Key Takeaways
- AI is forcing PE-backed technology companies to shift investment toward product, data, and infrastructure readiness, not just sales growth.
- Margin expansion will come less from broad cost cutting alone and more from redesigning workflows so AI actually improves how work gets done.
- AI-related M&A is likely to stay active, but sponsors should be selective. Talent, proprietary data, workflow fit, and integration feasibility matter more than simply buying something labeled “AI.”
Why AI strategy for private equity tech portfolios starts with capital allocation
In software and tech-enabled services, AI is changing the baseline. Features that once felt differentiated can become table stakes quickly.
That means the question for PE firms is not whether every portfolio company needs to build a frontier model. It is whether the company has a credible plan to stay relevant as customer expectations, product economics, and competitive moats shift.
PwC notes that software valuations are being reshaped by AI, while Cooley’s 2025 tech M&A review says AI influenced both which assets were acquired and how they were valued.
For sponsors, that changes the budget conversation. Capital that might previously have gone disproportionately to conventional go-to-market expansion may now need to move toward data quality, model access, product architecture, security, workflow redesign, and selective talent upgrades.
This is not a temporary spending spike. It is a reprioritization of what protects relevance and terminal value. That is especially important in private equity, where longer hold periods and tougher financing conditions leave less room for assets that are operationally outdated.
A useful way to think about this is to separate defensive and offensive investment.
Defensive AI investment protects the core business. It includes modernizing data foundations, enabling safe model use, improving search and reporting, supporting customer service automation, and preventing churn to faster-moving competitors.
Offensive AI investment creates a new advantage. It includes embedding AI into the product in ways that reduce customer labor, compress time to outcome, improve pricing power, or make the platform harder to replace.
Private equity teams do not need every portco to become an AI pioneer. They do need every portco to know which side of that line it is on.
The operating model matters as much as the product roadmap
The second implication is operational. Big technology companies are pairing AI investment with tighter cost discipline and flatter organizations.
Microsoft, for example, has explicitly pointed to cost efficiencies and fewer management layers while scaling AI infrastructure.
Reuters also reported on March 13, 2026 that Meta was considering major workforce reductions tied to AI costs and AI-enabled efficiency, though that plan was not finalized at the time of reporting.
For PE-backed companies, the lesson is not simply “cut headcount.” The better lesson is that AI only improves EBITDA when workflows change.
Bain’s research on software development found that modest productivity gains do not automatically translate into returns unless companies redesign the full process and redirect saved capacity to higher-value work.
McKinsey makes a similar point: software companies becoming AI-centric are automating internal operations across not just HR and finance but also sales, customer support, marketing, and professional services.
That distinction matters for sponsors. Buying copilots across the organization is easy. Building a repeatable operating model that changes labor intensity, cycle times, service levels, and management cadence is harder. That is where value creation lives.
Private equity portfolios are already seeing evidence of this. Bain reports that surveyed investors overseeing $3.2 trillion in AUM said a majority of portfolio companies were in some phase of generative AI testing and development, and nearly 20% had already operationalized use cases with concrete results.
Bain also cites examples such as Cengage improving productivity across customer care, content production, lead generation, and software development, and Shutterfly generating new revenue and engineering productivity gains from AI-enabled features and code assistance.
The practical takeaway is simple: sponsors should push management teams to move from tools to workflows, from pilots to operating metrics, and from experimentation to accountability.
Expect more selective AI-driven M&A
The third implication is deal strategy. When organic development is too slow, too uncertain, or too talent-constrained, AI capability will increasingly be acquired rather than built.
That trend is already visible in broader market activity. Bain reported that technology’s share of North American private equity deals rose to 22% in the first half of 2025.
Cooley’s review of 2025 tech M&A said AI remained the defining theme across the market, influencing both asset selection and valuation, and that nearly half of strategic technology deals above $500 million involved AI-native targets or transactions where AI-driven growth, cost savings, or positioning were central to the rationale.
For PE sponsors, that does not mean indiscriminate buying. It means more selective tuck-ins where one of four things is true:
- the target materially accelerates product roadmap timing
- the target brings scarce technical talent that is hard to assemble internally
- the target contributes proprietary data, workflow depth, or domain-specific IP
- the target strengthens exit positioning by improving the platform’s defensibility
Bain’s M&A research also suggests AI adoption will widen the performance gap between more active and less active acquirers.
Early adopters are using AI not just in products, but also in sourcing, diligence, integration planning, and program management. In other words, AI is changing both what gets bought and how deals get executed.
One important nuance: sponsors should not assume AI assets will automatically be cheap. Current evidence points in two directions at once.Some software assets facing AI disruption are under pressure, but high-quality AI assets, proprietary datasets, and scarce technical talent can still command strong valuations. The winning move is disciplined selectivity, not blanket enthusiasm or blanket avoidance.
What PE sponsors should do now
Private equity firms do not need a generic AI thesis. They need a portfolio-specific execution plan.
Start by asking five questions across each technology asset:
- Where is AI most likely to compress differentiation in the current product set?
- Which data, workflow, or domain assets could become a real moat if developed properly?
- Which functions can be redesigned to improve margins without weakening execution?
- Which capabilities should be built internally, and which are better acquired?
- What will a buyer or lender ask about this company’s AI readiness in the next exit process?
From there, management teams should translate the answers into a practical agenda:
- Define the defensive AI roadmap
- Identify one to three offensive use cases tied to revenue or retention
- Upgrade data and governance foundations
- Set operating metrics for AI-enabled productivity
- Screen tuck-in targets based on fit, not hype
- Connect AI priorities to the exit narrative early, not late
That is the important shift. In private equity, AI should not be treated as a separate innovation initiative. It should be embedded directly into value creation planning, M&A execution, and integration discipline.
The AI arms race is changing the rules for private equity tech portfolios. The old playbook of relying on growth alone, lean product investment, and late-stage efficiency moves is less reliable in a market where software defensibility is being re-underwritten in real time and value creation depends more on execution than on market beta.
The firms that respond well will do three things better than peers: reallocate capital with discipline, redesign operations instead of simply adding tools, and pursue selective acquisitions that strengthen capability and exit quality. The firms that do not will risk holding assets that still look profitable on paper but are becoming strategically less relevant in the market.
FAQ
Does every PE-backed tech company need to build proprietary AI?
No. Most do not need to build a foundational model. But every company does need a clear position on where AI threatens its product, where it can improve economics, and where it can strengthen defensibility. Buyers are increasingly evaluating software assets through that lens.
Should sponsors build or buy AI capability?
Usually both, but in different areas. Core workflow knowledge, proprietary data, and customer context often need to be built into the platform. Speed, specialist talent, or missing product capability may be better acquired through selective tuck-ins or acqui-hires.
What is the biggest mistake PE firms can make right now?
Treating AI as either pure hype or a standalone innovation budget. The bigger risk is failing to connect AI decisions to value creation, operating model design, diligence, integration, and exit preparation. That is where the real return or value leakage will show up.
If your firm is thinking through acquisition strategy, integration planning, or execution support, Xpertegic can help bring clarity and discipline to the process. Fill out our contact form to schedule a call.
