AI Hallucinations and M&A: Why Risk Management Must Catch Up

More companies are turning to agentic AI, but many are doing so blind–60% are adopting these tools, while more than half haven’t evaluated the risks they’re taking on, according to Deloitte, citing RiskConnect’s 2025 data.

“Misleading information can lead to poor strategic decisions, financial losses, and reputational damage,” Deloitte said. “In high-stakes sectors such as finance and healthcare, where precision and accuracy are paramount, the consequences of AI hallucinations can be particularly severe. A difference of only 0.5% could, in certain situations, amount to millions of dollars.”

And, the “risks are further compounded in Agentic AI models, where autonomous agents interact and act upon one another’s outputs,” the company continues. “When unchecked, hallucinations can propagate through interconnected systems, creating a chain of compounded errors in which small inaccuracies at each step accumulate into large-scale distortion of business processes and decisions.”

Most diligence frameworks still treat AI as a check-box: Is it there? What’s it used for? Can it scale? What’s often missing is the harder set of questions: How often does this system produce misleading or unverified results? Who is responsible for validating those outputs? What happens when the model is wrong, but no one notices?

Hallucinations occur when AI models generate content that appears credible but is factually inaccurate, misattributed, or entirely invented. Unlike intentional misinformation, these errors are unintentional, often driven by noise in training data, algorithmic limitations, or opaque model logic.

AI hallucinations are a failure of oversight. And as more AI systems interact with one another as autonomous agents, querying each other and taking actions based on each other’s outputs, errors start to compound. What begins as a small mistake can ripple through workflows and become a strategic misstep. If a company is valued on the strength of its AI capabilities, those systems must be interrogated, tested, and verified, just as financials or legal exposure are. The presence of AI is not inherently a value-add. What matters is whether it’s functional, trusted, and resilient in high-stakes environments.

That means due diligence needs to evolve. Risk teams must get deeper into the actual behavior of these systems. Test them under stress. Look at hallucination rates not in generic contexts, but in domain-specific scenarios. Examine the provenance of the training data and ask whether there’s a process for escalation when models produce suspect outputs, and whether anyone is watching for it in the first place.

Equally important is the integration risk. AI models are not plug-and-play. If the target company’s models operate under different assumptions, governance rules, or validation thresholds, integration will be slower and riskier than expected. Post-deal, that can translate into product delays, customer churn, or internal mistrust.

Hallucinations are not a reason to avoid AI. It’s a matter of perspective because they’re a reason to treat AI with the seriousness of any other critical infrastructure. Acquirers who understand this will not only avoid costly surprises but will build stronger, more transparent AI portfolios over time.

AI may not lie, but it does get things wrong, and it does so with confidence. The job of M&A leaders is to separate the promise of machine learning from the reality of machine error and to make sure they’re pricing, planning, and integrating accordingly.