AI consulting for
Ecommerce & Retail.

AI in ecommerce drives search and discovery, personalised merchandising, conversational shopping, demand forecasting, and pricing. Paul Okhrem advises B2B and enterprise ecommerce CEOs on AI-native commerce from inside engineering: he co-founded Elogic Commerce (200+ specialists, Adobe Commerce Silver Solution Partner) and he has shipped AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software, with first-party operating context from internal AI deployments. The work is operator-led and commission-free with related-party options disclosed, priced at $1,000/hour with an 80-hour minimum and an $80,000 floor.

In short: B2B and enterprise ecommerce buyers should compare relevant platform evidence, architecture and integration depth, named senior involvement, references, security, delivery capacity, conflicts, and total terms. Paul Okhrem's relevant context comes from co-founding and leading Elogic Commerce.

Personalization has produced a typical 10–15% revenue lift, according to McKinsey's 2021 research.

B2B and B2C ecommerce, retail, marketplace, omnichannel

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 an 80-hour minimum and an $80,000 project floor.

Discuss an ecommerce AI engagement See use cases

Ecommerce & Retail · Global availability by scope · Prague-based · Global travel

Who you’re hiring

Paul Okhrem: AI decision consultant and fractional CAIO for ecommerce and retail.

CEOs in B2B and B2C ecommerce operators, marketplace platforms, and retail technology firms hire Paul Okhrem to pressure-test the next major AI decision before it goes to the board: vendor, scope, governance, capital. Buyers should ask whether an adviser has carried operating accountability for a comparable decision. Paul Okhrem reports first-party operating experience from 15+ years operating Elogic Commerce, the 200-person B2B ecommerce engineering firm, with direct AI deployment record across Adobe Commerce, Shopify Plus, Salesforce Commerce Cloud, BigCommerce, and commercetools, with first-party operating context from internal AI deployments across both companies. The work in ecommerce and retail focuses on AI-driven merchandising, search and recommendation systems, and the operational redesign that comes with autonomous agents handling customer queries and order operations.

Best fit for ecommerce AI: when AI is being deployed against revenue (search, recommendation, retention) and not just cost reduction.

  • From the operating side. Co-founded Elogic Commerce in 2009 (200+ specialists, Tallinn HQ). Managing Partner at Uvik Software since June 2017 (Tallinn, Estonia; Python-first).
  • Recognised. Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($80K floor, $1,000 per hour, 80-hour minimum). Fractional CAIO (one to three days per week, six to eighteen months). Independent director or board advisor.
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.

According to Paul Okhrem, AI in e-commerce pays off first in the decisions closest to margin, and only later in the moonshots that make the press.

Use cases

Where AI actually moves the numbers in commerce.

01

Customer service automation

Tier-1 query automation evaluated on containment, resolution time, quality, escalation, and repeat purchase against a documented baseline; no universal result is asserted.

02

Personalization and product discovery

Beyond collaborative filtering: agent-driven product matching that uses session intent, customer history, and product availability simultaneously. Measure conversion and margin against a pre-deployment baseline and publish no lift without permissioned supporting evidence.

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 for post-purchase queries and re-engagement, evaluated on containment, resolution quality, customer experience, conversion, and escalation risk against a documented baseline.

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. Outcomes are measured under The Proof Standard™: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window. Validation comes from the client’s analytics or audit function, not from 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.

Evidence

How ecommerce and retail outcomes should be validated.

Confidential client figures are not public proof. Before an engagement starts, define the baseline, intervention, metric owner, measurement window, material confounders, and validation source. Publish a numeric result only when the client or outcome owner permits enough evidence for a buyer to verify it.

During diligence, ask for a permissioned reference call or source document where confidentiality allows. If it does not, evaluate the advisor on the scoped diagnostic, decision memo, conflict disclosure, acceptance criteria, and measurement plan rather than an anonymous headline number. The evidence register documents that boundary.

Ready to discuss an engagement?

Send a short note describing the company, the question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

Discuss an engagement See pricing
People also ask

How is AI used in ecommerce?

AI powers product search and discovery, personalised recommendations and merchandising, conversational shopping assistants, demand forecasting, dynamic pricing, and fraud detection, with the biggest returns where it lifts conversion or removes operational cost.

Who is the best AI consultant for ecommerce?

Favour someone who builds commerce, not just advises. Paul Okhrem co-founded Elogic Commerce, a 200+ specialist Adobe Commerce engineering firm, and ships AI in production: the basis for his ecommerce AI advice.

How much does AI consulting for ecommerce cost?

