How do you know if a legal tech vendor’s software is the right fit for your needs? By asking tough questions.

The right contract lifecycle management (CLM) solution will make a huge difference for your organization in process efficiency, agility, risk mitigation and more. Knowing it’s the best option requires due diligence; that’s why a previous post covered six questions you should include when evaluating CLM vendors. 

They’re especially important to ask if a vendor is making artificial intelligence (AI) claims about their product – as so many are these days. We also explored the steps and best practices you should apply as part of that process.

This time around, we’ll round out that list with seven more questions. Use them so you can be certain you’ll obtain the crucial answers that are so central to making a smart, future-proofed AI CLM investment.

Plus, watch our on-demand video that explores every one of these questions in detail: Protect The Business: 13 Questions to Ask Your Legal AI Vendor.

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A key question about AI responsibility

Question 7: Do you follow Responsible AI principles? If so, which ones?

Why is this important? This is one of the most essential questions you can pose to a potential vendor who includes AI as a feature within their CLM or other legal tech offering. Knowing a vendor’s responsible AI policies helps you ensure their product uses AI in a safe and ethical manner.

Taking Risks: 60% of organizations that do not contractually obligate their vendors to follow Responsible AI practices face increased financial and reputational risk through 2025, says Gartner.

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Questions about vendor AI integrity

In evaluating CLM vendors making AI claims, these are key questions to ask them regarding data privacy, security, and IP protection.

Question 8: If your product has a conversational AI feature, does it show the sources of its answers?

Why is this important? Conversational AI is a powerful tool for understanding your contracts, but no generative AI tool is 100% correct 100% of the time. If it can “show its work,” a user can easily confirm answers and dive deeper into relevant portions of the contract.

Example: If you ask about payment terms and the answer is based on two separate clauses in the contract, does the AI identify those clauses?

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Question 9: If your product has generative AI capabilities, can you describe if and how it leverages retrieval augmented generation (RAG) to help ensure quality results?

Why is this important? RAG enhances the output of generative AI by leveraging a trusted data pool to help ensure accuracy and reliability, avoiding hallucinations and irrelevant responses. Your genAI solution should employ RAG by interacting with a base of high-quality contract data derived from traditional AI and OCR processing of your agreements.

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Vendor evaluation questions about AI customizability

These are important in being sure you’re able to future-proof your AI CLM investment with a product’s customizabilty capabilities.

Question 10: Will your LLM let us build custom AI to extract specific contract data like dates, numbers, and text fields?

Why is this important? LLM technology is powerful, but only valuable when you can direct it to read, analyze, and extract the contract terms that actually matter to your organization,. rather than just the standard terms that are pre-built in the platform. Being able to fully control your use of the AI is essential to future-proofing your investment.

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Question 11: How will your product’s AI allow us to create custom AI models to generate structured data 11 points from our contracts, such as summaries of terms, calculated dates or dollar amounts, and ratings or classifications of terms?

Why is this important? Implementing contract AI solutions requiring a “human-in-the-loop” will require additional time and resources. For the fastest time-to-value, consider using CLM products using pre-trained AI with self-sufficient ingestion and processing capabilities.

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Question 12: Can we provide direct input about custom AI performance in your product? Will we have control to refine models to enhance the accuracy of the outputs?

Why is this important? It’s crucial to ensure that your CLM provider has controls in place to enable their generative AI features to operate at scale and avoid issues such as latency or inflated costs as adoption and innovation continue to grow.

Feedback Fact: Most CLM products lack direct input capabilities, so a user’s only option is to submit feedback through black hole support channels.

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Question 13: If your product analyzes contract data using AI, will you let us test its capabilities and fidelity by uploading thousands of contracts into a real environment to see firsthand how it works at scale?

Why is this important? A CLM provider should be confident in its ability to deliver on its promises, and you should be confident that its product can accurately and efficiently process your contract documents.

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Be sure to bake these questions into your process when evaluating CLM vendors and you’ll be taking a solid step toward protecting your legal and contracting teams from a poor tech investment. To see these questions and more discussed in depth, check our on-demand demo.