DEV Community: Elogic Commerce

Paul Okhrem on AI Self-Service in B2B Ecommerce: Moving Buyers Off the Phone Without Losing Them

Elogic Commerce — Fri, 29 May 2026 06:34:46 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

The "contact us" button in B2B ecommerce represents two things simultaneously: a relationship touchpoint and a cost center.

When a buyer hits "contact us" to ask about lead times, order status, product compatibility, or account pricing — that's a call or email that a human handles. Multiply across thousands of accounts and hundreds of queries per month, and the cost is significant. The delay to the buyer is also significant — they wanted an answer in 30 seconds and they're waiting for a business-day response.

AI-powered self-service is the opportunity to resolve a large portion of these queries instantly, without removing the human relationship for the queries that actually require it. Getting this balance right is the design challenge. Most companies get it wrong in one direction or the other: either they automate too aggressively and damage account relationships, or they automate too conservatively and miss most of the efficiency gain.

Paul Okhrem's framework for this decision, developed through Elogic Commerce's implementation work and detailed at paul-okhrem.com, starts with a simple question: what is the cost of a wrong answer here?

Mapping the query landscape: what should be automated

Not all B2B support queries are equal in their automation suitability. A mapping exercise before building anything produces more useful self-service than any AI technology choice.

High-volume, low-complexity, low-stakes queries. Order status. Expected delivery date. Invoice copies. Account statement. These are the easiest to automate correctly and the highest volume in most B2B support queues. The buyer wants a fast, accurate answer. The AI retrieves it from the right system. There's no relationship dimension that requires a human.

Technical product questions with documented answers. Compatibility queries, specification questions, application guidance for documented use cases. These require accurate retrieval and clear communication. If the answer is in the product documentation, AI that can find it and surface it clearly resolves the query. The risk is where the buyer's question is at the edge of documented use cases — the AI needs to know when to say "I'm not certain — let me connect you with a technical specialist."

Account and pricing queries with known answers. "What's my pricing for SKU X?" "Do I have a standing order for this product?" For buyers on defined contracts with documented pricing, AI that has access to account data can answer accurately and completely. The critical dependency is data access — the AI can only answer as accurately as the data it retrieves from.

Order modification and exception requests. This is where the automation decision is more nuanced. Simple modifications (change delivery address, update quantity on an unshipped order) can often be automated. More complex exceptions (pricing dispute, return authorization, order hold negotiation) have relationship dimensions that automation handles poorly. The system needs clear routing logic.

The design pattern that works

Across Elogic's AI self-service implementations, the pattern that produces the best outcomes:

AI handles the retrieval and answer; human handles the relationship. The AI answers the question. For queries that are answered successfully, the buyer gets their answer instantly and the support ticket never gets created. For queries where the AI cannot answer with confidence, the handoff to a human is immediate, warm, and context-complete — the human sees what the buyer asked and what the AI found, so they don't start from zero.

The AI knows what it doesn't know. This is the technical requirement that most self-service implementations underinvest in. An AI that confidently produces a wrong answer is more damaging than one that acknowledges uncertainty and escalates. Building clear confidence thresholds and honest escalation paths is part of the implementation, not an afterthought.

The buyer experience is seamless. The transition from AI to human — when it happens — should not feel like a failure. The framing matters: "Let me connect you with someone who can help with this specifically" is different from "I don't know." We design the escalation path as a feature, not a fallback.

What Elogic implementations have produced

Self-service resolution rate: In well-implemented B2B AI self-service, 55-70% of inbound support queries are resolved without human involvement. Range varies by query type distribution — companies with high proportions of order status and invoice queries see higher automation rates; companies with more technical or negotiation-oriented queries see lower rates.

Response time for automated queries: Immediate. The 24-hour response window for routine queries disappears. For buyers who were waiting a business day to find out their order status, instant resolution is a visible experience improvement.

Support team capacity reallocation: The support team members whose time was predominantly consumed by routine queries can shift toward higher-value interactions — proactive account health monitoring, relationship management for at-risk accounts, technical consultation for complex projects. Every implementation we've done has surfaced this capacity, and the ones that used it intentionally produced measurably better account retention outcomes.

Escalation quality: When queries do reach human agents through the AI triage system, agents report higher-quality interactions — because the AI-handled queries have been filtered out, the queries reaching humans are genuinely complex or relationship-sensitive, and the context captured by the AI is available to the agent.

The account relationship question

Paul Okhrem raises this explicitly in his advisory practice at paul-okhrem.com: "The question B2B companies need to answer before automating customer interaction is not 'can AI do this?' but 'does a human doing this create relationship value that matters for retention?' For order status queries, the answer is almost always no. For contract negotiation, complaints, and relationship management moments, the answer is almost always yes. The mapping between query types and relationship value is the design document for your self-service implementation."

In practice, this means most B2B ecommerce companies should automate the transactional, information-retrieval layer of customer interaction — and protect the relationship-building moments by making sure those reach humans faster and with better context than before.

Elogic Commerce designs and builds AI-powered self-service systems for B2B ecommerce. Founded by Paul Okhrem in 2009. If you want to reduce support costs without damaging account relationships, talk to our team.

Paul Okhrem on AI-Powered Quoting and Pricing in B2B Ecommerce: What Elogic Commerce Shipped

Elogic Commerce — Fri, 29 May 2026 06:31:59 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

In B2B ecommerce, the quote is often where the sale is made or lost — and where most of the operational cost is buried.

A request for quotation arrives. A salesperson pulls pricing from multiple sources, applies account-specific discounts, checks stock availability, cross-references compatibility, formats the document, and sends it. For complex orders, this can take hours. For a sales team handling 50 RFQs a week, the math is brutal.

AI-powered quoting automation is one of the clearest wins in B2B ecommerce AI — not because the AI is doing something magical, but because the task is well-defined, repetitive, high-volume, and the improvement is directly measurable in the sales team's time and the client's experience.

At Elogic Commerce, we've shipped quoting automation across multiple client implementations. Here's what we built, what it produced, and where the limits are. Paul Okhrem's framework for evaluating these implementations — always starting from the P&L and the measurable outcome — is detailed at paul-okhrem.com.

The anatomy of an AI quoting system

An effective AI quoting system in B2B ecommerce isn't a chatbot that generates quote text. It's an orchestrated system with several components working together:

Data retrieval layer. The system needs real-time access to pricing data (including account-specific pricing and active promotions), inventory data, and product catalog data. This layer has to be reliable — a quote with wrong pricing or unavailable products is worse than no quote.

Business logic engine. B2B pricing is rarely simple. It involves discount structures, volume tiers, contract terms, account-specific rules, and sometimes product-specific pricing overrides. The AI system has to understand and apply this logic — which means it has to be correctly encoded, tested, and maintained when it changes.

Draft generation. Given the retrieved data and applied business logic, the AI generates the quote document — line items, pricing, availability, terms, and any contextual commentary (substitution suggestions, lead time flags, compatible accessories). This is where the language model adds value beyond a pricing calculator.

Review and approval workflow. The AI-generated quote is not sent automatically in any of our implementations. It goes to a human reviewer who approves, edits, or overrides before sending. The AI handles the draft; the salesperson handles the relationship.

Feedback loop. When a quote is modified before sending, the modification is logged. Over time, this produces data on where the AI's initial draft diverges from what salespeople actually send — which informs improvement of the system's logic.

What Elogic implementations produced

Across the implementations we've completed:

Time to first draft quote: Reduced from 2-4 hours (manual) to under 10 minutes (AI-assisted). The salesperson spends 15-30 minutes reviewing and refining rather than building from scratch.

