Recently, we talked about “sleeper clauses” in legacy SaaS agreements, that carry unnecessary risk and uncertainty now that many of the products in your tech stack are AI-enabled. As renewal season approaches, now is a good time to approach your long-term vendors about rationalizing some of these clauses.

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As you triage the project, the first question is not “Which vendors cost the most?” It is: “Which vendors sit closest to sensitive data, important workflows, or newly expanded AI functionality?”

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The most urgent contracts to review are usually the ones where the vendor now has access to information, interactions, or business processes that create outsized legal, operational, or governance risk if legacy language is left untouched.

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How to identify the most urgent contracts

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Start with vendors that score highly on one or more of these factors:

 

1. Sensitive or regulated data
Prioritize vendors that process confidential business information, personal data, employee data, customer communications, regulated data, proprietary workflows, or other sensitive content. The more sensitive the data, the less comfortable you should be relying on older “improve the services” or de-identified data language.

 

2. Newly activated AI features
Focus on vendors that were originally purchased as conventional SaaS, but now offer copilots, assistants, generative search, summarization, drafting, classification, recommendation tools, or agents. These are often the contracts where the paper stayed the same while the functionality changed.

 

3. Embedded operational reliance
Elevate contracts where the business is starting to rely on AI-enabled outputs in meaningful workflows, such as customer communications, HR, finance, support, sales enablement, legal review, or decision support. Even if the contract value is modest, the downstream impact may be significant.

 

4. High-volume user interaction
Look closely at platforms that capture prompts, free-text entries, annotations, uploaded documents, corrections, or repeated user behavior. These environments may generate exactly the kinds of data that vendors want to use for tuning, training, and product development.

 

5. Broad internal adoption
A vendor used lightly by one team may present less urgency than a platform rolled out across the enterprise. Broad adoption increases the chance that sensitive data, inconsistent practices, and unreviewed AI use cases are already flowing through the service.

 

6. Customer-facing or externally visible use
Prioritize tools that influence communications, outputs, or decisions experienced by customers, counterparties, job candidates, patients, or other external stakeholders. These uses can create faster legal, reputational, and compliance consequences.

 

7. Vendor opacity
If the vendor’s current AI practices are hard to understand from the contract, DPA, product terms, or public documentation, that uncertainty is itself a review signal. Ambiguity is often where sleeper clauses can have the most impact.

 

8. Upcoming renewal, expansion, or renegotiation
The best contract to review is often the one where you have leverage now. Renewal, upsell, feature activation, and procurement review are practical moments to raise the issue, even if another contract may be theoretically riskier.

 

A practical triage approach

 

For a first review cycle, legal teams should usually start with a short list of vendors that meet several of these criteria at once.

 

A strong starting group might look like this:

• widely-deployed productivity or collaboration suite now layering in AI features

• CRM or support platform capturing large volumes of customer communications

• HR, recruiting, or employee tools platform handling sensitive workforce data

• knowledge management, search, or workflow tool now offering generative features

• any vendor whose AI-enabled functionality is spreading quickly inside the organization

• integrations to third-party platforms and plug-ins that provide services on your product/platform

 

The point is not to review everything at once. It is to identify the contracts where the combination of data sensitivity, AI enablement, and business reliance creates the greatest disconnect between old language and current risk.

 

A practical next step for legal teams

 

Once you identify the highest-priority vendors, the next move is not necessarily to reopen the entire master agreement. No one wants to break the seal on a long-standing agreement, with all the other internal revisions and complexity that creates.

A more efficient place to start is often an AI Addendum that can be shared with critical vendors to frame the conversation around current expectations. Your discovery process with the vendor can surface whether the vendor is using AI for day-to-day internal efficiency and productivity, versus using AI to build products; it’s really only the second that you are likely worried about.

 

An AI Addendum can help surface and negotiate issues such as:

• whether customer data may be used for training, tuning, testing, or model improvement

• whether prompts, outputs, logs, annotations, and corrections are treated as Customer Data

• limits on use of de-identified, aggregated, telemetry, or feedback-derived information

• disclosure of relevant AI subprocessors and downstream model providers

• transparency around AI-enabled functionality and material changes to data use

• deletion, retention, and residual model-use questions

• responsibility for output-related risk, provenance issues, and governance controls

 

For many organizations, this is the most practical way to begin: identify the contracts that matter most, then use a targeted addendum to test whether the vendor’s current AI practices match the customer’s legal, privacy, security, and governance expectations.

 

This conversation is now part of ordinary vendor management. A long-standing vendor's reluctance to engage on an AI Addendum, or suggestion to use their standard terms and conditions, is by itself a risk indicator that should not be ignored.

Need a starting point? I help legal teams identify the highest-priority vendor contracts for AI review and draft AI addenda that can be used to begin the conversation with critical vendors.