer spending their days fighting fires. Instead, they are supervising systems that can detect, diagnose, and remediate outages on their own. Autonomous agents are now responsible for:

Enforcing access and identity policies.
Optimizing routing for inference workloads.
Coordinating performance across cloud and edge environments.

As Snorre Kjesbu from Cisco noted, human intelligence remains at the center, but it stops being the bottleneck. This shift allows technical teams to focus on strategic initiatives (like architecture and innovation) rather than maintenance.

Reliability as a Ranking Factor

This operational reliability isn’t just an IT concern; it’s a commercial one. As mentioned earlier, marketplace algorithms are now penalizing businesses for backend failures. If your autonomous systems can’t maintain uptime or manage data flow efficiently, your front-end visibility suffers. Operational excellence is now directly tied to revenue.

The Shift from Contract Buyers to AI-Discovered Buyers

In the B2B sector, a distinct divide has emerged. For years, B2B eCommerce was synonymous with complex procurement portals and negotiated contracts. While those still exist, a new path has opened up.

Two Distinct Journeys

Contract Buyers: These are traditional procurement teams operating through negotiated pricing, approvals, and strict compliance rules. They value stability and integration. In 2026, their experience is accelerated by platforms that automatically enforce entitlements and account-level pricing, removing friction from complex transactions.
AI-Discovered Buyers: These are new, long-tail buyers entering through public channels: AI engines, marketplaces, and comparison platforms. They value speed, transparency, and ease of access.

The mistake many businesses made in 2025 was forcing both groups through the same funnel. If you put a login screen or a “request a quote” form in front of an AI-discovered buyer, they won’t wait. They’ll find an alternative in seconds. In 2026, successful merchants are separating these journeys and measuring them differently.

The New Benchmark: Time to Change

Speed is the currency of 2026. The new essential metric for eCommerce performance is time to change. How fast can your organization respond to a tariff adjustment? How quickly can you onboard a new brand or route around a supply chain disruption?

Companies on monolithic, rigid architectures are losing years of competitiveness in months.
Companies on modular, composable architectures (MACH) are adapting in real-time.

Every hour spent waiting for a code deployment is margin lost.

Tariff Changes and Supply Chains

a cargo ship loaded up with containers in the harbor

Perhaps the most tangible structural change in 2026 is the end of the de minimis exemption in the United States. For years, this policy allowed low-value imports (under $800) to enter the U.S. duty-free, fueling the rise of direct-to-consumer giants from overseas.

The End of Duty-Free Imports

The U.S. formally ended this exemption on August 29, 2025. Now, even small, low-value packages require formal customs entry and are subject to duties. This policy shift has effectively erased the cost advantage of shipping millions of cheap items directly to consumers from overseas warehouses.

Reshaping Logistics Gravity

This change has forced a massive rethinking of network design. Companies are moving away from cross-border direct shipping and are instead investing in:

Regional Warehousing: Positioning inventory closer to the end consumer within the U.S.
Nearshoring: shifting assembly and fulfillment to Mexico and Canada.
Logistics Hubs: We are seeing a shift in “logistics gravity” toward regions like Chattanooga, Columbus, Kansas City, and Phoenix—cities with favorable cost structures and transportation access.

In 2026, fulfillment is no longer just a cost center; it is a demand accelerator. The ability to navigate these tariff changes and position inventory strategically is a key competitive differentiator.

Connected Intelligence

All these trends (AI discovery, personalization, autonomous ops, and logistics shifts) converge on one concept: Connected Intelligence.

Siloed systems are the enemy of AI. You cannot have a generative AI assistant on your storefront if it can’t access real-time inventory data from your warehouse. You cannot optimize supply chain routing if your order management system doesn’t talk to your logistics provider.

In 2026, data synchronization is a revenue lever. Event-driven data flows are replacing old-school batch processing. This means data moves instantly between your ERP, PIM, WMS, and CRM. The result is:

Accurate reorder dates.
Automated contract enforcement.
Faster customer support resolution.
Higher conversion rates.

As BigCommerce executives have noted, data velocity and data cleanliness are merging. The operational cost of stale data is now visible on the balance sheet.

How to Thrive in 2026

view of the ocean sunset through a person holding a camera lens.

So, where does this leave your business? The structural reckoning of 2026 is not a warning to fear; it is an invitation to adapt.

To thrive in this landscape, mid-to-enterprise businesses must focus on three core areas:

Structure Your Data: Ensure your product data is clean, consistent, and structured for AI. Your catalog is your new storefront.
Embrace Composable Tech: Move away from rigid monoliths. Adopt modular systems that allow you to reduce your “time to change.”
Unify Your Operations: Break down silos. Connect your systems so that data flows freely from the back office to the front end.

At BlueBolt, we have spent over two decades helping businesses navigate exactly these kinds of shifts. We don’t just build websites; we architect the digital ecosystems that power growth. Whether you are navigating the complexities of B2B procurement or trying to capture the AI-discovered buyer, we act as an extension of your team to ensure your technology stack is ready for the future.

The future of eCommerce isn’t just about AI; it’s about the intelligence that connects it all.

Honey Olesen

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26th of December — 2025

The Blueprint for AI-Driven Success

Honey Olesen

A rtificial intelligence is reshaping the business landscape, and new terms like AIO, AEO, GEO, and GAO are gaining traction among organizations aiming to leverage AI for meaningful results.

Each concept addresses a different facet of optimization, but when thoughtfully integrated, they form a powerful toolkit for driving business growth, enhancing customer engagement, and staying ahead in a competitive market.

Let’s break down how these four strategies work together to help businesses fully realize the value of AI investments.

Understanding the Four Building Blocks

For businesses looking to harness AI, it’s essential to understand how each of these pillars supports practical goals:

AIO (Artificial Intelligence Optimization): AIO focuses on making your AI systems operate more efficiently, faster, and with greater accuracy. This might mean refining an AI-powered personalization engine so it delivers recommendations instantly, or reducing server costs by optimizing model performance. For any business relying on AI—whether for analytics, automation, or direct customer interaction—AIO ensures the technology foundation is robust and reliable.
AEO (Answer Engine Optimization): With consumers increasingly searching for direct answers—through search engines or voice assistants—a business’s content needs to be structured to provide those answers quickly and clearly. AEO techniques ensure your website’s information surfaces as a trusted solution in response to customer queries, improving visibility and reducing friction in the buyer’s journey.
GEO (Generative Engine Optimization): As generative AI tools become central to information discovery, GEO involves preparing your business’s digital assets to be recognized and used by these advanced engines. By optimizing content, data, and brand signals, you enable generative AI systems to accurately represent your expertise when generating content for users or business customers.
GAO (Generative AI Optimization): GAO takes optimization beyond content and search by systematically adopting generative AI to improve products, services, and operations. This could involve using generative AI to streamline support, personalize marketing at scale, or develop entirely new digital offerings, all within a scalable, ethical framework.

business owner working on GAO: AI Strategy

How These Strategies Create Business Value

While each pillar has its strength, the real transformation comes from a unified approach. Let’s see how they work together in practice:

1. Building a High-Performance AI Foundation with AIO

Every AI-driven business starts with robust infrastructure. AIO ensures your AI models—whether powering search, recommendations, or business analytics—deliver results efficiently. With optimized AI, you can scale operations, reduce response times, and limit infrastructure expenses. For example, an e-commerce company can process shopper behavior data in real time, delivering instant, relevant recommendations to boost engagement and sales.

2. Making Your Business Discoverable with GEO

Once your AI foundation is strong, GEO prepares your business to be found and cited by generative AI engines. Optimizing structured data, publishing in-depth resources, and maintaining up-to-date brand information all help ensure that when AI applications generate summaries or answers, your business is included as a reliable expert. This increases your brand’s reach, influences buying decisions, and builds authority with both humans and AI-driven platforms.

3. Becoming the Go-To Source with AEO

Modern customers expect clear, authoritative answers at their fingertips. AEO streamlines your website and content so that when existing or prospective customers have questions, your business provides the solutions directly in featured snippets, FAQs, and voice search responses. This not only attracts more qualified leads but also shortens the path from search to action, driving conversions and customer satisfaction.

4. Turning Capability into Impact with GAO

With a solid AI infrastructure, discoverable expertise, and optimized answers, GAO empowers your business to activate these assets throughout your operation. By embedding generative AI into customer service, marketing automation, or product development, you can personalize interactions, generate tailored proposals, or design new workflows—creating measurable business impact and allowing your organization to adapt rapidly to changing market conditions.

A Practical Example: Unified AI Optimization in Action

Consider a SaaS company adopting a comprehensive AI strategy:

AIO optimizes its recommendation engine and chatbots, enhancing speed and relevance while controlling computational costs.
GEO ensures that detailed product guides and success stories are recognized by generative AI tools, making the company’s expertise visible in synthesized industry insights.
AEO structures onboarding resources, knowledge bases, and service pages for maximum visibility in search and direct answers. Customers find helpful content easily, improving satisfaction and reducing support tickets.
GAO integrates generative AI models to generate custom implementation strategies and support documentation for each client, based on their specific configurations.

The result? Better customer acquisition through increased visibility, greater retention due to responsive support, streamlined internal operations, and scalable marketing and product innovation—all built on a shared, optimized AI backbone.

The Competitive Edge: Why Integration Matters

Businesses that embrace AIO, AEO, GEO, and GAO as a holistic framework experience several key benefits:

Faster, Data-Driven Decision Making: Optimized AI supports real-time analytics and actionable insights.
Higher Customer Engagement: Discoverability and direct answers meet users’ needs where and when they search.
Operational Efficiency: Generative AI unlocks automation and creativity at scale, reducing manual effort.
Future-Proof Digital Presence: Being recognized as a trusted authority by both users and AI platforms secures your place in tomorrow’s business landscape.

Taking the Next Step

Adopting this unified strategy doesn’t require an overhaul of your existing operations. Start by evaluating existing AI systems and content for optimization opportunities. Structure your digital assets to answer core customer questions, and focus on becoming a source of knowledge in your industry. Invest incrementally in generative AI capabilities and look for high-impact workflows to transform with GAO.

As businesses accelerate their adoption of AI, those who connect the dots between AIO, AEO, GEO, and GAO will be best positioned to lead. By viewing these four strategies not as isolated buzzwords, but as interconnected pillars supporting practical goals, your organization can drive sustainable growth, foster lasting customer relationships, and thrive in an AI-powered world.

Honey Olesen

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18th of December — 2025

What is AIO? How AI Optimization Powers Smarter Website Performance

Honey Olesen

A rtificial Intelligence Optimization (AIO) is quickly becoming a cornerstone for savvy businesses.

Most conversations about AI and your website focus on one question: are you showing up when AI platforms like ChatGPT or Perplexity answer questions in your space? That’s GEO and AEO territory and they matter enormously.

But there’s a second question that’s equally important: once a visitor lands on your site, is AI working to convert them?