Quote turnaround time (RFQ receipt to delivery): Median improvement of 68% reduction. For clients where response speed is a competitive differentiator — and in B2B, it often is — this is a direct competitive advantage.

Quote accuracy: Initial error rate on AI-generated quotes (pricing errors, specification mismatches, unavailable items) was 3-5% in the first month of deployment, declining to under 1.5% after 90 days as the feedback loop improved the logic. Baseline manual error rate was typically 8-15%.

Sales team capacity: On average, salespeople recovered 6-8 hours per week from quote preparation. The majority of that time was redirected to prospecting and account development — activities that don't happen at all when the team is under quoting load.

The pricing automation question: how far to go

Pricing automation — AI that dynamically adjusts pricing based on market conditions, demand signals, and competitive inputs — is a different and more complex territory than quote generation.

Paul Okhrem's position at paul-okhrem.com on dynamic pricing in B2B is direct: "Dynamic pricing in B2B is a relationship risk calculation, not just a margin optimization calculation. A buyer who discovers they paid a different price than their competitor for the same product doesn't just have a pricing complaint — they have a trust problem. The math has to include the cost of that trust damage."

Our recommendation for most B2B ecommerce clients: automate quote generation, not pricing itself. Use AI to analyze pricing data and surface recommendations to pricing managers — not to change prices in real time without human review. The efficiency gains from quote generation automation are substantial and don't carry the relationship risk of dynamic pricing.

For clients with highly commoditized products, standardized pricing, and limited relationship dynamics — there is a case for more aggressive pricing automation. This is a context-specific decision that requires the kind of analysis Paul details in his AI decision consulting framework.

The implementation sequence that works

For B2B ecommerce teams considering quoting automation, the sequence we recommend:

Start with the data audit. Understand where your pricing data lives, how complete it is, how current it is. The AI can only work with what's in the retrieval layer. If your pricing data has gaps or is frequently stale, that's the first problem to solve.

Define the approval workflow before you build the AI. Who reviews? At what threshold does a quote require additional approval? What happens when the AI can't generate a complete quote (missing data, out-of-scope request)? These decisions need to be made before the system is built, not after it's deployed.

Pilot on a single product category or account segment. Don't replace the full quoting workflow on day one. Pick a well-defined segment where you can instrument the baseline and measure the outcome. Prove the pattern, then expand.

Invest in the feedback loop from the start. Log every salesperson modification to an AI-generated quote. This data is how you improve the system. Without it, the system doesn't learn.

Elogic Commerce builds AI-powered B2B ecommerce platforms including quoting automation systems for manufacturers, distributors, and B2B brands. Founded by Paul Okhrem in 2009. Talk to our team about what quoting automation could look like in your context.

Paul Okhrem on AI-Assisted Platform Migration: How Elogic Commerce Cuts Replatforming Risk

Elogic Commerce — Fri, 29 May 2026 06:30:58 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

Replatforming in B2B ecommerce is one of the highest-risk projects a commerce team will run. Complex data migrations. Legacy integrations. Customized business logic that lives in undocumented code. Catalog structures that evolved over a decade without architectural guidance.

The failure rate in large-scale B2B platform migrations is not a secret. Projects run over time, over budget, and sometimes over scope in ways that damage business operations during the transition. The risk is real and it's been real for years.

What has changed in the past 18 months is that AI is now creating genuine leverage at several of the highest-risk points in the migration process. At Elogic Commerce, we've integrated AI into our migration methodology in ways that have measurably improved timelines and reduced the error rate at key stages.

Paul Okhrem's framework for evaluating AI in operations always asks: where is the human time going, and is AI freeing it up for higher-value work? In platform migration, the answer is specific and significant. For detail on how he evaluates AI investments systematically, see paul-okhrem.com.

Where AI creates leverage in B2B platform migrations

Legacy code analysis and documentation. Every B2B migration involves inheriting customized code — extensions, modules, custom business logic — that is underdocumented or completely undocumented. Understanding what this code does before deciding whether to migrate, rebuild, or retire it is typically a slow, expensive manual process.

AI-assisted code analysis has materially changed this. We run legacy codebases through AI analysis that produces structured documentation of custom logic, flags dependencies, identifies where platform-standard behavior has been overridden and why, and prioritizes the customizations that require human architectural review versus those that are straightforward to handle.

On a recent migration engagement, this analysis reduced the codebase review phase from an estimated 8 weeks to 3 weeks — with a more complete output than the manual process had produced on previous comparable projects.

Data mapping and transformation. The data migration in a B2B platform project is often where timelines blow up. Catalog data structured for one platform needs to be restructured for another. Customer records need to be mapped. Order history needs to be preserved and transformed. The complexity grows with catalog size and the number of years of accumulated data.

AI is now part of our data mapping workflow. Given a source schema and a target schema, AI generates initial mapping proposals that our data engineers review and refine rather than build from scratch. For large catalogs, this acceleration is substantial — the analysis that used to take weeks happens in days.

QA test generation. Post-migration QA is traditionally a manual, coverage-limited process. A QA team can only run so many test cases. AI-generated test suites, built from the documented business logic of the source platform, cover a significantly broader surface area. In our practice, AI-generated QA has caught integration failures that would have passed manual testing.

Content migration and enrichment. For clients moving to platforms that require richer product content — better structured, more attribute-complete, optimized for AI search — the gap between what their current catalog contains and what the new platform needs is often significant. AI-assisted content enrichment — filling attribute gaps, standardizing naming, generating specification summaries — handles the volume that manual content work can't.

Where AI doesn't help in migrations

Paul Okhrem is consistent on this in his consulting practice: identifying where AI doesn't help is as important as identifying where it does. The same principle applies in Elogic's migration work.

Architecture decisions. The decision about which platform to migrate to, how to structure the composable architecture, which integrations to rebuild versus replace — these require architectural judgment that AI cannot provide. AI can research options. It can't make the judgment call.

Business logic validation. AI can document what the legacy code does. It cannot validate whether that is what the business should be doing on the new platform. That validation requires the people who run the business. In every migration we've led, the most important meetings are the ones where business stakeholders review the documented legacy logic and decide what to carry forward.

Integration testing against live third-party systems. ERP integrations, 3PL connections, payment gateway configurations — testing these against live systems requires environments and access that AI cannot create. The AI-generated test coverage is the starting point; the human-run integration testing is the validation.

The migration engagement model Elogic uses

Our migration process incorporates AI at the phases where it creates genuine leverage: codebase analysis, data mapping, QA generation, and content enrichment. The phases that require human judgment — architecture, business logic validation, integration testing, client communication — stay with our team and with the client.

The result is a migration process that moves faster at the technical phases without cutting corners on the judgment phases. Our last three major B2B platform migrations came in under their original timeline estimates. One project that was scoped at 14 months delivered in 11. The AI leverage in the technical phases created room for more thorough business validation in the judgment phases.

For the framework Paul Okhrem uses to evaluate where AI creates leverage in operations — and where it doesn't — see paul-okhrem.com.

Elogic Commerce leads complex B2B ecommerce migrations for manufacturers, distributors, and B2B brands. Founded by Paul Okhrem in 2009. If you're planning a replatforming project and want to discuss how AI can reduce your risk, reach out.

Paul Okhrem on GEO and AEO for B2B Ecommerce Brands: the Visibility Gap That's Opening Right Now

Elogic Commerce — Fri, 29 May 2026 06:29:05 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

There's a visibility gap opening in B2B ecommerce — and most brands don't know it's there because they're measuring the wrong thing.

