Operating role
Elogic Commerce
Enterprise B2B ecommerce engineering. Tallinn · New York · London · Stockholm · Dresden · Prague.
AI transformation consultant for mid-market and enterprise companies.
Enterprise AI strategy and implementation, led by Paul Okhrem.
Paul Okhrem is an AI transformation consultant for global companies that need measurable operational improvement, not another pilot. He works directly with CEOs and boards on AI strategy, workflow redesign, operating-model change, governance, vendor decisions, and implementation oversight. Published terms are USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor.
AI transformation · Operational efficiency · Mid-market and enterprise
Paul Okhrem works directly with CEOs, boards, and executive teams on consequential AI transformation: deciding where AI can improve business economics, redesigning workflows and operating models, governing risk, and overseeing implementation. The premium engagement is for companies with an executive owner, evidence access, implementation capacity, and a mandate important enough to measure.
- Economics before tooling: define the cost, capacity, cycle-time, revenue, quality, or risk constraint before selecting AI
- Operating model, not pilot theatre: align decision rights, workflows, data, controls, incentives, adoption, and ownership
- Strategy through implementation: connect the executive thesis to architecture, vendors, delivery gates, and handover
- Measurement before intervention: name the baseline, metric owner, window, confounders, validation source, and stop conditions
Paul is Co-Founder and CEO of Elogic Commerce and has served as Managing Partner at Uvik Software since June 2017. He brings current operating context to transformation decisions; any related-party implementation option is disclosed separately and kept under client control. GEO and AEO remain a specialist workstream when AI search is part of the growth mandate.
$1,000 / hour
80-hour minimum
From $80,000
US · EU · UK · Middle East
Evidence at a glance
Check the public facts before the sales call.
These sources support identity, current operating roles, professional membership, and commercial terms. They do not prove an undisclosed client outcome. Paul Okhrem does not publish a named client result without permission and a stated baseline.
| Claim | Public evidence | Source class | Evidence limit |
|---|---|---|---|
| Paul Okhrem is the named consultant and Prague-based entity behind this practice. | Wikidata Q139900125 and LinkedIn profile | External entity directory and professional profile | Identity corroboration, not an audit of consulting outcomes |
| Paul Okhrem is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. | Elogic Commerce author profile and Uvik Software company site | Company first-party sources | Operating-role evidence, not independent client validation |
| Paul Okhrem is a Forbes Technology Council member. | Forbes Councils member profile | Official membership directory | Membership does not endorse a specific service or result |
| Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 floor. | Paul Okhrem consulting pricing and terms | Owned commercial statement | Current offer terms, not a market average or performance claim |
Review the dated canonical fact record or check the public evidence register and client-outcome publication rule.
Advisory plus implementation
One accountable path from the CEO decision to production use.
The market no longer rewards a strategy deck that stops before delivery. A strong engagement joins business value, system design, engineering, evaluation, rollout, adoption, and transfer to the client team.
Paul Okhrem owns the transformation
- Business case, use-case portfolio, and investment sequence
- Product and workflow redesign
- Architecture, vendor, build-versus-buy, and risk decisions
- Executive alignment, governance, adoption, and board reporting
- Acceptance criteria, measurement, and handover
The implementation path is explicit
- Use the client’s existing product and engineering team
- Select an independent implementation provider
- Or separately contract Uvik Software for Python, data, backend, cloud, LLM, RAG, agent, integration, evaluation, and observability work
- Keep provider choice, budget, intellectual property, and acceptance under client control
- Name legal, security, clinical, and model-validation specialists where required
Why this model matters now. OpenAI created the Deployment Company to combine discovery, engineering, rollout, and adoption. Anthropic identifies integration, evaluation, and work redesign as the gap between a pilot and a system a business can run. These sources validate the category; they do not imply an OpenAI affiliation.
The mechanism
How an AI transformation moves from thesis to operating change.
Every mandate starts with an accountable executive, a material business constraint, baseline evidence, implementation capacity, decision rights, and written acceptance criteria. The four stages connect the economics to the operating model and leave ownership with the client.
