Why AI Uncertainty Is Reshaping Software Dealmaking, and Redirecting Capital

Artificial intelligence is accelerating nearly every part of the M&A process. Deal teams can analyze more data, surface risks earlier, and move through diligence faster than ever before. Yet for all that speed, software dealmaking has become more selective, and, in some cases, slower to close.

This isn’t because deal activity has stalled or capital has retreated. In fact, global deal formation continues to grow. New deals initiated on Datasite, which annually facilitates about 19,000 new deals, rose 9% year over year in 2025, and that momentum has carried into 2026, with a further 6% global increase in the first two months of the year. Technology, media, and telecoms remain among the most active sectors. Since these are deals at inception, rather than announced, it can provide a look at what’s ahead.

The shift underway is more subtle. AI is changing how decisions are made. Faster insight is exposing new forms of uncertainty, particularly in software, and that uncertainty is reshaping where capital ultimately lands.

Faster insight, harder decisions

For decades, software M&A relied on relatively stable valuation frameworks. Buyers underwrote recurring revenue, customer retention, margins, and growth rates with confidence that strong fundamentals would hold over time. AI has complicated what those metrics mean.

The pace of AI innovation is compressing product cycles and redrawing competitive boundaries. Capabilities that once supported premium valuations, including specialized features, workflow ownership, or perceived data moats, can now be replicated or redefined quickly. New entrants emerge faster and platforms absorb entire categories. Cost curves can shift with little warning.

AI-enabled diligence brings these risks to the surface earlier than ever. Agentic tools can scan thousands of documents, connect insights across financials, contracts, HR policies, and compliance records, and highlight issues. With AI-driven automation, deal teams can close transactions an average of 22 days faster than the industry norm, cutting weeks from diligence timelines and potentially saving hundreds of thousands of dollars in review costs alone.

But faster diligence does not guarantee faster decisions. Investment committees slow deals by design

Investment committees exist to challenge assumptions and pressure-test the story. AI helps teams bring better data to those discussions, but it also surfaces more questions earlier in the process. Committees now confront strategic uncertainty sooner, particularly around how defensible a software business will be as AI capabilities evolve.

In other words, AI reduces informational uncertainty while increasing strategic uncertainty. When committees see risks earlier, they debate them longer. That dynamic does not stop deals, but it does stall marginal ones. Pricing gaps widen and conviction matters more.

The result is a clear rise in software selectivity.

As investment committees and deal teams face heightened uncertainty and tougher scrutiny, the focus has shifted from pursuing every opportunity to carefully choosing which software investments to make. This increased selectivity means that only businesses with strong fundamentals and a credible plan for adapting to AI disruption are moving forward in the deal process.

Private equity enters a period of portfolio reassessment

Private equity firms are responding by reassessing portfolios and prioritizing capital discipline. Many are reviewing existing software holdings and pausing new bids while they evaluate how AI could reshape revenue models, pricing power, and competitive positioning.

Capital remains available, but firms are now demanding clear answers to critical questions. Businesses that gain leverage from automation are prioritized. Companies facing margin compression due to AI lowering barriers to entry are scrutinized. Firms that depend on features vulnerable to AI commoditization are considered high risk. Only management teams with a credible plan to adapt are viewed as viable for investment.

Capital is rotating to other sectors

Against that backdrop, it is not surprising that capital is flowing toward sectors where disruption is easier to model and alignment comes faster. Industrials, transport, defense, consumer, and retail businesses are seeing increased interest. In fact, new global industrial deal kickoffs on Datasite, which annually facilitates about 19,000 new deals, rose 16% in this year’s first quarter, compared to the same time a year ago.

These sectors face technological change, but it is typically incremental rather than existential. AI may improve forecasting, logistics, or customer engagement, but it rarely invalidates the core business model overnight. Cash flows are easier to model. Asset bases are tangible. Valuations are easier to defend in front of investment committees.

This rotation reflects a preference for clarity. Where AI enhances operations without forcing a wholesale rewrite of the investment thesis, deals move more predictably.

For software sellers, the implications are clear. Growth alone is no longer enough. Buyers want to understand how AI reshapes the business, including where it creates leverage, where it introduces risk, and how management plans to stay ahead.

For buyers, patience has become a competitive strategy. Firms winning in this market are not those moving fastest, but those underwriting resilience with discipline and aligning stakeholders early.

Deal formation continues to rise. Diligence is faster and more efficient than ever. Yet decisions, especially in software, now demand greater conviction. Capital is flowing toward assets and sectors where long-term value can be defended with confidence.

In that sense, AI is doing what markets ultimately demand. It is forcing tougher questions earlier. The next phase of M&A will reward teams that use AI not just to move faster, but to build clarity, and then execute with discipline across every stakeholder involved.