AI Optimization (AIO) is the third discipline in BlueBolt’s GAO framework. Where GEO and AEO focus on getting your brand found and cited by AI platforms, AIO focuses on using AI to make your website itself perform better personalizing experiences, improving conversion rates, and automating the optimizations that used to require constant manual effort.

Think of it this way: GEO and AEO bring the right visitors in. AIO makes sure your site converts them when they arrive.

A quick note on terminology: AIO gets used two different ways in the industry. Some use it to mean using AI tools to produce content faster a workflow efficiency play. BlueBolt’s definition is different and more specific: AIO is about using AI to improve how your website performs for visitors once they arrive. Personalization, conversion rate optimization, on-site search, load performance. If GEO and AEO are about getting found, AIO is about what happens next. That distinction matters, and it’s what this post covers.

Defining Artificial Intelligence Optimization for Websites

As the on-site performance component of BlueBolt’s GAO framework, AIO is the process of using intelligent algorithms to fine-tune and automate vital website functions. Rather than relying solely on manual setups or traditional analytics, AIO makes it possible to continuously test, adapt, and improve elements of your site at scale. Its primary goal? To help your website operate more efficiently: boosting speed, delivering more relevant content, and providing smooth, engaging user journeys.

Think of your website’s recommendation engine, search bar, or personalized landing pages. These are areas where AI can make micro-adjustments, improving what products show up first, which blog posts get recommended, or how content is structured for different users. Through AIO, you can continually optimize these experiences, turning one-time visitors into repeat customers.

AIO doesn’t replace GEO or AEO; it completes them. A brand that earns AI citation through GEO work but sends visitors to a poorly optimized site loses the opportunity GEO created. The three disciplines are most powerful when they operate together under a unified GAO strategy.

How AIO is Transforming Businesses

AIO is already making significant impacts by unlocking new avenues for growth, engagement, and efficiency:

Personalized Content Delivery: AI can quickly analyze user behavior and preferences to recommend relevant blog posts, videos, or products. AIO takes this a step further by automatically testing and optimizing which recommendations drive the most engagement or sales, and then implementing changes in real time.
On-Site Content Intelligence: AIO analyzes how visitors interact with your content, which pages drive engagement, where users drop off, which CTAs convert, and surfaces recommendations for improvement. This is distinct from GEO and AEO, which optimize for how AI platforms find and cite you externally. AIO optimizes what happens after they arrive.
Conversion Rate Optimization (CRO): From A/B testing call-to-action buttons to personalizing homepage layouts by user segment, AIO streamlines traditional CRO tactics. AI-powered platforms can simultaneously test hundreds of variations and automatically adjust to the layouts or offers that drive the best results.
Faster Load Times and Technical Performance: AIO tools can monitor site speed and automatically compress images, adjust scripts, or recommend optimizations. These improvements not only enhance user experience but can also positively affect SEO rankings.

Key Benefits and Use Cases of AIO for Websites

Within BlueBolt’s GAO program, AIO delivers measurable on-site gains that compound the visibility work done through GEO and AEO. Core benefits include:

Enhanced User Experience: Deliver content, products, and layouts that are most relevant for each individual visitor. With AIO-driven personalization, websites feel more engaging and intuitive, reducing bounce rates and increasing time on site.
Increased Conversions and Revenue: By optimizing every step of the user journey, from navigation to checkout, AIO helps convert more visitors. Whether you run an eCommerce shop or provide digital services, AIO can uncover the subtle changes that lead to more sales.
Operational Efficiency: Reduce the manual work in tasks like SEO updates, content recommendations, or A/B testing. Your marketing and development teams gain more time to focus on strategy and growth.
Real-Time Adaptation: Website trends can shift rapidly. With AIO, your site can learn and respond to new user behaviors on the fly, adapting content, offers, or experiences as needed.

Practical Applications:

Dynamic Page Optimization: Automatically test headlines, layouts, CTAs, and content sequencing across visitor segments. AI identifies which combinations drive engagement and implements changes in real time, replacing the slow cycle of manual A/B testing.
On-Site Search Intelligence: AI-powered search understands intent, not just keywords surfacing the right product or page even when a visitor’s query doesn’t match exact terminology. It also surfaces patterns: what people are searching for that you don’t have, or can’t find.
Conversion Path Analysis and Optimization: AIO tools identify where visitors drop off, which pages stall decisions, and which CTAs convert by segment then surface specific recommendations for improvement rather than leaving teams to interpret raw analytics.
Predictive Personalization: Serve different homepage layouts, featured content, or offers based on traffic source, prior behavior, or firmographic signals. A visitor arriving from a ChatGPT referral has a different context than one from organic search. AIO lets your site respond accordingly.

Challenges and Considerations

While the promise of AIO is significant, it’s important for businesses to be mindful of a few challenges:

Complexity and Oversight: Fully automated changes should still be overseen by your team to maintain brand voice and ensure changes align with your business goals.
Data Privacy and Ethics: When using visitor data for personalization, ensure you’re up to date with privacy regulations and that your AI systems are transparent and fair in their recommendations.
Keeping AIO Aligned with GEO and AEO: As AI platforms evolve, the signals that drive external citation (GEO/AEO) and the signals that drive on-site conversion (AIO) need to stay coordinated. Optimizing one in isolation can create mismatches, for example, driving high AI referral traffic to pages that aren’t conversion-optimized. BlueBolt’s GAO framework manages all three disciplines together to prevent this.
Computational Scale: At the enterprise level, AI optimization at scale requires meaningful computing resources and data volume. For mid-market businesses, this is less of a barrier than it used to be AI-powered personalization and CRO tools are increasingly accessible, and BlueBolt scopes AIO implementation to what’s appropriate for your business size and goals, not the maximum possible footprint.

The Future of AIO in Website Optimization

As artificial intelligence continues to advance, so will the ways it optimizes websites. Future AIO systems are expected to become even more “intelligent”; automatically adjusting for seasonality, user trends, device types, and more. We’ll see a greater focus on explainability (understanding why a certain change was made), making it easier for website teams to trust and adopt AIO-driven recommendations.

Additionally, with advances in low-code and no-code platforms, even smaller website operators will be able to harness the benefits of AIO, democratizing access to powerful optimization tools previously reserved for large enterprises.

One trend worth watching specifically for AIO: the rise of AI-powered shopping agents. As platforms like ChatGPT begin facilitating purchases directly, not just answering questions, the line between AI citation (GEO) and on-site conversion (AIO) will blur. Businesses that have both disciplines running in a coordinated program will be positioned to capture that traffic end-to-end. This is exactly why BlueBolt built AIO as a component of GAO rather than a standalone service.

Frequently Asked Questions

What does AIO stand for?

AIO stands for AI Optimization. In BlueBolt’s GAO framework, AIO refers specifically to using artificial intelligence to improve on-site website performance — personalization, conversion rate optimization, load times, and user experience.

How is AIO different from GEO and AEO?

GEO and AEO focus on getting your brand found and cited by external AI platforms like ChatGPT and Perplexity. AIO focuses on what happens once a visitor lands on your site — making sure AI is working to convert them. All three are components of BlueBolt’s GAO framework.

Do I need AIO if I’m already doing SEO?

Traditional SEO and AIO address different problems. SEO optimizes for search engine rankings. AIO optimizes for on-site behavior, personalization, and conversion — using AI to make decisions that previously required constant manual testing and adjustment.

Is AIO only for large enterprises?

No. While enterprise platforms have led adoption, AI-powered personalization and CRO tools are increasingly accessible to mid-market businesses. BlueBolt implements AIO at a scope appropriate to each client’s size and goals.

How BlueBolt Implements AIO

BlueBolt’s AIO work begins with a site performance audit tied to the GAO Readiness Assessment — identifying where AI-driven optimization will have the highest impact for your specific business. From there, implementation follows a phased approach:

Phase 1: Baseline audit of personalization, CRO, and on-site search performance
Phase 2: Implementation of AI-driven recommendations, A/B testing frameworks, and dynamic content tools
Phase 3: Ongoing monitoring and reporting, integrated with GEO and AEO metrics in the unified GAO dashboard

AIO works best when it’s not a standalone initiative. As part of the full GAO program, every on-site improvement is measured against the AI visibility gains happening in parallel.

Honey Olesen

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11th of December — 2025

What is AEO? How Answer Engine Optimization Gets You Featured in AI and Search Responses

Honey Olesen

A s the digital landscape rapidly changes, businesses are encountering a new era of search.

When someone types a question into Google and gets a direct answer without clicking any links, or asks Alexa a question and gets a single spoken response, that’s an answer engine at work. And either your content is the source it draws from, or someone else’s is.

Answer Engine Optimization (AEO) is the discipline of structuring your content to win those featured positions. It is one of three components in BlueBolt’s GAO framework, sitting alongside GEO (which targets broader AI platform citation) and AIO (which optimizes on-site performance). Where GEO is about being recommended by AI, AEO is about being selected as the direct answer.

AEO has expanded beyond featured snippets and voice search. Today, it covers the full range of direct-answer surfaces, including AI Overviews, ChatGPT, and Perplexity, anywhere a platform synthesizes an answer rather than returning a list of links. The structural principles are the same: content that is concise, authoritative, and machine-readable. The stakes are higher, because AI-generated answers increasingly replace the click entirely.

What is Answer Engine Optimization (AEO)?

Answer Engine Optimization (AEO) is a strategic approach to structuring and presenting your website’s information so that search engines, virtual assistants, and AI-powered platforms can easily extract and display accurate answers to user queries. Instead of only vying for traditional organic rankings, businesses now need to position their content for inclusion in featured snippets, “People also ask” boxes, and voice search responses.

AEO leverages advances in natural language processing and conversational AI. Search platforms are continuously improving at interpreting questions and discerning which website offers the most authoritative answer. If your website delivers that concise, useful answer, you can earn visibility that was once only possible through paid or top organic listings.

Within BlueBolt’s GAO framework, AEO is the discipline most directly tied to traditional search infrastructure, Google’s featured snippets, People Also Ask boxes, and voice assistant responses, while GEO extends that visibility into the generative AI platforms like ChatGPT and Perplexity that don’t use traditional search ranking at all.

Why Should Businesses Care About AEO?

For businesses, the transition to zero-click searches (where users get their answers directly from the results page) is reshaping digital competition. Here’s why AEO matters:

Maximize Visibility in Search: Achieving a featured snippet or a voice search response places your website at the very top, often above even paid ads. This prime placement drives more awareness, even if users don’t always click through.
Build Authority and Trust: Being selected as the answer source signals to users (and algorithms) that your business is a trusted expert in your field. This credibility can increase conversions and customer loyalty.
Engage Voice-First Audiences: With more searches happening via mobile and smart speakers, concise, well-structured answers tailored to voice interfaces can expand your reach to new segments.
Outpace Competitors: In the answer box, the winner truly takes all. Outranking competitors in this space means you become the go-to resource, increasing brand recall and consideration.
Bridge Between Traditional Search and AI Discovery: AEO-optimized, answer-first content structure, FAQ schema, and authoritative sourcing are also foundational to effective GEO work. Getting AEO right doesn’t just win featured snippets; it builds the content infrastructure that makes your brand more citable across ChatGPT, Perplexity, and Google AI Overviews simultaneously. It’s the highest-leverage starting point in the GAO program.