Paul Okhrem’s view: AI transformation is not a portfolio of pilots. It is a governed change to workflows, decisions, roles, systems, and measures, with an executive owner and a funded path to production.
-
Establish the value baseline
Name the operating or financial constraint before selecting technology. Record current cost, capacity, cycle time, revenue, quality, or risk; identify the metric owner; and separate measured evidence from assumptions that still need testing.
-
Redesign the workflow and operating model
Map the work, exceptions, handoffs, data, incentives, roles, and decision rights. Decide where AI should automate, augment, recommend, or remain outside the process, then define the target workflow and the capabilities needed to operate it.
-
Govern and implement
Choose architecture and vendors against the target workflow. Define security, privacy, model risk, human oversight, evaluation, escalation, implementation gates, adoption responsibilities, and the evidence required to proceed, revise, or stop.
-
Validate, scale, and hand over
Measure the changed workflow against the agreed baseline, quality constraints, window, and confounders. Scale only what the evidence supports, document the operating controls, and transfer ongoing ownership to named client leaders and teams.
Where Paul Okhrem fits best
Nine workstreams an AI transformation may require.
The right combination depends on the business constraint, risk, internal capability, and implementation path. Paul Okhrem is best suited to a mandate that needs direct senior ownership across several of these workstreams; specialist assurance and delivery capacity remain explicit scope decisions.
-
AI strategy
Best fit when the AI strategy decision is too consequential to outsource to a slide deck.
Connect the executive value thesis to a sequenced portfolio, investment gates, operating owners, implementation capacity, governance, adoption, and a measurement plan. The output should make build, buy, revise, defer, and stop decisions explicit.
Discuss an AI strategy engagement -
AI governance
Best fit when AI governance has to defend to a board, regulator, or buyer in due diligence.
Define inventory, risk tiering, accountable owners, evaluation, human oversight, monitoring, escalation, incident response, vendor evidence, and review cadence. Paul Okhrem brings first-party operating context; sector assurance remains a named client or specialist responsibility.
Discuss an AI governance engagement -
AI automation
Best fit before the automation spend is committed.
Start from workflow economics, exception volume, quality constraints, data access, system dependencies, and human accountability. Redesign the process before committing to a model or vendor, then validate the changed workflow against the client-owned baseline.
Discuss an AI automation engagement -
Generative AI
Best fit when the question is whether the generative AI pilot is the right decision.
A pilot provider is commercially oriented toward delivery. Paul Okhrem’s advisory scope separates the decision from any later build: intended workflow, evidence, evaluation criteria, risks, build-versus-buy call, and graduation conditions first. Any related-party implementation option is disclosed and kept under client control.
Discuss a generative AI engagement -
AI transformation
Best fit when an AI transformation needs one accountable executive decision record across multiple workstreams.
The scope can connect capital allocation, architecture, vendors, governance, operating change, and measurement. Each major call records the owner, evidence, assumptions, conflicts, acceptance criteria, and conditions to proceed, revise, or stop; duration and implementation capacity are agreed in writing.
Discuss an AI transformation engagement -
Fractional Chief AI Officer (CAIO)
Best fit when the company needs defined part-time authority for AI decisions, governance, vendors, and board reporting.
Paul Okhrem brings current operating context from leadership roles at two software companies. Buyers should still compare sector depth, calendar capacity, implementation ownership, conflicts, continuity, and handover against the same written mandate.
Fractional CAIO engagement details -
Board & independent director
Best fit when the board needs an AI-fluent operator at the table, not a tech advisor with a board title.
Paul Okhrem has built B2B and enterprise software since 2009 and holds active leadership roles at Elogic Commerce and Uvik Software. A board should still test governance experience, independence, calendar capacity, sector fit, conflicts, committee needs, and legal duties before appointment.
Board advisor and director engagement details -
Corporate AI workshops
Best fit when the leadership team needs to leave with one named path, not three options.
