Every associate on your deal team is already using AI. They paste CIM pages into ChatGPT to summarize them, ask Claude to draft the first cut of an IC memo, and run market research through whatever tab is open. None of it is on a budget line, none of it is logged, and most partners assume it is free productivity. It is not. The cost is real; it compounds, and it lands where a PE firm can least afford it: memo quality, the firm’s memory, and the hours that were supposed to be saved.
This is the hidden tax of ad-hoc AI in private equity. Not the license fee, which is trivial, but the operational drag of a dozen people using powerful tools with no standard, no record, and no review. The technology works. The way most firms use it does not. And the gap between those two facts is where the money quietly goes.
Most coverage of AI in private equity celebrates the speed of the individual task and stops there. But a firm does not run on individual tasks. It runs on the cumulative quality of what those tasks produce, and ad-hoc usage degrades that quality in ways that never show up on a dashboard.
The Ad-Hoc Pattern, And Why It Feels Free
Ad-hoc usage looks productive because individual tasks get faster. An associate turns a 60-page CIM into a summary in four minutes instead of an hour. At the level of that one task, it is a real win.
The problem is that the task is not the unit that matters. The deal is. Across a deal, the same prompt run by four associates produces four different summaries, in four formats, with four definitions of what counts as a red flag. When those land on a principal’s desk, someone has to reconcile them. The four minutes saved upstream become two hours spent downstream making the outputs comparable, and nobody counts that time because it is buried in “review.”
The gain is visible, and the cost is invisible. That is exactly why it persists.
Where The Money Actually Leaks
Break the ad-hoc pattern into its cost centers and the tax becomes concrete.
- Inconsistent outputs and rework. Without a standard template, every output is bespoke. A partner reading five deal screens cannot compare them at a glance because each associate structured the analysis differently. The rework to standardize is pure waste, and it scales with deal throughput. Run 30 CIMs a quarter through five associates, and you are paying for the same reconciliation 30 times.
- Hallucinated research that reaches the memo. A general-purpose chatbot handed a 200-page CIM with embedded financial tables and inconsistent formatting does not fail loudly. It fails quietly, with a confident, wrong number that looks exactly like a right one. The risk in deal work is not slow output. It is an untraceable error in an investment committee memo that no one caught because there was no source citation to check against. One bad figure in one memo can cost a firm far more than every AI license it will ever buy.
- Lost institutional memory. This is the most expensive leak and the least visible. When an associate runs analysis in a personal chat window, the reasoning evaporates when the tab closes. The firm learns nothing. Six months later, another associate screens a similar target and starts from zero, because the first analysis lived in someone’s browser history, not in the firm’s memory. Years of deal work that should compound into an institutional asset instead decays into nothing. The fix is treating deal intelligence as structured, queryable data the firm owns, not as disposable output.
- Quality control that does not exist. Ad-hoc AI has no review layer. There is no way to know which outputs were checked, which were grounded in real documents, and which were taken on faith. For a registered adviser, that is not just an efficiency problem. It is an examination risk, and the regulators now ask about AI use directly.
Add these leaks together, and the picture is clear. The promise of AI in private equity is throughput, the ability to run six or eight deal processes concurrently where a team used to run three. Ad-hoc usage caps that throughput when inconsistent outputs become impossible to reconcile. You do not get parallel capacity from parallel chaos. You get a bottleneck that moves from the associate to the partner, the most expensive place a bottleneck can sit.
The Numbers Behind The Tax
The pattern is measurable, and the research on AI in private equity is unambiguous about where it goes wrong.
The clearest finding comes from MIT’s Project NANDA, whose 2025 study of enterprise generative-AI pilots found that roughly 95 percent showed no measurable return. Read that against a contrasting figure from FTI Consulting’s 2026 Private Equity AI Radar: about 95 percent of deliberate, funded PE AI initiatives meet their business case. Both numbers are true, and the gap between them is the whole story. Scoped AI with an owner and a standard pays off. Casual AI with neither does not. Same technology, opposite outcomes, and the only variable is whether it was run as infrastructure or as a personal hack.