AEO Strategies Tailored for Businesses

To become the answer that search engines select, consider these proven strategies:

1. Identify and Prioritize High-Value Questions

Use analytics, customer interactions, and tools like Google’s “People also ask” or site search logs to discover the specific questions your customers are asking about your services, industry, or products.

2. Deliver Clear, Concise Answers

Provide direct responses to these questions within your content. Use language that is accessible and includes the relevant keywords naturally. A rule of thumb: keep answers between 40–60 words and make them easy to find within the page.

3. Structure Content for Both Users and Machines

Divide your content into clear headings (H1, H2, H3), use bullet points and numbered lists for step-by-step guidance, and consider summary tables. Implement FAQ and How-To sections, and use schema markup (such as FAQPage and QAPage) to help search engines identify and extract your answers.

3a. Configure llms.txt and AI Crawler Settings

AEO increasingly extends beyond Google. Ensuring your robots.txt allows AI crawlers from OpenAI, Anthropic, and Google, and implementing llms.txt to signal your most authoritative content, applies AEO principles to the AI platform layer. BlueBolt handles this as part of every GAO technical implementation.

4. Optimize for Featured Snippets and Rich Results

Analyze which search features appear for your key terms (paragraph snippets, lists, tables) and match your content format to what is commonly displayed. Providing instruction-based content or clear definitions can increase your chances of being featured.

5. Build Topical Authority

Develop content clusters around your core expertise. Interlink related articles and resources to showcase depth and breadth, making it easy for search engines to see your site as a comprehensive authority on your subject.

6. Prepare for Voice and Conversational Search

Voice assistants often present the single, best answer. Use conversational, natural language in your answers and anticipate the kinds of questions people might ask aloud (“How do I…?”, “What is the best way to…?”).

7. Build Author Authority Infrastructure

Search engines and AI platforms weigh E-E-A-T signals heavily when selecting answer sources. Content attributed to recognizable, credentialed authors earns more citations than byline-free pages.

Key Benefits for Businesses

AEO has the potential to deliver benefits that compound with GEO and AIO work running in parallel:

Prominent Placement Without Paid Ads: Secure top-of-page positions organically, often above traditional search results.
Boosted Brand Trust: Consistently being sourced for answers builds a reputation of reliability with both users and search engines.
Increased Engagement: Even when users don’t click, being the answer source keeps your website top-of-mind, supporting future visits and conversions.
Stronger Support for New Technologies: As AI, voice search, and chatbots become more prevalent, well-optimized answers will surface across diverse touchpoints, not just search engines.
Foundation for the Full GAO Program: AEO-optimized content structured, authoritative, and answer-first is the raw material that GEO and AIO build on. Businesses that start with AEO create a content infrastructure that accelerates results across the entire GAO framework.

Challenges and How to Address Them

AEO isn’t without hurdles. Businesses should be aware of the following:

Zero-Click Implications: When AI answers a question using your content, the visit may never happen but the brand impression does. Counter this by treating AEO as a top-of-funnel awareness channel, not just a traffic driver. Structure answers so they establish authority and name your brand explicitly (“BlueBolt’s approach to X is…”) rather than presenting generic information that gets cited without attribution. The goal is that even a zero-click encounter leaves the reader knowing who provided the answer.
High Competition: Only a few sources earn featured spots. Regularly update and refine your content to remain at the forefront.
Keeping Content Fresh: Out-of-date answers lose authority; schedule regular content reviews.
Technical Implementation: Structured data and page speed improvements may require investment in web development, but the payoff in visibility can be significant.
Changing Algorithms: Keep up with SEO and AEO best practices to adapt swiftly when search engines update their answer selection criteria.
Coordination Across GAO Disciplines: AEO implemented in isolation can create gaps, for example, winning a featured snippet but having no GEO strategy for the same query in ChatGPT, or driving answer-engine traffic to pages that aren’t AIO-optimized for conversion. BlueBolt’s GAO framework manages AEO alongside GEO and AIO to prevent these gaps from undermining each other.

The Future: Why AEO is Essential for Website Success

The answer engine landscape is expanding beyond Google faster than most businesses realize. Google AI Overviews, ChatGPT’s Browse mode, Perplexity, and voice assistants are all answer engines, each with different technical requirements for citation. AEO as a discipline is evolving to cover all of them, not just featured snippets.

The businesses best positioned for this shift are the ones building structured, authoritative, consistently maintained content now, not waiting until a new platform reaches mainstream adoption. In BlueBolt’s experience, the technical foundation of good AEO work transfers directly to GEO performance on newer AI platforms. The two disciplines share more than they diverge, which is exactly why GAO treats them as complementary rather than competing priorities.

Looking ahead, expect answer platforms to introduce more explicit source attribution, creating direct traffic and credibility benefits for cited businesses. Structured, E-E-A-T-rich content will be the determining factor in who gets attributed and who gets omitted.

Frequently Asked Questions

What does AEO stand for?

AEO stands for Answer Engine Optimization. It is the practice of structuring website content to be selected as the direct answer in Google featured snippets, voice search responses, People Also Ask boxes, and AI-generated answer platforms.

How is AEO different from SEO?

Traditional SEO optimizes for ranking position in a list of search results. AEO optimizes for being selected as the single featured answer, which appears above organic rankings and is increasingly the only result a user sees or hears.

How is AEO different from GEO?

AEO targets structured answer positions primarily in Google and voice assistants. GEO targets citation and recommendation within generative AI platforms like ChatGPT, Perplexity, and Gemini. The techniques overlap significantly, which is why BlueBolt implements both under the unified GAO framework rather than as separate engagements.

What is the relationship between AEO and BlueBolt’s GAO program?

AEO is one of three disciplines in BlueBolt’s GAO framework, alongside GEO and AIO. It is typically the first phase of implementation because strong AEO content infrastructure accelerates results in GEO and AIO work that follows.

Conclusion

AEO is where most GAO programs begin because structuring content for direct answers lays a foundation that benefits every other discipline in the framework. Get AEO right, and GEO and AIO work becomes faster and more effective.

Honey Olesen

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4th of December — 2025

What is GEO? How Generative Engine Optimization Gets Your Brand Cited by AI

Honey Olesen

W hen someone asks ChatGPT to recommend a B2B ecommerce platform, or asks Perplexity which agency specializes in Shopify migrations, the answer they get isn't a list of links. It's a synthesized response; pulled from sources the AI has already decided are authoritative.

If your brand isn’t one of those sources, you’re not in the conversation.

Generative Engine Optimization (GEO) is the discipline of ensuring your business is one of the sources AI platforms draw from when composing those answers. It sits at the core of BlueBolt’s GAO framework, alongside AEO and AIO, and it’s the component most directly tied to AI-driven discovery and brand visibility. GEO’s specific job is the middle layer: ensuring that platforms like ChatGPT, Perplexity, and Google AI Overviews recognize your brand as a credible, relevant source when they generate responses in your space.

This post covers what GEO is, why it’s structurally different from traditional SEO, and the specific steps BlueBolt uses to build AI citation authority for clients.

What is Generative Engine Optimization (GEO)?

GEO is one of three disciplines within BlueBolt’s GAO framework. Where AEO focuses on structured answers in traditional search, and AIO focuses on AI-powered website performance, GEO specifically addresses how generative AI platforms (ChatGPT, Perplexity, Gemini, Google AI Overviews) understand, reference, and recommend your brand.

The practical goal of GEO is simple: when an AI platform composes an answer relevant to your business, your content should be a source it draws from and your brand should be one it recommends.

Why GEO Matters for Website Businesses

AI search isn’t replacing traditional search overnight, but it’s already a parallel discovery channel that most businesses aren’t optimizing for. Here’s why that matters:

Direct Responses Over Click-Throughs: Increasingly, users ask AI tools questions and receive direct answers instead of scrolling through website links. Your website must be structured so generative engines can include your content in those answers.
Brand Authority and Trust: AI-generated responses reflect the data they ingest. If your website has accurate, well-structured, and up-to-date content, you position your business as a reliable source, enhancing trust and authority in your space.
Cross-Platform Presence: Generative AI powers numerous platforms, from voice-activated speakers to chatbots on ecommerce sites. GEO ensures your site shows up wherever potential customers interact with intelligent systems, not just in traditional search engines.
Mitigating Misinformation: Proper GEO practices help prevent generative engines from misrepresenting your business or displaying outdated information, protecting your brand’s online reputation.
Your competitors are mostly unprepared: Structured GEO implementation is still early-stage for most industries. Businesses that establish AI citation authority now build a compounding advantage that is increasingly difficult for late movers to close.

GEO Strategies That Build AI Citation Authority

GEO isn’t a checklist; it’s a signal-building process. AI platforms like ChatGPT and Perplexity don’t publish their citation criteria, but current research and ongoing testing point to consistent patterns. Here’s what actually moves the needle.

1. Implement Structured Data and Schema Markup — Specifically for AI Extraction

Schema markup isn’t new, but its role in GEO is more targeted than in traditional SEO. The goal isn’t just to help Google parse your page; it’s to give AI crawlers unambiguous signals about who you are, what you do, and why you’re authoritative.

Priority schema types for GEO:

Organization: name, URL, logo, founding date, social profiles. Establishes entity identity.
FAQPage: direct Q&A pairs that AI can extract verbatim as answer candidates.
HowTo: step-by-step content that matches instructional query patterns.
Article with author, datePublished, and publisher: E-E-A-T signals AI models use to evaluate source credibility.

If your structured data isn’t implemented or is inconsistent across pages, AI systems have less confidence in your content, and lower-confidence sources get cited less.

2. Build Content Around the Questions AI Is Already Answering

GEO starts with understanding what’s being asked, not just what you want to rank for. Run the queries your buyers are likely to use in ChatGPT or Perplexity, “best Shopify Plus agency for B2B,” “how to migrate from Magento to BigCommerce,” “what does an ecommerce discovery phase include”, and see what sources get cited. Those are your competitors in AI search.

A 2024 study from Princeton, Georgia Tech, and the Allen Institute for AI (the paper that formally introduced GEO as a discipline) tested nine optimization tactics across 10,000 queries and found that adding citations, statistics, and quotations improved AI visibility by up to 40%. Content that consistently earns citations in practice shares a few structural traits:

Answer-first format: the direct response appears in the first 1–2 sentences, before supporting detail
Concise, low-ambiguity language: AI models prefer content that doesn’t require interpretation
Clear headings: H2/H3 structure that maps to how questions are phrased
Topic completeness: covering the full question, not just a partial answer

3. Establish Your Brand as a Named Entity

AI platforms reason about entities, brands, people, products, and locations, not just keywords. If your brand name, key personnel, and core services don’t appear consistently across your own site, third-party mentions, and structured data, LLMs will have weak entity signals for you.