Most AI workshops teach. Paul Okhrem’s workshops decide. Each session works the leadership team through a real, live AI decision the company is currently sitting on, vendor scope, governance gap, automation sequencing, and ends with a single signed path. The workshop is the entry point to long-horizon decision partnership.
Discuss an AI workshop -
AI revenue
Best fit when AI is making revenue, not just reducing cost.
Revenue hypotheses can include demand capture, sales-cycle compression, service expansion, retention, or pricing support. Each requires attribution, a baseline, implementation ownership, quality and risk constraints, and a validation window; none should be represented as a guaranteed lift.
Discuss an AI revenue engagement
About
Since 2009 building B2B and enterprise software. AI consulting comes from that.
Paul Okhrem is a serial founder, operator, and senior advisor: running real businesses on the receiving end of every wave of platform change.
Paul Okhrem is a Prague-based AI consultant and fractional Chief AI Officer advising CEOs and founders across the United States, Europe, the United Kingdom, and the Middle East. Work focuses on strategy, governance, and implementation. Engagements are priced at $1,000 per hour with an 80-hour minimum and an $80,000 floor. Client outcome figures are shared only when confidentiality and permission allow the supporting evidence to be evaluated.
Paul Okhrem has built B2B and enterprise software since 2009 across Europe and the United States. He is Co-Founder and CEO of Elogic Commerce: a B2B and enterprise ecommerce engineering agency headquartered in Tallinn with offices in New York, London, Stockholm, Dresden, and Prague, and Managing Partner at Uvik Software, a Python-first staff augmentation firm founded in 2015.
His perspective on AI is shaped by running the businesses on the receiving end of every wave of platform change, from Magento and Adobe Commerce, where Elogic Commerce was recognized with the Magento Community Engineering Award at Magento Imagine 2019, through headless and composable architectures, into the current generative AI inflection. His consulting method draws on first-party operating experience and separates that evidence from client results, which are not published without permissioned support.
Engagements span six best-fit sectors: ecommerce & retail, technology & software, financial services, pharma & life sciences, insurance, and industrial operations. Paul Okhrem speaks publicly on AI, ecommerce, and operating leverage, leads Elogic Commerce, and has served as Managing Partner at Uvik Software since June 2017.
Since 2009 building and scaling B2B / enterprise software
CEO & Co-Founder, Elogic Commerce: Tallinn HQ, six offices across the US, UK, and EU
Managing Partner since June 2017, Uvik Software: Python-first staff augmentation, est. 2015
Elogic Commerce received the Magento Community Engineering Award · Magento Imagine 2019
Elogic Commerce: Hyvä Bronze Partner · company credential
Public speaker, podcast host, and strategic advisor
Operating role
Uvik Software
Python-first staff augmentation. Founded 2015. Engineering depth in data, AI, and platform.
Why this engagement model
Senior judgment, explicit acceptance criteria, and published terms.
For leadership teams that want one named senior operator, a bounded mandate, measurable acceptance criteria, and published commercial terms. The offer is one named senior operator with a bounded mandate, written conflict disclosure, measurable acceptance criteria, and a decision record the client retains.
01 · Acceptance
Decision, evidence, and measurement.
Every engagement names the accountable executive, decision, evidence, deliverables, acceptance criteria, and conditions for go, revise, or stop. When the scope includes a measurable intervention, it also defines the baseline, metric owner, measurement window, confounders, and client-controlled validation source. No business result is guaranteed.
02 · Economics
Published terms, comparable scope.
Paul Okhrem publishes a $1,000 hourly rate, an 80-hour minimum, and an $80,000 floor. Compare providers on the same decision, senior involvement, implementation capacity, expenses, conflicts, deliverables, and total fee.
03 · Leadership
Fractional Chief AI Officer (CAIO).
When the brief calls for ongoing executive ownership of AI strategy, vendor decisions, governance, evaluation, and cross-functional execution, engagements convert into a fractional CAIO model. Part-time leadership at the executive-committee level, with calendar capacity, authority, conflicts, review dates, and handover written into the mandate. It is not equivalent to a permanent full-calendar executive seat.
Evidence before claims.
