60 Seconds on Tech & Sourcing: Avoiding the Landmines of Extending AI Access to Third Parties

The Scenario: 

Your company licenses an AI tool for internal use. The tool performs well, so you extend access to your resellers, distributors or other third-party partners. Your upstream AI agreement permits this expanded use, but what additional exposure does this create?

The Landmines:

1. The Middleman Problem. Your third-party partner has no privity of contract with your AI vendor, so if the AI tool produces inaccurate outputs and your partner suffers harm, the partner's claims run to you. Even if you have indemnification or other remedies against the vendor, you still bear the upfront defense costs and the burden of pursuing those remedies in a separate proceeding.
2. The Coverage Gap. Most enterprise AI agreements are negotiated for internal use. Output disclaimers and liability caps were acceptable when access stayed in-house. But when you extend access to partners, you change the risk profile without changing the contract, and your partners will expect protections from you that your vendor never gave you.
3. The Use-Case Drift. Once you extend access to a third-party partner, you lose visibility into how the tool is actually used. If your partner deploys it for prohibited use cases or high-risk applications, you may find yourself in breach of your vendor agreement and out of compliance with applicable regulations.

Mitigation Strategies:

1. Allocate the Risk Downstream (With Your Partners). Your agreements with third-party partners should expressly address AI-related risks, including:
   1. Use restrictions defining permitted use cases and prohibiting high-risk uses
   2. Output disclaimers and requirements that users independently verify AI-generated outputs before relying on them
   3. Liability caps and consequential damages exclusions for AI-related claims
   4. Flow-down provisions requiring partners to comply with upstream restrictions
2. Strengthen Your Upstream (Vendor) Protections.  Assess whether additional protections are available, including:
   1. Performance commitments tied to agreed performance metrics or documented accuracy benchmarks
   2. Disclosure of known failure modes and material model changes
   3. Indemnification coverage reflecting the expanded deployment model
3. Implement Operational Controls. Contractual protections alone may not be sufficient.  Operational controls reinforce those protections and create a record of responsible deployment:
   1. Prominent disclosures about AI limitations at the point of output delivery
   2. Click-through acknowledgments confirming that users understand AI limitations
   3. Human-review requirements for higher-risk use cases
   4. Usage logging to support incident investigation

If you are extending AI access to third parties, the gap between the risk you are assuming and the protection your contracts provide may be wider than you think.  Address that gap now, while you still have negotiating leverage.