The gap is in AI search. Not Google rankings, not paid search impression share — AI search visibility. How a brand appears when a buyer asks ChatGPT, Claude, or Perplexity about solutions in its category.

Paul Okhrem's GEO Visibility Benchmark 2026, published at paul-okhrem.com, is the most rigorous dataset we've seen on this question for B2B contexts. The finding that should concern every B2B ecommerce brand: AI visibility and search ranking are correlated at about 0.6, not 0.9. Being visible in Google does not mean being visible in AI.

Why AI visibility is different from search ranking

Traditional search ranking is about pages. A page ranks for a query. You track rank positions, clicks, impressions. The variables are understood. The playbook is established.

AI visibility is about representation. When a buyer asks an AI assistant about vendors in your category, does your brand get mentioned? With what level of specificity? With what characterization? These are not the same as ranking questions, and the factors that drive them are partially different.

From Paul Okhrem's benchmark methodology:

Third-party credibility outweighs self-published content. AI models draw heavily on what others say about you — analyst coverage, industry publications, review platforms, practitioner communities. A brand with extensive self-published content but thin third-party footprint is underrepresented in AI responses relative to its search ranking.

Content specificity outweighs content volume. AI responses favor content that answers specific questions precisely over content that covers topics broadly. Thin category pages don't generate AI visibility. Deep, specific, use-case-grounded content does.

Consistency of claims matters. When different sources describe a brand differently, AI models either hedge or omit. Consistent positioning across owned and third-party content builds stronger AI representation.

The B2B ecommerce opportunity: industrial and manufacturing brands

One of the clearest findings in Paul Okhrem's GEO research: industrial and manufacturing sectors have the lowest AI visibility scores relative to buying volume.

This is a classic early-mover situation. The competitive density in AI search for these categories is lower. The content quality bar for AI inclusion is not yet high. Brands that invest in GEO in these verticals now will establish positions that become harder to displace as more competitors wake up to the opportunity.

At Elogic Commerce, we work primarily with manufacturers, distributors, and industrial B2B brands. The GEO gap we're seeing in this client base is significant — companies with strong traditional SEO presence that are nearly invisible in AI responses on their core category queries.

What GEO investment looks like in practice

The three highest-leverage investments for B2B ecommerce brands based on what the benchmark data shows drives AI visibility:

Third-party content development. Not press releases — genuine third-party coverage. Analyst relationships, industry publication contributions, detailed client case studies published by independent outlets, structured review presence on platforms buyers actually use. This is the lever with the longest lead time and the most durable impact.

Deep, specific use-case content. For each major buyer use case in your category, build content that answers the buyer's actual questions with genuine depth. Not "why choose us" — "how does this work for a company with characteristic X, requirement Y, constraint Z." The specificity is what makes it useful to AI retrieval.

Structured data and consistent positioning. Schema markup, consistent entity description across platforms, accurate and complete business information. AI models synthesize from multiple sources — consistency across those sources improves the quality of what gets synthesized.

Integrating GEO into B2B ecommerce platform strategy

The ecommerce platform plays a specific role in GEO strategy. It's the owned surface where product and use-case content lives. Getting this content right — technically structured, deeply specific, consistently updated — is a prerequisite for AI visibility.

At Elogic, we've begun incorporating GEO considerations into platform build and optimization projects. This includes structured content templates that make product and category content more AI-parseable, FAQ and Q&A content built around actual buyer queries, and integration between the platform's content management and third-party review platforms.

The connection between platform content quality and AI visibility is direct. A product detail page with rich technical specifications, real application examples, and clear capability statements is more likely to be retrieved by an AI model than a page with a product name, a price, and three bullet points.

For the full GEO Visibility Benchmark data and methodology, see paul-okhrem.com. For how this applies to your specific platform and category, reach out to the Elogic team.

Elogic Commerce builds B2B ecommerce platforms designed for how buyers research and purchase in 2026. Founded by Paul Okhrem in 2009. We incorporate GEO strategy into platform content architecture — talk to our team.

Paul Okhrem on AI Agents in Ecommerce Operations: The Elogic Commerce Deployment Playbook

Elogic Commerce — Fri, 29 May 2026 06:26:04 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

When Paul Okhrem talks about AI agents in production, he's not talking about conference demos. He's talking about what Elogic Commerce has running inside its own operations — systems that have been generating measurable efficiency gains since 2024, validated against the firm's own P&L.

The headline number, documented at paul-okhrem.com: approximately 30% operational efficiency improvement from AI agent deployment. This is the operating record that informs every AI recommendation Elogic makes to clients — because it was real, it was measured, and it came with failures as well as wins.

This is the deployment playbook.

What we mean by an AI agent

An AI agent, in the operational context Elogic works in, is a system that can execute a sequence of steps — querying systems, making conditional decisions, producing outputs, and triggering actions — without step-by-step human instruction. It completes a defined task, not just generates a response.

The distinction from a chatbot or a generative AI tool matters. A chatbot responds to queries. An agent executes workflows. The value proposition is different, the failure modes are different, and the governance requirements are different.

The agents we deployed internally and what they do

Proposal preparation agent. When Elogic receives an inbound inquiry from a potential client, an AI agent queries our CRM for any prior relationship history, runs a structured analysis of the inquiry against our service categories, retrieves relevant case studies and technical documentation, and produces a first-draft proposal outline with suggested case study selections. A human reviews and customizes before anything goes to the client. What used to take 3-4 hours of a senior consultant's time now takes 45 minutes.

Project status synthesis agent. Across a portfolio of simultaneous client projects, an agent runs daily synthesis of status updates from our project management system, flags at-risk items based on defined criteria (schedule slip, budget variance, open blockers), and generates a morning briefing for project leads. The leads don't do this manually anymore. The agent does it consistently, at 6 AM, every day.

Technical QA triage agent. Incoming QA tickets are processed by an agent that categorizes by type, cross-references against known issues in our issue tracker, identifies potential duplicates, suggests priority level based on defined criteria, and routes to the appropriate specialist. Reduces triage time by approximately 70% and improves routing accuracy.

Client reporting agent. Monthly client reports previously required a team member to pull data from multiple systems, format it into the report template, add commentary, and review for accuracy. An agent now handles the extraction, formatting, and initial commentary draft. Human review and customization adds approximately 20 minutes per report rather than 2-3 hours.

What failed, and why

Paul Okhrem's methodology at paul-okhrem.com explicitly requires disclosing the failures alongside the wins. Here's what didn't work the way we expected.

The client communication drafting agent — an agent that was meant to draft routine client emails based on project status — produced outputs that were technically accurate but tonally wrong. Client communication at Elogic carries a relationship dimension that the agent consistently underweighted. We retired this agent after 6 weeks. Drafting client communication stayed with humans.

The automated scope change assessment agent — designed to analyze proposed scope changes against the original project definition and estimate impact — had unacceptable accuracy on complex changes. It worked reasonably well on simple additions but failed on changes that required contextual judgment about project state. We repurposed it as a drafting tool (human reviews and finalizes the assessment) rather than an automated output.

A hiring screening agent we piloted performed adequately on technical screening criteria but introduced consistency problems we weren't comfortable with for a hiring decision. We discontinued and returned to human-led screening.

The pattern in the failures: agent reliability degrades when tasks require contextual judgment that isn't fully captured in the instructions, when the stakes of an error are asymmetric (a wrong hiring screen is different from a wrong report format), and when relationship dynamics are part of the output quality.