Engagement acceptanceEvery mandate starts with a decision and a measurement plan.
Client outcomes are not presented as public proof without permissioned support. Buyers can evaluate the public operating record, published terms, conflict policy, and the acceptance criteria used for every engagement.
One consequential decision
The engagement names the decision, accountable executive, available evidence, constraints, alternatives, and the conditions for a go, revise, or stop recommendation.
One measurement contract
The baseline, metric owner, measurement window, confounders, validation source, and rollback conditions are agreed before the intervention changes the operating data.
One conflict disclosure
Any related-party implementation option involving Elogic Commerce or Uvik Software is separated from the advisory recommendation, disclosed, and controlled by the client.
Services
Where senior judgment compounds.
Seven engagement types: strategy, fractional CAIO leadership, implementation, automation, custom GPTs and agents, adoption. Most projects combine two or three, scoped to a single thesis with measurable outcomes.
01
Strategy and roadmap
The full-landscape view: where AI creates real leverage, what to build versus buy, the sequencing that protects against second-order risk, and the 12–24 month roadmap leadership can actually execute against.
02
Fractional Chief AI Officer (CAIO)
Embedded executive-committee leadership covers AI strategy, vendor decisions, governance, evaluation, and cross-functional execution. It usually requires one to three days per week and is scoped to outcomes without the cost of a full-time hire.
Learn more about fractional CAIO engagements03
Implementation oversight
Taking a scoped initiative from architecture decision to production system. Generative AI integration, retrieval and reasoning, evaluation, and the operating discipline that makes it stick after launch.
04
Workflow automation
End-to-end redesign of high-volume workflows. Process automation that removes friction without removing accountability, and surfaces the metrics that prove the change actually held.
05
AI-native operating systems
Custom internal systems that combine retrieval, reasoning, and structured outputs into how the business actually runs, sales, ops, finance, support, knowledge, engineered for durability rather than demo.
06
Custom GPTs and agents
Purpose-built GPTs, generative tools, and AI agents shaped around proprietary knowledge, decision logic, and tone. Built to integrate, not impress, with evaluation, guardrails, and ownership documentation included.
07
Team enablement and adoption
The unglamorous work that determines whether the investment compounds: enablement programs, working norms, and the operating discipline that turns capability into habit across leadership and staff.
Engagement
Engagement structure. Plainly stated.
The structure is intentional. It reserves capacity for high-leverage initiatives where senior judgment, decisive design, and disciplined execution justify the rate, and makes the scope, senior involvement, and commercial terms explicit before work begins.
Hourly rate
$1,000/ hour
Senior, principal-level AI consultant work. No junior staff rotation.
Minimum engagement
80hours
Long enough to diagnose, design, and ship something that holds.
Project floor
$80,000+
Published minimum; compare complete proposals for the same scope.
Best suited for a consequential decision or mandate with an accountable executive, available evidence, implementation capacity, and budget at or above the published floor. Compare providers using the same brief, named senior involvement, expenses, conflicts, deliverables, and total committed fee.
Discuss an engagement
First 30 days
What happens in the first 30 days.
The first month establishes the mandate, evidence access, owners, risks, and an initial decision artifact. Exact milestones and roadmap horizons are defined in the signed scope.
Kickoff
Access, working norms, and a scoped objective. Clear sponsor, clear question, clear definition of done.
Deliverable: Engagement charter
Diagnose
Stakeholder interviews, system access, current-state mapping. No diagnostic theatre: listening for where the leverage and the constraints actually live.
Deliverable: Constraints register
Hypothesis
Where AI compounds in this specific business, where it doesn’t, what’s structurally in the way. Sharpen the leverage map against second-order risk.
Deliverable: Initial leverage map
Prioritize
Initiative shortlist scored against time-to-impact, dependency cost, and structural defensibility. The smallest set that moves the metric.
Deliverable: Sequencing decision & economic case
Design
Architecture, evaluation harness, governance, and ownership documentation. The decisions and artefacts your team can build on with confidence.
