The Real M&A Challenge in the Age of AI

Artificial intelligence has rapidly become the strategic battleground for companies across industries. Nowhere is this more evident than in enterprise software, where companies are racing to acquire AI capabilities to strengthen their offerings and reposition themselves for the future. The surge in acquisitions of AI assets reflects a clear reality: AI is reshaping competitive dynamics.

Yet acquiring AI is not, in itself, a strategy. Too often, companies pursue AI deals because competitors are doing so or because the technology appears transformative.  M&A creates real value only when it is tightly linked to an overarching strategic ambition. The real question is not whether a company has acquired AI capabilities, but whether those capabilities will fundamentally reshape its competitive position.

The first step is clarity of intent. AI is not an end goal. It is a means to redefine how a company competes. Any acquisition must fit within a coherent strategic thesis about where and how the business intends to win.

In the AI era, that thesis should be tested through scenario planning: considering two or three plausible AI futures and clarifying what capabilities the company should own versus rent in each case. Because model capabilities, user adoption patterns, and underlying economics are evolving rapidly, this roadmap needs to be refreshed frequently rather than treated as static. The second challenge lies in due diligence. Traditional financial and commercial diligence remains essential, but it is no longer sufficient.

Acquirers must assess whether AI will revolutionize, transform, or simply augment the target’s business. They must validate the target’s differentiated intellectual property, particularly its differentiated data assets and end-to-end workflows that make its capabilities defensible. Understanding how AI affects competitive positioning, cost structure, and long-term sustainability is central to determining whether the asset can create durable value.

Revenue synergies also require rethinking. AI acquisitions often involve companies with limited customer bases, which means cross-selling opportunities are typically one-directional, bringing the acquired capabilities into the acquirer’s installed base. Unlocking value therefore demands a deliberate go-to-market approach: defining a focused set of sales plays, aligning pricing, equipping the salesforce, and preparing customer onboarding. Much of this work should begin before closing, using the pre-close period to build momentum for post-close execution.

Product integration presents another critical test. Acquiring AI rarely means simply adding a feature. It often requires reimagining product architecture and reprioritizing the roadmap. Companies need to be explicit about the go-forward R&D envelope and any one-time integration investments required. This is particularly important when the acquisition includes a small but highly specialized technical team.

Finally, talent is frequently the decisive factor. AI acquisitions are fundamentally about people. A well-developed integration thesis should make the future operating model explicit, clarifying what will fully integrate, what will partially integrate, and what will remain autonomous. In some cases, reverse integration may even be appropriate. Beyond financial incentives, teams need a compelling vision of how their work will scale and create impact. Settling people-and-power decisions early is critical to retaining key talent and avoiding friction that erodes value.

In short, buying artificial intelligence is not enough. Real value emerges when acquisitions are anchored in strategy, informed by AI-specific diligence, supported by thoughtful revenue and product integration, and grounded in a deliberate approach to talent and operating model design. In the age of AI, the winners will not simply be those who buy the most technology, but those who integrate it with discipline and clarity of purpose.