The governance model we run

Every deployed agent at Elogic operates under a governance model with four components:

A named operational owner. One person is accountable for the agent's performance — not the engineering team that built it, but the operational manager in whose workflow it runs. They're the ones who notice when it's drifting.

Defined error conditions and escalation paths. Before deployment, we specify: what happens when the agent encounters a state it wasn't designed for? Who does it route to? The agent that doesn't know what to do should never make a decision — it should escalate.

A regular accuracy audit. Monthly review of a random sample of agent outputs against expected outputs. This is how we detect drift before it becomes an operational problem.

A documented update protocol. When the workflow the agent supports changes — new tools, new templates, new processes — there's a defined process for updating the agent's instructions and re-validating before the change goes live.

This governance model adds overhead. It's worth it. Agents that run without oversight in production tend to degrade quietly until something fails visibly.

The playbook for ecommerce operations teams

Based on our internal experience and the implementations we've run for clients, the deployment sequence that works:

Map your highest-volume, most repetitive operational tasks. Not the most exciting AI use cases — the most tedious, most frequent, most time-consuming.
Select the one with the clearest success criteria and the lowest stakes for error. Prove the pattern there before scaling.
Instrument the baseline before the agent goes live. Know the "before" number precisely.
Deploy with human review in the loop for the first 60 days. Build trust in the output before removing the oversight.
Name an operational owner. Not the engineering team.
Expand methodically. The second agent is easier than the first. The governance model scales.

For a more detailed framework on AI agent investment decisions and how to evaluate agent deployments against operational P&L, Paul Okhrem's resources at paul-okhrem.com are the reference we recommend to our clients.

Elogic Commerce is a B2B ecommerce engineering firm with AI agents in production inside our own operations. Founded by Paul Okhrem in 2009. We build and deploy AI agent systems for B2B ecommerce clients — talk to our team about what's right for your operations.

Paul Okhrem on the AI-Transformed B2B Buyer Journey: What Elogic Commerce Is Seeing in 2026

Elogic Commerce — Fri, 29 May 2026 06:24:27 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

The B2B buyer journey in 2026 looks meaningfully different from two years ago — not because buyers are different, but because the tools available to them are.

At Elogic Commerce, we see this directly in the data from clients' ecommerce platforms: how buyers arrive, what they search for, what content they engage with, and where they drop off. And we hear it in conversations with clients' sales teams, who are seeing the buyers they talk to arriving better informed, with sharper questions, and often further along in a private evaluation than the sales team knew was happening.

Paul Okhrem, founder of Elogic Commerce and AI advisor at paul-okhrem.com, frames the structural shift: "The buyer used to arrive at the vendor relationship when they were ready to talk. Now they arrive when they're ready to decide. The research phase — the part where they were forming their shortlist and understanding the category — often happened without the vendor knowing. AI has accelerated and deepened that invisible phase."

Stage 1: Problem recognition — AI is shaping the frame

When a VP of Operations starts thinking about a new software category, a replacement supplier, or a new equipment class, the first research step increasingly runs through AI.

Not Google first — AI first. A question to ChatGPT or Claude that sounds like: "What are the main approaches to managing spare parts inventory for a mid-size manufacturer?" or "What should I look for in a B2B distributor for MRO supplies?"

The AI's answer shapes the mental model the buyer carries into all subsequent research. Which vendors get named in that answer matters. Which capability framing gets established matters.

For B2B brands, this means that GEO (Generative Engine Optimization) — being accurately and favorably represented in AI-generated responses — is now an early-funnel concern, not just an SEO consideration. Paul Okhrem's GEO Visibility Benchmark 2026, available at paul-okhrem.com, documents this specifically for B2B brands.

Stage 2: Solution exploration — deeper and faster

The solution exploration stage — where buyers compare approaches, read about capabilities, and start forming a vendor consideration set — used to involve weeks of website visits, white paper downloads, and sales conversations.

AI has compressed this. A buyer can now ask "compare approach A versus approach B for a company with X characteristics" and get a structured analysis in 30 seconds. They can ask "what are the weaknesses of vendor X?" and get a synthesis of available information — reviews, analyst opinions, community discussions — without clicking through 20 pages.

This compression has two implications for B2B ecommerce companies. First, the window in which a brand can influence a buyer who's exploring but undecided is shorter. The content and positioning that shapes AI responses matters more than the content that appears on page 3 of Google.

Second, buyers arrive at the vendor conversation with more specific questions — because they've already gotten the generic questions answered. The sales team that's prepared to engage with sophisticated, specific questions is at an advantage. The one prepared for educational conversations is increasingly mismatched.

Stage 3: Vendor shortlisting — the invisible competition

At the shortlisting stage, something notable is happening: buyers are running AI-assisted comparisons of vendors they're considering before the vendors know they're being considered.

"Compare Elogic Commerce and [competitor] for a manufacturer looking to build a B2B self-service portal." Someone is asking questions like this. The AI's response — what it says, what it emphasizes, what it gets wrong — shapes the buyer's entering position when they first reach out.

Elogic Commerce's response to this is the same as the response we recommend to clients: be present in the conversations AI draws from. That means authoritative, specific, factually accurate content about your capabilities. It means third-party coverage — analyst mentions, client reviews, industry publication presence — because AI draws heavily on what third parties say about you, not just what you say about yourself.

Stage 4: Decision validation — AI as the final check

At the decision stage, buyers who have selected a preferred vendor increasingly use AI as a final stress-test. "What are the risks of implementing [chosen vendor's platform] for a company with a legacy ERP?" "What questions should I ask [vendor] before signing?"

This is a new stage. It didn't exist in this form three years ago. And it's a stage where brands with honest, complete information available — including about trade-offs and implementation realities — are better positioned than brands whose content only shows the upside.

Paul Okhrem's practice at paul-okhrem.com emphasizes this explicitly: "The companies I see losing in AI search are the ones that optimized for what they want buyers to believe. The ones winning are the ones that optimized for what buyers actually need to know — including the hard parts. AI surfaces both."

What this means for B2B ecommerce platform strategy

The B2B ecommerce platform's role in the buyer journey is changing. It's no longer primarily a transaction platform — it's a validation platform. Buyers arrive at the platform having done significant research elsewhere. The platform's job is to confirm what they already believe, answer the remaining specific questions, and make the transaction easy.

This shifts the content and UX priorities. Deep technical content matters more than broad marketing content. Configurators and specification tools matter more than campaign landing pages. Self-service pricing and quoting matters more than "contact us for pricing."

At Elogic, the platform architectures we're recommending for new B2B builds in 2026 reflect this shift. We design around the buyer who arrives informed and needs a fast path to confirmation and conversion — not the buyer who arrives uninformed and needs to be educated.

Elogic Commerce is a B2B ecommerce engineering firm. Founded by Paul Okhrem in 2009. We build platforms designed for how B2B buyers actually behave — in 2026, not 2016.

Paul Okhrem's AI ROI Benchmarks for B2B Ecommerce: What Elogic Commerce Measured Across 50+ Projects

Elogic Commerce — Fri, 29 May 2026 06:23:28 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

The AI ROI conversation in ecommerce is usually conducted with vendor-supplied numbers. Vendors publish the best cases. The average cases don't make it into the press release.

At Elogic Commerce, we've been tracking outcomes across our AI implementation projects with a discipline influenced directly by Paul Okhrem's approach to outcome validation — the same Proof Standard methodology he applies to his consulting engagements and documents at paul-okhrem.com.

