AI implementation services: from pilot to production
Paul Okhrem's AI implementation consulting for enterprise teams starts at USD 80,000 for an 80-hour engagement. He helps move an approved use case into daily work. The scope covers system design, vendor choices, release checks and risk controls. Your team or chosen partner builds the system. Engineering, software and external specialists are separate unless included.
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.
What are AI implementation services?
AI implementation connects a use case to data, software, people and production support. It includes integration design, access controls, evaluations, rollout and measurement. A model demonstration alone does not cover that work.
Paul’s service is senior implementation leadership and oversight. The statement of work names the builders, system owner and release authority. If the business case is not ready, start with AI strategy consulting or an AI Growth Readiness Audit before funding a build.
Who AI implementation leadership fits
The service fits an approved AI use case with a sponsor, build team and decision deadline.
Best fit
- A working pilot needs a clear route into production.
- A stalled build needs a review of evidence, costs and ownership.
- A team needs release checks before AI can affect customers or systems.
Not a fit
- A request for developers or ongoing production support within the consulting fee.
- A build with no agreed business case or accountable owner.
When fixed automation beats an AI agent
Use fixed automation when steps are known, inputs are structured and errors are costly. Use an AI agent when variable inputs require choices within clear limits.
Paul Okhrem reviews the whole workflow before recommending either option. Some steps can follow fixed rules, while exceptions need a person or a controlled agent.
| Approach | Fit | Main check |
|---|---|---|
| Process change | Duplicate entry or unnecessary approvals | Can the work be removed or simplified? |
| Fixed automation | Stable inputs and explicit rules | Integration reliability and exception handling |
| AI copilot | Variable information reviewed by a person | Output quality and review effort |
| Controlled agent | Runtime choices and permitted actions | Permissions, testing, recovery and oversight |
Measure the completed process
Collect volume, task mix, waiting time, handoffs, error rates and current operating costs. Name the process owner and metric owner.
Include human review, exceptions, software usage and support. Faster routine steps are not useful if they create more correction work elsewhere.
Design the exception path before release
Test missing information, conflicting records and failed system calls. Define retry limits, approvals, escalation and rollback.
The scope can include a process map, option comparison, cost model, acceptance tests and release plan. Builders and operating teams need separate ownership and budgets.
If the use case is still open, review generative AI use cases before choosing the model. When an agent is the right answer, see agentic AI consulting. Use the AI governance framework to assign controls.
What does an AI implementation consultant do?
Paul translates the approved business decision into a delivery plan. He reviews vendor proposals, tests architectural assumptions, defines acceptance criteria and reports risks to the executive sponsor. A gate review can recommend proceed, revise or stop; the client retains the authority agreed in the mandate.
- Before build: baseline, user workflow, data access, costs and controls.
- During build: review evidence, integration risks and changes to scope.
- Before release: evaluations, support ownership, training and rollback.
- After release: compare outcomes with the agreed baseline and monitor regressions.
What are the five stages of AI implementation?
| Gate | Decision | Required evidence |
|---|---|---|
| 1. Decide | Is this worth testing? | Use case, owner, baseline, risk limits and budget |
| 2. Design | Can it work in this environment? | Architecture, vendor roles, permitted data and integration plan |
| 3. Prove | Does the pilot meet the agreed criteria? | Representative test cases, failure analysis, quality and cost results |
| 4. Release | Can the company operate it safely? | Monitoring, access controls, support, training and rollback plan |
| 5. Measure | Should the company scale, revise or stop? | Client-owned results, costs, adoption and incident evidence |
Review windows can include 30- and 90-day checks where the business cycle supports them. They are not a promise that every implementation finishes or pays back in 90 days.
Need a decision, not a report? Send a private brief. Paul replies within two business days.
Send a private briefWhy do AI pilots fail to reach production?
Common planning gaps include unavailable data, weak integration ownership, unclear value, underestimated running costs and no support team. These are risks to test, not a claim that every stalled pilot has the same cause.
Gartner’s June 2025 forecast predicted that more than 40% of agentic AI projects would be canceled by the end of 2027. That is a forecast for agentic projects, not an observed failure rate for all AI implementations. The AI agent statistics guide explains that distinction.
How do you test an AI system before release?
Use cases should reflect real work, including missing data, unusual requests and likely misuse. Test task quality, incorrect outputs, permissions, latency, cost per completed task and escalation to people. Compare with the current process, not an invented baseline.
For agents that can act, test tool permissions, spending limits, approval steps and recovery after a failed action. Use the agent controls register. The client’s security and legal owners must review the controls relevant to their responsibilities.
AI integration consulting: delivery and support
The client's team or a selected delivery partner owns engineering under a separate scope. AI integration consulting defines the system boundaries, data access, tests and handover that the build must support. Paul reviews those decisions; the advisory fee does not include a development team or managed production service.
Paul is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. If either is proposed for implementation, the relationship and selection criteria must be disclosed in writing before the decision. The client may choose another builder.
When your team lacks build capacity, forward deployed AI engineers can work inside your stack under your controls. Their delivery work needs a separate scope and budget.
How much does AI implementation leadership cost?
Paul’s published consulting rate is USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. The scope states which gates and reviews are included. Build capacity, software, model usage, cloud, security testing and ongoing support need separate budgets.
A useful brief names the workflow, sponsor, current systems, available data, build team and target date. Review published terms before sending confidential detail.
Frequently asked questions
Can Paul take over a stalled AI pilot?
Yes, where the scope fits his experience and capacity. The first step is to review the business case, current code and data access, test results, costs and ownership. A restart is not always the right outcome.
Is model selection the first step?
No. Start with the business process, permitted data, required quality and operating constraints. Those requirements inform whether to buy software, use a model, build a smaller automation or stop.
What does AI implementation consulting cost?
Paul Okhrem charges USD 1,000 per hour with an 80-hour minimum and USD 80,000 floor. The scope sets which design decisions, release checks and reviews he owns. Engineering, software and external specialist costs are separate unless included. The client agrees the builders, system owner and terms before work starts.
Does the consulting fee include software development?
No. The published fee covers Paul’s scoped leadership and review work. Engineering, licenses, infrastructure and production support are costed separately.
Is a 30% improvement required for every AI release?
No. Acceptance criteria depend on the workflow and risk. A quality-critical system may need a different threshold, and an improvement in speed does not override safety, legal or accuracy limits.
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.
Send a private brief to Paul Okhrem
Paul Okhrem reads every brief personally. Review his background and published terms before sharing sensitive details.