AI and Data Trends in Private Equity (2026): Nine Shifts and the Bottleneck Underneath Them

TL;DR: Generative AI in private equity crossed from experiment to default in about eighteen months. Deloitte found 86% of corporate and PE dealmakers using it in M&A workflows, with 88% of PE firms having spent more than $1M on it. Then Bain's GP Outlook found that nearly 40% of GPs do not expect material financial impact from AI in 2026. Both are true, and the space between them is a data problem. Sponsors have told portfolio company CFOs to adopt AI at a rate of 98%, and 68% of those CFOs say they do not know where to begin. This is a review of the nine trends worth tracking, what the primary research says, and what has to be true about your portfolio data before any of it reaches EBITDA.

Two findings published within months of each other describe private equity's position better than any narrative.

Deloitte's 2025 survey of 1,000 senior corporate and PE leaders found that 86% already use generative AI in their M&A workflows, and that 65% of those started within the last year. Among PE firms specifically, 88% have invested more than $1M, and 81% expect measurable return within one to three years.

Bain and StepStone's 2026 GP Outlook, drawn from more than 100 investment and investor relations professionals, found that nearly 40% of GPs do not expect material financial impact from AI in 2026.

Nobody is confused. Firms are spending because the capability is real and the competitive risk of sitting out is obvious. They are also honest that the spend has not yet shown up in the numbers. What follows is the nine trends behind that, and the specific reason most programs stall.

Key takeaways

• Adoption is effectively complete at the deal level. Deloitte: 86% of dealmakers using generative AI in M&A, 65% having started within the past year, 88% of PE firms past $1M invested. Bain found systematic use more than doubled to 45% in a year.

• The value shows up first before the signature. GPs report their highest returns from AI in deal sourcing and diligence. McKinsey found roughly 20% cost reduction on deal processes and 10% to 30% shorter timelines.

• The bottleneck is named, and it is data. Deloitte's top two adoption blockers are data security at 67% and data quality or availability at 65%.

• Sponsors are ahead of their portfolio companies. Accordion found 98% of sponsors have told portco CFOs to prioritize AI, while 68% of those CFOs say they do not know where to begin.

• AI-ready data now shows up in price. In the same survey, 85% of buyers said they consider AI-enabled finance capabilities in valuation.

• AI is repricing the asset class, not just the workflow. EY found technology fell from about 30% of global PE deployment by value in 2025 to 12% in Q1 2026, with 60% of GPs adding diligence on AI disruption risk.

• The liquidity backdrop is the forcing function. Bain: distributions below 15% of NAV for four straight years, roughly 32,000 unsold portfolio companies, exit value up 47% to $717B in 2025.

Trend 1: Generative AI at the deal level went from pilot to default

The speed here is the story. Deloitte's finding that 65% of the 86% started within the last year means most of this adoption happened inside a single cycle. Bain's Global M&A Report 2026, based on a November 2025 survey of roughly 300 senior M&A executives, found 45% used AI tools in M&A in 2025, more than double the prior year, with about a third using it systematically or redesigning processes around it.

The spending pattern says these are not experiments. Eighty-eight percent of PE firms have put more than $1M into generative AI for M&A, ahead of corporates at 77%. Fifty-four percent of PE firms plan slight increases and 24% plan significant increases.

Where it lands is consistent across surveys. Deloitte found the most traction pre-signature: M&A strategy and market assessment at 40%, target identification and screening at 35%, due diligence at 35%. Those are all reading and synthesis jobs performed under time pressure on unfamiliar material, which is exactly the shape of work language models handle well.

Trend 2: Sourcing and diligence pay first, and the returns are measurable

Bain's Global Private Equity Report 2026 found GPs report their highest returns from generative AI in deal sourcing and due diligence. McKinsey's February 2025 survey work, cited in its 2026 M&A outlook, puts numbers on it: roughly 20% average cost reduction on deal processes and 10% to 30% shorter deal timelines.

Those are meaningful without being transformational, and the honest framing matters. A 20% cost reduction on deal process is real money at volume, and a 10% to 30% timeline compression can be the difference in a competitive process. Neither one changes the fundamental economics of a fund.

What changes the economics is what the compression enables. If diligence takes 30% less time at the same quality, a firm can either do the same number of deals cheaper or look at more deals. The second is where the return is, and it depends on whether the top of the funnel can be widened, which is a sourcing question. Our diligence acceleration demo shows reported against adjusted figures reconciled with a risk register attached, and our deal sourcing demo shows target ranking on thesis fit, both on fictional data.