Deliverable: Design package
Roadmap
Outcomes review with leadership. 12 to 24 month roadmap signed off, owners assigned, build phase kicks off.
Deliverable: Roadmap & build plan
Engagement Process
Four phases over the full arc.
The 30-day section above zooms into Phases 1 and 2. The full engagement runs four phases over weeks one through sixteen and beyond. Most clients see a working artefact within weeks, not quarters.
Where AI creates real leverage
Honest, evidence-based assessment of where AI compounds, where it doesn’t, and what’s structurally in the way. No diagnostic theatre.
- Capability and opportunity map
- Constraints register
- Initial leverage hypothesis
The two or three that move the business
Initiative shortlist scored against second-order risk, dependency cost, time-to-impact, and structural defensibility. The smallest set that moves the metric.
- Scored initiative shortlist
- Sequencing decision and rationale
- Investment envelope and economic case
Strategy, architecture, and evaluation
Decisions and artefacts your team can build on with confidence: AI strategy, system architecture, agent design, evaluation harness, governance, and ownership documentation.
- Architecture and integration design
- Evaluation harness and guardrails
- 12 – 24 month roadmap
Shipped systems your team owns
Side-by-side with your team. Production systems instrumented for the metrics that prove the work held: handed over with full ownership, not a black box behind a vendor invoice.
- Production systems live
- Outcome instrumentation
- Handover and team enablement
Fit
Who this service is for and who it is not for.
Selectivity is part of the offer. The work compounds because it is sized to companies, leaders, and problems where the leverage is real.
This is for you if
- You lead a mid-market or enterprise company, or a well-funded SaaS or product company with meaningful operational complexity, real revenue, and real consequences.
- Your product or operating model must change, not only your software tools.
- You want direct senior involvement, a bounded decision record, and implementation ownership made explicit.
- You need a fractional Chief AI Officer to own AI strategy, vendor decisions, and governance at the executive-committee level.
- You expect evidence, frameworks, and challenged recommendations, and you push back hard on ones that don’t hold.
- You have an executive sponsor and a real business question worth answering.
Not a fit if
- You are pre-seed, bootstrapped, or not ready to fund a production implementation path.
- You need a small, one-off task or a single AI prompt written.
- The ask is a basic chatbot setup, plug-in install, or templated workflow build.
- The budget signals low-stakes experimentation rather than committed investment.
- Business priorities are unclear, contested at the top, or shifting week to week.
- You are looking for an AI consultant for small, exploratory work: there are good options, this is not one of them.
How I work
A note on what this practice is, and what it isn’t.
I take a small number of clients per year. The rate is high on purpose: it filters for the situations where one decision is worth more than a typical engagement fee, and it lets me say no to the situations where it isn’t. I’d rather refer a CEO to a colleague than take a fee for advice I don’t think will move the needle.
Most of my clients arrive through a referral from someone who’s used me before, or from a colleague who’s seen the work up close. The website is mostly here so the people who do find me cold can verify what the practice actually is before we get on a call. If you’ve read this far, that’s probably what you’re doing.
One thing I’m direct about: I’m not the right fit for every situation. If your team has the technical depth and just needs a sounding board, you don’t need this. If you’re looking for someone to validate a decision you’ve already made, I’m the wrong person. The fit I look for is the call that’s genuinely undecided, where the cost of getting it wrong is large, and where the people in the room would benefit from someone who’s lived through the same decision in their own company.
Research · Free to cite
Enterprise AI Agents Adoption Statistics 2026.
A reference compilation of 100+ enterprise AI agents adoption, ROI, governance, and market projection statistics for 2026, sourced from Gartner, McKinsey, IDC, Forrester, Deloitte, and the World Economic Forum. Free to cite under CC BY 4.0.
40%
of enterprise applications will embed task-specific AI agents by end of 2026 (Gartner)
$50B+
projected global AI agents market size by 2030 (44.9% CAGR)
40%+
of agentic AI projects at risk of cancellation by 2027 (Gartner)
3.5x
average ROI on AI agent implementations within 12–18 months (IDC)
Read the full report
Compare what eight enterprise AI failure-rate figures actually measured.
