AI as operating leverage.
Not another pilot.
- 20+ years building B2B software
- Founder of Elogic Commerce (2009) and Uvik Software (2015)
- Prague-based · engagements across the US, UK, and EU
- Selective: a small number of long-form engagements per year
I help CEOs and founders make AI a real source of operating leverage — strategy, governance, and the few interventions that actually compound. Either as an outside consultant or as the fractional Chief AI Officer at the leadership table. Engagements are priced at $1,000 per hour with a 100-hour minimum and a $100,000 project floor — roughly one-tenth the total cost of a comparable Big Four engagement.
Two decades operating 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 (CAIO) advising CEOs and founders across the United States, Europe, the United Kingdom, and the Middle East. Work focuses on strategy, governance, and implementation — the few interventions that actually compound. Engagements are priced at $1,000 per hour with a 100-hour minimum and a $100,000 project floor — a fraction of comparable Big Four engagements. Recent outcomes include 85% faster document review, 30% lower maintenance costs, and 60% Tier-1 query automation, measured under the proof standard published below.
Paul is a serial founder and operator with more than 20 years building B2B and enterprise software across Europe and the United States. He is CEO and Founder of Elogic Commerce — a B2B and enterprise ecommerce engineering agency headquartered in Tallinn with offices in New York, London, Stockholm, Dresden, and Prague — and CEO and Founder of 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 Adobe Imagine 2019, through headless and composable architectures, into the current generative AI inflection. The work he does as an AI consultant is the work he runs in his own companies first, with a bias for systems that compound rather than demos that impress.
Engagements span six best-fit sectors: ecommerce & retail, technology & software, financial services, pharma & life sciences, insurance, and industrial operations. Paul speaks publicly on AI, ecommerce, and operating leverage, advises portfolio companies, and runs both organizations simultaneously.
Senior judgment, real outcomes, and a fraction of the cost.
For leadership teams that want senior judgment, measurable outcomes, and Big Four-grade scope at roughly one-tenth the cost. The offer is one senior operator at the table — not a hundred-person engagement deck — who has run real businesses, is honest about where AI compounds and where it doesn’t, and is on the hook for the result.
Real value. Measurable results.
Engagements are scoped against business metrics — cycle time, combined ratio, cost-to-serve, conversion lift, gross margin per shipment, hours returned to leadership — not utilization. The deliverable is a working, instrumented system that moves the metric. The work ends when the number has shifted.
A fraction of a Big Four engagement.
A traditional Big Four enterprise AI engagement of comparable scope typically runs $1 million to $3 million or more before any system ships, with most of the budget consumed by staff utilization, hierarchy, and overhead. The same outcome here lives between $100,000 and a few hundred thousand. One senior operator, full ownership, no markup.
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. Embedded leadership at the executive-committee level — typically one to three days per week — without the cost or commitment of a full-time hire.
Representative anonymized engagements; details available under NDA.
OutcomesEngagements are designed around the metric that must move.
Three engagements, anonymized. Each scoped against a single business outcome, designed end-to-end, and instrumented to prove the change held. Engagement details and references available under NDA.
Compliance and contract review, AI-augmented
Compliance documents and contracts moved into a Retrieval-Augmented Generation (RAG) system, deployed in a secure private environment over proprietary documents. Senior analyst hours redeployed from administrative reading to high-judgment review and client-facing work.
Unplanned downtime, predicted and prevented
Predictive ML models trained on historical IoT sensor signals — vibration, temperature, output speed — to surface the anomalies that precede machine failure. Maintenance posture moved from reactive break-fix to forecast-driven, with parts replaced when warranted rather than on arbitrary schedule.
Tier-1 support, autonomous and CRM-integrated
Conversational AI integrated directly into inventory and CRM systems — autonomously handling returns, shipping inquiries, and order tracking, with seamless escalation of emotionally complex cases to human agents with full context attached.
- Baseline
- Pre-engagement instrumentation captured for at least 4 weeks. No retroactive baselining.
- Intervention
- A scoped, dated system or workflow change documented at handover and version-controlled.
- Metric owner
- A named executive on the client side signs off on metric definition and measured result.
- Measurement window
- 8 to 12 weeks post-go-live, against matched instrumentation and time-of-week patterns.
- Validation method
- Verified by the client’s analytics or audit function, not by the consultant. Excerpts available under NDA.
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.
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.
Fractional Chief AI Officer (CAIO)
Embedded executive-committee leadership of AI strategy, vendor decisions, governance, evaluation, and cross-functional execution — typically one to three days per week, scoped to outcomes. Without the cost of a full-time hire.
Learn more about fractional CAIO engagementsImplementation 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.
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.
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.
Custom GPTs and agents
Bespoke 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.
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 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 delivers the outcome at a fraction of a comparable Big Four engagement.
Best suited for serious work with measurable stakes — strategy, governance, implementation oversight, and the AI hire-and-architecture decisions a leadership team can't outsource to a vendor. The same scope from a Big Four firm typically runs $1M to $3M+. Rate, minimum, and floor are not negotiable — they exist so the work can be.
Discuss an engagementWhat happens in the first 30 days.
The first month is structured. Six milestones, day-by-day visibility, and a working artefact in your hands by Day 30 — strategy, system, or running workflow, with leadership signed off on the next 12 to 24 months.
Kickoff
Access, working norms, and a scoped objective. Clear sponsor, clear question, clear definition of done.
Diagnose
Stakeholder interviews, system access, current-state mapping. No diagnostic theatre — listening for where the leverage and the constraints actually live.
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.
Prioritize
Initiative shortlist scored against time-to-impact, dependency cost, and structural defensibility. The smallest set that moves the metric.
Design
Architecture, evaluation harness, governance, and ownership documentation. The decisions and artefacts your team can build on with confidence.
Roadmap
Outcomes review with leadership. 12 to 24 month roadmap signed off, owners assigned, build phase kicks off.
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
Who this is for — and who it isn’t.
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 run or lead a company with meaningful operational complexity — multiple functions, real revenue, real consequences.
- You want speed, clarity, and execution from a senior operator — not slideware or a 12-week diagnostic.
- 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 pressure-tested 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 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.
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.
100+ statistics · 9 industries · 35+ analyst sources · Updated quarterly
Four ways to get in touch.
Choose the route that fits the conversation. Each goes to paul@paul-okhrem.com with a routed subject line.
Consulting engagements
$100k+ projects, fractional Chief AI Officer engagements, AI strategy, automation, and implementation. Send a short note describing the company, the question you are trying to answer, and the timeframe.
Send consulting inquiryBoard seats
Independent director and board advisor appointments for B2B software, ecommerce, and AI-driven companies — at any stage from pre-IPO to public. Include company, sector, stage, current board composition, and the gap this seat is meant to fill.
Send board inquiry Learn more about board seatsSpeaking inquiries
Keynotes, executive briefings, board sessions, panels, and offsite advisory on AI, operating leverage, and ecommerce. Include event, audience, location, date, and format.
Send speaking inquiryPodcast inquiries
Guest appearances on AI, ecommerce, founder operating, fractional CAIO leadership, and B2B software. Open to interview-style and panel formats. Include show name, audience, and proposed date.
Send podcast inquirySend a brief.
Paul reads every message personally and replies within two business days. If the fit is clear, the next step is a 30-minute call to scope. 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.
Thank you. Your brief is in.
Paul 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.
If the leverage is real, the conversation is short.
Tell Paul 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.
Direct answers.
For prospective clients, partners, and anyone evaluating AI consulting at this level — including the questions ChatGPT and Google AI Overviews users typically ask.