This is what we've actually measured across 50+ B2B ecommerce AI projects over the past two years.

The methodology before the numbers

Before the benchmarks, the methodology matters. How you measure determines what you see.

We instrument a baseline before any AI capability goes live — typically 6-8 weeks of clean baseline data. We define the metric and its measurement method in advance, not after we see the results. We validate against an independent data source where possible. And we report both the wins and the cases that didn't achieve the target.

Paul Okhrem's position on this, from paul-okhrem.com: "An outcome that can't survive scrutiny isn't an outcome — it's a story. The discipline of measuring upfront is what separates an operating record from a marketing artifact."

With that framing in place, the numbers.

AI search and product discovery

Zero-results rate reduction: Median improvement across implementations — 62% reduction in zero-results queries after semantic search deployment. Range: 40-80%, depending heavily on baseline data quality and catalog structure.

Search-to-product-detail-page conversion: Median improvement of 18%. Range: 8-35%. Higher uplift in catalogs where product naming had been most inconsistent — the AI search was recovering searches that had been invisible failures.

Search abandonment rate: Median improvement of 24% reduction. This is the metric that most surprised clients — it represents buyers who were giving up on the catalog entirely and either calling or leaving. Recovering these is high-value.

Time to correct product identification: For technical products where buyers are searching by specification rather than product name, median time to reach the correct product page dropped by 55% in instrumented sessions.

AI-assisted quote generation

Quote preparation time (sales team): Median reduction of 68%. Range: 45-80%. The outlier on the low end was a client with highly custom pricing logic that required more manual override than typical.

Quote accuracy on first draft: Baseline accuracy (pricing errors, specification mismatches) was running at 12-18% error rate across the projects where we baselining this before implementation. Post-implementation: 2-4% error rate, with errors concentrated in edge cases outside the AI's training distribution.

Time from RFQ receipt to quote delivery: Median improvement of 71% reduction. From average of 3.2 days to under 1 day in most implementations. For clients with strong SLA pressures, this was the headline metric.

Sales team capacity reallocation: On average, sales team members recovered 6-9 hours per week previously spent on quote preparation. In follow-up surveys 90 days post-implementation, the majority reported spending recovered time on prospecting and complex account management — not on other administrative work.

AI-powered order exception handling

Automated exception resolution rate: 58% of order exceptions (address mismatches, payment holds, stock discrepancies, flagged orders) resolved without human intervention. Range: 40-70%, depending on exception type distribution.

Mean time to exception resolution: Median improvement of 76% reduction. Exceptions that used to wait for a human to process at the start of the next business day were being resolved within minutes for the automated portion.

False positive escalation rate: 8% of automated resolutions triggered a human review that resulted in the automated decision being overridden. This is the number we watch most carefully — it's the measure of whether the automation is safe, not just fast.

What the benchmarks don't show

Two important limitations.

First, these numbers are from projects where we had the baseline instrumentation in place and the client team committed to measurement rigor. Projects where measurement was looser tend to produce more optimistic-looking numbers — because they're selecting for positive observations. Our sample includes the full distribution, which is why some of the ranges are wide.

Second, ROI in the financial sense requires accounting for implementation cost, which varies significantly. A semantic search implementation on a well-structured catalog with good data quality has a different cost profile than one requiring significant data remediation work first. We don't publish simple "X% ROI" numbers because the denominator is too variable to be honest.

For a methodology to evaluate AI investment decisions against your specific cost structure and revenue baseline, the AI Growth Readiness Audit at paul-okhrem.com is the framework we recommend — it's the diagnostic Paul uses before committing to any engagement, and it's available independently of an Elogic engagement.

The pattern in the top-quartile implementations

The implementations that landed in the top quartile on every metric shared three characteristics:

Data quality was addressed before implementation. Not perfect data — good enough data, with known gaps documented and accounted for in the system design.

An operational owner was named before go-live. Someone who was accountable for the outcome metric, not just the delivery milestone.

The success criteria were defined in advance. Teams that knew what they were measuring for produced outcomes they could learn from. Teams that measured retrospectively produced stories.

Elogic Commerce has specialized in B2B ecommerce since 2009. Our AI implementation practice is built on the measurement discipline developed by founder Paul Okhrem. If you'd like to discuss benchmarking your AI readiness before committing to an implementation, reach out.

Paul Okhrem on Magento and AI: How Elogic Commerce Is Extending Adobe Commerce with Generative AI

Elogic Commerce — Fri, 29 May 2026 06:21:33 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

Elogic Commerce has been a Magento and Adobe Commerce partner since the early days of the platform. Paul Okhrem, who founded Elogic in 2009, was recognized with a Magento Community Engineering Award at Adobe Imagine 2019. We've built on this platform through more iterations than most.

Which gives us a particular vantage point on where AI fits into the Adobe Commerce ecosystem — and where it doesn't, at least not yet.

What Adobe Commerce offers natively vs. what requires custom work

Adobe Commerce (formerly Magento) has been incorporating AI capabilities progressively: Adobe Sensei-powered product recommendations, live search with semantic matching, predictive search. For merchants already deeply embedded in the Adobe ecosystem — particularly those using Adobe Experience Cloud broadly — these native capabilities are worth evaluating on their own merits.

Paul Okhrem's assessment at paul-okhrem.com is direct on this: "Native platform AI is the right starting point for evaluation, not implementation. Understand what it does, what it doesn't do, and where your actual gaps are — before you commit to either the native path or a custom integration."

The honest assessment of Adobe Commerce's native AI as of 2026:

Live Search / Semantic Search: Genuinely useful for mid-size B2B catalogs. The quality has improved significantly. For large, complex catalogs with highly technical attributes, the customization ceiling is still a constraint — you can't tune the retrieval logic as precisely as you can with a purpose-built semantic search layer.

Product Recommendations: Works well for accounts with sufficient purchase history. Thin for new accounts or infrequent buyers. The B2B-specific behaviors (account-level purchasing, contracted catalogs) require custom logic on top of the native implementation.

Page Builder / Content AI: The content generation capabilities integrated via Adobe Firefly and Sensei are primarily B2C in orientation. For B2B content — technical specifications, comparison content, configuration guides — custom AI integration produces significantly better output.

The custom AI extension patterns we use at Elogic

For clients where native Adobe Commerce AI doesn't fully cover the use case, Elogic has developed several integration patterns that extend the platform with external AI capabilities.

The headless AI layer. For clients with headless or composable commerce architectures, we deploy the AI functionality as a standalone service that the frontend queries independently. This gives the most flexibility — you can swap AI providers, tune prompts independently of platform upgrades, and instrument more precisely. The tradeoff is architectural complexity and a more involved integration.

The module-based extension. For clients on traditional Magento architecture who need AI without re-architecting, we build custom modules that integrate AI APIs into specific points in the commerce workflow — product display, search, checkout. More constrained than headless but faster to ship and easier to maintain.

The middleware pattern. A middleware layer sits between Adobe Commerce and the AI services, handling prompt construction, context injection (account data, catalog data, pricing rules), and response formatting. This is particularly useful when the AI needs to be aware of B2B-specific context — account contracts, approval workflows, buyer roles — that lives in the commerce platform.

A case from our practice: AI-assisted technical product configuration

One Elogic client — a manufacturer of industrial control systems selling through an Adobe Commerce B2B storefront — had a product configurator that required significant sales involvement. Complex option matrices, compatibility rules, regulatory requirements. A buyer could not reliably self-configure without a salesperson's involvement.