Practically, this means:

Author bios tied to published content (name, title, area of expertise, link to LinkedIn)
Consistent brand name and service descriptions across your site, press mentions, partner pages, and directories
Building coverage on platforms LLMs draw from heavily: industry publications, G2/Capterra reviews, LinkedIn, relevant Reddit threads, and Wikipedia if applicable

4. Audit and Control What AI Crawlers Can Access

Many sites are inadvertently blocking AI bots through misconfigured robots.txt files or Cloudflare settings. If AI platforms can’t crawl your pages, they can’t cite them.

Key technical checks:

Review robots.txt for disallow rules that catch AI user agents (GPTBot, PerplexityBot, ClaudeBot, Google-Extended)
Implement or audit your llms.txt file — an emerging standard that gives AI models direct guidance on which content is authoritative and how to represent your brand
Confirm that important content is server-rendered, not client-side only, AI crawlers don’t execute JavaScript the way browsers do
Verify canonical tags are clean, so AI systems don’t fragment your authority across duplicate URLs

5. Monitor What AI Is Actually Saying About You

GEO without measurement is guesswork. BlueBolt’s AI visibility audits assess how your brand currently appears or doesn’t across ChatGPT, Perplexity, Gemini, and Google AI Overviews. That means running structured prompt sets in your category, capturing citation rates, and identifying gaps by topic, competitor, and query type.

What you’re tracking:

Mention rate: how often your brand appears across a defined set of prompts
Citation accuracy: whether what AI says about you is correct and current
Competitive share of voice: who’s getting cited when you’re not, and why
Sentiment: whether the framing is favorable, neutral, or problematic

Examples of GEO in Action for Businesses

These are the business types where BlueBolt’s GEO work has the most immediate impact:

eCommerce Stores: By adding structured product data and up-to-date inventories, retailers can have their items highlighted in AI-driven shopping assistants or voice purchase flows.
Service Providers: Clear service descriptions and FAQs, formatted with schema, help AI chatbots and voice agents recommend your services accurately to potential customers.
Local Businesses: Consistent map data, reviews, and contact information allow generative engines to provide the right information in local searches or in-car voice systems.
Content Publishers: Educational websites and blogs that offer open, well-organized content see their articles, guides, or resources cited and summarized in AI-powered learning and discovery platforms.

In each case, the underlying work is the same: structured data, authoritative content, technical AI readiness, and consistent monitoring delivered as part of the BlueBolt GAO program.

The Future of GEO: Trends Businesses Should Watch

GEO is an ongoing practice, and several trends are influencing its future:

AI platforms are beginning to facilitate purchases, not just answer questions. ChatGPT’s shopping integrations and Perplexity’s commerce features mean the line between “AI cites your brand” and “AI sells your product” is already blurring. For ecommerce businesses, GEO citation authority is becoming a direct revenue channel not just a visibility metric. Brands that are already recognized sources for AI platforms will be the first ones recommended when those platforms begin completing transactions.
Agentic AI will raise the bar on structured data. As AI agents begin executing tasks autonomously, comparing vendors, shortlisting agencies, and filling out contact forms, they’ll depend even more heavily on clean, machine-readable signals. Businesses with consistent entity data, well-implemented schema, and accessible llms.txt files will be preferred sources for agent-driven decisions. Incomplete or inconsistent structured data won’t just hurt citations — it will exclude you from consideration entirely.
Citation attribution is becoming visible to end users. AI platforms are under increasing pressure to show where their answers come from. As that attribution becomes more prominent in the UI, being a cited source transforms from an invisible influence into a direct brand awareness moment. GEO work done now builds the citation record that will be surfaced to users as transparency standards evolve.
Share of model becomes a tracked metric like share of voice. Within 12–18 months, AI visibility dashboards will be as standard as SEO rank trackers. Mention rate, citation accuracy, and competitive share of voice across ChatGPT, Perplexity, and Gemini will be reported metrics in every serious marketing review. The brands that have already established baselines through a structured GEO program will have a measurable head start on competitors who are just beginning to audit.

Frequently Asked Questions

What is GEO in digital marketing?

GEO stands for Generative Engine Optimization. It is the practice of structuring your website’s content and technical infrastructure so that AI platforms like ChatGPT, Perplexity, and Gemini cite and recommend your brand in their generated responses.

How is GEO different from SEO?

SEO optimizes for ranking in traditional search engine results pages. GEO optimizes for visibility in AI-generated answers, where there are no rankings only cited sources and omitted ones.

How does GEO fit into BlueBolt’s GAO framework?

GEO is one of three disciplines within BlueBolt’s GAO program, alongside AEO (Answer Engine Optimization) and AIO (AI Optimization). GAO brings all three together into a single managed strategy.

Which AI platforms does GEO target?

The primary platforms BlueBolt optimizes for are ChatGPT, Perplexity, Google Gemini, and Google AI Overviews. As new AI search platforms emerge, the GAO program adapts accordingly.

Conclusion

GEO occupies a specific, critical role in the AI discovery stack: it’s how your brand earns the right to be cited before a potential buyer ever visits your site.

But GEO doesn’t work in isolation. The content authority you build through GEO feeds the answer extraction that AEO depends on. The traffic GEO generates needs AIO to convert. And both are most effective when managed together under a unified GAO strategy with visibility audits, structured implementation, and reporting that connects AI citation signals to actual business outcomes.

The window to establish GEO position is open right now. Most businesses in your space haven’t run a single AI visibility audit. The brands that build citation authority early will be harder to displace as AI search matures — for the same reason early SEO movers still hold organic authority today.

If you want to know where your brand stands in AI-generated answers, that’s where BlueBolt starts.

Honey Olesen

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26th of November — 2025

What is GAO? BlueBolt Unified Framework for AI Visibility and Optimization

Honey Olesen

H ow people find information, make purchase decisions, and evaluate vendors has been fundamentally changed by AI.

When someone asks ChatGPT, Perplexity, or Google’s AI Overviews to recommend a solution or product like yours, your ability to appear in the answer is no longer determined by traditional SEO alone.

Generative AI Optimization (GAO) is BlueBolt’s unified framework for ensuring your business is visible, credible, and citable across AI-driven search and answer engines. It brings together three interconnected disciplines (AEO, GEO, and AIO) into a single, managed strategy.

The industry is still catching up on terminology. GAO, GEO, AEO, and AIO are used inconsistently across the web. BlueBolt’s framework gives clients a clear, actionable structure rather than a confusing alphabet soup and backs it with a real deliverable suite at every phase.

In this post, we’ll discuss how GAO applies to businesses, explore the benefits it brings to online operations and content, and outline practical strategies for leveraging this powerful toolset.

What is Generative AI Optimization (GAO) for Businesses?

What is GAO?

GAO is BlueBolt’s unified framework that unifies three optimization disciplines under one strategy:

AEO (Answer Engine Optimization): Structuring your content so it gets cited in direct AI answers on Google AI Overviews, voice assistants, and answer boxes.
GEO (Generative Engine Optimization): Ensuring your brand is recognized, referenced, and recommended by generative AI platforms like ChatGPT, Perplexity, and Gemini.
AIO (AI Optimization): Using AI tools to improve your website’s internal performance personalization, content efficiency, and user experience.

By focusing on these areas, businesses can ensure that the generative AI powering their digital experiences is not just a novelty, but a driver for measurable improvements.

Why Does GAO Matter for Businesses?

The rapid advancement of generative AI has enabled websites to automate key functions previously handled manually. However, without optimization, these models can underperform; creating generic, off-brand, or even confusing user experiences.

AI is now a discovery channel: A growing share of purchase research starts with a ChatGPT or Perplexity query, not a Google search. If your brand isn’t surfaced there, you’re invisible to that audience.
Traditional SEO isn’t enough: Ranking on page one of Google doesn’t guarantee you appear in AI-generated answers. GAO addresses the gap.
Credibility signals have changed: LLMs favor brands with authoritative, consistently structured, well-attributed content. GAO builds those signals deliberately.
The window to establish position is open now: Most competitors haven’t implemented a structured AI visibility strategy yet. Early movers gain citation advantages that compound over time.

Key benefits of GAO for businesses include:

AI Citation Presence: Your brand appears in ChatGPT, Perplexity, and Google AI Overviews responses when buyers are researching solutions in your space before they ever run a traditional search.
Accurate Brand Representation: AI platforms reflect what your content and structured data tell them. GAO ensures what they say about you is correct, current, and credible not a misattribution pulled from an outdated source.
Competitive Positioning While the Window Is Open: Most businesses haven’t yet audited their AI visibility. A structured GAO program builds citation authority now, when displacement costs are lower.
On-Site Conversion of AI-Driven Traffic: GEO and AEO bring visitors in from AI platforms. AIO ensures your site is optimized to convert them once they arrive. The three work as a system.
Measurable Visibility Reporting: GAO isn’t a set-it-and-forget-it engagement. Mention rate, citation accuracy, share of voice, and on-site conversion data are tracked together in a unified dashboard.

Practical Applications of GAO Services Website Operations

1. Content Creation for AI Citation

Restructure existing content with answer-first formatting, FAQ schema, and E-E-A-T signals that make it easier for LLMs to cite your pages as authoritative sources.

Example format aligns with how AI retrieves answers:

Answer-first formatting → immediate extraction
Concise, factual language → low ambiguity
Clear headings + bullets → easy parsing
FAQ schema compatibility → matches query patterns
Entity clarity → reinforces relevance

2. AI Search Visibility

BlueBolt assesses how your brand currently appears (or doesn’t) in responses from ChatGPT, Perplexity, Gemini, and Google AI Overviews identifying gaps and opportunities by topic, competitor, and query type.

Example of Opportunities from Audit:

Own the Definition Layer: Create clear, citation-ready definitions for core industry terms.
Build Comparison Authority: Develop “vs” and “best solution” content to enter AI-generated lists.
Structure for Extraction: Reformat pages with: direct answers, bullet summaries, FAQ sections and expand topic clusters.
Cover full journeys: Awareness → Consideration → Decision

3. Technical AI Readiness

We audit and implement the technical infrastructure LLMs rely on: robots.txt for AI crawlers, llms.txt, JSON-LD structured data, and metadata optimization.

