Independent AI transformation consulting
Paul Okhrem's AI transformation consulting for enterprise teams starts at USD 80,000 for an 80-hour engagement. The work connects AI investment to workflows, owners, data, controls and adoption. Each project needs a baseline and evidence before it scales. Paul leads the agreed decisions and reviews. Your business and delivery teams retain their named responsibilities.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. Reply within two business days. Read the published terms.
Discuss an AI transformation Explore the transformation roadmap
What does AI transformation consulting change?
Transformation changes how a company works, not just which AI tools it buys. It can change how work enters a process and who handles unusual cases. It can also change which decisions use AI, how teams share data and how managers review results.
Paul leads the agreed executive decisions and oversees the build within the scope. Business, data, risk and delivery teams keep the duties set out in the mandate.
Who an AI transformation engagement fits
The work fits a company that must change several connected workflows and assign clear owners.
Best fit
- AI pilots exist, but leaders need one operating plan.
- A rollout needs changes to roles, data, controls and team routines.
- An executive sponsor needs a clear sequence of investment decisions.
Not a fit
- A small tool setup or a request for extra developers.
- A program without a sponsor or access to the evidence.
AI transformation starts with decisions
Paul Okhrem helps leaders decide what to fund, test, change or stop before scaling AI. Each decision needs an owner, deadline, realistic options and evidence.
Review whether to build, buy, improve existing software or make no change. Test vendor claims against the workflow, available data, running costs and required controls.
Make the decision explicit
- Write the business question, deadline and person authorized to decide.
- Separate verified facts, estimates, vendor claims and missing evidence.
- Compare cost, delivery capacity, operating risk and the ability to change course.
- Recommend a path, with conditions and a date to review the decision.
| Memo section | Required detail |
|---|---|
| Decision and scope | What is being approved, and what is outside the review |
| Evidence and alternatives | Sources, assumptions, missing information and realistic options |
| Cost and dependencies | Known fees, estimates, internal capacity and required specialist reviews |
| Risks and conditions | Controls, remaining uncertainty and reasons to pause |
| Owner and next review | Who acts next and what would trigger a different decision |
The client keeps the right to decide. A small test, ordinary software or no investment can be the right recommendation.
A defined decision across several projects may fit AI strategy consulting. Ongoing executive ownership may need a fractional Chief AI Officer.
Digital transformation versus AI transformation
Digital transformation changes systems, data, processes and ownership. AI transformation adds model-driven decisions or actions where they solve a defined problem.
A useful change does not always need AI. A platform upgrade, better ERP links or clearer workflows may solve the problem first.
Compare the current system with realistic alternatives
Record business needs, system design, dependencies, full cost and tests for sign-off. Include the option to improve the current platform.
Compare vendors on the same terms: security, data rights, system links, support, custom code and exit costs. Disclose related-party options and use a separate delivery agreement.
AI operating model consulting sets who owns the work, how teams use it and how to hand it over. Name an owner for each measure before release. Include the cost of moving to the new process.
For commerce and ERP decisions, see ecommerce operations consulting. For recurring IT ownership, compare the fractional CIO mandate.
A four-phase AI transformation roadmap
- Baseline: define the problem, current results, data limits and gaps in ownership.
- Build and prove: choose a few supported use cases and test quality, costs, benefits and controls.
- Controlled scale: expand only where evidence supports it. Train users and watch for new failures.
- Embed and hand over: pass the work routines, support, decisions and measures to permanent owners.
Phases can overlap, and a failed gate can stop a project. The detailed AI transformation roadmap is a planning guide, not a promised delivery timetable.
When does a 12-month embedded transformation fit?
A 12-month embedded option fits a company that needs senior involvement across several phases. An illustrative sequence uses months 1 to 3 for baseline and priorities. Months 4 to 6 cover controlled implementation. Months 7 to 9 cover evidence-led scale, and months 10 to 12 cover handover.
The written scope sets the actual hours, cadence, dependencies and total fee. The USD 80,000 consulting floor is an 80-hour minimum, not a fixed price for a year of delivery. If the main requirement is ongoing part-time executive AI ownership, compare the separate fractional CAIO offer.
Which operating-model decisions need an owner?
Assign owners for funding, daily work, data, controls, support and staff adoption.
- Business priorities and funding decisions.
- Workflow design, exceptions and human review.
- Data ownership, access and quality.
- AI governance, tests and the right to approve a release.
- Engineering, vendor and live-support duties.
- Training, incentives, adoption and results.
Use governance consulting for controls. Use AI adoption consulting for the work people must do differently. Do not leave either until the software is complete.
IBM reports this finding in its 2026 CEO study. Organizations with an AI-first approach to C-suite design scaled 10% more AI initiatives enterprise-wide than their peers. This is a survey association, not proof that changing leadership roles caused the difference.
Need a decision, not a report? Send a private brief. Paul replies within two business days.
Send a private briefAI transformation governance: who decides what
AI transformation governance sets who may approve funding, workflow changes and releases, and who may accept risk. Paul Okhrem's framework gives each decision an owner, required evidence and a review point.