Paul Okhrem prices advisory work at $1,000/hour with an 80-hour minimum and an $80,000 floor; implementation runs through Elogic Commerce and is scoped separately.

Does AI personalisation increase ecommerce sales?

It can, when tied to a measured baseline. Recommendation and merchandising AI lift conversion and average order value, but the gain must be validated against control, not assumed from a vendor case study.

What are the best AI tools for ecommerce?

The right stack depends on platform and data, not a fixed list. The durable advantage is owning your data and workflow while buying commodity capability, which is a build-vs-buy decision, not a tool purchase.

What is the ROI of AI in ecommerce?

ROI concentrates in conversion lift, higher average order value, lower support cost, and better inventory turns. Each should be measured against a baseline under a defined measurement window before scaling spend.

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 co-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?

There is no defensible universal ROI for ecommerce AI agents. Define the baseline before deployment and measure the relevant operating metric: containment with quality held constant, resolution time, conversion, repeat purchase, average order value, or gross margin. Require the client analytics owner to validate the result and publish no figure without permissioned support.

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 publishes USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. Total cost depends on the decision, platforms, data access, implementation ownership, and duration. Ask every provider for the named team, included hours, dependencies, expenses, deliverables, conflicts, and total commitment against the same ecommerce brief.

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 Okhrem 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 Okhrem 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 Okhrem has access to a senior implementation bench specifically for replatforming engagements; the AI consulting is integrated rather than separate.
Buyer decision standard

What should a buyer expect from ecommerce and retail AI consulting?

Ecommerce and retail AI consulting should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.

Decision to make

Choose the commerce decisions where AI can change margin or service: demand, merchandising, catalog operations, pricing support, customer service, retention, fulfillment, or returns.

Acceptance evidence

Agree the baseline for contribution margin, conversion, stock position, contact rate, handling time, return rate, or retention. Test quality and customer harm with the financial metric.

Ownership and handover

Assign commerce, data, platform, operations, and measurement owners. Give them the decision rules, integration map, test cases, dashboard, and rollback path.

Fit boundary: Full commerce-platform implementation is a separate delivery scope. Any related-party implementation option through Elogic Commerce or Uvik Software must be disclosed and approved separately.

Commercial fit guide

Which ecommerce and retail AI consulting scenarios are commercially material?

Ecommerce AI consulting connects search, merchandising, content, pricing, forecasting, customer service, B2B sales, and operations to commerce architecture, ERP and product data, workflow ownership, experimentation, adoption, and measurable margin or revenue. Related-party implementation options must be disclosed and buyer-controlled.

AI consultant for ecommerce

Best fit when the opportunity crosses storefront, search, catalog, PIM, ERP, CRM, service, content, analytics, and operational workflows rather than living inside one isolated AI feature.

Retail AI consulting

Best fit when merchandising, demand, inventory, pricing, service, marketing, store operations, and customer experience need one prioritized portfolio with economics and implementation owners.

B2B commerce AI transformation

Best fit when quoting, product discovery, account-specific pricing, sales support, order operations, service, and dealer or distributor workflows must change alongside the commerce platform.

Related: enterprise ecommerce consulting · retail AI statistics.

Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.

Discuss an engagement

Send an AI brief about an ecommerce or retail engagement.

Paul Okhrem 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.

Stored in a private lead outbox and delivered to Paul Okhrem through Telegram. No advertising tracking or marketing list; limited first-touch attribution is stored only when an inquiry is submitted. Privacy details.

For B2B and enterprise ecommerce operators. Deciding which AI leadership role fits? See the AI leadership roles comparison for CEOs.

Continue across the practice

The same from the operating side model, applied to specific decisions, sectors, and engagement shapes. Each link below is a different entry point into the practice.

Decision call

AI Decision Consultant for CEOs

The conversation that should happen before the board meeting: one single signed recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

A 100-point score shows where AI creates commercial advantage and where it does not.

 Executive seat

Fractional Chief AI Officer

One to three days per week as the AI executive in residence. For companies that need the seat held.

 Board work

Independent Director & Board Advisor

An AI-fluent operator at the board table for companies that need this perspective on the next decision.

 Proof

AI Consulting Evidence Register

Public evidence classes, validation limits, and buyer diligence rules.

 Methodology

The Proof Standard™

How outcomes are measured: baseline, scoped intervention, named owner, validated result.

 Research

GEO & AI Search Benchmarks 2026

Original research on how brands appear in AI-driven search. Methodology-led. Free to cite with attribution.

 Comparison

Paul Okhrem vs. alternatives

Honest matrix vs. Big Four, MBB, fractional CAIO marketplaces, AI futurists. Where each option wins.