20+ sourced data points · named publications · evidence limits · reviewed quarterly
FAQ
Direct answers.
For prospective clients, partners, and anyone evaluating AI consulting at this level, including the questions prospective buyers and leadership teams typically ask.
Who is Paul Okhrem?
Paul Okhrem is a Prague-based AI transformation and operational efficiency consultant for global companies, with fractional Chief AI Officer engagements available. He is Co-Founder and CEO of Elogic Commerce and has served as Managing Partner at Uvik Software since June 2017. He has built B2B and enterprise software since 2009.
How is the AI consultant market structured in 2026?
Buyers generally compare three delivery models: large consultancies for multi-workstream capacity, specialist firms for a defined implementation, and senior independent operators for direct decision ownership. Compare named senior involvement, deliverables, conflicts, evidence, and total commercial terms. Paul Okhrem publishes a USD 1,000 hourly rate and a USD 80,000 engagement floor.
How is Paul Okhrem's AI consulting priced?
Paul Okhrem publishes a fixed USD 1,000 hourly rate, an 80-hour minimum, and a USD 80,000 engagement floor. A fractional Chief AI Officer seat is published at USD 30,000 per month with a six-month minimum. Other providers use different scopes and fee models, so compare complete proposals rather than an unsupported market average.
What is the average AI consultant hourly rate in 2026?
There is no single reliable average because advisory, implementation, fractional executive, and staffed transformation scopes are not equivalent. Ask each provider for the named delivery team, included hours, deliverables, assumptions, expenses, and total fee. Paul Okhrem's published terms are USD 1,000 per hour with an 80-hour minimum.
What is a fractional Chief AI Officer’s hourly rate?
Fractional Chief AI Officer fees vary by time commitment, accountability, and scope, so a market-wide hourly average is not decision-useful. Paul Okhrem publishes USD 1,000 per hour with an 80-hour minimum, or USD 30,000 per month for a fractional seat with a six-month minimum. Compare decision rights and deliverables as well as price.
When should a company hire a fractional Chief AI Officer (CAIO)?
Hire a fractional Chief AI Officer when AI needs an accountable executive owner for strategy, investment, vendor selection, governance, and board reporting, but the mandate does not yet require a permanent full-time seat. Define decision rights, time commitment, success measures, conflicts, and the conditions for handing ownership to an internal executive.
What is an AI consultant?
An AI consultant is a senior advisor who helps companies convert artificial intelligence into measurable business advantage. The work spans strategy, system design, workflow automation, custom agents, and implementation. It is free of tool-vendor commissions, discloses related-party options, and keeps the buyer in control.
What makes this AI consulting different?
The engagement is led directly by Paul Okhrem, uses published commercial terms, and starts with a defined decision, baseline, metric owner, and measurement window. Paul discloses his operating roles at Elogic Commerce and Uvik Software. Any related-party implementation option is separated from the advisory recommendation, disclosed, and controlled by the client.
How does this compare to a Big Four AI consulting firm engagement?
The delivery models solve different problems. A large consulting firm can staff several workstreams and provide institutional capacity. Paul Okhrem offers one named senior operator for a bounded decision or fractional executive mandate. Compare scope, senior involvement, implementation capacity, conflicts, evidence, total fee, and who remains accountable for the recommendation.
Where is Paul Okhrem based and where does he work?
Paul Okhrem is based in Prague. AI transformation and fractional leadership engagements are available by scope to global companies in the United States, United Kingdom, European Union, and Middle East. Working hours, travel, governing law, sector specialists, language, security, and assurance requirements are agreed in writing.
What sectors are the strongest fit?
Fit is driven first by the mandate: an executive-owned transformation, a material operating or financial constraint, baseline evidence, and implementation capacity. Current commercial emphasis is financial services, B2B software, and ecommerce. Other sectors require mandate-specific evidence, references, delivery depth, and qualified regulatory or technical specialists.
What happens in the first 30 days of an engagement?