Key outputs from the audit and implementation could potentially be:

Robots.txt Outcome

Ensures controlled, intentional access for LLM crawlers
Improves likelihood of content ingestion into AI systems

llms.txt (AI-Specific Content Guidance) Outcome

Provides contextual signals for AI summarization
Reinforces topical authority and brand positioning

JSON-LD Structured Data Outcome

Improves machine readability and answer extraction.
Increases eligibility for AI-generated summaries

Metadata Optimization for AI Outcome

Aligns with how AI selects and summarizes content
Increases inclusion in AI-generated answers

Internal Linking & Crawl Depth Outcome

Strengthens semantic relationships between pages
Improves AI confidence in topic authority

Overcoming Challenges in GAO for Businesses

Like any advanced digital technology, effective GAO comes with obstacles:

The terminology is still evolving: GEO, AEO, AIO, and LLMO are used inconsistently across the industry. BlueBolt’s GAO framework provides clients with a stable, coherent strategy that doesn’t require rebuilding whenever a new acronym emerges.
AI platforms don’t disclose their ranking signals: Unlike Google, ChatGPT and Perplexity don’t publish how they select cited sources. GAO strategies are built on current research and ongoing testing, not assumption.
Results compound over time: AI visibility isn’t a switch you flip. It builds as your content authority and citation signals strengthen, which is why a phased, monitored approach matters.

GAO Best Practices for Website Teams

To make the most of Generative AI Optimization, businesses can focus on:

Iterative Training: Continuously gather feedback from users and internal stakeholders to refine prompts and retrain models as needed.
Collaborative Workflows: Integrate AI into content teams’ workflows, allowing human editors to review outputs before publishing.
Comprehensive Testing: Employ automated QA tools to check for accuracy, SEO alignment, and adherence to guidelines at scale.
Ethics and Transparency: Clearly indicate when content is AI-generated and ensure compliance with data privacy standards.

Asia young business woman sit busy at home office desk work code on desktop test IT deep tech ai design skill online html text

Looking Ahead: How GAO Evolves as AI Search Matures

AI platforms are beginning to facilitate purchases, not just answer questions. ChatGPT’s shopping integrations and Perplexity’s commerce features are early signals that the line between “AI cites your brand” and “AI sells your product” is blurring. Businesses running coordinated GEO and AIO programs will be positioned to capture that end-to-end, from first AI mention to completed transaction. This is exactly why BlueBolt built AIO as part of GAO rather than a standalone service.
Agentic AI will raise the bar on structured data. As AI agents begin executing tasks autonomously (comparing vendors, filling out RFQ forms, booking demos) they’ll depend even more heavily on clean, machine-readable data. Businesses with well-implemented schema, accurate entity signals, and accessible llms.txt files will be preferred sources for agent-driven decisions.
Citation attribution is getting more transparent. Platforms are increasingly expected to show where their answers come from. Businesses that have built citation authority now will benefit as that attribution becomes more visible to end users, turning AI mentions into a direct brand awareness channel, not just an invisible influence.
Share of model becomes a tracked metric like share of voice. Within 12–18 months, AI visibility dashboards will be as standard as SEO rank trackers. The brands that have already established mention rates and citation accuracy baselines will have a measurable head start on competitors who are just beginning to audit.

Frequently Asked Questions

What does GAO stand for?

GAO stands for Generative AI Optimization. It is BlueBolt’s unified framework combining AEO, GEO, and AIO into a single AI visibility strategy.

How is GAO different from SEO?

SEO optimizes for traditional search engine rankings. GAO optimizes for visibility in AI-generated answers, the responses you see in ChatGPT, Perplexity, Gemini, and Google AI Overviews.

What is included in a GAO service?

BlueBolt’s GAO service includes an AI visibility audit, content structuring for citations, technical AI-readiness implementation, and ongoing monitoring and reporting across all major AI platforms.

How long does it take to see results from GAO?

Initial technical and content changes can improve citation signals within 60–90 days. Full visibility gains compound over 6–12 months as authority builds.

Conclusion

AI search is not a future trend; it’s where your buyers are looking right now. GAO is how BlueBolt helps businesses show up in those moments with authority and consistency.

Our framework, AEO, GEO, and AIO, working together, is backed by a structured audit process, phase-by-phase deliverables, and ongoing reporting. It’s not a blog post strategy. It’s a managed program.

Honey Olesen

Find out where your brand stands in AI search.

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7th of November — 2025

AI + Shopify: How Smart Merchants Are Winning Big in 2025

Aaron Shapiro

O nce upon a time, launching a Shopify store meant uploading some products, crossing your fingers, and hoping customers showed up.

Fast forward to 2025, and that fairytale is long gone. Today’s merchants are embracing artificial intelligence (AI) not just to survive—but to thrive.

From creating personalized shopping experiences to running operations on autopilot, AI is the ultimate Shopify sidekick. Let’s take a walk down this digital runway and explore how AI is revolutionizing the way merchants do business—and why, frankly, if you’re not using it, you’re falling behind.

The Age of Customer Insights: AI as Your Data Whisperer

Picture this: a customer walks into your virtual store. They click, scroll, pause. That behavior? It’s a goldmine of insight. But without AI, you’re essentially panning for gold blindfolded.

AI agents analyze mountains of customer data in milliseconds, pulling out patterns you wouldn’t spot in a lifetime. They track:

Browsing habits
Purchase history
Abandoned carts
Even micro-signals like hover time on a product image

This isn’t about creeping on your customers (let’s leave the spy work to the movies). It’s about serving up exactly what they want. Personalization powered by AI is no longer a nice-to-have. It’s expected.

And the payoff? According to McKinsey, companies that ace AI-driven personalization see revenues jump by 5 to 15 percent, all while trimming marketing costs by as much as 30 percent. That’s not pocket change—that’s game-changing [McKinsey].

Operations on Autopilot: AI, the Manager You Never Knew You Needed

Remember the good old days of manually tracking inventory with spreadsheets? Neither do we, and we’re all better for it.

AI on Shopify is the operations manager who never takes a coffee break. It keeps your stock levels optimized, pricing competitive, and fulfillment smooth. Here’s how:

Inventory automation: AI predicts demand spikes and keeps you ahead of stockouts.
Dynamic pricing: Prices adjust in real time based on competitor moves, customer demand, or even the weather (because yes, people do buy more boots when it rains).
Smart fulfillment: AI flags bottlenecks in your supply chain before they derail delivery timelines.

And it works. Gartner reports that companies using AI in supply chain operations see stockouts reduced by 25 percent and inventory turnover improved by 30 percent [Gartner]. That’s the kind of efficiency that turns headaches into high-fives.

AI Chatbots: Because Nobody Likes Waiting on Hold

Picture this: it’s 11:47 PM, your customer wants to know where their order is, and your human support team is, understandably, asleep. Enter AI chatbots—your store’s tireless, endlessly patient front line.

AI chatbots on Shopify can:

Handle hundreds of inquiries simultaneously (no hold music required)
Provide real-time order tracking, returns info, and sizing guidance
Offer personalized product suggestions based on browsing history

And they’re not just good at saving time—they’re good at saving sales. Salesforce reports that AI-powered bots reduce cart abandonment by up to 20 percent. Even better, 62 percent of consumers actually prefer using self-service tools for simple tasks [Salesforce].

The secret sauce? These bots don’t just parrot FAQs. They engage customers in ways that feel tailored, relevant, and dare we say… helpful.

Upselling and Cross-Selling: The Art of the Friendly Nudge

Let’s face it—we’ve all been on the receiving end of a bad upsell. But AI makes upselling (and cross-selling) feel less like a pushy sales pitch and more like a well-timed suggestion from a stylish friend.

Shopify’s AI apps analyze customer behavior to:

Identify the perfect moment for a premium upgrade
Suggest complementary items at checkout (that hat does look great with that jacket)
Bundle products in a way that feels natural, not forced

The result? Forrester found that personalized upsell and cross-sell strategies can boost average order values by 10 to 30 percent [Forrester]. That’s revenue that adds up—without annoying your customers.

Where It’s All Headed: The Future of AI on Shopify

Think AI on Shopify is impressive now? We’re just getting started. The next wave promises experiences so immersive, shoppers may never want to leave your store. Here’s what’s on the horizon:

AR + AI product configurators: Shoppers will be able to visualize furniture in their living room or see how that watch looks on their wrist before clicking buy.
Voice commerce: AI shopping assistants will guide customers through purchases using natural language—no typing required.
Predictive customization: Your store will anticipate customer preferences down to the last detail, from color to fit to feature set.

McKinsey puts it bluntly: 71 percent of consumers now expect brands to deliver personalized interactions, and 76 percent get frustrated when they don’t [McKinsey]. The message is clear: AI isn’t optional. It’s essential.

woman shopping - uses a screen to check her clothes

Why All This Matters (and What You Should Do About It)

Here’s the bottom line: AI lets Shopify merchants punch above their weight. Whether you’re a solo entrepreneur or a scaling brand, AI helps you:

Serve customers better
Run operations smarter
Sell more (without feeling like you’re selling)

And it doesn’t require a computer science degree to get started. Shopify’s ecosystem is packed with AI apps and integrations designed for merchants, not engineers.

So, what’s your move? If you’re not using AI, now’s the time to start experimenting. Begin with chatbots or product recommendations. Test dynamic pricing. Dip your toe into predictive analytics. The tools are there—and so is the opportunity.

Ready to Future-Proof Your Shopify Store?

AI is changing eCommerce faster than you can say “machine learning.” The question isn’t if you’ll use AI. It’s how soon you’ll let it help your store work smarter, not harder.

Need help figuring out the right AI tools for your business? Want guidance on setting up AI-powered apps in Shopify? Get in touch.

Shopify + AI

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19th of June — 2025

AI Agents in eCommerce

Aaron Shapiro

I n the fast-evolving world of eCommerce, customer expectations are rising—and businesses are turning to artificial intelligence (AI) agents to keep up.

These digital powerhouses do more than just answer FAQs; they create seamless, personalized experiences that help brands scale and shoppers buy with confidence. From product recommendations to intelligent chat support, AI agents are redefining how online retailers engage, convert, and retain customers in an increasingly competitive landscape.

What are AI Agents and Why are They Important to eCommerce

A primary benefit of AI agents lies in automation. AI can handle repetitive tasks—such as responding to FAQs, recommending relevant products, and processing orders—freeing up customer service teams to focus on more complex issues. This improves response times, reduces errors, and creates a more streamlined customer experience.

AI agents also play a critical role in customer engagement. They make shopping more interactive by guiding users through purchasing decisions, providing instant assistance, and personalizing each interaction. With AI-driven support, customers receive timely and relevant recommendations, keeping them engaged and reducing friction in the shopping journey.

For eCommerce businesses aiming to scale, AI agents are invaluable. They enable efficient, round-the-clock support and deliver a personalized shopping experience that encourages conversions and customer loyalty. Whether through chatbots, product configurators, or recommendation engines, AI agents are reshaping the future of customer service and sales in online retail.

The UX Benefits of Using AI in Product Configurators

As eCommerce continues to evolve, customer expectations for smooth, intuitive shopping experiences are higher than ever. AI-driven product configurators play a key role in meeting these expectations by providing personalized, real-time guidance and making the decision-making process simpler for users.

One of the biggest advantages of AI product configurators is their ability to offer tailored suggestions. Instead of customers manually sorting through endless options, AI dynamically adjusts product recommendations based on their preferences. For example, when shopping for custom-built electronics or personalized home goods, AI can present relevant choices in response to user input, ensuring customers feel supported in their journey.

