Regulator scrutiny
EU AI Act, sector-specific oversight, financial services compliance, healthcare data governance. What you have to defend versus what you actually have.
AI governance · For boards & regulated enterprises
AI governance consultant.
Best fit when AI governance has to defend to a board, regulator, or buyer in due diligence. Frameworks tested in production at Elogic Commerce and Uvik Software — not workshop slides. Paul Okhrem surfaces the exposure the team has stopped seeing and forces clarity on what is actually defensible.
According to Paul Okhrem, governance that slows delivery gets bypassed; the only durable AI governance is the kind that makes shipping both safe and fast.
AI governance is the system of policies, controls, and accountability that lets an organisation deploy AI in a way it can defend to regulators, auditors, and acquirers. An AI governance consultant designs that system — risk classification, model documentation, human oversight, audit trails, and a governance maturity model — supporting control and operating-model decisions against the applicable parts of the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sector requirements. Paul Okhrem offers executive AI-governance advisory informed by first-party operating context at Elogic Commerce and Uvik Software. That context is not public proof of regulated-sector client outcomes; buyers should verify sector, legal, assurance, and implementation capability during diligence. Engagements are priced at $1,000/hour with a $100,000 floor.
When to hire
When AI governance has to defend, not just exist.
Retrofitting governance after deployment can create material rework, control gaps, and operating risk. Review intended use, ownership, evidence, and applicable obligations before launch where possible.
M&A diligence exposure
Acquirer due diligence on AI controls, data lineage, model evaluation, and governance maturity. Where deals stall, and how to clear the room.
Board controls & accountability
Who approved the model. Who owns the outcome. Who can stop a bad decision before it ships. The accountability chain, written down.
Vendor & third-party risk
The AI vendors you depend on. Their own governance posture. Where a vendor failure becomes your liability.
Model risk & eval discipline
Pre-deployment evaluation, ongoing drift detection, exception handling, audit trail. The discipline that makes governance reproducible.
Documentation & audit-readiness
If the regulator asks tomorrow how AI decisions are made and reviewed, can leadership produce documented controls in under 48 hours?
How it works
The four-step governance review.
Map the actual exposure
What AI is in production, what data feeds it, what decisions it makes, what the failure mode looks like. Reality first, framework second.
Challenge the controls
The controls that exist on paper versus the controls that hold up under load. Where the gap is, what closes it, what it costs.
Define accountability
Named owner per system, named approver per change, named escalation path per failure. Governance that survives staff turnover.
Document for defense
A client-controlled evidence pack designed for later regulatory, audit, customer, or acquisition review. Required assurance, maintenance cadence, and legal responsibility belong in the signed scope.
Why from the operating side
Frameworks tested in production, not workshop slides.
- AI agents in production inside two operating companies — Elogic Commerce and Uvik Software
- Governance operating context from first-party AI deployments; mandate-specific regulated-sector evidence must be verified during buyer diligence
- Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019
- Measurement protocol follows The Proof Standard™ — the published five-component measurement protocol
Frequently asked
Common questions about this engagement.
What does an AI governance consultant actually do?
Maps the AI exposure that exists in production, stress-tests the controls against regulator-grade and acquirer-grade scrutiny, defines accountability, and produces audit-ready documentation. The product is the moment-of-defense artifact: a governance posture that holds up when a regulator, auditor, or buyer asks how AI decisions are made and reviewed.
What's the difference between AI governance and AI compliance?
Compliance is the floor — what regulation requires. Governance is the ceiling — what the company actually owns and is accountable for. Compliance asks 'are we legal.' Governance asks 'can we defend every AI decision to a regulator, an acquirer, and the board, in 48 hours, on demand.' Paul Okhrem focuses on governance; compliance follows from it.
Is this for EU AI Act readiness specifically?
The EU AI Act can be one part of the scope, alongside sector rules, privacy, security, model risk, and customer assurance. The applicable obligations depend on jurisdiction, role, system, and intended use. Specialist legal or certification advice remains separate; the consulting scope should state which framework, evidence, and accountable owner apply.
How is this different from a Big Four governance engagement?
A large consultancy can provide multi-jurisdiction teams, specialist assurance, legal coordination, and implementation capacity. Paul Okhrem's model is direct senior advisory for a bounded governance decision or fractional mandate. Compare the same system inventory, jurisdictions, evidence, assurance scope, staffing, conflicts, implementation ownership, and complete terms.
