Why AI M&A deals fail: 3 questions every buyer must ask

While artificial intelligence (AI) holds potential to replace jobs, many of its capability claims remain a concern, making it risky for corporate buyers and investors to rely on traditional checklists alone.

The rise in M&As over the past five years has given the impression that acquiring a new capability will be as quick as buying a new car. Recent data from PitchBook revealed that, in Q1 2025, AI made up 71 percent of total venture capital deals.

Venture capital is not the only source of funding driving the mega-investments in AI. Major tech companies have also invested in AI ventures. Hundreds of millions of dollars are being poured into big data processing, language models and generative AI. Everything goes according to plan, until performance falls short of expectations.

In this article, I’ll explore three questions to ask when acquiring new capabilities.

• Can the company reproduce it? Product demos can only showcase marketing capabilities. They say very little about the actual technology. The real test is whether AI capabilities can be replicated within the acquiring company’s infrastructure.
• Can they sustain it? AI has become such a broad term that it’s often unclear whether it refers to a genuine in-house technology. No matter how flashy it looks, it must still be sustainable.
• Can they keep building new capabilities in the newly merged environment? Companies need to demonstrate that they can develop new capabilities to respond to unanticipated changes.

Mounting pressures to catch up have led to acquisitions between companies from completely different worlds. A software company with an R&D team and a sporting goods retailer have vastly different expectations when it comes to technology. One might think AI would solve the problem and that inefficiencies from the old world would disappear. That’s far from reality. There is no guarantee that the same capabilities seen in product demos can be replicated elsewhere. The true measure of value is whether a capability can be reproduced and sustained in the newly merged environment.

Can they reproduce the capabilities?

As of now, no one is entirely sure of AI’s full potential. Even the creators of the world’s largest AI platforms admit to its unpredictability. Capabilities that look financially and technically promising don’t guarantee the same success when reproduced elsewhere. M&A leaders are already familiar with post-merger failures as the norm. It is often only after the acquisition that ineffective capabilities become visible.

According to IDC, US$3.4 trillion will be invested in digital and AI initiatives by 2026. The potential value of AI lies in automated IT teams, sales optimization and medical diagnosis. Yet AI has barely touched these areas, let alone reproduced capabilities beyond a call center.

Nearly all technology now incorporates some form of AI, promising to revolutionize the world. These include app creation, content generation, sales prediction and medical diagnosis. To date, these methods still rely heavily on human design and oversight. To capture AI’s full potential, its capabilities must impact the core business and be reproducible.

While AI is filled with marketing hype, it has made some progress in critical areas like healthcare. In recent tests, AI has outperformed human specialists in medical diagnosis with greater accuracy and reliability. The question, then, is not whether AI is more accurate, it’s whether its predictive capability can be reproduced in a newly merged environment. Can that success be replicated in other healthcare settings? Can the same level of medical diagnosis be delivered using the resources, team and infrastructure of another company?

Can they sustain it?

If one company releases updates every month, it may not be able to maintain that rhythm after being acquired. Due diligence on AI should go beyond checklists and include “field testing” capabilities. The acquired company should demonstrate that it can reproduce and sustain the same capability using the infrastructure provided.

In 2016, Builder.AI made headlines with its “AI-powered” site, claiming to have automated the entire app-building process. By 2019, rumors emerged that its AI capabilities were overstated. Valued at $1.5 billion in 2023 with backing from Microsoft and Qatar’s sovereign wealth fund, the company filed for bankruptcy protection after revelations that its “AI-powered” platform relied on hundreds of engineers in India while executives allegedly orchestrated a years-long financial deception.

Verifying whether a company can sustain its product or service is not only good due diligence. It’s vital to ensuring post-integration success. Product demonstrations only verify marketing capability; they don’t reveal the resources or time required to deliver it.

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Can they build new capabilities?

Applying an existing capability is not the same as creating a new one. Building new capabilities is an entirely different challenge. It’s only post-acquisition that companies realize which capabilities they lack, by which point it’s often too late to be adapted or developed from scratch. Companies serious about survival will attempt to transform capabilities and develop new ones. While COVID lockdowns disrupted many professional services, many pivoted towards online delivery. We shouldn’t expect companies to have these capabilities instantly, but we should expect them to develop them over time.

We must confront the reality that merging two companies is rarely smooth. Given the consistent issues with post-merger integration, it’s wise to begin with the assumption that things are broken. The 100-day integration plan can be counterproductive if each day only reports underperformance. Those 100 days would be better spent identifying which capabilities are broken and which can be salvaged.

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