AI also enhances user experience by enabling real-time interaction. When customers make adjustments to their selections, such as changing size, color, or features, AI responds instantly, updating the available options. This immediate feedback reduces friction and keeps users engaged, helping to maintain momentum in the shopping process.

Finally, AI product configurators simplify complex purchasing decisions. By breaking down intricate customization steps into manageable, guided choices, AI makes even the most detailed configurations feel straightforward. This combination of real-time feedback, personalization, and simplification creates a seamless user experience that meets the modern shopper’s demand for convenience.

robot hand selecting a person symbol out of many

How AI-Driven Product Configurators Increase Conversion Rates

One of the biggest challenges in eCommerce is keeping customers engaged and guiding them smoothly toward completing their purchases. AI-driven product configurators are powerful tools for increasing conversion rates by offering personalized experiences, reducing decision fatigue, and improving the overall shopping process.

AI configurators work by providing personalized product recommendations in real-time, helping customers find exactly what they need without overwhelming them with too many options. This level of customization makes the shopping experience more relevant and enjoyable, encouraging customers to move forward in the purchase process. For example, when browsing for a custom pair of shoes, an AI configurator can narrow down choices based on preferred size, style, and color—ensuring customers see only the products that fit their preferences.

Another way AI product configurators boost conversion rates is by reducing decision fatigue. When customers face too many choices or complex configurations, they often abandon their carts. AI eliminates this frustration by breaking down decisions into manageable steps, offering clear guidance along the way. Instead of feeling overwhelmed, customers feel confident in their choices, leading to a higher likelihood of completing the transaction.

Lastly, AI-driven product configurators enhance the overall shopping experience by speeding up the decision-making process. By automatically presenting the most relevant options, AI helps customers find what they’re looking for faster. This reduces friction, keeps customers engaged, and significantly lowers the risk of cart abandonment—key factors in improving conversion rates.

Related Data Sources

Customized Product Offerings Drive Conversions: A report from Deloitte highlighted that 36% of consumers express interest in purchasing personalized products, and 1 in 5 consumers who wanted personalization would be willing to pay a 20% premium . AI product configurators play a key role in offering personalized product variations, helping customers make confident purchase decisions and leading to higher conversion rates.

Conversational Commerce Boosts Engagement: According to a study by Capgemini, 70% of consumers who used conversational commerce (such as chatbots and voice assistants) reported high levels of satisfaction, and 40% of these consumers went on to make a purchase as a result of their interaction with an AI assistant . Product configurators using conversational AI fall under this umbrella, as they help guide the user to configure a product based on specific needs.
https://www.capgemini.com/us-en/resource/conversational-commerce-dti-report/

Product Configuration Reduces Abandonment: Research by Statista found that the average cart abandonment rate in eCommerce is 69.57%, but streamlined, interactive experiences like those provided by AI configurators help reduce this number. Customers who are able to easily customize and visualize products are more likely to complete their purchase instead of abandoning their cart.

The Platform-Agnostic Benefits of AI in eCommerce

Platform Flexibility and Easy Integration

One of AI’s major advantages is its platform-agnostic nature, meaning it can integrate seamlessly with different eCommerce systems—whether on Shopify, WooCommerce, or custom-built sites. This flexibility allows businesses to adopt AI tools without needing major technical overhauls, making it easier to start offering personalized experiences.

Benefits Across Multiple Industries

From fashion and electronics to home goods, AI product configurators provide industry-specific benefits. For example, fashion retailers can offer custom fit recommendations, while electronics stores can help customers configure products with specific technical features. This level of customization boosts customer satisfaction by helping them find exactly what they need.

Long-Term Business Value

AI configurators contribute to scalability and reduce human errors by automating tasks. As businesses grow, AI tools help streamline operations, ensuring consistent service quality and reducing bottlenecks. This operational efficiency not only saves time but also helps maintain customer loyalty by offering a smooth, reliable shopping experience.

AI-driven product configurators enhance eCommerce by providing seamless integration, industry-specific advantages, and long-term scalability. Businesses adopting AI see stronger customer relationships and improved operational performance across any platform they choose.

Preparing Your Business for AI-Driven Product Configuration

Data Collection and Organization

To power AI effectively, start by collecting and organizing high-quality product data. This includes details on product options, customer preferences, and historical sales data. Clean, structured data enables the AI to provide accurate recommendations and configurations, making the shopping experience more relevant and personalized for customers.

Selecting the Right AI Tools

Choose AI tools that fit your business needs and platform. Look for configurators compatible with your eCommerce system, whether Shopify, WooCommerce, or custom setups. Many AI solutions offer different levels of customization, so evaluate which features—such as dynamic pricing, real-time recommendations, or custom sizing—align best with your business goals.

Optimizing Infrastructure for AI

AI configurators require a robust infrastructure to operate efficiently. Assess your website’s load times, server capacity, and data processing speed to ensure it can handle real-time customer interactions. Investing in a scalable, cloud-based infrastructure will help your business manage increased demand as AI-driven configurators boost engagement and traffic.

Preparing for AI-driven product configuration involves strategic data management, tool selection, and infrastructure planning. With these foundations, businesses can successfully integrate AI, creating personalized shopping experiences that engage customers and drive growth.

Conclusion

AI agents are no longer a futuristic luxury—they’re essential tools for modern eCommerce success. By automating tasks, simplifying complex purchasing decisions, and delivering highly personalized experiences, AI-driven solutions like product configurators boost engagement, reduce cart abandonment, and increase conversions. Whether you’re selling sneakers or smart home devices, integrating AI across platforms positions your business to grow, scale, and thrive in a customer-first digital economy.

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24th of January — 2022

Website Testing and Experimentation: Leverage Data with Optimizely

Chris Risner

F or too long, marketing and business executives have been left in the dark as to what their customers really wanted. Yes, some datasets were available based on results from a promotion, but those results often took weeks, if not months, to receive – and were usually only able to be calculated after a campaign was complete.

Good news. Times have changed. The challenge now is to harness the data that is coming at us at the speed of light. Thankfully, software platforms like Optimizely Intelligence Cloud offer a data-driven model of testing and experimentation that creates usable, actionable reports. This also enables your team to prove the ROI of marketing actions, optimize strategies to improve performance and make intelligent, customer focused marketing decisions. In the age of website testing and experimentation, brands can leverage data, thanks to Optimizely more easily than ever.

In practical terms, testing and experimentation with Optimizely Intelligence Cloud empowers you to:

Prove the effectiveness of marketing campaigns
Shutter ineffective strategies and expensive mistakes
Innovate and test new design ideas
Improve campaigns and pages
Assess new marketing plans
Generate informed business decisions

Marketing experimentation is the fastest path towards true digital innovation and, more importantly, standing head and shoulders above your competition.

Website testing with Optimizely Intelligence Cloud

Getting Started with Optimizely Intelligence Cloud

To enable your marketing team to chalk up some quick wins with Optimizely testing and experimentation, their team has put together directions on ten common Optimizely Intelligence Cloud experiments including:

Geographical Differences

Do you want to know what matters to your customers in different parts of the United States? How about the entire world? This is a great experiment to see what content and imagery appeals to consumers in various locations. It’s even more powerful for commerce companies looking to see buying patterns in various geographic areas, as this can drive promotions or entire lines of new business.

CTA’s for New Visitors vs. Subscribers

Delivering clear and compelling content for each of your user personas is critically important, so the team at Optimizely created an experiment that centers around testing content for new users versus subscribers. After all, would you want a loyal customer to be greeted with a “let’s get started” form?

Remove Distractions from the Checkout Funnel

Have you been challenged to improve online sales? One culprit that hinders most sales funnels is distraction. This test enables you to look at a variety of steps where your customers may be getting hung up. Could it be convoluted navigation? Could it be too many steps in your checkout process? There are so many ‘virtual squirrels’ that compete for your customer’s attention. Minimizing the interruptions they face will facilitate them across your goal line.

Optimize Your Pricing Pages

How a pricing or subscription page is arranged can truly deliver the goods for your team. The question is, what styling changes will deliver the magic combination? Prior to testing and experimentation, this was largely anyone’s best guess based on sales results. Fortunately, testing and experimentation takes the guesswork out of the picture leading to higher conversions and increased sales.

Highlight Key Value Propositions

Do you offer multiple purchase choices customers can make, or new offerings you would like to highlight? Would adding a phrase like “most popular” sway your customers to make a choice they may not have made otherwise? The good news is you can test all these variables and get clear answers with multi-variate testing.

Symmetric Messaging

One of the truest marketing sayings is “the devil is in the details.” Many a marketing team has had battles over the tiniest differences in messaging and/or which images should accompany the text. The great thing is that with Optimizely’s ability to test multiple phrasing and pictures through A/B/n testing, teams will now know who gets bragging rights.

Website testing with Optimizely Intelligence Cloud - 4

Personalize Based On Cookies

One of the easier ways to deliver personalized content is to leverage the cookies your users download from your website. However, this can also get a bit over the top creepy. Finding the balance between what is a good use of cookie-based personalization is what testing can help you identify. You may have users who love a highly personalized website or you may have a customer who will jump ship. The only way to know is to hypothesize and evaluate based on actual trials.

Test Promotion Formats

Do you have a promotion that has gone over exceptionally well with your customer base? What would happen if you expanded that promotion from your website into an email or vice versa? When does the promotion reach its limit and run its course with your customer base? All these questions – and more – can be answered with Optimizely.

Optimize a Form

With website forms being a key component of demand gen pipeline, it’s very important for marketers to use them in the most effective way possible. For example, one company in the UK experimented with a long form that asked clients 1-2 questions per page over four pages. They found a 70% increase in their customer base completing the entire form versus having 5-8 questions in a one-page form. As one can imagine, a 70% uptick in form completion can do a lot for pipeline. In their case, it led to a whole new product line offering.

Adding Social Proof

Adding social proof is a theory that examines the impact of whether adding a testimony will influence a customer’s decision to commit. With Optimizely’s multi-variate testing, it’s possible to also add sophistication with cookie-based personalization to test whether a testimonial from the consumers’ geographic area would be more trusted and influence their buying decision.

The good news about these 10 different experiments is that they are truly the tip of the iceberg with Optimizely Intelligence Cloud. The biggest challenge with this powerful platform is to be disciplined and stick to a strategic testing roadmap. Otherwise you may find yourself with a lot of disjointed data making it difficult to build actionable and measurable campaigns. An experienced partner like BlueBolt ensures the platform is implemented properly and enables your team to test often and fail, maximizing your investment for FY22 and beyond.

14th of August — 2017

The Difference Between Machine Learning and Artificial Intelligence

Chris Risner

W hen performing an Internet search on artificial intelligence or machine learning, the two terms are often used interchangeably. However, while the two are similar in nature and cross paths more than just a few times, there are major differences between machine learning and A.I.

As the industry continues to progress and both are utilized more and more, understanding the difference becomes necessary, both for the average consumer and for corporations looking to implement the technology within the business itself.