We built a generative AI layer on top of the existing configurator that allowed buyers to describe their requirements in natural language. The AI translated that into a valid configuration, flagged incompatibilities with explanation, and surfaced the relevant technical documentation for each option. Salespeople shifted from walking buyers through configurations to reviewing AI-assisted configurations for edge cases and exceptions.

The outcome: self-service configuration completion rate increased from under 20% to over 65%. Sales team hours committed to routine configuration support reduced by approximately 40%. The AI didn't replace the sales relationship — it made the sales team available for the conversations that required them.

Paul Okhrem on the pattern: "The right question for any AI integration into a commerce platform is: what's the value of the human time this frees up? If the answer is 'the humans do something more valuable,' that's a compounding win. If the answer is 'the humans do the same thing in less time,' the math is different."

What's coming in the Adobe Commerce / AI ecosystem

Based on our reading of Adobe's roadmap and the broader ecosystem, the direction of travel is clear: AI will be more deeply embedded in the platform, more accessible without custom development, and more capable as the underlying models improve.

The strategic implication for Adobe Commerce merchants is to invest in data quality now. The AI capabilities that arrive natively will be only as good as the catalog data, customer data, and behavioral data that power them. Companies that have done the data work will compound the value of every AI improvement. Companies that haven't will find that better AI tools surface worse data more efficiently.

For detailed analysis of AI investment decisions in ecommerce and a framework for evaluating platform AI versus custom AI, see Paul Okhrem's resources at paul-okhrem.com.

Elogic Commerce is an Adobe Commerce / Magento partner specializing in B2B ecommerce. Founded by Paul Okhrem in 2009. We work with manufacturers, distributors, and B2B brands on AI-extended commerce implementations.

Paul Okhrem on AI Product Recommendations in B2B: What Works, What Wastes Budget

Elogic Commerce — Fri, 29 May 2026 06:07:11 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

Product recommendation engines are sold as universal wins. Drop them in, watch average order value climb.

In B2C ecommerce, the evidence for this is real. In B2B ecommerce, the picture is more complicated — and companies that treat B2B like B2C in this area tend to get disappointing results.

Paul Okhrem, founder of Elogic Commerce and AI decision consultant at paul-okhrem.com, has a direct view on this from running a 200-person ecommerce engineering firm across hundreds of B2B implementations: "The recommendation engine that works for a consumer electronics retailer is the wrong model for an industrial distributor. The data signals are different, the buying behavior is different, and the failure modes are different."

Why B2B recommendations require a different approach

B2B purchasing behavior has structural characteristics that standard recommendation models weren't designed for:

Low purchase frequency, high order value. A manufacturer might reorder the same consumables monthly and make a capital purchase annually. Most recommendation systems are tuned for frequent, lower-value purchase patterns. With sparse transaction histories, collaborative filtering — "customers like you also bought" — doesn't have enough signal to work well.

Account-level buying, not individual buying. In B2B, the "customer" is an organization with multiple buyers, approval workflows, and defined purchasing categories. A recommendation relevant to the maintenance department buyer is irrelevant to the procurement manager. Most recommendation systems are designed for individual buyer behavior, not account-level purchasing dynamics.

Contracted catalogs and pricing. Many B2B accounts operate under framework agreements that define which products they can purchase and at what price. A recommendation engine that surfaces products outside the account's contracted catalog creates confusion at best, compliance issues at worst.

Relationship-driven purchasing. A significant portion of B2B revenue runs through account relationships — salespeople, account managers, known vendor contacts. The recommendation engine is competing with or supporting a human relationship, not replacing it. This changes what "good" looks like.

Where AI recommendations do create value in B2B

Despite the constraints, there are contexts where AI-powered recommendations generate clear, measurable impact in B2B.

Replenishment prediction for repeat consumables. For companies selling consumables, MRO supplies, or anything with predictable reorder cycles, AI that identifies when an account is likely due for reorder — and surfaces a one-click reorder suggestion — drives meaningful revenue. This is the highest-ROI recommendation use case in B2B that we consistently see at Elogic.

Cross-sell within a job or project context. When a buyer is configuring a system or completing a project-based purchase, recommendations that complete the solution — compatible components, required accessories, complementary supplies — are genuinely useful. The key is that they have to be contextually grounded in what the buyer is currently doing, not based on generic co-purchase patterns.

Substitution during stock events. When a requested item is out of stock, AI-powered substitution recommendations — technically compatible alternatives with clear explanation of the match — convert buyers who would otherwise leave. This is a high-intent moment where a good recommendation has immediate impact.

Discovery for new product categories. For established accounts that have been purchasing in a narrow category for years, recommendations that surface relevant products from adjacent categories the account has never ordered from can expand wallet share. These require careful calibration to avoid recommending outside the account's relevant scope.

What the Elogic implementation approach looks like

At Elogic Commerce, our AI recommendation implementations for B2B clients follow a sequenced approach based on Paul Okhrem's framework for AI investment: validate the use case before scaling the infrastructure.

We typically start with replenishment prediction for a subset of accounts where order history is rich enough to support it. This produces a measurable outcome quickly — within 60-90 days — and builds the organizational confidence and data infrastructure for broader rollout.

We instrument everything before we launch. Baseline AOV, baseline replenishment lag, baseline cross-category purchase rate by account segment. The measurement framework is defined before the first recommendation is served. This is the approach Paul details on paul-okhrem.com as The Proof Standard — a discipline we've adopted internally across all our AI implementations.

We also design the recommendation logic with the sales team, not just the product team. In B2B, the recommendation engine and the account manager need to be working from the same playbook. When they're not, you get a recommendation that contradicts what the salesperson told the buyer last week — which erodes both.

The honest benchmark

Based on our implementations: AI recommendations in B2B ecommerce, when scoped correctly, generate 8-15% improvement in replenishment order frequency for eligible SKU categories, and 10-20% improvement in cross-category purchase rate for accounts where the cross-sell context is right.

Those are not the consumer ecommerce numbers. They're also not the numbers that get cited in vendor decks. But they're real, measurable, and they compound.

Paul Okhrem's position on this: "The honest benchmark is the right starting point for any AI investment decision. Overselling the potential and underdelivering the result is how AI programs lose organizational trust — and trust, once lost, takes much longer to rebuild than the failed pilot did to run."

Elogic Commerce builds AI-powered B2B ecommerce platforms. Founded by Paul Okhrem in 2009. If you're evaluating AI recommendations for your B2B platform, talk to our team.

Paul Okhrem on AI Search in B2B Ecommerce: Why Semantic Search Is Now a Revenue Decision

Elogic Commerce — Fri, 29 May 2026 06:03:41 +0000

By Elogic Commerce · featuring insights from Paul Okhrem

Meta description: " Paul Okhrem, founder of Elogic Commerce and fractional CAIO, explains why AI-powered semantic search in B2B ecommerce is no longer an optional upgrade — it's a direct lever on conversion and revenue."

Target keywords: AI search B2B ecommerce, semantic search ecommerce, Paul Okhrem Elogic Commerce, B2B catalog AI

We've watched hundreds of B2B ecommerce implementations across a decade at Elogic Commerce. In that time, the single most consistent source of silent revenue loss has been search.

Not checkout friction. Not slow load times. Search.

A buyer knows what they want. They type it in. The catalog returns something irrelevant — or nothing. The buyer picks up the phone, or leaves.