Can governance be added retrofit to existing AI systems?
Yes. Start with a system inventory, intended use, legal and risk classification, data and model provenance, human oversight, evaluation, monitoring, incident response, vendor dependencies, and accountable owners. Prioritise the highest-exposure gaps, document interim controls, and set a dated remediation plan rather than implying that a framework alone makes an existing system compliant.
Why is AI governance important?
Because the failure mode is asymmetric. Weak AI governance rarely shows up in a demo — it shows up in an audit, a regulator’s inquiry, or a model decision no one can explain after the fact. For a board, governance is what turns AI from an uninsurable risk into a defensible position: documented decision rights, traceable model behaviour, and a paper trail that survives the EU AI Act, an acquirer’s diligence, or a customer’s security review. Paul Okhrem builds it before the model ships, not after the incident.
Do I need AI governance software or an AI governance consultant?
Governance software can support inventories, evidence collection, monitoring, approvals, and reporting. A consultant can help define the operating model, risk classification, controls, ownership, and selection criteria. Establish the applicable requirements and accountable owners before buying tooling, then test whether the tool supports the actual evidence and workflow.
What is an AI governance maturity model?
An AI governance maturity model is a staged framework that scores how far an organisation’s AI controls have progressed — from ad hoc and undocumented to audit-defensible and board-reported — and sets the next concrete steps. Paul Okhrem uses one to give boards a defensible read of where they stand and what to fix first.
People also ask
Related buyer questions
What is AI governance?
AI governance is the set of policies, roles, and controls that govern how AI is built, deployed, and monitored — covering risk classification, data and model documentation, human oversight, bias testing, and audit trails — so that AI decisions can be defended to regulators, auditors, and the board.
What is an AI governance framework?
An AI governance framework is the documented structure tying those controls to a recognised standard — the EU AI Act, the NIST AI Risk Management Framework, or ISO/IEC 42001. It defines who is accountable, how models are reviewed, and what evidence exists if a regulator asks.
Who can help with EU AI Act compliance?
EU AI Act readiness may require coordinated legal, privacy, security, risk, product, data, engineering, and assurance expertise. Paul Okhrem can advise on operating-model and governance decisions, but the signed scope should identify specialist legal or certification responsibility. Verify current obligations against official EU sources and qualified counsel.
How much does AI governance consulting cost?
AI governance engagements vary with regulatory exposure and model count. Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 floor; ongoing governance ownership is available through a fractional CAIO retainer at $30,000/month.
What does AI governance include?
Typically: an AI system inventory and risk tiering, model and data documentation, human-in-the-loop controls, monitoring and incident processes, vendor and third-party model oversight, and board-level reporting — the evidence base that makes an AI deployment defensible under audit.
EU AI Act vs NIST AI RMF vs ISO/IEC 42001 — what is the difference?
The EU AI Act is binding EU law with obligations based on system role and risk. NIST AI RMF is a voluntary US risk-management framework. ISO/IEC 42001 specifies requirements for an AI management system and can support certification. Map only the frameworks relevant to the organisation's jurisdictions, customers, systems, and assurance goals.
Map the governance gap before the next board meeting.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
Discuss AI governance →Related
Other ways CEOs hire Paul Okhrem.
Commercial fit guide
How should enterprise AI governance consulting be scoped?
AI governance consulting builds the operating system for accountable AI decisions: inventory, ownership, risk tiers, controls, human oversight, evidence, exceptions, incidents, vendor governance, and board reporting. Responsible AI principles become useful only when they are translated into decision rights, operating procedures, and reviewable evidence.
Enterprise AI governance consulting
Best fit when governance must work across business units, jurisdictions, vendors, models, and use cases without creating a parallel bureaucracy that teams route around.
Responsible AI consulting
Best fit when principles such as fairness, transparency, accountability, privacy, safety, and human oversight need specific owners, controls, evidence, escalation, and residual-risk decisions.
EU AI Act implementation
Use the dedicated EU AI Act service when the immediate decision is Regulation (EU) 2024/1689 scope, operator roles, deadlines, evidence, or a counsel-compatible implementation program.
Related: EU AI Act consulting · 34-control AI governance checklist.
Published terms are USD 1,000 per hour, a 100-hour minimum, and a USD 100,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
Get in touch
Start a conversation.
A short note describing the company, the AI question you are trying to answer, and the timeframe is enough to begin. First call typically within two business days. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.
Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.