AI, Automation, and Robotics Are Reshaping Value Creation for Private Equity

I have spent close to three decades sitting across deal tables from private equity sponsors, and for most of that time the game looked the same. Buy a good business, fix the capital structure, drive operational discipline, dress it up, and sell it for a multiple of what you paid. Financial engineering on one side of the table, operational engineering on the other. That was the playbook, and it worked.

It still works, but it is not enough on its own anymore.

What I am seeing in deal rooms across Silicon Valley and the rest of the state is that sponsors have stopped opening with the old questions. The first question I get now is some version of, where does AI live inside this business, and what is it worth if we get it right?

Let me put some context around that, because it is not theoretical. It is a function of where the market actually sits right now.

The exit door is jammed

Coming into 2026, the industry is living with the consequences of three years of stretched hold periods. DPI, distributions to paid-in capital, has become the metric every LP wants to talk about and that very few GPs want to discuss. The prolonged government shutdown at the end of 2025 took what should have been a strong Q4 IPO window and turned it into a lost opportunity. Companies didn’t disappear. They stacked up. Figma cleared the window. CoreWeave cleared the window. Cerebras cleared the window. The rest are queued, and they are going to stay queued until interest rates come down and risk-on sentiment returns to support the multiples needed to clear the backlog.

When you cannot get out the front door, you find other ways. We saw a real wave of secondaries last year, with sponsors packaging assets into continuation vehicles, sometimes single asset, sometimes baskets, and selling them into other funds. I worked on more of those in 2025 than I had in any twelve-month period in my career. That tool works once or twice, but it does not solve the underlying problem, which is that you have to actually create value during the hold. And in a slower exit environment, you have to create more of it than the model said you needed when you signed the LOI.

That is what is really driving the AI conversation in PE right now. It is not about chasing a trend. It is about defending and expanding margin during a hold that is lasting longer than anyone underwrote.

AI has moved from experiment to operating budget

Across my portfolio company clients, AI has stopped being a slide in the strategic plan and started being a line item. Back office automation, forecasting, supply chain optimization, customer service deflection, and code generation inside engineering are now part of everyday operations. The lean manufacturing crowd from the 1990s would have a hard time recognizing what is happening to SG&A inside well-run portfolio companies right now.

I had a conversation recently with an operating partner at a mid-market sponsor who told me about their thesis that every new platform deal now starts with one question: what percentage of headcount is doing repetitive cognitive work that an agent could do tomorrow? Two years ago, that was a footnote. Today, it is the model.

The numbers track with what I am seeing on the ground. AI and machine learning private equity deal value went from roughly $42 billion in 2023 to north of $140 billion in 2024, and the momentum kept building through 2025. Q1 of 2026 broke every record on the books, with AI taking roughly 80 percent of the venture dollars deployed. When capital moves that decisively, sponsors who are not running an AI-first value creation thesis are going to find themselves selling into a market that prices one in, whether they delivered it or not.

Robotics is no longer just industrial

I came up in the Valley watching robotics live in factories and warehouses. That is not where the next leg is. Advances in computer vision, autonomous systems, and sensor technology have opened up applications that were not economically viable five years ago. Logistics yards, food service, construction, field services, healthcare delivery, defense tech, and space are all emerging areas. Government contracts in particular provide five to ten years of revenue visibility, offering stability that is hard to find in AI infrastructure markets.

For sponsors looking at industries with persistent labor shortages, wage inflation, or seasonal volatility, robotics is a credible operational lever. It is the kind of investment that can permanently change a business’s unit economics—exactly what is needed when hold periods extend beyond initial expectations.

A diligence war story

The diligence I am running on technology-enabled deals today looks very different from 2022.

We now spend real time on data rights. Whose data is the model trained on? Do you have the license? Was it scraped? Are there indemnities? Are the outputs clean for commercial use? In one recent deal, a target company had trained its model on restricted third-party data. The deal survived, but it was renegotiated and the seller took a financial hit.

We also spend time on regulatory exposure. The EU AI Act and state-level laws in California, Colorado, and New York are reshaping compliance requirements. If AI is used in hiring, pricing, credit, or insurance decisions, regulatory diligence becomes unavoidable. Insurance carriers are also beginning to introduce AI-specific exclusions, reflecting real-time risk pricing.

Cybersecurity is another major focus. AI expands the attack surface through prompt injection, model extraction, and training data poisoning. These are not theoretical risks—they are active concerns.

Finally, workforce impact matters. Automation has legal implications, including WARN Act requirements, severance considerations, and union obligations. Reducing headcount requires a legally sound plan, not just a strategic vision.

A tale of two worlds in the talent market

At the TED AI conference last year, I described the AI talent market as a tale of two worlds. Large tech firms and AI labs are offering compensation packages that startups cannot match. I have seen founders lose key engineers to nine-figure offers.

For PE sponsors, this creates two challenges. First, retention strategies must evolve, including equity refresh grants and strong compensation structures. Second, founder and engineer concentration risk has increased significantly—losing just a few key individuals can destabilize the entire company.

Operational engineering has become technology engineering

The top-performing sponsors today act more like technology transformation platforms than traditional financial buyers. They bring AI expertise, vendor relationships, and operational clarity. Within weeks, they can identify automation opportunities, deploy agentic systems, and optimize workflows.

Those who treat AI as a cost-cutting tool will see only short-term gains. Those who treat it as a strategic investment will achieve lasting margin expansion, scalability, and stronger exit valuations.

Where this leaves us

I tell my clients the same thing consistently: hope is not a strategy, and preparation is not optional. AI, automation, and robotics are no longer secondary factors—they are central to value creation.

The convergence of private equity, artificial intelligence, and robotics is accelerating. Sponsors who embrace this shift—by building teams, structuring deals, and focusing diligence around it—will lead the market. Those who do not will struggle to deliver returns and explain performance gaps to their investors.