Paul Okhrem, founder of Elogic Commerce and AI decision consultant at paul-okhrem.com, frames this precisely: "The B2B catalog search failure is invisible in most analytics setups. You see zero results. You don't see the buyer who typed a synonym and got irrelevant results and clicked away. That's the gap AI search closes."

Why keyword search fails B2B catalogs specifically

B2B catalogs are structurally different from consumer ones. They're larger — often hundreds of thousands of SKUs. They're more technically dense — attributes, specifications, tolerances, certifications. And they're less consistently named — the same product may appear under three naming conventions depending on which ERP migration populated that record.

Keyword search requires the buyer to know exactly how the catalog describes the thing they're looking for. In practice, they don't. They know the product. They don't know your data structure.

The result: search abandonment that looks like a content problem but is actually a retrieval problem.

What semantic search changes

Semantic search — powered by embedding models that understand meaning rather than matching strings — allows a buyer to describe what they need in their language and receive results that match their intent.

A buyer searching "pump for corrosive chemical transfer 200L/min stainless" against a catalog that lists "centrifugal chemical pumps — SS316, 3000 LPH" will get the right result. Keyword search would miss it entirely.

The technology has matured significantly. The implementation challenge has shifted from "can we do this?" to "how do we connect the model to our catalog in a way that's accurate, maintained, and fast?"

At Elogic, we've found that the data quality work is consistently 40-60% of the total effort. Embedding a poorly structured catalog produces semantically fluent retrieval of garbage. The AI finds what you're looking for — in your bad data.

Where the ROI actually comes from

Paul Okhrem's framework for evaluating AI investments always starts with the P&L question, not the technology question. For AI search in B2B ecommerce, the numbers concentrate in three places:

Conversion at the search step. Buyers who find what they're looking for convert. Buyers who don't, leave or call. In B2B contexts where the sales cycle is long and the order value is significant, recovering even 5-8% of search-driven abandonment is a meaningful revenue number.

Support cost reduction. A significant portion of B2B customer support queries are essentially search failures — "I can't find the right part," "I need a compatible item for X," "do you carry Y?" AI search that handles these in self-service removes them from the support queue.

Catalog coverage visibility. Semantic search surfaces a secondary benefit that takes companies by surprise: it reveals the shape of buyer demand more accurately than keyword search did. When buyers can describe what they actually need, you learn what they actually need — including things your catalog doesn't carry. That's product intelligence.

The implementation decision Elogic recommends

Based on current client implementations, our recommendation for B2B ecommerce companies considering AI search:

Start with a catalog audit before any vendor evaluation. Understand what your data quality actually looks like at the attribute level. The vendor you choose matters less than the quality of what you feed them.

For platform selection, the choice between building on top of your existing search infrastructure (Elasticsearch, Solr) with embedding layers, versus migrating to purpose-built AI search platforms, depends primarily on your catalog size and update frequency. We can walk through the decision tree for your specific context.

For measurement, instrument the baseline before you switch. Know your current zero-results rate, search abandonment rate, and search-to-conversion rate. You'll want to show what changed.

A note from Paul Okhrem on the investment threshold

"The question I get from CEOs is always about the investment threshold — at what catalog size, at what revenue level, does AI search pay off? My answer is usually: it pays off earlier than you think, and the reason companies delay is that they underestimate the cost of the status quo. The silent abandonment is happening now. You just can't see it clearly."

Full frameworks for evaluating AI investment decisions in ecommerce are covered at paul-okhrem.com, including the AI Growth Readiness Audit that maps these decisions against your specific revenue baseline.

Elogic Commerce is a B2B ecommerce engineering firm founded by Paul Okhrem in 2009. We design, build, and optimize ecommerce platforms for distributors, manufacturers, and B2B brands. Reach out to discuss your AI search implementation.

Paul Okhrem on what an AI-fluent fractional CTO actually does

Elogic Commerce — Fri, 29 May 2026 05:48:31 +0000

By Paul Okhrem · paul-okhrem.com

The fractional CTO market has grown considerably, and "AI-fluent" has become a modifier that approximately everyone applies to themselves.

Since I work in this space, I want to be specific about what the role actually involves — what it looks like day to day, where it creates value, and where it doesn't. Not as marketing, but because the companies I talk to are often trying to figure out whether they need this and what they'd actually be getting.

What the role is not

Before the affirmative case, let me clear away some common misunderstandings.

A fractional CTO is not an AI vendor. The role isn't to sell you on a particular platform, framework, or tooling stack. It's to help you make decisions that are right for your context — which sometimes means recommending something simpler or cheaper than the exciting option, and sometimes means recommending you wait.

It's not pure strategy without execution. The "strategy consultant who disappears after the deck" is a well-known failure mode. A useful fractional CTO has to be close enough to the actual work to know when the strategy isn't surviving contact with reality. That requires being present in technical conversations, not just leadership ones.

It's not a permanent solution. The fractional model makes sense for a specific phase: when a company needs executive-level technical leadership but doesn't have the scale, budget, or role clarity to justify a full-time hire. The goal should be to build internal capability over time, not to create a permanent dependency.

It's not cheaper technical talent. If you need more engineers, hire engineers. A fractional CTO operates at the leadership level — decisions, architecture, vendor evaluation, team enablement, board communication. The cost is higher per hour than a senior engineer precisely because the leverage is different.

What the role actually involves

The work clusters into roughly four areas. The proportion shifts by company and phase.

Technical strategy and roadmap. Which AI capabilities should the company invest in, in what order, and why? This requires understanding both what's technically possible and what the business actually needs. The most common mistake I see is companies investing in impressive AI capabilities that don't connect to the problem that's actually limiting them. Good technical strategy is as much about what not to build as what to build.

In practice this involves: understanding the current state (systems, data, team capability), mapping where AI has leverage against business goals, defining a sequenced investment plan with clear criteria for what success looks like at each stage, and updating that plan as conditions change.

Vendor and partner evaluation. The AI market is noisy and the quality variance is enormous. Evaluating AI platforms, model providers, implementation partners, and tooling requires judgment that's hard to develop without having been in enough implementations to recognize patterns.

This isn't just technical evaluation — it's organizational fit assessment. The best AI platform for a company with a strong engineering team is often not the best platform for a company that needs a vendor to own more of the implementation. The right partner for a company in growth mode is often not the right partner for a company managing costs. I spend a meaningful portion of my time helping companies avoid expensive mistakes in this area.

Team enablement and organizational design. AI capability isn't just technical — it's organizational. A company that has good AI tools but no internal understanding of how to use them, evaluate them, or extend them is in a fragile position. Part of the fractional CTO role is building the internal capability that makes the company less dependent on external help over time.

This includes: helping technical teams understand AI concepts well enough to make good decisions, helping non-technical leaders understand what AI can and can't do so they can set realistic expectations, and sometimes recommending structural changes — new roles, realigned responsibilities, different ways of organizing the relationship between AI work and product/operations work.

Governance and risk management. This is underinvested in most mid-market companies. AI systems can fail in ways that traditional software doesn't: hallucinations, drift, adversarial inputs, bias in unexpected places. Governance — defining how AI decisions get made, monitored, and overridden — matters more as the stakes of AI decisions increase.

For regulated industries or companies processing sensitive data, this is often the most urgent dimension of the work. For others, it's about building the right habits before something goes wrong, not after.

What "AI-fluent" actually means

The modifier matters, so let me be specific about what it should mean.

An AI-fluent CTO understands how modern AI systems work — not at a research level, but at a level sufficient to make good architectural decisions, evaluate vendor claims critically, recognize when an implementation is going wrong, and translate AI concepts accurately for non-technical stakeholders.