The constraint on both is the same. AI reading a data room is only as good as its ability to check what it reads against something. A model that summarizes a QoE report is useful. A model that compares the QoE adjustments against your firm's own history of what adjustments held up post-close is considerably more useful, and that requires your deal archive to be queryable rather than filed.

Trend 3: Data quality is the blocker firms name themselves

Deloitte's adoption blockers are worth reading carefully because they are not what the discourse assumes. The top two are data security at 67% and data quality or availability at 65%. Model capability does not appear at the top.

This tracks with what firms actually experience. The failure mode in PE is not that the model writes badly. It is that a firm asks a question spanning eleven portfolio companies and gets a fluent answer built on figures that were never comparable. One portco recognizes multi-year contract revenue at signature, another at go-live. One puts customer success salaries in cost of revenue, another in SG&A. Gross margin is not comparable, so the ranking built on it is wrong, and nothing in the output signals that.

A federation is structurally harder than a single company here. A corporate deploying AI on its own ERP has one chart of accounts and one definition of revenue. A PE firm has as many as it has portfolio companies, plus its own fund-level systems, plus documents that govern the terms. Any AI initiative that skips the reconciliation step inherits the inconsistency and presents it with confidence.

We went through the mechanics of fixing this without an ERP migration in private equity portfolio monitoring in 2026.

Trend 4: Sponsors are far ahead of their portfolio company CFOs

The most actionable research this cycle is Accordion's survey with Wakefield Research, fielded September 2025 across 200 PE sponsor executives and 200 CFOs of PE-backed companies with $50M or more in revenue.

Ninety-eight percent of sponsors have directed portfolio company CFOs to prioritize AI adoption. Sixty-eight percent of those CFOs say they do not know where to begin.

That gap explains a great deal of the frustration on both sides. The sponsor sees a mandate issued and little movement. The CFO sees a directive with no specificity, arriving on top of a close calendar, a lender reporting package, and a finance team that is already thin. Neither party is wrong.

The survey has a second finding that reframes the whole conversation: 85% of buyers now consider AI-enabled finance capabilities in valuation, and CFOs who embed AI in planning, forecasting, and reporting are reported to be twice as likely to achieve smoother exits. Related, 97% of sponsors expect an always exit-ready posture while only 20% of CFOs operate that way, a misalignment sponsors say can cost one to three turns of exit multiple.

Read together, the portco data layer stopped being an IT line item and became a value creation lever with a price attached. That is a different conversation to have with a CFO than "please adopt AI."

Trend 5: Value creation is industrializing into platforms

The 2024 pattern was portfolio companies running their own pilots. The 2026 pattern is firms building repeatable platforms and pushing them down.

Vista Equity Partners says 30 of its portfolio companies are already generating revenue from agentic AI, with another 30 to 40 expected to follow in the coming months, supported by what it calls an Agentic AI Factory built to scale agentic tooling across its enterprise software portfolio.

Hg reports more than 1,600 AI projects across its portfolio representing roughly $260M of budgeted EBITDA impact, a fivefold increase since 2024, backed by about 100 AI builders at its Hg Catalyst incubator and more than 150 AI value creation specialists. Roughly 20% of its portfolio companies generate more than 10% of new bookings from AI initiatives.

Blackstone runs a data science team of more than 50 inside Portfolio Operations working alongside deal teams globally, and hired Prakhar Mehrotra, previously leading a 400-person data science organization at Walmart, to head applied AI across a portfolio of well over 200 companies.

EQT's Motherbrain, now about ten years old, covers sourcing, diligence, and portfolio value creation using interpretable scoring over unstructured material including PDFs, conversations, and market research, combined with internal relationship data. It is historically credited with sourcing a number of investments, though the specific figures repeated in secondary coverage trace to older case studies rather than current disclosure.

The pattern across all four is the same: centralized capability, standardized tooling, repeated deployment. Hg's number is the one to watch, because $260M of budgeted EBITDA impact is a claim stated in the unit that matters, which invites the follow-up question of how much lands.

For firms building rather than buying that capability, we laid out which agents justify themselves and what to fence off from autonomy in AI agents for private equity workflows.

EY's Private Equity Pulse for the first half of 2026 confirms the direction more broadly: 76% of PE firms have increased focus on AI, automation, and data infrastructure as operational value creation levers at portfolio companies.

Trend 6: AI is repricing the asset class, not just the workflow

The most underappreciated finding of the year is what AI has done to PE's own underwriting.

