AI for private equity: deal teams and portfolio companies
AI consulting for private equity from Paul Okhrem starts at USD 80,000 for deal teams and portfolio leaders. The work tests AI claims before signing and builds value plans after close. Paul connects workflow costs, data access and management capacity to funded decisions. Each initiative needs an owner, a baseline and a review date.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. See the published consulting terms. Engineering, software and external specialists cost extra unless included in writing.
When private equity teams need senior AI advice
Paul's engagement fits a deal or operating decision that lacks clear evidence, ownership or priorities. It does not replace a fund's full-time operating partner.
- Before signing: a fund needs to test a target's AI claims and risks.
- Across the portfolio: an operating partner wants common evidence rules for different companies.
- Before a board review: a portfolio CEO needs a funded AI value plan.
- Not a fit: full-time operating-partner coverage, investment advice or a promise of portfolio returns.
Private equity AI consulting: what is included
Private equity AI consulting from Paul Okhrem covers three moments in the deal cycle. The written scope selects the companies, decisions and evidence to review.
- Before signing: test the target's AI claims, data rights, unit economics and team. See the AI due diligence checklist.
- First 100 days: build an AI value-creation plan with priorities, owners and baselines.
- Hold period: review portfolio AI controls, vendor choices and board reporting through a scoped engagement or fractional role.
A private equity AI consultant should test claims and stop weak investments, not only find new use cases. The same tool or playbook may not fit companies with different data, margins or operating teams.
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A 100-day post-close AI plan
Start with the investment thesis and workflow costs, then test one bounded use case. The example below is an operating sequence, not 100 days of included consulting or a promised launch date.
| Period | Work | Decision evidence |
|---|---|---|
| Days 1 to 30 | Check the thesis, inventory current AI and map workflow costs | Baseline, sponsor, data owner and risk register |
| Days 31 to 60 | Compare build, buy and no-AI options; select a bounded pilot | Approved scope, total cost, tests and stop criteria |
| Days 61 to 100 | Review pilot results and the operating handover | Scale, fix or stop recommendation; funded owner and next review |
Data readiness, approvals and delivery capacity determine the schedule. Do not fund the next stage before the evidence supports it.
Which portfolio AI scenarios are worth reviewing?
Review scenarios with a known workflow cost, a usable data source and a manager who can change the process. Paul helps compare the options rather than assume every company needs an AI build.
Back-office work across several companies
Review document handling, finance operations and staff knowledge work. Check process differences before sharing software or data. Similar department names do not mean the workflows are the same.
Commercial operations in a B2B company
Test quotation support, account-service triage or proposal preparation against cycle time, rework and customer experience. Deduct review time and running costs before reporting a benefit.
An AI feature inside a software company
Check customer demand, tenant permissions, test coverage, model usage costs and support ownership. A demonstration is not proof of customer retention or pricing power.
A stalled pilot or vendor program
Rebuild the record of spend, approvals and the starting baseline. Separate a fixable delivery problem from a use case without a sound business case.
How to report AI value without overstating EBITDA
Finance must validate the effect before it enters the value plan. Time saved, usable capacity and cash savings are different measures. Faster work does not automatically create revenue.
Record adoption, quality, cash cost and new review work. Show the difference between a measured operating change and a modeled benefit. The AI outcome measurement method sets out the evidence record.
Keep the decision log useful: what was funded, blocked, stopped or moved into production, and why? Use the AI use-case prioritization framework as a starting point for the portfolio review.
Keep diligence findings distinct from post-close value
Pre-close diligence tests claims within limited access. After close, management chooses and owns the operating changes. Paul can translate a finding into an action, budget owner and review gate.
Keep unresolved questions visible during that handover. Do not turn a diligence hypothesis into a promised synergy. Legal, accounting and investment advisers retain their specialist decisions.
Fees and scope for private equity AI work
Senior consulting costs USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. A multi-company review needs an agreed sample and evidence request list. The minimum does not buy an audit of an entire portfolio.
Travel, software, delivery teams and specialist reviews are agreed separately. Set access, interviews, deadlines and the decision owner before work starts.
Review the private equity AI research for market context, not an investment forecast. Paul's Elogic Commerce and Uvik Software roles provide operating experience, not proof of PE client outcomes or investment returns.
AI operating partner or fractional CAIO?
An AI operating partner usually works for the fund across several portfolio companies. A fractional CAIO works inside one portfolio company, under that company's mandate.
| Need | Scope to consider | Accountability |
|---|---|---|
| Portfolio thesis and common operating standards | Fund operating partner with a scoped AI adviser | The fund sets priorities and owns investment decisions |
| One company needs ongoing AI leadership | Fractional CAIO mandate | The company sets authority, budget and review gates |
| Approved design needs daily implementation | Engineering or specialist delivery team | Delivery owners build, test and operate the system |
Funds may need more than one role. Paul can provide a scoped advisory engagement or an agreed fractional seat, not full-time fund coverage. A fractional seat costs USD 30,000 per month, for at least six months, up to 30 hours monthly.
FAQ
Can Paul compare AI opportunities across a portfolio?
Yes, with an agreed company sample and common evidence rules. The review compares workflow cost, data readiness, adoption, risk and management capacity. Paul does not assign the same tool to every company. The output identifies decisions, owners and review dates rather than promise portfolio-wide returns.
How much does private equity AI consulting cost?
The consulting rate is USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. Agree the company sample and outputs before work starts. A fractional role has different terms: USD 30,000 monthly, a six-month minimum and up to 30 hours per month.
Is the 100-day plan included in the minimum engagement?
The exact outputs must be scoped. The 100-day plan shows an example operating sequence, not 100 days of included consulting. It is not a guaranteed deployment date. Data access, approvals and the delivery team's capacity determine what can be completed and when.
Does an AI pilot prove an EBITDA improvement?
No. Finance must validate the effect on earnings before reporting it as realized value. Saved staff time, available capacity and cash savings are different measures. Count model usage, human review and support costs. A successful pilot can justify another test without proving a financial return.
Do we need an AI operating partner or a fractional CAIO?
Use the fund's operating partner for portfolio priorities and investment oversight. A fractional CAIO can own an agreed AI mandate inside one company. Paul can support either through a defined scope. A full-time operating partner or daily engineering team covers different responsibilities and capacity needs.
What should an operating partner share before the first call?
Send the company type, deal stage, current AI initiatives, decision owner and deadline. State whether you need pre-close diligence or a post-close value plan. Start with a non-confidential summary. Agree an NDA and secure process before sharing target documents, customer data or data-room access.
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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Paul Okhrem reads every brief personally. Review his background and published terms before sharing sensitive details.