Keep one view of all projects, with a business owner for each. Legal, security, privacy and sector specialists keep the duties that require their expertise.
| Decision | Accountable owner | Evidence and gate |
|---|---|---|
| Portfolio admission | Executive portfolio owner | Baseline, sponsor, dependencies and risk screen; fund, revise or reject |
| Workflow redesign | Business process owner | Process, exceptions and role changes; approve the operating design |
| Data and tool access | Data and security owners | Purpose, permissions, retention and audit trail; grant, restrict or deny |
| Model and vendor choice | Technology executive | Requirements, evaluation, cost and exit plan; select, build or reject |
| Risk classification | Qualified risk executive | Intended use, jurisdiction and impact; assign the risk tier and obligations |
| Evaluation plan | Business and technology owners | Representative cases, thresholds and failure tests; approve testing |
| Human oversight | Business executive | Review triggers, reviewer skills and override process; approve oversight |
| Production acceptance | Named release approver | Evaluation, security, resilience, monitoring and rollback; go, revise or stop |
| Value validation | Business or finance executive | Baseline, quality, costs and measurement window; scale, revise or stop |
| Material change | Technology and business executives | Impact, retesting and approvals; release or hold |
| Incident response | Incident executive | Severity, containment and corrective action; continue, restrict or suspend |
| Retirement | Business and technology owners | Data disposition, replacement and access removal; retire and verify |
Download the full AI transformation RACI (CSV). It names who does the work, who gives input, when reviews occur and who may stop a project.
Test governance on one workflow in the first 30 days
- Week 1: write the business decision, baseline, affected users, systems and deadline.
- Week 2: name business, technical, control, metric, release and incident owners.
- Week 3: test quality, permissions, human review, fallback, support and cost thresholds.
- Week 4: record the go, revise or stop decision and schedule an operating review.
Keep one record of assumptions, evidence, conflicts, approvals and next reviews. Pilot counts and tool licenses alone do not prove value.
The framework and CSV are original guidance by Paul Okhrem, licensed CC BY 4.0. They do not establish legal compliance or certification.
Related references: AI governance framework, NIST AI Risk Management Framework and AI adoption consulting.
What does an agentic workforce transformation mean?
It means changing selected tasks to use software agents. People keep defined authority and handle exceptions. Check task limits, skills, workload, access rights and effects on jobs before changing roles.
This does not mean an agent can replace an entire team. Start with a bounded workflow and the agentic AI advisory scope. In some processes, ordinary automation or better information is the more reliable choice.
How does this differ from a large consulting program?
Paul offers one named senior operator within an agreed mandate. A large consultancy can provide several teams, work across regions and offer specialist assurance. An internal transformation office can own the work day to day.
Compare the same decisions, senior time, build capacity, access to evidence and full commitment. Paul's Elogic Commerce and Uvik Software roles provide operating experience and potential conflicts to disclose. They are not independent proof that a client's transformation will succeed.
How are results measured?
Track business outcomes alongside quality, costs, adoption and risk. Record the starting point, measure owner, change made, test period and important outside changes. For decision-only work, sign-off depends on the agreed outputs. Do not claim a revenue result that has not been measured.
McKinsey's 2025 survey gives context for the gap between AI use and value. About 6% of organizations qualify as AI high performers, attributing 5% or more of EBIT to AI. McKinsey also required respondents to report significant value from AI. EBIT means earnings before interest and taxes. These are self-reported survey results, not a promised client outcome.
The Proof Standard defines the evidence method. Use the free readiness assessment for an initial review. Choose a paid Growth Readiness Audit when the foundations need a deeper evidence review.
Fees and practical requirements
Consulting costs USD 1,000/hour, with an 80-hour minimum and USD 80,000 floor. The full budget depends on the signed scope, your team's capacity, the build and external costs. Read published terms.
The engagement needs an executive sponsor, named delivery owners and access to evidence. A short strategy decision or a need for extra developers calls for a different scope.
Frequently asked questions
Is AI transformation the same as digital transformation?
They overlap. Digital transformation can change platforms, data and processes without AI. AI transformation uses AI as a material part of how the business works.
Does every AI transformation take 12 months?
No. Twelve months is one embedded planning option, not a fixed rule. The time needed depends on decisions, systems, data, risk and the team's ability to deliver.
Does USD 80,000 cover a full AI transformation?
No. USD 80,000 is the consulting floor for the 80-hour minimum at USD 1,000 per hour. The written scope sets the decisions, reviews and time included. A wider program needs its own budget. Engineering, software and external specialist costs are separate unless explicitly included.
Who builds the systems?
The client’s team or a selected provider. Paul’s leadership scope does not automatically include engineers or production support. Related-party options are disclosed and separately contracted.
Can we stop a use case after starting?
Yes. Set stop, revise and scale criteria before funding each stage. Stopping a weak investment on evidence can be a useful outcome.
About Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
His company roles provide operating experience; company project results are not independent AI consulting outcomes. Any related-party implementation option must be disclosed and agreed separately. Read the evidence and measurement method.
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