AI value creation in private equity means finding the jobs inside each portfolio company where AI adds earnings, building the systems that do them, and proving the gain in the numbers the sponsor already tracks. The mandate is in place at most firms. The method is what is missing.
In Accordion's May 2026 survey of 150 PE operating partners, 98 percent of sponsors had mandated AI adoption, while only about half of portfolio companies were actively implementing (Accordion, The PE AI Adoption Benchmark). The gap sits inside the companies: in which jobs to change first, who builds the change, and whether anyone uses it.
Where AI adds EBITDA
AI reaches earnings through three levers. Each one runs through specific jobs people do by hand today.
| Lever | Example jobs | What moves |
|---|---|---|
| More revenue from the same team | Finding companies about to buy, drafting quotes, following up on every open quote | Win rate, quote speed, new accounts |
| Lower cost to serve | Reading orders and invoices, answering routine customer questions, preparing reports | Hours per order, error rate |
| Less cash tied up | Chasing overdue invoices, flagging slow-moving stock, catching billing errors | Days sales outstanding, inventory days |
Step 1: Start from earnings, not tools
The work starts with the diligence a buyer would run. Commercial diligence tests the market, customers, pricing and pipeline for revenue left behind. Performance diligence follows the work itself: where hours go, what each order costs to serve, and where it waits. Every finding is tied to earnings, so opportunities compare on what they are worth. The same diligence can run on a company before close, so the AI case enters the value creation plan on day one.
Step 2: Rank by worth, cost and payback
Each opportunity gets a business case: the earnings it adds, from a baseline in the company’s own records; the cost to build and run it; and the month the gain covers the cost. Ideas that fail the numbers are retired in writing. The top one leaves with a design and a priced build plan. How the business case is built.
Step 3: Build inside the company
The system is built with the people who will use it, in the email and software they already work in, over 10 to 14 weeks. A person approves anything that reaches a customer. The engineering standard covers how each system is kept running, safe and checked.
Step 4: Get people using it
A system nobody uses adds nothing to EBITDA. One named person meets the team every week for the first 90 days, against a goal of 7 in 10 intended users active every week by day 90.
Step 5: Report it the way the sponsor reads results
Each company reports on one scoreboard: how much the system is used, how good its output is, the business result against the baseline, and what it costs to run. The format is the same in every company, so the operating team can compare the portfolio on one page.
Why portfolio AI stalls
- It starts from a tool, so nobody can say which earnings line it serves.
- The pilot never reaches the work the team does every day.
- Nobody owns adoption after launch, so use fades.
- There is no baseline, so the gain cannot be proven.
- Each company reinvents the approach, so nothing compares across the portfolio.
The mandate is in place. The method is the gap.
A checklist for operating partners
- Every AI opportunity has a business case stated in earnings.
- Every case has a baseline taken from the company’s own records.
- Every build has a named owner for adoption and a usage goal with a date.
- Every system reports use, quality, results and running cost each month.
- Every company reports in the same format.
- Ideas that fail the numbers are retired, with the reason written down.
This is how ClearForge runs portfolio work: one diagnostic model across the companies you choose, systems built where they pay, and results reported to the operating team. How portfolio work runs.