Chris Brown, President at VASS Intelygenz, drives AI and deep tech innovation and implementation across industries, delivering tangible ROI.
The private equity playbook is being rewritten. Debt is pricier, exits are slower and limited partners scrutinize every basis point.
Over the past quarter, a common mandate has called for funds to use AI to sharpen deal sourcing and portfolio execution but prove the ROI first. McKinsey’s 2025 Global Private Markets Report highlights AI as a rising priority for private-market managers, and a separate McKinsey survey finds only 1% of companies consider their AI usage mature. Value‑creation windows have tightened, so investment committees don’t want to see another proof of concept; rather, they want to be shown the return.
Anchor AI To Investment Value
Smart funds start with a value hypothesis. To implement AI and see real investment value, it’s imperative to spell out the business problem, the workflow to improve and the payback window. Tying every line of code to a deal-thesis metric keeps data scientists, operators and deal teams marching in lockstep. If the model cannot move an EBITDA lever or widen an exit multiple, it must be shelved.
Win The First Mile
Ambitious visions of AI implementation inspire, but solution credibility is earned with the first concrete win. One mid-market fund we worked with started small by deploying a signals engine that scanned open-source data and conference agendas. The tool flagged that the CEO of a high-priority target would be speaking across the country. A deal-team principal then hopped on a plane, secured a face-to-face meeting and moved a once-cold opportunity into active diligence.
The lesson is to focus on narrow, data-rich use cases where success criteria are clear. Early wins convert skeptics, generate proprietary data and bankroll the next wave of bets long before generative tools mature for enterprise-grade use.
Three Use Cases That Move the Needle
Private equity investors are taking a closer look at how AI can deliver everyday results. One area that draws repeated attention is deal sourcing. Machine learning models now review everything from hiring trends to patent filings to surges in web traffic, surfacing promising targets months before bankers or intermediaries send out teasers. Through our clients, we’ve seen unique applications of AI, and at one top-performing fund, these signals contributed to nearly a third of its new pipeline.
The diligence process is evolving just as quickly. Today’s large language models can digest thousands of pages of contracts or ESG documents in just a few hours, flagging hidden risks or opportunities and letting deal teams focus on actual decision-making instead of endless paperwork.
On the portfolio side, firms are leveraging autonomous agents to fine-tune pricing, limit procurement losses and monitor contract renewals. The impact is already visible in day-to-day operations as these technologies are delivering real, measurable improvements across the industry.
Proof That It Works
Several leading private equity firms are translating ambition into results. KKR’s cloud-native data platform now helps standardize portfolio KPIs and automate valuations while speeding up deal flow. Vista is channeling investment into agentic AI, moving well past dashboards into automation that can actually act on insights. At Blackstone, more than 50 data scientists work across dozens of portfolio companies, and a community of 300 analytics professionals shares best practices and discoveries every week.
What was once theory is now driving material performance and competitive advantage for those at the forefront.
Overcome The Predictable Barriers
One of the biggest obstacles to successful AI in private equity is not the technology itself but the scattered nature of the data. It is common to find key details tucked away in separate systems, including KPI dashboards, market feeds, diligence memos, Slack threads and CRM records—all on different platforms. Real concerns around privacy and sharing confidential board discussions or call transcripts add another layer of complexity, slowing down progress and making change management feel daunting.
The first practical step is to pull essential data into a single, highly secure environment that satisfies both SEC disclosure requirements and LP audit trails. Any AI project should begin with strong data quality standards, built-in safeguards and trial runs in real-world test settings to confirm that at least one ROI metric (e.g., deal-sourcing hit rate, days in diligence, EBITDA lift) moves in the right direction. Many off-the-shelf AI solutions are still maturing, so top-quartile funds are investing in their own data infrastructure and portable models that can scale with the next acquisition.
The silver lining is that building good foundations does not require extravagant spending. McKinsey modelling indicates that allocating roughly 1-1.5 % of existing IT budgets is enough to create the right level of security, oversight and scalability to enable robust AI in private equity.
Looking Around The Corner
The next frontier is agentic workflows that execute. Early movers will own proprietary data flywheels, sharper underwriting models and self-improving playbooks. The competitive gap between adopters and laggards is poised to widen, not narrow.
Private equity has always thrived on information asymmetry. AI supercharges that advantage for firms prepared to act now. Commit board-level sponsorship and a scalable budget, pilot two tactical use cases to build momentum and then scale one signature workflow that resets the fund’s operating model. Those who operationalize AI responsibly today will set the pace for the industry’s next decade.
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