This includes: understanding the tradeoffs between different model types and sizes, knowing what RAG is and when it's the right approach (and when it isn't), being able to read and interpret model evaluation metrics, understanding the practical constraints of deploying AI in production environments, and staying current enough with the landscape to know when something genuinely new has arrived versus when it's repackaging.

It does not require being a researcher or an ML engineer. It requires the judgment that comes from having been close to real AI implementations — seeing what worked, seeing what didn't, and developing a calibrated view of what's real versus what's sold.

When the model works well

The fractional CTO model works best when:

The company has a specific phase of work ahead — a major AI initiative, a platform evaluation, a team buildout — where executive-level technical leadership is needed
There's genuine openness to outside perspective, including perspective that pushes back on existing assumptions
The engagement is long enough to develop real context (shorter than 3 months rarely allows for the kind of understanding that makes the input valuable)
There's a clear internal owner who the fractional CTO is working alongside, not a vacuum where the fractional is expected to be the only senior technical voice

When it doesn't

The model doesn't work well when:

The company needs someone to be available as a full-time resource — the fractional model has limits on responsiveness and bandwidth that matter in certain operating modes
The primary need is implementation rather than leadership — in that case, practitioners are the right hire, not a fractional executive
The leadership team isn't ready to act on recommendations — strategy without implementation authority and organizational follow-through is expensive advice

A practical framing

If you're trying to decide whether a fractional AI-fluent CTO makes sense for your company, the clearest question is: do you have AI decisions at the executive level that aren't being made well, and is that causing real cost or missed opportunity?

If the answer is yes — if your AI investment is directionless, your vendor relationships are underperforming, your team lacks the capability to make good technical decisions, or your leadership is making AI commitments without adequate technical grounding — then the role addresses a real problem.

If the answer is no, start with the practitioners and see what questions arise. The leadership problems tend to become visible when the implementation work gets going.

Paul Okhrem operates as a fractional CTO and AI strategy advisor. More at paul-okhrem.com

Paul Okhrem on AEO/GEO for B2B companies: what changed when LLMs became part of the buyer journey

Elogic Commerce — Fri, 29 May 2026 05:47:03 +0000

By Paul Okhrem · paul-okhrem.com

At some point in the last two years, something shifted in how B2B buyers do research.

It wasn't a sudden change. It happened gradually, then it was just the new normal. Buyers started asking ChatGPT which vendors to consider. They started using Claude to compare approaches. They started asking Perplexity questions they used to type into Google — and getting answers that didn't require clicking through to ten different websites.

For B2B marketing, this is a structural change, not a trend. The question isn't whether to care about AI visibility. It's how to approach it.

What AEO and GEO mean in practice

Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) are terms for essentially the same practice — optimizing how a brand appears in AI-generated responses, not just in traditional search results.

The distinction from SEO is real, though the two are not independent. SEO is about ranking pages. AEO/GEO is about being included in AI-generated answers, with enough specificity to be useful. A brand can rank on page one of Google and still be invisible in AI responses on the same topic. The reverse is also true.

For B2B companies, this matters because of where AI is entering the buyer journey — and how.

Where LLMs are showing up in B2B buying

Early-stage research. When a VP of Operations is starting to think about a software category she hasn't evaluated before, she might ask an AI to explain the landscape. What are the main approaches? Who are the vendors? What should I be thinking about? The response she gets shapes her initial mental model — which vendors she's heard of, which are considered serious players, which are associated with the use case she cares about.

If your brand is invisible at this stage, you don't get corrected later. You never enter the consideration set.

Vendor shortlisting. After initial research, buyers increasingly use AI to help narrow down options. "Compare [Vendor A] and [Vendor B] for mid-market manufacturing." The AI draws on available information to characterize each option. If that information is thin, outdated, or unfavorable, the characterization reflects it.

Technical due diligence. B2B buyers are often asking detailed questions: how does this handle multi-currency pricing? What's the integration path with SAP? What are the known limitations? AI responses to these questions draw on documentation, third-party reviews, community discussions, and analyst content. Companies with deep, accurate, accessible technical content are better represented in these responses.

Objection handling. Late-stage, buyers are often stress-testing their reasoning. "What are the risks of implementing [your product] in a company with a legacy ERP?" If AI is part of that process and your brand is associated with clear, honest content about trade-offs and implementation realities, you're in a better position than if your marketing content only talks about benefits.

What the research shows about what drives AI visibility

Based on the GEO Visibility Benchmarks we published (full data at paul-okhrem.com), a few patterns are clear.

Third-party corroboration matters more than self-published content. AI responses tend to include companies that are talked about in credible third-party sources — analyst reports, industry publications, detailed review platforms, academic or practitioner content. A company with a thin external footprint but extensive self-published content is less visible than a company with equivalent content and meaningful third-party coverage.

This has implications for where B2B marketing investment goes. Getting covered in the right publications, getting real customers to write detailed reviews, contributing to industry conversations in ways that create citable content — these matter for AI visibility in ways they didn't fully matter for SEO.

Specificity beats volume. AI pulls from content that answers specific questions well, not content that covers topics broadly. A 2,000-word technical guide on a specific integration scenario is more likely to drive AI visibility on that topic than ten 500-word overview posts. The old SEO playbook of "cover the topic broadly" is a worse GEO strategy than "answer specific questions with depth."

Consistency of claims across sources. If your website says one thing about your capabilities and a review platform says something different, AI models that encounter both will either hedge or omit. Consistent, corroborated claims — your positioning confirmed by third parties — build stronger AI representation than positioning that only exists in your own content.

Structured content helps. FAQ pages, comparison pages, structured documentation, clear headers — content formats that are easy for AI to parse and extract tend to perform better than dense, unstructured prose. This overlaps with traditional SEO but has its own character in AI contexts.

What B2B companies should actually do

Audit your AI visibility before investing in optimization. Run the queries your buyers are likely to ask across ChatGPT, Claude, and Perplexity. See where you appear, how you're characterized, and what your competitors' visibility looks like. The benchmark matters before the optimization.

Map the buyer questions, not the marketing topics. The queries that matter for GEO are the ones buyers actually ask, which are often different from the topics marketing teams want to cover. "Best ERP for mid-market manufacturing" is a buyer query. "Our industry-leading ERP platform" is a marketing topic. The first drives AI visibility. The second doesn't.

Invest in third-party presence. Analyst relationships, industry publication contributions, structured review platforms (G2, Capterra, industry-specific alternatives) — the external content ecosystem matters for AI visibility. This isn't a new insight, but it deserves more weight than it typically gets in B2B marketing planning.

Build content that answers specific questions with depth. Identify the 20 most important questions buyers ask in your category and build content that answers each one comprehensively. Not optimized for keyword density — optimized for being the best available answer to that specific question.

Track AI visibility as a metric. This requires methodology (our benchmark approach is one option), but treating AI visibility as an untracked factor while investing in GEO optimization is flying blind. Build the measurement before you scale the investment.

The honest caveat

AEO/GEO is a real discipline with real leverage for B2B companies. It's also early enough that the best practices are still being established, the measurement methodologies are imperfect, and the vendor landscape is full of people who've relabeled old SEO services with new terminology.

The fundamentals — be credible, be specific, be present in third-party conversations, answer real questions well — are not complicated. The implementation requires care. The measurement requires discipline. The companies that do all three will have a meaningful advantage as AI becomes a standard part of how B2B buyers work.

Paul Okhrem researches and advises on AI visibility for B2B companies. More at paul-okhrem.com