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How agentic AI is redefining private equity in 2026

5-MINUTE READ

December 15, 2025

After years of volatility and valuation resets, the Private Equity (PE) industry enters 2026 on firmer but still uneven ground. Signs of recovery are evident: deal activity and exits are rising, liquidity to limited partners is improving and financing conditions are easing. Yet fundraising remains selective, and the global backdrop of geopolitical instability continues to test conviction.

In this environment, advantage no longer stems from access to capital alone but from the ability to harness intelligence. In 2026, the smartest firms will not just use AI; they will deploy agentic AI to sense, decide and act in real time, and we’re seeing this play out in three forces.

A more stable, more selective market

While confidence is returning, discipline remains.

Global PE deal value already climbed from $1.45 trillion in 2023 to $1.75 trillion in 2024, signaling renewed investor appetite. By the end of Q3 2025, with a 21% quarter-on-quarter increase, PE’s transaction value had already matched the prior full year’s total. This happened even as deal volumes fell 13%. It’s a clear indicator that execution-ready theses now matter more than raw volume, as this reduces the grapple with the cost of a ‘dead deal.’

Dry powder remains high, and firms are deploying capital selectively, prioritizing targeted deployment and measurable value creation over financial engineering. In this environment, intelligence is what separates those who act with conviction from those who wait for clarity.

One of the most visible shifts is the acceleration of AI’s role across deal flow and diligence. AI and machine-learning (ML) global PE deal value more than tripled from $41.7 billion in 2023 to $140.5 billion in 2024, and momentum continued into 2025. These deals represented 8% of total deal value in 2024, up from 3% in 2023. With agentic AI rapidly proving its value potential, we anticipate this trend to continue into 2026 as well. Investors are clustering around product-centric and hard-asset adjacencies—data centers, battery storage and code-intensive platforms—where AI can amplify returns. The signal is clear: technology-led investing is moving from the periphery to the core of PE strategy.

Within the deal cycle itself, PE teams are moving from AI-enabled analysis to AI-orchestrated workflows where intelligent agents scan markets, model scenarios, raise diligence red flags and support integration in real time. These practices are likely to become baseline soon.

Is it time for a new playbook?

Firms are competing not on access to funds, but on the ability to convert insight into value faster than rivals.

An Accenture survey of 250 PE professionals found that leadership confidence in customer segmentation and targeting rose from 3.5 in 2023 to 3.9 in 2025 (on a five-point scale), reflecting a pivot toward data-driven, customer-centric growth.

PE’s operating model is being reframed:

From leverage to learning speed.
From balance-sheet engineering to operational precision.
From scale of capital to depth of insight.

Its future value model will rest on four interlocking shifts that embed intelligence at every stage of the deal lifecycle:

1. Intelligent origination and screening
Agentic AI compresses the gap between signal and action. By autonomously scanning filings, sentiment and sector chatter, AI surfaces hidden targets before the market reacts, turning origination from reactive deal flow into proactive market shaping.

2. AI-augmented due diligence
Diligence is evolving from static snapshot to living model. Agentic systems continuously ingest financial, operational and ESG data, automate document review and simulate synergy confidence ranges to drive faster, evidence-based investment decisions.

3. Self-optimizing portfolio operations
Post-acquisition, AI agents monitor KPIs, learn from interventions and recommend next-best actions like adjusting pricing, refining segmentation and optimizing working capital. CEOs list segmentation as a growth driver, and AI amplifies this precision across commercial levers.

4. Talent and operating model transformation
AI-fluent operators are becoming indispensable. Incentives are shifting toward measurable value levers—pricing, productivity and digital growth. In GP back offices, high-throughput workflows are automated, freeing teams to focus on insight generation and strategic intervention.

Execution discipline: From pilots to performance

The ability to embed AI and transform organizations at scale is what will separate leaders from followers.

Firms capturing the full potential of agentic AI adhere to four core principles:

Select and scale: Start with two or three high-value domains—such as dynamic pricing or finance-ops interoperability—then scale across the portfolio.
Strengthen your core: A foundation-first approach—with a strong architecture and technology backbone—helps contain risk, control costs and deliver early wins that reinforce adoption.
Measurable outcomes: Each AI agent is tied to clear outcomes—EBIT uplift, cycle-time reduction—ensuring measurable business impact and return on invested capital (ROIC) for Limited Partners (LPs).
Governed autonomy: Define human-loop thresholds and control gates so autonomy accelerates performance while preserving trust and oversight.

A large North American financial institution with a complex capital markets and retirement franchise modernized its core before touching agentic AI. Working with a technology partner, it consolidated data centers, modernized its network and cyber architecture, and migrated critical applications to a cloud-based digital core. Only then did it introduce AI agents—initially to support disaster recovery, incident management and operations teams. Those same foundations are now being reused to embed AI into client-service journeys, moving from scattered pilots to a governed, repeatable execution model.

The convergence of capital, intelligence and execution

Three structural tailwinds define PE’s next phase:

Valuation discipline is the new baseline. Returns must come from operational excellence optimized with intelligence. Differentiation depends on how fast firms translate data into action.
Generative AI has crossed the pilot chasm. Investors now expect generative AI to drive top-line growth and tangible value creation—not just back-office efficiency.
Agentic AI is emerging as the orchestrator of performance, blending autonomy with governance to deliver scale, speed and precision.

In a market where sitting on dry powder increasingly signals paralysis rather than prudence, General Partners (GPs) can no longer “wait for clarity.” Agentic AI turns intelligence into decision velocity, giving firms the ability to see more, decide earlier and act with greater precision than peers drawing on the same information.

For leadership teams, this translates into three concrete commitments:

Make intelligence-led deployment the default
Put agentic AI at the front of origination and underwriting. Require that every investment case shows how agents have shaped target selection, valuation assumptions and risk views—and how they will keep refining those views post-close. Persistent dry powder should trigger tough questions about the firm’s sensing and decision-making machinery, not just about “market conditions.”

Run portfolios as self-optimizing systems
Rewrite value-creation plans as live systems rather than static decks. Instrument key levers—pricing, churn, productivity, working capital—with agents that surface signals, recommend next-best actions and track uplift against plan. Portfolio teams should operate in hybrid models where human operators and AI co-pilots work together to test, learn and scale what moves the numbers.

Build a hybrid, governed AI capability
Treat agentic AI as a core capability of the firm. Focus on a small number of high-value domains, invest in the data and architecture they require, and put in place clear guardrails for autonomy, oversight and explainability. Build teams that combine investors, operators, data talent and AI specialists, and anchor this in governance that ICs, management teams and LPs can trust.

The next era of private equity will be defined by hybrid models—where human insight and AI co-pilots blend seamlessly. Talent will emerge as a critical differentiator, as will responsible AI governance that builds transparency and trust. Agentic AI is not another technology wave; it is the structural shift that will separate managers who use intelligence to deploy capital with speed and conviction from those who watch opportunities pass them by.

The author would like to thank Nicole Cohen, Jordan Griffiths, Conrad Devin and Ranjan Ramdas for their contributions to this article.