Every discussion about AI in private equity lands in the same place. One side imagines fully automated diligence. The other admits most AI pilots never get beyond a proof of concept – and both are true.
After working with hundreds of private equity clients at TresVista, we’ve seen the same pattern play out repeatedly. A firm rolls out ChatGPT or Claude, analysts start using it for small tasks, productivity gets a temporary lift, then adoption plateaus.
The technology rarely hits the wall. The workflow does.
That distinction matters because most conversations focus on what AI can do, not what firms are prepared to support.
The cost of waiting
A lot of mid-market firms are still sitting on the sidelines, and there are good reasons for that. Compliance teams worry about confidential information. Legal teams worry about governance. In private equity, a mistake involving sensitive data is expensive. These concerns call for governance, not avoidance, with clear limits on what AI can see and decide.
Research hasn’t helped ease those concerns.. MIT’s Project NANDA, in its 2025 report The GenAI Divide, found that despite $30–40 billion in enterprise AI spending, around 95% of organizations still aren’t seeing measurable financial returns. It’s an easy statistic to point to when the safest decision is to wait.
Waiting doesn’t make the workload any lighter.
Deal teams are handling more information than they were a few years ago, hiring has slowed, and LPs increasingly expect firms to explain how their AI investing decisions are paying off. Sourcing and diligence are usually the first places those expectations show up.
Sourcing still depends on judgment
AI is excellent at sorting information.
It can scan thousands of companies, organize messy datasets, identify businesses that match an investment thesis, and summarize results in minutes. That’s valuable work, but the decision about whether a company is worth pursuing still belongs to the investment team.
No model knows which management team will become difficult during diligence, or whether recent growth came from a sustainable customer base versus a single contract that happened to renew at the right time. Those decisions depend on context, conversations, and experience, so the strongest sourcing projects combine automation with review.
One recent engagement, a private equity AI use case, involved building a database of compounding pharmacies using state-level records spread across PDFs, spreadsheets, downloadable files, and other inconsistent sources. Scripts extracted the data, one model structured it, another classified it, and a human review process validated everything against external references. A project that would have required roughly 500 manual hours was reduced by about 80%.
That’s a much more realistic picture of AI in sourcing. It removes repetitive work, so analysts spend more time making decisions.
Diligence improves in layers
Due diligence is probably where expectations get ahead of reality.
Anyone who’s opened a financial model that’s been passed between investment banks for several years knows how messy those files become. Nested formulas, broken links, overlapping assumptions, hidden tabs, and inconsistent formatting aren’t problems AI can magically clean up.
Research is different.
Once the underlying structure exists, AI can review filings, industry reports, market research, and company materials at the same time instead of forcing analysts through each source one by one.
The gains are usually steady rather than dramatic.
Research and Databook preparation often start with time savings around 28%. CIM preparation has produced improvements close to 24%. Those numbers tend to increase over time because prompts improve, templates become more consistent, and teams learn where human review adds the most value.
Most successful diligence workflows weren’t perfect on day one.
Portfolio monitoring depends on the data first
Portfolio monitoring probably has the biggest long-term opportunity, but it’s also where weak data becomes impossible to hide.
An AI model can’t answer questions that no system has captured. Customer profitability, operating metrics, working capital trends, or product-level performance all depend on structured, reliable data before automation can add much value.
When that foundation exists, the results can be dramatic.
K-1 reconciliation has traditionally taken about 25 to 30 minutes per investor across funds with roughly 200 investors. Python automation with built-in validation reduced that process to about one minute per K-1, roughly a 90% reduction in manual effort.
PCAP reviews followed a similar pattern. Manual teams often reviewed only about 20% of statements because reviewing every file wasn’t practical. Automation made full coverage possible without adding proportional effort.
Neither improvement started with AI. Both started with usable data.
Why pilots stall
After enough projects, why AI fails in private equity usually comes down to a few predictable problems. .
Many firms still don’t have clean data. Others buy software without changing the process around it. Success is often measured by licenses issued or prompts submitted instead of hours saved, review quality, or turnaround time.
The firms seeing meaningful returns aren’t chasing every new model release. They’re investing in the less glamorous work first: data quality, governance, repeatable workflows, and review processes.
If you’re trying to figure out where AI belongs inside your firm, don’t start with the tool.
Start with the workflows that already run on clean, structured data.
Those are usually the first places where AI earns its place.
If you’re wondering where enterprise AI for private equity belongs inside your firm, don’t start with the tool. Start with the workflows that already run on clean, structured data. Those are usually the first places where AI earns its place. If you’re mapping where AI belongs in your own workflows, get in touch – we’re glad to walk through what we’ve learned.