EY found that technology fell from roughly 30% of global PE deployment by value in 2025 to just 12% in the first quarter of 2026. The reason given is that AI reshaped assumptions about growth, pricing power, and defensibility in software. When a category's moat can be re-examined by a model in an afternoon, the terminal value assumptions in a software LBO deserve a second look.

GP behavior reflects it. Sixty-four percent increased selectivity, 60% increased diligence specifically on AI disruption risk, and 44% increased focus on AI-enabled software.

Meanwhile the venture side shows where capital did go. PitchBook data indicates AI captured roughly two thirds of global venture deal value in 2025 out of $512B total, and that 87.5% of all US venture dollars went to AI in the first half of 2026, with AI companies receiving a 2.2x median valuation step-up against 1.6x for non-AI.

The practical consequence for a buyout firm is that "does AI disrupt this business" has become a standard diligence workstream alongside quality of earnings and customer concentration. Answering it well requires knowing what the target actually does at a level of detail that marketing material does not provide, which again comes back to whether diligence material can be interrogated rather than read.

Trend 7: LPs expect AI, and expect it to widen the spread

Coller Capital's 44th Global Private Capital Barometer, published June 2026 and covering 108 institutional investors overseeing more than $2 trillion, gives the clearest read on the LP position.

Seventy percent of LPs expect GPs to deploy AI primarily as a cost efficiency tool. Twenty-two percent see it as a source of return outperformance and 8% as risk management. Sixty-seven percent believe AI adoption will widen return dispersion between leading and lagging GPs. Sixty-one percent report no change in the importance of gut instinct in fund selection.

That is a sober set of expectations, and it is useful. LPs are not expecting AI to generate alpha directly. They are expecting it to separate operators, and they are watching which side a manager lands on. For a GP, the implication is that AI capability is becoming a diligence topic in fundraising, and the credible answer is specific and operational rather than aspirational.

Trend 8: The liquidity backdrop is what makes this urgent

Bain's Global Private Equity Report 2026 sets the context that makes AI a priority rather than a project.

Global exit value rose 47% to $717B in 2025 and buyout deal value rose 44% to $904B, both second-best ever. And distributions as a share of NAV have stayed below 15% for four consecutive years, an industry record, with roughly 32,000 unsold portfolio companies still in inventory. Twenty-five percent of GPs have recently launched or completed a continuation vehicle and about 40% expect to explore one within one to two years.

That combination puts unusual weight on operational performance and on exit preparation. When holds run long and buyers are selective, the ability to demonstrate a company's performance credibly, quickly, and with lineage becomes a transaction advantage. Accordion's finding that AI-enabled finance capability affects valuation and that exit-readiness misalignment can cost one to three turns of multiple is the same point measured from the sell side.

Our portfolio monitoring demo and margin leakage demo show what continuous visibility looks like against that backdrop, on fictional data.

Trend 9: Some GPs are now financing AI's physical layer

The last trend is a category shift. KKR launched Helix Digital Infrastructure in June 2026 with more than $10B in committed capital to finance AI hyperscale data centers, power generation, and connectivity, with founding investors including the Kuwait Investment Authority, NVIDIA, and Vistra, led by former AWS chief executive Adam Selipsky.

That is a different relationship to AI than using it internally. For firms of that scale, AI is now simultaneously a tool, an underwriting variable, and an asset class. For everyone else, the relevance is indirect but real: the capital going into compute and power infrastructure is a signal about how long the demand curve is expected to run, which is an input to any technology underwrite.

The gap between 86% and 40%, explained

Return to the two numbers at the top. Eighty-six percent using generative AI. Nearly 40% expecting no material financial impact this year.

The explanation is not that the technology underdelivers. It is that most of the 86% deployed AI against material a model can read in a single context: a data room, a set of documents, a market scan. Those uses work because the material is self-contained.

The uses that would actually move a fund's numbers are not self-contained. Cross-portfolio benchmarking, continuous margin analysis, early detection of covenant risk, value creation plan tracking, and exit readiness all require reasoning across systems that were never designed to be read together. Eleven portfolio companies, nine ERPs, four definitions of recurring revenue, and a set of credit agreements sitting in a document folder.

An AI pointed at that produces confident, non-comparable answers. Someone catches one, the number is wrong, and the initiative loses its sponsor. That is the mechanism behind Deloitte's 65% naming data quality and Accordion's 68% of CFOs not knowing where to begin. The CFOs are not confused about AI. They are looking at a mandate that presumes a data foundation their company does not have.

The same pattern shows up in every vertical we work in. It is visible in commercial real estate, where roughly half of executives name data fragmentation as their top problem, and in healthcare operations, where 80% of health systems are acting on revenue cycle AI and 3% report significant returns. We wrote about the underlying concept in what is an AI context layer and about the sequencing in data unification in 2026.

