AI reshapes the assumptions behind software M&A

Artificial intelligence has become a recurring theme in software dealmaking. Research from PwC suggests that roughly a third of the largest transactions completed in 2025 referenced AI as part of their strategic rationale. That narrative has continued into 2026 as AI influences valuations, as well as the logic boards use to justify acquisitions in the first place.

 

This shift is happening amid a wider rebound in deal activity. Global M&A deal value rose 43 % in 2025 to $4.7 trillion, which is 20% above the ten-year average, reflecting a stronger appetite for growth and expansion.

 

Yet, the ambitions around AI-led growth are often defined before organisations have a full picture of how their combined systems will interoperate. That gap between expectation and visibility is where AI-centric deals can often begin to stall. Once the deal closes, ambitions meet operational reality. 

 

 

The efficiency promise, and the integration burden

Efficiency is usually one of the main promises behind any acquisition. For executive teams and investors, efficiency usually translates into margin improvement and faster growth. 

 

For technology leaders, it often means something more grounded, maintaining stability, security and compliance while consolidating platforms and workloads.

 

AI widens the distance between those perspectives because much of the investment happens early. Compute costs grow as data engineering pipelines are developed, new tooling is introduced and specialist talent is brought in to support ambitious roadmaps. All of this often happens before duplicate systems have been retired or a clear data integration strategy has been agreed upon.

 

In practice, AI programmes are layered onto estates that are still fragmented. Overlapping applications often remain in place, datasets vary in quality and do not align which means costly transformations and processing is required before value can be realised. Teams are expected to move quickly, while often carrying more complexity than before.

 

Flexera research suggests that over 90% of IT leaders admit that they need to invest in new tools and technologies to extract value from their existing data.

 

In a post-deal environment, where investor scrutiny is high and delivery timelines are compressed, the inability to unlock value from newly acquired datasets can become a critical barrier to realising the deal’s promise.

 

AI expectations can amplify these conditions, as they can increase infrastructure demand and intensify reliance on data quality and platform consistency, precisely where integration work is still underway.

 

 

Why early assumptions are hard to revisit

As integration progresses, another dynamic tends to emerge: the reluctance to challenge early AI assumptions once investment has been committed.

 

By the time organisations have funded new data platforms, aligned teams and publicly linked growth expectations to AI capability, momentum becomes tough to slow. Early warning signs can start to surface. Data maturity might be lower than expected, or operating costs climb, and systems remain more fragmented than forecast. Yet even when these signals appear, reversing course can feel riskier than pressing ahead.

 

It is tempting to attribute these outcomes to insufficient due diligence. The reality can be more nuanced, as no diligence process can fully reveal the true cost profile of AI workloads at scale. Also, the quality of datasets, or the operational dependencies that surface when environments are combined.

 

Integration challenges have always taken time to resolve, and AI is compressing the feedback loop. Misalignment, cost pressure and risk exposure all become visible sooner. If early warnings signals are not addressed, organisations can risk finding themselves locked into technical and commercial decisions that are expensive to unwind.

 

 

Slowing down to move with control

The organisations that extract durable value from AI-influenced acquisitions tend to show discipline at key moments. They resist the instinct to accelerate integration before foundational visibility is in place.

 

That visibility starts with a clear understanding of the technology estate, which applications are actively used, where datasets overlap, how existing data is used and shared, and how cloud commitments scale with consumption. It also means modelling how AI workloads will change infrastructure demand over time, rather than assuming linear growth.

 

Without that clarity, AI investment risks being layered onto inefficiency. So instead of reducing cost and complexity, it can entrench both.

 

For boards and executive teams, transparency can create options. A clear view of the full data estate spanning quality, overlap, usage, ownership and underlying infrastructure and cloud commitments enables leaders to reassess assumptions and sequence decisions with confidence. That clarity also makes it easier to prioritise rationalisation and to phase AI investment in line with operational readiness.

 

 

A shorter distance between decision and consequence

AI does not rewrite the fundamentals of post-merger technology integration. Organisations still need coherent architecture, clean data, cost discipline and governance. What AI changes is the speed at which consequences appear.

 

Cloud spend accelerates quickly, and technical debt compounds earlier. Risk exposure can become more visible, which can lead to the window to course correct narrowing.

 

In that environment, the defining capability is the ability to see the estate clearly, respond early to emerging friction and continuously optimise.

 

Software acquisitions justified by AI can deliver meaningful value. But that value is not secured at the announcement. It is realised, or eroded, in the months that follow, when ambition meets infrastructure, and strategy is tested against the realities of integration.

 

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Jesse Stockall is Chief Architect at Flexera

 

Main image courtesy of iStockPhoto.com and amgun