Press and speaking: Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. This page holds his bios, approved headshot, research and contact details for journalists, podcast hosts and event organizers.
Short bio
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer. He advises CEOs and boards on AI strategy, governance and agentic AI decisions. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
Long bio
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer (CAIO) for mid-market and enterprise companies. He serves companies in the United States, United Kingdom, European Union and Middle East. He advises CEOs and boards on AI strategy, governance, agentic AI decisions and board reporting. He publishes his fees and works under written mandates.
Paul is Co-Founder and CEO of Elogic Commerce. He has been Managing Partner at Uvik Software since June 2017. He has built B2B and enterprise software since 2009. He is a Forbes Technology Council member. His research includes enterprise AI agents statistics. The enterprise AI failure rate study compares what each source measured.
Topics Paul comments on
Paul comments on executive AI decisions, operating controls and the evidence behind adoption claims. Send the business question and audience with your request.
- AI strategy and investment decisions for CEOs
- Agentic AI: where agents fit and how to control them
- The fractional Chief AI Officer model
- AI governance and executive readiness for the EU AI Act
- How to measure AI ROI
- What AI failure-rate studies actually measure
Research you can cite
Use the dated source and measurement definition beside each figure. Paul's research distinguishes reported results, survey findings and forecasts.
- Enterprise AI agent statistics: KPMG's Q3 2026 US survey reported 62% building, developing or deploying agents. This includes development and does not measure production use alone.
Suggested citation: Okhrem, P. (2026). Enterprise AI agents adoption statistics 2026. paul-okhrem.com/enterprise-ai-agents-statistics-2026/. Original figure: KPMG, Q3 2026 US AI Pulse.
- Enterprise AI failure-rate comparison: S&P Global's 2025 publication reported 42% of organizations stopped most proofs of concept. It is not a failure rate for all projects.
Suggested citation: Okhrem, P. (2026). The Enterprise AI Failure Rate: What the Studies Actually Measured. paul-okhrem.com/enterprise-ai-failure-rate/. Original figure: S&P Global Market Intelligence, 2025.
- Companies using AI: sourced examples grouped by industry. Cite each company's source and year; the directory does not establish one comparable adoption rate.
Suggested citation: Okhrem, P. (2026). Companies using AI in 2026: examples by industry. paul-okhrem.com/companies-using-ai/.
- Companies using AI in finance: named bank deployments and the distinct measures each source reports. Deployment scale does not prove a financial return.
Suggested citation: Okhrem, P. (2026). Companies using AI in finance: banks, use cases and adoption. paul-okhrem.com/companies-using-ai-in-finance/.
- AI conferences calendar: organizer dates, locations and audience fit. Confirm event details with the organizer before travel.
Suggested citation: Okhrem, P. (2026). AI conferences 2026. paul-okhrem.com/ai-events/. Include the access date.
- Five-level AI maturity model: Paul's practical assessment across six dimensions. This original framework is not a survey or compliance certificate.
Suggested citation: Okhrem, P. (2026). AI maturity model: five levels and how to move up. paul-okhrem.com/ai-maturity-model/.
Follow each page's reuse terms. Third-party figures remain attributed to their original publishers. Browse the research library for methods and source notes. Follow the research updates Atom feed for material updates.
Datasets and industry statistics
These collections retain source dates and measurement limits. Cite the original publisher for each figure.
- AI consulting statistics 2026: consulting demand, market estimates and pricing context.
- AI governance statistics: governance practices, oversight and control evidence.
- Fractional CAIO statistics: executive ownership and the limits of role surveys.
- AI in financial services statistics: banking, fintech and insurance evidence.
- AI in healthcare statistics: clinical, administrative and adoption evidence.
- AI in insurance statistics: claims, underwriting and operating controls.
- AI in manufacturing statistics: maintenance, quality and industrial workflows.
- AI in private equity statistics: diligence and portfolio operations.
- AI in retail statistics: service, merchandising and forecasting evidence.
- GEO benchmarks 2026: a method for tracking AI mentions, citations and referrals.
Company evidence and source standards
Company examples retain the original publisher's claims and limitations. A company result does not establish Paul's personal consulting performance.
- Insilico Medicine AI case study: research milestones and clinical evidence.
- Klarna AI customer service case: reported support changes and their limits.
- Zillow AI pricing case study: model limits, inventory exposure and the iBuying exit.
- Consulting evidence register: separate company delivery evidence from personal consulting claims.
- Proof Standard measurement method: record baselines, owners, measurement windows and limits.
Disclosed provider comparisons
Paul publishes these first-party comparisons and offers one of the services. They are not independent analyst research or objective rankings.
- Best agentic AI consulting companies compared: delivery models and production responsibilities.
- Best AI strategy consulting firms compared: strategy scope and implementation capacity.
- Best healthcare AI consulting firms compared: provider, payer and pharma buyer needs.
- Best AI consultants for CEOs: senior advisory scope and executive ownership.
- Best fractional Chief AI Officers compared: leadership mandates, fees and conflicts.
Tools and templates
These working aids support assessment and planning. They do not certify compliance or predict a client's return.
- AI ROI calculator: test costs, benefits and assumptions.
- Enterprise AI readiness assessment: score readiness with named evidence owners.
- AI governance checklist: connect controls, evidence and accountable owners.
- AI governance maturity model: assess the next control improvement.
- AI transformation roadmap: plan phases, outputs and decision gates.
- AI use-case prioritization framework: compare value, readiness and risk.
- AI risk assessment: document harm scenarios and remaining risk.
- AI policy template: adapt staff rules for tools, data and human review.
- EU AI Act compliance checklist: organize role, risk and evidence questions.
- ISO/IEC 42001 readiness checklist: plan management-system work before an independent assessment.
- Chief AI Officer job description: define the mandate and responsibilities before hiring.
- AI due diligence checklist CSV: request evidence before an investment decision.
Guides for leadership and provider selection
Use these guides to define the decision, responsible people and evidence needed before selecting a provider.
- Chief AI Officer role guide: define the executive mandate.
- AI leadership roles compared: compare CAIO, Head of AI, CTO and other roles.
- Forward deployed AI engineer guide: distinguish engineering delivery from executive ownership.
- AI consulting rates and pricing: compare scope, delivery models and fees.
- How to choose an AI consultant: review proposals, evidence and conflicts.
- AI governance framework: connect system owners, controls and decisions.
- Agentic AI governance controls: define permissions, approval gates and stop procedures.
- AI transformation governance framework: assign decision rights through a RACI template.
- AI in banking implementation guide: connect workflow selection, controls and production ownership.
Headshot
Use the approved portrait for editorial coverage of Paul Okhrem with the credit “Paul Okhrem”. Other uses require permission.
Download the editorial portrait JPEG, 1200 px on the long edge.
Copyright Paul Okhrem. Free to use with credit for editorial coverage of Paul Okhrem. All other rights reserved.
Quotes
Quotes on request. Paul replies within two business days.
Press contact
Email paul@paul-okhrem.com with the outlet, topic and deadline. Paul replies within two business days.
For speaking requests, include the date, audience, format and location. On-site attendance and travel must be agreed in advance.
Disclosures
Paul's roles at Elogic Commerce and Uvik Software are recorded on the facts page. Company delivery results are not results of Paul's independent consulting.
Use the Proof Standard to understand evidence labels and the editorial standards for source handling.
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