Build vs Buy AI in Private Equity: What 2026 GP Survey Data Shows
Taylor Lowe
The firms that have gone furthest with AI are the ones reporting the hardest problems.
Private equity and private market firms are split on whether to build or buy AI. In Private Equity Wire's Q3 2026 GP Survey of more than 100 GPs, 41% of firms said they build their AI stack internally, 23% assemble models and point solutions, 20% buy a purpose-built platform, and 16% are still in pilot.
The more revealing data comes after the decision. Firms above $10bn AUM build most often, and are more than three times as likely as smaller firms to say fragmented data holds their AI back. The firms furthest along aren't reporting fewer problems, just different ones.
Key findings
59% of firms still define AI by efficiency; 33% have moved on to conviction, edge and compounding knowledge.
41% build, 23% assemble, 20% buy and 16% are still piloting. Firms above $10bn AUM build most.
63% of firms above $10bn cite fragmented data as a barrier, vs 19% of smaller firms.
38% of firms above $10bn say their infrastructure goes out of date too quickly, vs 11% of smaller firms. In private credit, it's 44%.
Buyout firms build most (60%) and overrun most on cost and time (40%).
Download the full report: Dilemmas and Dichotomies, Private markets approaches to AI implementation
What Do Private Equity Firms Want From AI in 2026?
Efficiency remains the default case for AI. 59% of GPs surveyed name operational efficiency as their primary AI goal.
A third aim higher, at outcomes a general LLM can't reach on its own: sharper conviction on deals (13%), a durable competitive edge (11%) and institutional intelligence, the proprietary knowledge that compounds (9%). The remaining 8% have no defined goal yet.
That split matters for the build decision. A general LLM model, such as ChatGPT and Claude, handles the task in front of it well: summarizing a CIM, drafting a first pass, answering from a single document. Conviction, edge and institutional intelligence depend on something no model ships with: the firm's own record of what it has seen, decided and still believes.
Scale sharpens both ends. Firms above $10bn AUM are more likely to lead with efficiency (67% vs 57%) and more likely to name institutional intelligence as the goal (11% vs 8%). Only 5% of them lack a defined AI goal, against 11% of smaller firms. That clarity tracks resources. Larger firms tend to have more mature technology teams and more room to test AI inside their own process before they commit to what it is for.
Are Private Equity Firms Building or Buying AI Infrastructure?
Private equity firms are split almost evenly between building and buying AI infrastructure. Building leads at 41%, but as many firms assemble or buy (43%), and the split shifts with scale.
Approach to AI infrastructure | All firms | Under $10bn AUM | Over $10bn AUM |
|---|---|---|---|
Build: AI stack owned internally | 41% | 39% | 50% |
Assemble: integrated models, layers and point solutions | 23% | 22% | 25% |
Buy: purpose-built platform | 20% | 19% | 25% |
Early stage: still in pilot | 16% | 19% | 5% |
Scale doesn't push firms toward one answer. It pushes them to commit. Large firms are more likely to build, since they tend to have the engineering talent to own a stack. They are also more likely to buy, and almost none are still piloting: a bigger budget lets a firm test several vendors, absorb the failures and decide sooner.
Why firms choose to build
For many private equity firms, the case for building AI in-house starts with data security. A firm that builds keeps custody of its data in-house, controls how that data is secured, and can shape the system around its own investment process rather than a vendor's roadmap. With frequent cyberattacks targeting sensitive client data, that control carries weight with LPs.
The tradeoff is adaptability. Frontier models turn over every few months, and an internally built stack has to absorb each change on its own.
How Do Buyout and Private Credit Firms Approach AI Differently?
60% of buyout firms build their AI stack internally and only 10% assemble point solutions. In private credit, a third of firms assemble, reflecting an asset class that already runs on a wide ecosystem of administrators, data providers and service agents.
Each path shows up in the barrier that strategy reports most.
Buyout | Private credit | |
|---|---|---|
Build internally | 60% | 44% |
Assemble point solutions | 10% | 33% |
Barrier: more time and cost than expected | 40% | 22% |
Barrier: infrastructure goes out of date too quickly | 10% | 44% |
Buyout firms, the most committed builders, report the most cost and time overruns. Private credit firms, the most likely to assemble, are four times as likely as buyout firms to say their infrastructure goes stale. Building front-loads the cost; assembling spreads it across integrations that each age on their own schedule.
What Is Holding Back AI Adoption in Private Equity?
Cost, fragmented data and deal team trust are the main barriers to AI adoption in private equity.
30% of firms say AI has taken more time and money than expected, 27% say their data is too fragmented, and 27% say deal teams do not trust the outputs. A quarter report no significant limitations yet. Unclear ROI (23%), infrastructure that ages too quickly (16%) and talent (11%) follow.
Why Do the Largest Firms Report More Fragmented Data?
With deeper pockets, larger firms feel cost and time less. They report fragmentation and obsolescence instead. Smaller firms are twice as likely to say they've hit no significant limits, and twice as likely to be unsure about ROI.
Read next to the build data, this is not a story about large firms struggling. It is what scale does to a firm’s data. More deals and more teams spread the firm’s history across more systems, and each team sees only its own slice. Each team then builds its own AI workflows on that slice. The deal team, the portfolio team and IR end up with different answers to the same question, and no shared view of what the firm actually knows.
For whoever runs technology at the firm, this is a governance problem before it is a model problem. There is no single place to manage access, correct an error once, or make sure everyone’s AI reads from the same source of truth. Fragmentation stays invisible while AI summarizes one document at a time. It surfaces when a firm asks AI to reason across every deal it has seen.
That is not a gap in competence but accumulation: more deals, more systems, more teams, more judgment sitting where nobody has mapped it.
Each firm’s history runs across many systems, where each holds a different piece of the deal and none of it resolves into a pattern the firm can carry forward. Across teams, where everyone builds their own AI and no admin can govern the result.
Taylor Lowe, CEO & Co-founder of Metal
What Does the Data Mean for the Build vs Buy Decision?
Building is a defensible choice, and the most committed firms make it. The data shows where its cost lands: not in the first version, but in keeping the system current as models, sources and teams change. 38% of the largest firms and 44% of private credit firms already say their AI infrastructure goes out of date too quickly.
The Harder Cost Arrives After Launch
A working prototype proves a firm can build. It says little about year two. Foundational AI infrastructure carries a standing workload that never closes:
Entity resolution that is never finished. Every new deal brings new aliases, codenames and corporate structures to reconcile.
Connectors that break mid-process. Live connections into the CRM, data room, document store and portfolio systems fail when a source changes its schema, often during active diligence.
Model turnover. Each new frontier model forces re-validation of every workflow built on the last one.
Permissions that drift. People move between deal teams and funds, and access rules have to follow them.
Engineers who leave. Talent turns over fastest once the work shifts from architecture to upkeep.
None of this is a talent problem. It is the shape of the work.
Draw the Line Between Plumbing and Edge
The decision that matters is not build versus buy. It is how a firm dedicates its engineering capacity. Every competitor can reach the same frontier models, so no firm builds an edge by rebuilding the plumbing that carries them. The edge is in what only that firm has: its proprietary deal record, sector theses, conviction framework, the workflows shaped around how it actually invests.
Taylor Lowe, CEO & Co-founder of Metal
The hard part of being AI-ready is not the AI. A firm's record of what it has seen, decided and still believes is the one part of the stack that appreciates.
Where does your firm's engineering capacity go today: the plumbing, or the part only your firm owns? Download the full report to benchmark your approach against your peers.