Four things have to be true before AI reaches EBITDA:

One definition per metric, owned by the firm. ARR, gross margin, EBITDA adjustments, net revenue retention, and working capital each need one canonical definition, precise enough that two analysts compute the same number, with each portco's local source mapped to it in code rather than in a reporting template nobody follows. This is a finance artifact, it needs no technology, and everything downstream depends on it.

Entity resolution across the portfolio. One customer, one supplier, one company, regardless of how eleven systems spell them. Without it, concentration analysis and cross-portfolio supplier leverage are guesses.

Documents made computable. Credit agreements define covenants. Customer contracts define pricing and renewal rights. A covenant headroom calculation that has not read the credit agreement is an estimate presented as a fact.

Lineage on every figure. Traceable to source in seconds. This is the direct answer to the accuracy fear that stops most programs, and it is what allows a controller to endorse a number rather than dispute it in a board meeting.

None of this requires standardizing portfolio company software, and firms that try usually spend two years on a migration that does not need to be won.

How OutcomeCatalyst fits

OutcomeCatalyst builds the layer between your portfolio companies' systems and the AI you want to use. We connect the finance, CRM, workforce, and industry-specific operational systems each company already runs, plus the documents that govern them, into one governed model your team and your agents can reason over. No ERP migration and no asking eleven companies to standardize.

For a sponsor that means:

• A firm-owned metric registry, with each portco's local source mapped to your canonical definition in code.

• Entity resolution across the portfolio, so shared suppliers, overlapping customers, and true concentration become visible.

• Documents made queryable, so covenant terms, pricing clauses, and renewal rights are facts an agent can compute against.

• Lineage on every number, which is what makes an IC memo figure checkable rather than trusted.

• Permissions and deal-team walls enforced in the layer, not by convention.

It also gives the portfolio company CFO an answer to the sponsor's AI mandate that starts with something concrete rather than a platform selection exercise, which matters given that 85% of buyers now weigh AI-enabled finance capability in valuation.

If you are weighing an internal build against a bought layer, we compared the two paths honestly in buy versus build an AI context layer, and covered the broader architecture in AI agents, workflows, and knowledge graphs in private equity.

See the full private equity workflow set, or start a conversation about your portfolio.

Common questions about AI and data in private equity

How many private equity firms are using AI in 2026?

Deloitte's 2025 survey of 1,000 senior corporate and PE leaders found 86% of dealmakers using generative AI in M&A workflows, with 65% having started within the past year and 88% of PE firms having invested more than $1M. Bain separately found systematic use in M&A more than doubled to 45% in a year.

Where does AI deliver the most value in the deal cycle?

Before the signature. GPs report their highest returns in deal sourcing and due diligence, and Deloitte found the most traction in strategy and market assessment at 40%, target screening at 35%, and diligence at 35%. McKinsey measured roughly 20% cost reduction on deal processes and 10% to 30% shorter timelines.

Why do so many PE AI initiatives stall?

Because portfolio companies define metrics differently and run different systems, so anything requiring a cross-portfolio view produces confident but non-comparable answers. Deloitte's top blockers are data security at 67% and data quality or availability at 65%, not model capability.

What are sponsors asking portfolio company CFOs to do?

Accordion found 98% of sponsors have directed portco CFOs to prioritize AI adoption, while 68% of those CFOs say they do not know where to begin. The gap is usually a data foundation problem rather than a reluctance problem.

Does AI capability affect exit valuation?

Buyers say it does. In Accordion's survey, 85% consider AI-enabled finance capabilities in valuation, and CFOs who embed AI in planning, forecasting, and reporting are reported twice as likely to achieve smoother exits. Separately, 97% of sponsors expect an always exit-ready posture while only 20% of CFOs operate that way, a gap sponsors associate with one to three turns of multiple.

How is AI changing what private equity buys?

EY found technology fell from about 30% of global PE deployment by value in 2025 to 12% in the first quarter of 2026 as AI reshaped assumptions about growth and defensibility in software. Sixty-four percent of GPs increased selectivity and 60% added diligence on AI disruption risk.

What do LPs expect from GPs on AI?

Coller Capital's June 2026 barometer of 108 institutional investors found 70% expect AI to be used primarily for cost efficiency, 22% for return outperformance, and 8% for risk management, with 67% expecting AI adoption to widen return dispersion between leading and lagging managers.

Do we need to standardize portfolio company ERPs first?

