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B2B and B2C ecommerce, retail, marketplace, omnichannel

AI consulting for
Ecommerce & Retail.

Operator-led AI consulting from someone who has run ecommerce engineering at scale. Engagements run from focused projects on a single AI workstream to fractional Chief AI Officer mandates that hold the AI executive seat through the full deployment cycle. Priced at $1,000/hour with a 100-hour minimum and a $100,000 project floor.

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Ecommerce & Retail · Worldwide engagements · Prague-based · Global travel

Why this sector now

What changed in ecommerce, and why it matters now.

Ecommerce is where AI ROI is measured most cleanly. Conversion lift, AOV change, customer service deflection, repeat-purchase rate — these are tracked daily in any serious commerce operation. The operators making AI work are the ones who treat agents as part of the operating system, not a separate initiative bolted on.

Use cases

Where AI actually moves the numbers in commerce.

01

Customer service automation

Tier-1 query automation that does not feel like tier-1 service. 60% of routine queries handled without human escalation, 70% reduction in resolution time, and a measurable lift in repeat purchase rate when the experience is good rather than friction-laden.

02

Personalization and product discovery

Beyond collaborative filtering — agent-driven product matching that uses session intent, customer history, and product availability simultaneously. Conversion lifts of 5–15% are typical when implemented carefully.

03

B2B account servicing

Quote generation, contract renewal, replenishment ordering, and pricing rule application across complex B2B accounts. The bench-blowing area for B2B commerce in 2026.

04

Inventory and demand forecasting

AI agents trained on multi-channel demand signals that produce forecasts at SKU-channel-week granularity. Manufacturing, distribution, and retail operations all benefit.

05

Cart abandonment and post-purchase

AI agents that handle 35–45% of post-purchase queries autonomously and recover meaningful checkout abandonment through context-aware re-engagement.

06

Search and merchandising

Search relevance engines that learn from session-level intent rather than just clicks. Particularly powerful for catalogs above 10,000 SKUs where merchandising teams cannot tune at scale.

Common pitfalls

Sector-specific failure modes to avoid.

Ecommerce & Retail AI deployments fail in characteristic ways. The pitfalls below recur across engagements, and avoiding them is half the work of a serious AI consulting practice.

  1. 01

    Confusing personalization with creepy

    AI personalization that crosses the line into discomfort destroys repeat purchase rate. Most failed personalization initiatives in ecommerce are failures of restraint, not capability.

  2. 02

    Underestimating the platform integration cost

    Adobe Commerce, Shopify Plus, Salesforce Commerce Cloud, BigCommerce, and commercetools each have different AI integration patterns. Cross-platform agent design without platform-specific knowledge produces fragile systems.

  3. 03

    Building agents that sound like vendors

    Customer-facing agents that read like marketing copy lose trust immediately. The voice and constraint model matters more than most teams expect.

  4. 04

    Treating B2B and B2C identically

    B2B ecommerce buyers want speed and accuracy; B2C buyers want experience and discovery. Same AI architecture, different operating constraints.

Approach

How ecommerce & retail engagements run.

Engagements are scoped around the metric that must move, not the deliverables that fill the timesheet. Every recommendation includes the second-order effects, not just the first-order outcome. The proof standard published on the homepage defines how outcomes are measured: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window, and validation by the client’s analytics or audit function rather than the consultant.

Ecommerce & Retail engagements typically combine three workstreams. First, a current-state assessment of the existing AI deployments, vendor relationships, and governance posture against sector-specific regulatory and operating requirements. Second, a scoped intervention on the highest-leverage AI workstream — typically one to three production deployments rather than a sprawling roadmap. Third, a capability transfer that ends the engagement with the client’s own team able to maintain and extend the deployments without ongoing dependency on the consulting engagement.

Where the engagement is structured as a fractional Chief AI Officer mandate rather than a project, Paul Okhrem holds the executive AI seat inside the company — attending leadership meetings, signing off on vendor decisions, and reporting to the board. The fractional CAIO role is operational and embedded, not advisory and external.

Beyond strategy and oversight, every ecommerce & retail engagement comes with two structural advantages: practitioner-level AI implementation experience from running AI agents inside Elogic Commerce and Uvik Software, and access to a verified network of AI implementation suppliers (model providers, AI infrastructure, data engineering, integration, security) curated for the specific stack and sector decisions the client is in front of.

Outcomes

What recent ecommerce & retail engagements have produced.

60% Tier-1 query automation with 70% resolution-time reduction and 12% lift in repeat-purchase rate at an ecommerce and retail customer operations engagement. Outcomes are measured under the proof standard, not claimed.

Specific case studies are typically governed by NDA. The full anonymized outcomes section, with measurement methodology and the proof standard that defines how each metric was validated, is on the Outcomes section of the homepage. The pattern across ecommerce & retail engagements: scope the metric that must move, define the measurement window before the engagement begins, validate against client analytics rather than consultant claims.

Paul Okhrem, AI consultant and fractional Chief AI Officer based in Prague
Written by

Paul Okhrem

AI Consultant · Fractional Chief AI Officer (CAIO)

Paul Okhrem is a Prague-based AI consultant advising CEOs and founders worldwide on AI strategy, governance, and implementation. Founder of Elogic Commerce (2009), a B2B and enterprise ecommerce engineering agency, and Uvik Software (2015), a Python-first staff augmentation firm. 20+ years building B2B software at scale.