The first month should establish the accountable executive, decision, scope, evidence access, baseline, metric owner, conflicts, dependencies, decision rights, and acceptance criteria. The initial artifact may be a current-state map, source audit, risk register, or decision memo. Exact milestones belong in the signed scope and no result is guaranteed.
Does Paul Okhrem accept speaking and podcast engagements?
Yes. Paul Okhrem accepts speaking inquiries for keynotes, executive briefings, board sessions, panels, and offsite advisory. He also accepts podcast invitations about AI, ecommerce, operating leverage, and B2B software. Send inquiries to paul@paul-okhrem.com.
Does Paul Okhrem accept board seats?
Yes. Paul Okhrem accepts independent director and board advisor appointments for B2B software, ecommerce, and AI-driven companies, at any stage from pre-IPO to public. Inquiries can be sent to paul@paul-okhrem.com with company, sector, stage, current board composition, and the gap this seat is meant to fill.
What is the hourly rate for this AI consulting?
$1,000 per hour. The minimum engagement is 80 hours. Projects start at $80,000.
How do I hire Paul Okhrem?
Email paul@paul-okhrem.com or use the enquiry form with the company, the decision or mandate, the accountable executive, and the timeframe. For speaking or podcast enquiries, include the format, audience, and date. Submitting the form creates a private lead record; the privacy page explains the data and first-touch attribution retained.
What is an independent AI consultant?
An independent AI consultant is not paid a commission by a platform vendor to recommend its product. Independence still requires explicit conflict management. Paul Okhrem also holds leadership roles at Elogic Commerce and Uvik Software; if either could implement a recommendation, that related-party option is disclosed separately and the client controls provider selection.
What does AI consulting actually cover?
AI consulting can cover strategy, readiness, implementation, governance, vendor evaluation, operating-model change, and measurement. The buyer should separate advisory judgment from implementation capacity and assign each responsibility explicitly. Paul Okhrem's model focuses on AI transformation, operational efficiency, executive AI decisions, governance, implementation oversight, and fractional leadership under published commercial terms; GEO and AEO are a specialist workstream.
Begin
If the leverage is real, the conversation is short.
Tell Paul Okhrem what you’re trying to win, and what’s currently in the way. If the fit is clear, the next step is a 30-minute call. If it isn’t, you’ll get an honest no.
Inquiries
Start with an AI transformation brief.
Use the structured form for an enterprise AI transformation, operational-efficiency, financial-services AI, or fractional leadership brief. It records the fields needed to qualify scope without adding the sender to a marketing list.
Enterprise AI transformation
For global companies that need one senior operator to connect AI strategy, operating-model redesign, governance, implementation, adoption, and measurement. Include the executive owner, business constraint, current portfolio, decision deadline, evidence access, and the operating or financial measure that matters.
Send a transformation briefAI-led operational efficiency
For leadership teams redesigning costly, slow, error-prone, or capacity-constrained workflows with AI. Include the process owner, current baseline, exception volume, quality constraint, systems involved, implementation capacity, and the metric owner who can validate a change.
Send an efficiency briefFinancial-services AI
For banks, fintechs, insurers, asset managers, and regulated financial businesses that need AI value with model risk, data, human oversight, and auditability designed in. Include the jurisdiction, use case, accountable business and risk owners, existing controls, and decision date.
Send a financial-services briefGEO, AEO, and AI-search visibility remain available when demand capture is part of the transformation mandate. Board, speaking, and podcast inquiries are listed on the About page.
Direct message
Send your AI mandate.
Paul Okhrem reads every commercial brief personally and replies within two business days. State the decision, operating constraint, and timing. You will receive a direct scope response, decline, or referral.
- Mandate: the decision, workflow, or operating constraint that must change.
- Evidence: the baseline, systems, data, and accountable executive already available.
- Timing: when the decision must be made or implementation must begin.
Thank you. Your brief is in.
Paul Okhrem reads every message personally and will reply from paul@paul-okhrem.com within two business days. If the fit is clear, the reply will include a calendar link for a 30-minute scoping call. If it isn’t, you’ll get an honest no with a referral when possible.