The target values its AI capabilities. But what dependencies lie beneath? What is the real EU AI Act exposure? Are models built on data that creates legal risks? Once closing is signed, these questions become your problems.
A missing AI due diligence is a value destruction risk that nobody put on the table.
A proprietary ML pipeline, data processing capability, an in-house tool. Targets value these as differentiators. But real value depends on what is truly controlled: is training data proprietary? Do models depend on third-party vendors whose conditions can change? Do performance levels rely on APIs that could be cut off?
Traditional AI due diligence focuses on code and infrastructure. It misses the essential: strategic dependencies and regulatory exposure.
We produce a strategic evaluation of the target's AI assets: real dependency mapping, EU AI Act exposure assessment, data risk analysis. An independent reading that complements legal and financial due diligence.
This intervention is relevant for:
→ acquisitions involving a target with AI systems in production
→ transactions where technology assets represent a significant share of value
→ deals with EU AI Act exposure post-closing
If the target has no AI systems embedded in its processes, this intervention is not necessary.
AI due diligence is the process of evaluating the assets, risks and dependencies related to artificial intelligence in the context of a merger or acquisition. It complements traditional legal, financial and technical due diligence by addressing dimensions specific to AI systems.
Rigorous AI due diligence covers three areas. Technology dependency evaluation: which AI vendors the target uses, under which contractual conditions, and what migration cost would be if conditions changed post-acquisition. EU AI Act regulatory exposure: are the target's AI systems classified as high-risk, are compliance obligations met, and what is the potential regulatory liability. Training data analysis: on which data were proprietary models trained, are usage rights documented, and are there litigation risks related to training data usage.
The absence of AI due diligence in an acquisition can lead to acquiring a hidden liability: undocumented vendor dependencies, EU AI Act non-compliance to rectify, or AI assets whose value rests on data whose rights are not clearly established.
We produce the independent strategic reading of your target's AI assets : real dependencies, EU AI Act exposure, data risks : before terms are locked in.
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