The productivity ceiling is real too. A controlled NBER study by Brynjolfsson, Li, and Raymond measured generative AI lifting worker output by about 14 percent on average, and up to 34 percent for the least experienced workers. That is the prize ad-hoc usage is chasing. But the gain concentrates in repetitive, high-volume, judgment-light work, precisely the work that has to be standardized to be trusted. You cannot capture a 34 percent lift on deal screening if every screen comes out in a different shape.
And the governance exposure has a number. Grant Thornton’s 2026 AI Impact Survey found about 78 percent of leaders doubt they could pass an AI-governance audit within 90 days. The same survey found firms with AI genuinely integrated into the business are about four times more likely to report revenue growth. The discipline that satisfies an examiner and the discipline that produces a return are the same discipline. Ad-hoc usage has neither.
Ad-Hoc Usage Versus Production Infrastructure
The distinction that matters is not which model you use. It is whether AI runs as a tool each person reaches for privately or as infrastructure the firm operates deliberately. The difference shows up on every axis a deal team cares about.
- A tool produces a different output every time, depending on who prompted it and how. Infrastructure produces the same structured output regardless of which associate ran it, because the standard is built in, not improvised.
- A tool forgets. Infrastructure remembers, because every analysis writes back to a searchable deal database that the next associate inherits.
- A tool asks you to trust the answer. Infrastructure grounds every answer in a source document and cites it, so a reviewer can verify the figure in the memo against the page it came from.
- A tool saves an individual a few minutes. Infrastructure saves the firm the reconciliation, the rework, and the re-learning, which is where the real hours live.
This is the shift from generative AI in private equity as a novelty to agentic AI in private equity as an operating layer. The first makes one person faster on one task. The second makes the firm faster on every deal, and keeps what it learns.
Systematization Is The Product, Not The Model
Here’s where most firms get it backward. The answer to messy, ad hoc AI is not a better model or another point tool bolted onto the stack. Most of the “AI tools” marketed to PE firms are thin wrappers around the same large language models associates already use ad hoc, which fixes the interface but leaves the real problem untouched. Adding a tool to an unstandardized process just gives you a faster way to produce inconsistent work.
The fix is a systematization layer: one place where deal-ops workflows run the same way every time, where every output follows the firm’s format, where every analysis is grounded and cited, and where everything the firm learns is captured and searchable. That is what a Growth OS is built to be. Not another model in the stack, but the layer that turns a dozen people’s private AI habits into one firm-level capability with standards, memory, and a record. The same discipline extends to the target itself: before you close, a commercial-maturity diagnostic reads the gap the CIM cannot show you.
The math changes the moment AI stops being something each associate does alone and becomes something the firm operates. The rework disappears because the format is fixed. The memory compounds because nothing is lost to a closed tab. The governance exists because every output is logged and grounded. The productivity gain the research promises becomes capturable, because the work is finally consistent enough to trust at scale.
This is the real dividing line for AI in private equity in 2026. Not which firms adopted the technology, because nearly all of them have, but which firms turned scattered adoption into a system before the compounding advantage went to someone else. Ad-hoc AI in private equity is not free. You pay for it in reconciliation, in lost knowledge, and in the risk of a wrong number in a memo. The only question is whether you keep paying that tax quietly or replace it with infrastructure that turns the same spend into an asset.
See It Run On Your Own Deal Flow
The fastest way to understand the difference between ad-hoc AI and a systematized deal-ops layer is to watch one read a real CIM and return a scored one-pager, live. That is what we run through during Tech Week. Join us at SF Tech Week or LA Tech Week, where we take an AI-native operating model through firm-level deal ops in real time and turn a single portfolio company into a roll-up platform on the spot. Or request a pilot on your own deal flow and see the systematization layer run against the work your team is already doing by hand.
Sources
- MIT Project NANDA, The GenAI Divide (2025)
- FTI Consulting, 2026 Private Equity AI Radar
- Grant Thornton, 2026 AI Impact Survey
- Brynjolfsson, Li, and Raymond, “Generative AI at Work,” NBER (2023)
