AI has quickly become part of the justification for many large software acquisitions.
According to PwC, around a third of the biggest deals completed in 2025 cited AI as part of their strategic rationale.
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Chief Architect at Flexera.
What’s changed is not just why deals are done, but what happens next. Once a deal closes, AI ambitions can conflict with operational reality.
Commitments to AI-led growth are frequently made before organizations have a reliable view of how their combined systems, data, and contracts actually fit together.
Boards expect value to materialize quickly. IT & Engineering teams, meanwhile, are still discovering what they’ve inherited. That gap between expectation and visibility is where many AI-driven deals begin to struggle.
Why “efficiency” becomes harder to deliver after AI-driven acquisitions
One of the earliest tensions in AI-driven M&A centers on efficiency. For deal teams, efficiency typically means margin improvement and speed. For technology leaders, it means keeping critical platforms stable, secure, and compliant while absorbing new systems and workloads.
AI widens that gap because much of the required investment arrives early. Spend on cloud computing infrastructure, data pipelines, tooling, and specialist talent often ramps up before organizations have resolved software overlap or decided which platforms will survive the deal.
In practice, this means AI investment is layered onto technology estates that are still fragmented. Duplicated systems remain in place, overlapping licenses can’t yet be retired, and contractual constraints limit how quickly rationalization can happen. Teams are asked to accelerate delivery while carrying more complexity than before.
That lack of visibility has a direct financial impact. In the UK, nearly one in every five pounds spent on SaaS is lost to unused or duplicate licenses, money that organizations under post-deal pressure cannot afford to waste.
AI does not create these inefficiencies, but it amplifies them by increasing IT infrastructure demand and placing greater strain on already fragmented technology estates.
The sunk cost trap sets in early
As integration progresses, organizations often encounter one of the hardest dynamics to escape in post deal technology strategy: the sunk cost fallacy.
Once investment has been committed to AI tooling, data migration, or platform alignment, it becomes increasingly difficult to revisit early assumptions.
Even when capabilities prove less mature than expected, data environments are more fragmented, or operating costs are materially higher than forecast, momentum tends to win out.
These outcomes are sometimes attributed to insufficient due diligence. In reality, no diligence process can fully surface the quality of data, the true cost of AI workloads at scale, or the operational dependencies that emerge after systems are combined.
Traditional integration challenges can take years to resolve. AI compresses this timeline. Misalignment often becoming visible much sooner, alongside the associated cost and risk.
If early signals are ignored, organizations can find themselves locked into decisions that are costly to unwind, with each passing quarter making it harder to change direction.
Clarity creates optionality
The organizations that deliver long-term form AI-driven acquisitions tend to slow down at the right moments. Rather than pushing ahead with integration before the basics are understood, the establish early clarity across their software, SaaS and cloud estates.
That clarity comes from knowing what software is actually being used, where licenses overlap, how SaaS and cloud commitments are structured, and how AI workloads will drive consumption over time.
Without it, AI investment is layered onto inefficiency, amplifying waste, rather than eliminating it.
For boards and executives, clarity creates leverage. A clear view of usage, entitlements, and contractual obligations allows leaders to challenge assumptions, prioritize rationalization, and pace investments before spend becomes locked in.
Deals deliver value when organizations remain willing to adapt as new information emerges. That includes revisiting integration plans, adjusting timelines, and setting realistic expectations for what AI can deliver in the near term.
AI shortens the distance between decision and consequence
AI does not change the fundamentals of software integration, but it accelerates the consequences of getting those fundamentals wrong. Spend accumulates faster, complexity compounds earlier, and the window to course correct narrows.
In that environment, the ability to see clearly, respond early, and continuously optimize becomes a defining factor in whether an acquisition delivers durable value or locks in long-term cost and risk.