In the Early Days

The best way to dive into the difference between machine learning and artificial intelligence is to dive back into the early days of AI. Now, the concept of artificial intelligence and machine learning has been around for hundreds of years. References to AI can be seen in literature dating back beyond the even earliest conception of a computer. However, the implemented idea of artificial intelligence didn’t truly begin to take shape until the 1950s.

In 1956, the Dartmouth Conference brought computer scientists from around the world together. Computers remained in the earliest of infancy, yet the idea and drive to create artificial intelligence proved to be a major topic of interest throughout the conference. Of course, the technology necessary to create artificial intelligence lacked significantly and, up until recently, very little in way of AI had occurred. Real change didn’t truly take place in the industry until around 2012 (Nvidia, 2016).

The Divergence of Artificial Intelligence

Before diving into the differences of A.I. and computer learning, it is necessary to understand the divergence of artificial intelligence. In the early days of conceptualized A.I., computerized devices could take on the exact same characteristics and intelligence of a human. They might have a primary function or skill, but in general, the computer would act human. The best example of this in modern pop culture is the droid C-3PO from the Star Wars series. While the droid had a primary function (of being fluent in over six millions forms of communication), it could still perform many, if not most of the same tasks as a human. This form of artificial intelligence is known as “General A.I.”

Of course, while this form of artificial intelligence continues to develop, it isn’t the most commonly utilized form of A.I. The form of artificial intelligence primarily used in both the consumer and commercial levels is known as “Narrow A.I” (in some circles, this is also referred to as weak artificial intelligence while the other is known as strong artificial intelligence). This kind of technology is used for a specific reason or task. Typically, when the narrow A.I. is utilized, it is because it can perform the given task faster, or more accurately, than a human (Forbes, 2016).

Narrow artificial intelligence is any kind of technology used to perform a specific task. One of the most used forms of narrow A.I. is the spam filter for any email account. It is used to identify undesirable emails and separate it from the rest of incoming messages. Other forms of narrow A.I. includes the newsfeed on user’s Facebook account, self-driving cars using GPS, navigational technology and sensors to drive safety, and, among other technological forms, machine learning (Tech Target, 2016).

Machine Learning: An Offshoot of Artificial Intelligence

Machine learning does in fact fall under a category of narrow A.I. However, simply suggesting machine learning is a form of artificial intelligence is a narrow sided and inaccurate assessment. Machine learning does technically fall under the category of narrow A.I., but in reality it is so much more than a spam folder or Facebook newsfeed. With these other forms of narrow A.I., an algorithm is input, allowing the computer system to analyze information in order to perform a very specific task. In the form of machine learning though, the system uses the input algorithms to learn from the data it receives, in order to make a possible prediction or educated assumption on the interactive world around it.

For example, with a spam folder (at least the standard spam folder used in most email services), the narrow A.I. is used to identify potential spam. From time to time it may miss a spam message, or flag a certain sender as spam. If a user identifies a file as spam, the spam folder will add this to the list of accounts to block (or, on the reverse side, remove the email from the spam list if the content is not spam). Although a user can add or remove information from the spam folder, it does not learn from the addition or subjection. It does not analyze the information included within the message, sender email address and title and use it to improve its spam filter ability. If it did, it would use computer learning (Forbes, 2016).

Examples of Computer Learning

There are many examples of computer learning, both large and small. One of the most popular currently is the digital assistant (Amazon’s Alexa/Echo, and Google Home are the two most advanced and widely used). These devices not only provide information, but learn on the fly in order to offer more specific results to each user.

The Blending of Machine Learning and AI

It is possible many confuse the term of artificial intelligence and machine learning because, in many cases, artificial intelligence used in technology has transitioned into machine learning. A prime example of this rests in a search engine’s performance. Google’s search results early on relied on keyword input. A user would input keywords and the search engine would utilize its specially crafted algorithm to provide results. However, if one user in Michigan and another in Nevada typed in the same basic keywords, they would receive the results. The search would use artificial intelligence to crawl through millions of data points to provide results, but it would not take into account the individual making the search request.

Eventually, search engines such as Google began to implement machine learning into search. This way, the search engine could not only provide desirable results based on the input algorithm, but it could learn from user interaction and adapt to these requests. In a way, Google Search is the poster child of narrow A.I.’s evolution into machine learning (Wired, 2016).

The Push for General AI

The quest for general A.I., such as the Star Wars droids, continues to be a major goal in artificial intelligence research. However, to reach these goals, computer learning will play a major role, as the computerized device must be able to learn and adapt to its environment. In this way, artificial intelligence will lead to the development of computerized learning, which leads to the continued development of A.I. So, while computer learning does stem from narrow A.I., it is in itself an evolved, elevated version of it.

While often subtle, the differences between machine learning and artificial intelligence can prove vast. Understanding this difference is necessary for an enterprise considering the implementation of such technology in current or future product releases or within the corporate network itself. As the industry progresses, the two technologies will continue to develop new traits, differentiating the two even further. However, for earlier adopters of the technology, in-depth knowledge of the two is a must.

If our team can help you harness the benefits of AI and Machine learning, please connect with us.

28th of July — 2017

How Personalization is Changing Content Marketing

Chris Risner

T he business world remains in constant motion and marketing is no different. With new marketing technologies and means of connecting with customers, companies need to remain in constant state of change and optimization in their quest to improve its products, productivity and production.

In recent years, the use of personalization strategies and technology as a means for staying ahead of the competition has significantly influenced the way businesses market and grow branding efforts. Because of this, content marketing has been shifting in the last few years. Content personalization has grabbed the market’s attention as widespread adoption of the personalization technologies and techniques occur. To stay competitive and achieve a higher return on investment in marketing expenses, it is becoming more and more critical to use personalization as one of the main tactics within an overall content marketing strategy. Failure to do so will likely be a mistake for any business in the long run.

What is Personalization?

A more detailed overview on personalization, what it is and how to implement it is available in a previous post located here. As well as a discussion of how CMS personalization can help convert more leads. However, as a general overview, personalization is when a website provides a customized, unique experience for each visitor to the site. So, instead of providing a universal website with the same displays and highlighted content, the unique experience is tailor made to better serve the specific visitor. By improving services and connectivity to the visitor, the likelihood of a sale or website conversion increases dramatically (Optimizely, 2017).

The Personalized Experience

The idea of a personalized experience is nothing new. In fact, offering unique shopping and purchasing experiences to consumers has been around for centuries. From monogrammed bath robes to customized sneakers, personalized experiences are offered by companies for two reasons. The first is to provide a premium service, above and beyond the average purchase. A personalized set of wine glasses gives a unique, one-of-a-kind feet to it, all while the produce sells for more. The second is to stand out from the competition and attract in customers.

With more and more companies now providing personalized services, it has become more of the norm than the exception. Major corporations such as Nike allowed customers to personalize just about everything purchased from the company, while Coca-Cola offers what it refers to as a “Freestyle” machine, which gives patrons access to 100s of flavor combinations. However, customization does not simply begin with a consumer coming into a facility to purchase goods or visiting a website in search of products. The personalization designed for new leads or prospects must begin at the first contact or first interaction. This is when a consumer or visitor is first made aware of the company and the services and products it offers. In other words, through an advertisement or other marketing effort (Forbes, 2016).

How to Personalize Content Marketing

Whenever a company interacts with a potential customer, there is the opportunity to make a sale or, at the very least, develop a lead. This interaction should leave a desirable impression on the consumer, and the most powerful tool to do this is to personalize content. In fact, according to a survey conducted by Lux Research (2017), consumers are willing to pay more money for a personalized experience.

Google and Amazon are two pioneers of personalization. The head of Amazon famously said early on in the existence of the website, if the company had a million customers he’d rather have one million versions of Amazon instead of one. As personalization has become more expected than anything else though, simply providing product recommendations on a store front no longer cuts it. Sending a customer discounts off of similar items they purchased monthly in the mail isn’t enough either. These are all staples of companies that have already connected with a consumer. Content marketing personalization is about connecting with a consumer the business has not yet sold to. Thankfully, personalizing content marketing doesn’t need to be overtly complex.

There are three easy steps to personalizing content marketing. For starters, the marketing material should not be bogged down with unnecessary information. It is always best to keep it simple over attempting to put too much information in. Providing recommendations based on both history and interest helps catch the customer’s attention.

The second step is to customize the marketing message to fit the need of the customer. Not all customers have the same needs, so the best way to connect with a potential client is to create a unique message. By taking into account the customer’s age, location, history and other data collected off of the customer’s IP address, it becomes easier to tailor forge a unique message.

Lastly, the content needs to be current. Not all customers want to be trend setters, but a vast majority want to go with what is new. Outdated marketing material, including images and other forms of media, can turn off a perspective customer. This is true not only for content produced several years ago but for a different season entirely (One Spot, 2017).

By taking into account these three steps and the information collected on the consumer, it becomes far easier to create advertisements with a personalized touch to it. The personalization should carry on through the marketing approach all the way through the website. For businesses not currently utilizing personalization in its marketing approach, it doesn’t take much additional effort to customize the company’s outreach potential. Despite this, there are businesses throughout the United States failing to incorporate these three simple steps in producing their personalized marketing and consumer outreach.

Why Some Marketers Don’t Use Personalization

Despite the proven benefit of content marketing personalization, many companies still turn a blind eye to the potential personalizing their content. Growing sales and increasing customer engagement through the use personalization can provide improved forms of communication and perceived value to customers and website visitors. Why do some marketers skip out on almost a sure fire way of boosting sales? According to a survey conducted by Demand Metric, 59% of marketers stated a lack of technology. In addition, many claimed that they did not have the necessary resources as one of the main reasons for failure to properly adopt personalization techniques.

Not taking advantage of content marketing personalization due to a lack of resources or technology simply is no longer a viable option, however. The risk of falling behind is too great and the advantages too enticing for marketing departments to wait to explore personalization options. Gartner published a study in 2015 indicating companies utilizing personalized elements within its content marketing would outsell those companies not using the marketing approach by at least 20% in 2018. As 2018 stands right around the corner, dropping by a 20% sales amount to the competition simply because of a “lack of technology” will fall more and more flat as an excuse. It also may be the reason why some companiesvstruggles to survive or even go out of business. Businesses with the available technology and resources will not take it easy on the competition. With the value of personalized content marketing increasing by the day, there are no more excuses. For a company to reach its fullest sales and growth potential, it must take advantage of personalized content marketing.

Customers have come to expect a personalized shopping experience. Convenience isn’t the only reason more consumers purchase goods through online retailers than in-person. The ability to receive a personalized service while visiting a website makes the entire visit to a website more beneficial and desirable for the consumer, which keeps them coming back. With the implementation of personalization, content marketing will never be the same, and businesses dragging their feet to bring about such advertising changes will suffer from the lack of customer connectivity. For any business serious about customer growth and providing the best shopping experience possible, personalizing content market is a must. Delaying any longer is simply no longer an option.

