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AI for VC and PE firms

AI creates value in a VC or PE firm when it is built into how the firm and its portfolio actually operate, on their own data and rails. Handing out tool licenses and hoping people adopt them rarely gets there, which is what the major studies on AI adoption keep finding.

Where AI helps inside an investment firm

For VC and PE firms, the durable uses cluster in a few places: sourcing and screening, diligence, portfolio monitoring and reporting, and internal operations. What these have in common is that each one runs on the firm's own data, its deal flow, portfolio numbers, and documents, rather than on a generic tool bolted onto the side of the business.

The value gap

Most firms are not seeing returns yet, and the reason is consistent across the research:

• BCG reports that most private equity firms cannot show meaningful returns from AI across many portfolio companies, and that most are deploying AI by handing out tool licenses "without changing anything about how the organization operates."
• Bain has described roughly four in five companies as stuck in "pilot purgatory."
• McKinsey finds only a small share of companies report high AI impact on the bottom line today.
• An MIT study (NANDA) found the large majority of generative AI pilots showed no measurable impact on profit and loss.

All of those figures come from the third-party studies named above, not from anything we have measured ourselves, and they point at the same underlying problem: distribution of tools is being mistaken for adoption of capability. The practical cost is familiar: seats that renew unused and pilots that stall before reaching production.

Production systems vs tool rollout

For firms whose bottleneck is not the model but scattered institutional context, the production system needs a governed foundation beneath it. Graph’s Central AI Memory Layer connects the firm’s records, history, and evidence to the interfaces the team already uses.

The operating-partner model

The approach that works is the one large firms are formalizing as the AI operating partner: a builder who owns AI outcomes across the portfolio. H.I.G. Capital created a "Forward-Deployed AI Specialist" role reporting to an "Operating Partner, AI," described as "a builder first... not an advisory role," and Google Cloud assigns forward deployed engineers to work alongside Vista Equity Partners' value creation team. Korn Ferry has named the AI operating partner as an emerging portfolio value-creation role. Firms that have not hired for the role can often get the same result by embedding an external team that does the same job.

VC and PE buy this differently

VC funds use Graph Advisors for both fractional CFO work and forward deployed engineering. PE firms, which usually have finance in-house, use us for forward deployed engineering aimed at portfolio value creation. Either way the model is the same: the work is built on your data, shipped to production, and handed off so the firm owns it.

How Graph Advisors does it

We organize the work around outcomes rather than software deliverables, and we ship production systems rather than prototypes. Because the systems run on your own data and rails, the engagement typically goes through the firm's security review and data processing agreements up front, with the firm's IT or security lead in the loop. For more detail, see our forward deployed engineering practice, the worked examples for VC funds and PE firms, what forward deployed engineering is, or why bringing AI into a PE firm fails.

Frequently asked questions