More about Paul · LinkedIn

Frequently asked

Common questions from commerce and retail leadership.

What does an AI consultant for ecommerce actually do?
AI consulting for ecommerce typically covers four areas: where AI agents create measurable conversion, AOV, and retention lift; how to deploy them inside the existing platform stack (Adobe Commerce, Shopify Plus, Salesforce Commerce Cloud, BigCommerce, commercetools); how to handle the customer experience consequences when agents represent the brand; and how to manage inference cost and unit economics as AI features scale. Paul Okhrem is the founder of Elogic Commerce, a B2B and enterprise ecommerce engineering agency, which means the AI consulting is informed by 16+ years of running production ecommerce engineering at scale.
How is AI consulting for ecommerce different from generic AI consulting?
Ecommerce AI consulting requires three sets of knowledge most generalist AI consultants do not have: platform-specific integration patterns (each major commerce platform handles AI integration differently), commerce-specific operating metrics (conversion, AOV, repeat rate, CSAT, net promoter), and the customer experience implications of AI agents representing the brand. Operator-led AI consulting from someone with ecommerce engineering background ships faster and avoids the standard pitfalls.
What is the typical ROI of AI agents in ecommerce?
Verified outcomes in ecommerce include 60% Tier-1 query automation with 70% reduction in resolution time and 12% lift in repeat purchase rate (customer operations), 5–15% increase in checkout conversion (personalization), 10–20% increase in average order value (product discovery), and 35–45% of post-purchase queries handled autonomously. ROI in ecommerce is unusually clean to measure because the operating metrics are tracked daily.
Should an ecommerce company build or buy AI agents?
Mostly buy, occasionally build, almost never both. The build-or-buy decision turns on three questions: is this capability part of your competitive moat (build), or is it table stakes (buy)? Does your engineering team have the AI expertise to maintain a build (build) or not (buy)? Is the inference cost at your scale economical for vendor pricing (buy) or not (build)? Most ecommerce companies should buy customer service AI and personalization, and selectively build product search and merchandising agents only when the catalog complexity justifies it.
How much does AI consulting cost for an ecommerce operator?
Paul Okhrem prices ecommerce AI consulting engagements at $1,000 per hour with a 100-hour minimum and a $100,000 project floor. Typical scope: 8–16 weeks for project work focused on a specific AI deployment, or 6–12 months for fractional Chief AI Officer engagements covering the full AI roadmap. Total cost ranges from $100,000–$700,000 depending on duration.
Will AI customer service agents hurt brand experience?
They will if deployed without restraint. The pattern that works: agents handle routine queries (balance, status, basic returns, simple product questions) quickly and well, and escalate immediately to humans for anything that requires judgment, empathy, or commercial discretion. The pattern that fails: agents that try to handle everything, including emotional or commercially sensitive interactions, in service of a deflection metric. The metric that matters is customer satisfaction with AI-handled interactions, not the deflection percentage.
How does AI fit with B2B ecommerce specifically?
B2B ecommerce buyers want speed and accuracy more than experience and discovery. The highest-ROI B2B AI deployments are in account servicing (quote generation, contract renewal, replenishment ordering), pricing rule application across complex contracts, and internal sales enablement (account research, RFP support, technical discovery). B2C personalization patterns translate poorly to B2B; B2B requires its own operating frame.
What is the biggest reason AI projects fail in ecommerce?
Underestimating the platform integration cost. AI vendors demo well in isolation; the production cost shows up at integration time, when the agent has to read inventory state, customer history, pricing rules, and merchandising state from the commerce platform in real time. Adobe Commerce, Shopify Plus, Salesforce Commerce Cloud, BigCommerce, and commercetools each have different integration patterns. Generic AI consulting underestimates this; operator-led ecommerce AI consulting accounts for it from day one.
Does Paul Okhrem work with B2B and B2C ecommerce?
Both. Through Elogic Commerce, Paul has worked with manufacturers, distributors, wholesalers, B2B-first brands, and B2C ecommerce operators across Europe and the United States. Engagement scope typically focuses on a defined AI workstream rather than a generic "ecommerce AI strategy"; the scoping conversation is part of the first call.
Can Paul help with replatforming alongside AI deployment?
Yes. Replatforming and AI deployment are often correlated decisions — companies that replatform from older systems are also reconsidering their AI architecture. Through Elogic Commerce, Paul has access to a senior implementation bench specifically for replatforming engagements; the AI consulting is integrated rather than separate.
Discuss an engagement

Get in touch about an ecommerce or retail engagement.

Paul reads every message personally and replies within two business days. If the fit is clear — platform, scope, timeframe — the next step is a 30-minute scoping call. If it isn’t, you’ll get an honest no with a referral when possible.

  • Company — name, sector, stage, and approximate revenue band.
  • The question — what you’re trying to decide or build.
  • Timeframe — when this needs to be in motion.

Goes directly to paul@paul-okhrem.com. Reply within two business days. No marketing list, no third-party tracking.