If our BlueBolt team can help your team increase personalization to engage your customers, please connect with us.

5th of July — 2017

Use CMS Personalization to Convert More Website Leads

Chris Risner

I n the world of marketing, establishing a connection with the key demographic and the individual is essential. Without a connection, clients have no reason to feel anything and need to a company. Instead they may turn to the competition, taking their business with them.

While marketing through traditional means does require some element of a wider generalization when reaching specific audiences, website personalization allows companies to tailor each visitor’s experience to better fit their own needs. This goes a long way in establishing a connection with the potential customer, increasing the chance of the visitor turning into a customer or, at the very least, a new potential lead. This is exactly why all business owners need to implement CMS personalization into their website.

What is a CMS?

More than likely, an enterprise is already going to run a CMS, but for those who don’t or those who are unsure of the system in place, CMS stands for content management system. It is a software application used to assist in the management and creation of digital content. While not exclusively for, a CMS is most commonly used at the enterprise level for managing the massive amount of information coming in and leaving a website (at the enterprise level it may also be referred to as Enterprise Content Management, or ECM). A CMS is essentially software for organizing and delivering the website to the world.

When an individual visits a website, they leave a trail of all sorts of useful information. From previous websites that they visited to the pages they access on the corporate page and how long they stay on a specific page, a considerable amount of data gets captured. With the help of a content management system, it is possible to collect all of this user data in one place. Having all data on hand in one location makes analyzing visitor information easier and more accurate (Tech Target, 2014). It also allows for easy access to the information to customize the experience. One simple example is using the geographic information of where the visitor is located to show relevant promotions for local events on the site rather than an event 1000 miles away.

Important Features of a CMS

There are dozens of service providers offering a content management system at the enterprise level. Each service provider does bring specific benefits and features to the table. However, nearly all CMS software does come with a handful of features. Some of the most important features available in a CMS includes:

Indexing
Format Management
Revision Features
Publishing

Indexing, searching and retrieving information in real time is important for any business. The ability to recall files and other information in real time makes performing edits and upgrades to a website to better fit the needs of a customer easier.

Websites may not include a host of different format types. While the majority of pages are written in an HTML document for Internet viewing, others are uploaded as PDF documents for easy downloading.

There are times when a website edit may not provide the desired results. A quality CMS provides revision features that allows Website admins to revert back to a previous release of the website. This way, even if new changes are made to the site, if these changes do not prove beneficial everything can be restored without issue.

Along with revisions, a quality CMS should provide publishing features, ranging from templates all the way to tools designed to help a website designer gain valuable methods to modify the website whenever necessary (HubSpot, 2011).

Personalization of the Internet

Personalization is not something that simply happened over night. While it may have seemed to come about relatively suddenly, services such as Google and Amazon have been experimenting with providing a unique, customized experience to visitors for nearly a decade. According to Search Engine Land (2009), Google released new personalized search services on a large scale in 2009 (although Google had been releasing gradual updates providing semi-personalized searches for several years prior).

Companies such as Google and Amazon have the ability to profit a considerable amount off of personalized searches. By showcasing similar search results based on a user’s past search history, stores such as Amazon can make sure visitors not only identify what they logged online for with less effort, but they may find additional products they originally had no interest in buying, but end up buying anyway, all due to website personalization. By implementing CMS personalization into a website, businesses around the world have the ability to offer a customized, unique visitor experience, which in turn boosts sales and helps convert more website leads.

What Can CMS Personalization Do for Your Team?

The bread crumb trail of data website visitors leave when searching a particular page can paint an in-depth picture of the individual. It not only indicates how they arrived on the page (Google, Facebook, direct link or so on), the device they are using, their geographical location, how long they visit and potentially more specific information. All of this information can then be used by the CMS to create a personalized, custom experience for the visitor. If a company provides services in a half-dozen different states, the information obtained through the user’s IP address can notify the website of their location, which in turn loads the correct information. This way, a user in Michigan may see visuals of the Great Lakes while someone in San Diego may see the Pacific Ocean. The localized personalization is just one way to produce a unique website experience.

As an individual interacts with the website, the site itself grows smarter and can produce a finer-tuned image of what the visitor might want. This may inform the website to recommend a specific product, or highlight a service the individual already clicked on. By showcasing what a visitor wants, CMS personalization has the ability to dramatically transform a company’s e-commerce presence (CMS Wire, 2017).

The True Benefits of Personalization

The fact of the matter is customers want a personalized experience. In a recent report published by Accenture, 75 percent of all consumers said they are more likely to purchase products and services through an e-commerce website that knows their name and can provide desirable recommendations based off of previous purchases. Additionally, 63 percent of consumers in the survey said they hold a specific company to a higher level and think more positively of the company by recognizing them upon visiting the site. Beyond all of it, one of the most telling statistics is nearly 80 percent of all consumers will only engage with a website that provides personalization and 77 percent of shoppers said they made purchases based specifically on recommendations from a service that recognized them (Accenture, 2016).

Nearly every study done on the subject points towards the importance of website personalization. At the enterprise level, additional assistance is required in order to implement these personalization elements. With the help of CMS personalization, any business can boost exposure and increase both sales and potential leads.

In the modern day of Internet browsers, users now experience a certain level of personalization. Due to more and more customized experiences while surfing the Internet, the need for instant customer gratification becomes much more vital in turning a website visitor into a potential customer or lead. With the help of CMS personalization, a website offers more information and content of interest to every single person who visits the page. So by taking advantage of the powerful connective aspects of CMS personalization, the business will grows its e-commerce department while converting more website leads at the same time.

If our talented, senior-level team can help you deliver on your next CMS project, please connect with us.

28th of June — 2017

What is Website Personalization and Why Is It Important?

Chris Risner

C onnecting with customers is the top priority of all marketing material to come out of a business. A company website, when utilized properly, provides a variety of benefits, ranging from e-commerce store and point of sale to literature and media on services provided.

Marketing and customer outreach are two additional aspects of the website. When a client arrives on site, a well designed website works as all quality advertising does. It connects with the customer, highlighting how it can improve their lives or businesses. The most most successful marketing campaigns are finely tuned to meet the personality of a company’s key demographics. That is exactly what website personalization is and why it should be implemented into any business site.

What Exactly is Website Personalization?

Regardless of the form of marketing, a blanket approach attempts to cover all demographics, yet fails to target any. These advertising approaches typically stem from businesses with either an inferior marketing department or a company that does not understand its own target demographic. Instead of going after everyone, a business with proper understanding of its clients should personalize all marketing and outreach methods, to better meet the needs of its customers. Website personalization takes the same approach. It offers a customized experience for visitors, dedicated to meet their individual needs. Personalization highlights products, services, or content that a particular customer might like while connecting to them on a more personal level. By establishing this connection, a potential client becomes more inclined to not only shop or use the website, but return to the site for future needs (Hubspot, 2014).

The Development of Website Personalization

Jeff Bezos, the creator of Amazon and its nearly $100 billion empire, started from the ground up in the late 1990s. Even in the early infancy of the consumer driven Internet, Mr. Bezos understood the importance of creating a unique experience for all visitors. In 1998, he told the Washington Post the goal of Amazon was not to have one store. Instead, he said “…if we have 4.5 million customers, we shouldn’t have one store. We should have 4.5 million stores.” Jeff’s vision took years for technology to catch up to, but now, every individual who visits Amazon has a slightly different user experience. They see product recommendations based not only on previous product searches within Amazon, but on searches performed outside of the service.

The major problem with creating a single website for all customers is major corporations likely spend hundreds of thousands of dollars, if not millions, annually to identify their target audience, understand what they like and determine what sells a product and what doesn’t. All of this information is vital to the development of varying marketing campaigns. Despite all of this, with a static, single website, all of the money spent and valuable knowledge obtained goes right out the window. Instead, with website personalization, a company has the ability to take this valuable data and implement it into the website. This way, much like Amazon and other major online retailers, it becomes possible to provide a unique visitor experience while on the site.

Nothing is (or Should Be) One Size Fits All

Even when a company’s key demographic is universally the same, individual clients and customers are not. They may shop for slightly different items or have different buying habits. This is where individualized personalization really comes into play. While it does not change the aesthetics of a website for every visitor, it does alter what products are showcased. For no retail outlets, the website can provide regionalized weather information, news reports, travel insights based on season and so on. Everything is designed to meet the needs of the individual.

Customer outreach has greatly shifted over the past decade. Individuals now expect a personalized experience, dedicated to providing information more akin to their preferences. With the ability to ask digital assistants (such as services offered through Amazon, Google and Apple) questions and receive instantaneous responses to verbally informing a television what kind of program they are interested in, a customized response is more important now than ever before as it is what customers now expect. Offering this personalized experience through website personalization is what all companies, from small to the enterprise level, should strive for.

How is a Website Personalized?

Data is the friend of any website. Data mined from a visitors can provide valuable insights not only into the key demographics of a site, what advertisements are working and how inbound marketing campaigns are working. Real time data analysis makes it possible to directly affect the way a user experiences a website. The first time a visitor comes to a site the company will not have any information based on the user yet. However, that changes nearly instantaneously. When arriving at a cite, the user’s unique IP address provides them with a unique identity. As they click on different images, display listings or other interact with videos, all of this information is sourced and logged. If a visitor is spending time looking at outdoor lighting on a landscape company’s page, highlighted information on the website can target additional recommendations based on the outdoor lighting the visitor looked at. By taking in and continually analyzing real time data, it becomes possible to offer on-the-fly website personalization for every visitor.

As the same IP address returns to a website, additional information is obtained, which allows fine tuning of the personalization. However, real time data analysis is not the only element when it comes to website personalization. Personalization doesn’t matter much if it doesn’t increase sales. To ensure not only an improved personalized experience but to improve lead generation and sales, additional planning and continual improvements need to take place on the site. Planning for visitors comes from a greater knowledge of a company’s key demographic. By understanding what a visitor is likely to look for or why they are on the site, the entire layout of the website can be altered to better fit the target consumer’s needs.

Lastly, understanding how certain website personalization works and continuously making improvements allows a website to identify what is working and what isn’t. Not all personalization will lead to a potential client purchasing services from the website. If part of the website personalization continually underperforms and does not connect with the customer, it is necessary to adjust, remove or implement other personalization changes to correct the lack of sales generation. All of this becomes possible with the help of continually monitoring website analytics and data (Search Engine Watch, 2014).

The moment a visitor arrives, a website needs to connect with their personal needs, wants and desires. By understanding key demographics, it is possible for a company to setup its website to prove more attractive and beneficial to customers. This acts as a welcome mat, waving the customer in. As the individual spends time on the site, continual data analysis allows for a personalized, custom experience, unique to them and them alone. By taking advantage of website personalization, companies not only connect directly with a potential client, but increase the chance of the customer both making a purchase and returning for future purchases. Due to this, implementing website personalization is a must for all businesses.

If our team can help deliver on your website and personalization projects, please connect with us.