From Renewal Question to Reusable AI Workflow
How contract professionals can cut through AI hype by starting with one practical, recurring problem.
The ever-growing volume of AI concepts and terminology can be overwhelming. So can figuring out which are important (or even relevant) for enterprise contracts.
An approach I’ve found helpful to address the information overload and FOMO is to focus on a smaller volume of high-impact concepts, one at a time, organized by concrete problems and use cases. Here’s an example.
Problem / Scenario
Let’s say that you’re an in-house counsel, contracts manager or account executive. You’ve received an email from a client or from an internal stakeholder asking about a renewal for a specific contract.
Here are four common things someone might want to know about a renewal:
Is there renewal language at all?
Renewal type (automatic vs. optional)
Renewal timing (when is the contract coming up for renewal)
Counterparty outreach necessary (do I need to provide notice or do anything else and if so, when)
Solving the Problem
One approach is to search the repositories you have access to for the contract using whatever criteria is available. Or if you don’t have access to all of the repositories, sending a note around internally to people who might know. If it’s a customer contract, sending a note to sales, if it’s a vendor contract, sending a note to procurement, etc.
For a single request, we also create a chat with an LLM to help us. E.g. ping Claude or ChatGPT, provide some context, and then move forward with the search.
There’s nothing wrong with these approaches for a single request, but they don’t scale well.
For many requests, for example if you get dozens or hundreds of similar requests, you would probably benefit from a more methodical approach.
One approach is to compile a rough estimate of the types of requests you receive in a given week (or month / quarter / year). Which are the common ones, the most cumbersome ones or the ones where it feels like you’re doing the most duplicative work?
For these we can create a skill, which is essentially a reusable instruction that reduces the need to repeatedly prompt an LLM-powered chatbot for the same thing.
See my article here for a specific example that is useful for managing contract intelligence requests.
The prompt doesn’t need to be over-engineered: for example for our renewal language you can start by simply asking “can you help me put together a skill to find renewal language in contracts”? Then you can provide additional context as needed and check the AI tool’s work.
This works in both ChatGPT and Claude, two of the most popular LLM tools. You can also go to https://chatgpt.com/skills to manage skills there. Please follow your organization’s policies on AI use regarding which tools and context are appropriate.
Summary
Don’t worry about learning every AI concept or about AI FOMO
Start with a concrete problem or use case
Determine whether it’s a recurring or duplicative task
If it’s truly a one-off task, use a regular chat
If it’s recurring or involves significant duplicative work, create a skill by prompting the LLM
Check the LLM’s work
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