October 18, 2026
Few subjects in private markets generate more noise and less precision than artificial intelligence, and the finance side of the industry sits squarely in the crossfire. The claim that AI will transform the deal team and the finance function is made so often and so loosely that it has stopped carrying information, so the more useful exercise is to set out where it is genuinely being used by private equity and venture professionals today, task by task, and where it is still mostly a promise. The current evidence shows a wide and persistent gap between how many firms are experimenting and how many have changed how they actually work, and that gap is where the real story sits.
The headline adoption figures look like saturation until they are read carefully. Deloitte's 2025 work found that the large majority of corporate and private equity dealmakers, around 86%, already use generative AI somewhere in their workflows. FTI Consulting's 2026 research tells the sober half of the story, finding that only about a third of portfolio companies use AI in day-to-day operations and just 7% describe it as fully integrated, with a similarly small share of businesses having operationalised it with measurable results. The distance between trying a tool and rebuilding a process around it is exactly the distance between those numbers, and most of the industry still sits on the experimentation side of it.
Where AI has genuinely earned a place, it clusters in a handful of well-bounded tasks. The first is deal sourcing and screening, where firms increasingly use AI to scan markets, enrich their pipelines and surface targets that fit a thesis, shifting origination away from manually built lists toward a more signal-driven process that widens the funnel and prioritises it faster. The second, and the most mature, is due diligence, where AI reads through data rooms, summarises contracts, flags anomalies and produces a first pass on a quality-of-earnings analysis, compressing work that used to consume weeks of junior time into a far shorter cycle. The third is portfolio monitoring, where automated dashboards pull operating data across a whole portfolio, standardise it, and flag the companies and metrics that need attention, so that operating partners see a problem forming earlier than a monthly pack would ever have revealed it. The fourth sits inside the portfolio companies themselves, in the finance function, where AI speeds the assembly of management reporting, runs scenarios in financial planning, and supports pricing and forecasting work that used to be slow and manual.
What connects all of these is that AI is landing hard in the mechanical layer of the work, the gathering, reconciling, summarising and presenting of information, and barely at all in the layer above it. It is not deciding which number to trust when two systems disagree, taking a defensible position in front of a lender, choosing which assumption in a model to stake a plan on, or judging whether a management team can deliver. The tooling has made the production of analysis faster and cheaper without replacing the person who decides what the analysis means, and in any firm I would take seriously, that judgement still rests firmly with people.
This is the part with real consequences for talent, and it points somewhere less obvious than the usual fear of headcount cuts. As the mechanical layer becomes faster and cheaper, the value of the judgement layer rises rather than falls, because a team that can produce analysis in minutes needs people who can interrogate it, contextualise it and act on it with more discernment, not less. The junior role built mainly around manual reconciliation, data gathering and first-draft analysis is genuinely exposed. The finance leader or investment professional who can take faster, cheaper information and turn it into a better decision becomes more valuable, because the bottleneck moves decisively from producing the numbers to understanding them. A fluency with these tools is quickly becoming a baseline expectation rather than a differentiator, in the way spreadsheet proficiency once was, while the durable advantage lies in the judgement that directs them.
What this becomes over the next few years is reasonably foreseeable, even if the pace is not. The immediate direction of travel is from AI as a set of copilots, used task by task by individuals, toward AI built into repeatable systems that run a whole workflow, so that diligence and portfolio monitoring in particular move from being accelerated by AI to being structured around it. Firms are already working to institutionalise these capabilities rather than leave them to individual initiative, which is the clearest sign that the technology is maturing from novelty into infrastructure. Continuous, near-real-time portfolio monitoring will become an expectation rather than an advantage, AI literacy will be a standard requirement in hiring across finance and investment teams, and a visible performance gap will open between the firms that have done the unglamorous work of fixing their data foundations, without which none of this functions, and those still buying tools and wondering why the transformation has not arrived. The firms that come out ahead will be the ones that used AI to free their best people for the judgement it cannot do, and built teams weighted toward exactly that, rather than the ones that simply adopted the most of it. In an industry where returns increasingly turn on decisions rather than on data, the scarce resource was never the analysis but the judgement that the machine now delivers everyone to the edge of, and no further.