No. Standardize the metric definition at the firm level and map each company's local source to it in code. Migrating a portfolio onto one ERP to fix reporting is a multi-year project with a high failure rate, and portfolio visibility does not require it.

What is the highest-leverage first step?

Writing the metric definition registry. Twenty to thirty metrics, agreed by the CFO and head of portfolio operations, defined precisely enough that two analysts compute the same number. It requires no software purchase and every AI initiative downstream depends on it.

Sources

• Deloitte, 2025 GenAI in M&A Survey, 1,000 senior corporate and PE leaders, released October 2025 (86% of dealmakers using generative AI; 65% started within the past year; 88% of PE firms invested $1M+; blockers of data security 67% and data quality/availability 65%; strategy 40%, target screening 35%, diligence 35%): deloitte.com

• Bain & Company, Global M&A Report 2026, survey of approximately 300 senior M&A executives, November 2025, published January 2026 (45% used AI tools in M&A in 2025, more than double the prior year; about one third using systematically): bain.com

• Bain & Company and StepStone, 2026 Private Equity GP Outlook, 100+ investment and IR professionals, published March 2026 (highest AI returns in sourcing and diligence; portfolio company benefits skew to cost and efficiency; nearly 40% of GPs expect no material financial impact in 2026): stepstonegroup.com

• McKinsey & Company survey work cited in its 2026 M&A outlook, via CFO Dive, February 2026 (approximately 20% average cost reduction on deal processes; 10% to 30% shorter deal timelines): cfodive.com

• Accordion with Wakefield Research, State of the PE Sponsor and CFO Relationship, 200 sponsor executives and 200 PE-backed CFOs, fielded September 2025, published November 2025 (98% of sponsors directing CFOs to prioritize AI; 68% of CFOs unsure where to begin; 85% of buyers weigh AI-enabled finance capability in valuation; 97% of sponsors expect always-exit-ready posture versus 20% of CFOs; one to three turns of multiple at stake): accordion.com

• EY Private Equity Pulse, Q1 2026 (technology fell from about 30% of global PE deployment by value in 2025 to 12% in Q1 2026; 64% increased selectivity, 60% increased diligence on AI disruption risk, 44% increased focus on AI-enabled software): ey.com

• EY Private Equity Pulse, H1 2026, published July 2026 (76% of PE firms increased focus on AI, automation, and data infrastructure as portfolio value creation levers): ey.com

• Vista Equity Partners via CNBC, January 2026 (30 portfolio companies generating revenue from agentic AI, 30 to 40 more expected; Agentic AI Factory platform): cnbc.com

• Hg / HgCapital Trust, June 2026 (more than 1,600 AI projects representing approximately $260M of budgeted EBITDA impact, a fivefold increase since 2024; about 100 AI builders at Hg Catalyst plus 150+ AI value creation experts; roughly 20% of portfolio companies generating more than 10% of new bookings from AI): hgcapitaltrust.com

• Blackstone Data Science and Institutional Investor (data science team of more than 50 within Portfolio Operations; Prakhar Mehrotra hired to lead applied AI): blackstone.com

• EQT Motherbrain profile via Tech.eu, November 2025 (roughly ten years in operation, spanning sourcing, diligence, and portfolio value creation with interpretable scoring over unstructured data): tech.eu

• KKR, June 2026 (Helix Digital Infrastructure launched with more than $10B committed to finance AI data centers, power generation, and connectivity; founding investors include the Kuwait Investment Authority, NVIDIA, and Vistra): businesswire.com

• Coller Capital, Global Private Capital Barometer, 44th edition, 108 institutional investors overseeing $2T+, June 2026 (70% expect AI primarily as cost efficiency, 22% return outperformance, 8% risk management; 67% expect wider return dispersion; 61% report no change in the importance of gut instinct): collercapital.com

• Bain & Company, Global Private Equity Report 2026 (2025 exit value up 47% to $717B; buyout deal value up 44% to $904B; distributions below 15% of NAV for four consecutive years; approximately 32,000 unsold portfolio companies; 25% of GPs recently launched or completed a continuation vehicle): bain.com

• PitchBook data via SiliconANGLE, January 2026, and Fortune, August 2026 (AI captured roughly two thirds of global venture deal value in 2025 out of $512B; 87.5% of US venture dollars went to AI in H1 2026; 2.2x median valuation step-up for AI companies versus 1.6x for non-AI): siliconangle.com

OutcomeCatalyst connects the systems you already run into a governed intelligence layer your team and your agents can reason over. Demos on this site use fictional data. To see this on your own portfolio, start a conversation.