Source: JD Supra

Developing AI capabilities is a strategic priority for companies and acquisitions are a principal route to accelerate those capabilities; boards must therefore focus on locking in value and managing risk in AI-related M&A. AI diligence must be technology-led and forward-looking, assessing whether the AI capability is genuine, the nature of the model and system architecture, third‑party dependencies (datasets, code, integrated models), guardrails and real‑time failure detection, and whether key‑person risks or access to talent are the primary deal drivers. Specialist legal "pre-diligence" of AI assets is recommended early—before term sheets or approvals—to surface technology, regulatory and commercial risks before material resources are committed.

The AI value chain is complex, spanning compute, connectivity, platforms, applications and an ecosystem of financings, partners and suppliers; understanding the target’s position in that chain is essential to evaluate competitive moats, upstream/downstream dependencies and AI sovereignty. Legal diligence should address IP (notably copyright and ownership of AI outputs), data privacy (including tensions with fundamental privacy principles and potential conflicts with laws like the GDPR), regulatory fragmentation (AI-specific laws plus technology-agnostic regimes such as export controls, cybersecurity, antitrust and foreign investment), contractual allocation of AI risks (AIaaS, model development and partnership agreements), and jurisdictional sensitivities across these areas. The memo also notes a broad disclaimer about generality and the need for specific legal advice.