What Foley & Lardner Covers on AI Deals and AI Infrastructure Investing

Updated

Foley & Lardner's AI coverage centers on two connected themes: what buyers scrutinize in AI and autonomy transactions, and how investors evaluate AI infrastructure beyond data centers. The firm frames both through its Private Equity and Innovative Technology practices, with dedicated "Foley Ignite" content on AI's role in private equity value creation and on AI infrastructure investing. This is insight and commentary for dealmakers, not legal advice on a specific transaction.

AI deals: six diligence priorities

Foley's "What Buyers Want in AI Deals" identifies six diligence priorities shaping AI and autonomy transactions. The framing matters more than the count: in AI deals, diligence is not only financial and contractual, it is also technical and operational. Buyers need to understand what the target's AI actually does, what it depends on, and what could change after closing.

For a buyer, that translates into concrete questions such as:

  • Data and model provenance — where training data and models came from, and what rights attach to them.
  • Third-party dependencies — reliance on external model providers, APIs, or compute that could shift in price or availability.
  • Talent and key-person risk — whether the people who built the system stay after the deal.
  • Regulatory exposure — how the target's use of AI maps to applicable rules in its markets.
  • IP ownership — whether the target owns or merely licenses the outputs and tooling it relies on.
  • Integration and scalability — whether the AI works outside the demo environment and at the buyer's scale.

The practical takeaway: treat AI diligence as a cross-functional exercise, not a checkbox handled by one advisor.

AI and private equity: where value creation comes from

Foley Ignite's "AI & Private Equity" examines how AI, automation, and robotics reshape value creation for private equity. The value-creation lens is different from the diligence lens. Diligence asks what you are buying; value creation asks what you can do with it after closing.

Typical areas where AI, automation, and robotics show up in a portfolio company:

  • Cost structure — automating repeatable processes to reduce headcount intensity or error rates.
  • Revenue — using data and models to improve pricing, targeting, or product features.
  • Speed — shortening cycle times in operations, underwriting, or service delivery.
  • Scalability — growing output without proportional additions to staff.

The caution is that these gains depend on data quality, process discipline, and adoption inside the portfolio company — not on the technology alone.

AI infrastructure investing: beyond data centers

Foley Ignite's "Investing in AI Infrastructure" is explicitly subtitled "Beyond Data Centers." That signals the scope is wider than the physical facilities most people associate with AI infrastructure. The category can extend to the layers that make data centers useful — power, connectivity, hardware, and the supporting services around them.

For an investor, the useful question is where in the stack you are taking exposure and what risk that implies. A data center asset, an energy contract, and a software layer have very different economics, timelines, and regulatory profiles even when all three are labeled "AI infrastructure."

How to follow this coverage

Foley publishes this material through its Insights hub and Foley Ignite, organized under practice areas including Private Equity and Innovative Technology. The firm also runs LearningLab, described as an on-demand hub offering state-approved CLE credit and industry-relevant streaming content on your schedule.

If you want to track a specific thread, the practical path is:

  1. Go to the Insights section and filter by practice area (Private Equity, Innovative Technology) or by topic (AI).
  2. Use Foley Ignite for the AI-and-private-equity and AI-infrastructure series.
  3. Check LearningLab if you need CLE credit alongside the substantive content.

What this coverage is and is not

It is commentary and practical framing for dealmakers — diligence priorities, value-creation angles, and investment scope. It is not a substitute for transaction-specific legal advice, and the firm's own materials are written as general insight rather than guidance for a particular deal. If you are evaluating an actual AI transaction or infrastructure investment, the relevant questions will depend on your structure, jurisdiction, and counterparty, and should be worked through with counsel.