It’s not easy finding the bandwidth to uplevel your contract team’s skills, especially with cost-cutting and financial constraints in place in so many companies. Here are six ways that you can add additional training without breaking your budget.
Strategy #1 - Start by Developing One Person’s Skills
How to implement: Identify someone on your team with both technical curiosity and the ability to teach others. Support their deep dive into AI contracting, whether through a major conference, specialized courses, or time to study. This person can lead internal training sessions, document playbooks for common scenarios, pair with teammates on negotiations, and become the first line of review for AI-specific issues. Over time, this reduces external counsel spend on routine AI contract questions.
Why this works: Instead of spreading training budget thinly across the entire team, investing deeply in one person creates a multiplier effect. That person becomes your go-to resource and trains others over time.
How to Contract option: Our AI Training Hub includes 80+ hours of training videos on AI contracting topics, with 75+ hours created since March 2025.
Strategy #2 - Embed AI Learning in Existing Meetings
How to implement: Consider rotating responsibility for short presentations connecting technical concepts to practical contract issues. When someone has to explain how a technology works and why it matters for your negotiations, they develop deeper understanding than passive learning provides.
Why this works: Creating separate training programs requires dedicated time and resources. Integrating learning into existing team meetings makes education sustainable without adding to everyone's calendar.
How to Contract option: Our enterprise plan will include a license to share training videos two times per month as part of internal training programs, including with people who are not members of How to Contract.
Strategy #3 - Test AI Tools Against Your Area of Expertise
How to implement: Select contract provisions where you have deep expertise and strong opinions. Then have your AI tool review and suggest redlines. You can evaluate what it catches versus what it misses. That approach lets you identify patterns in its strengths and blind spots. Then adjust your workflow to compensate for identified weaknesses.
Why this works: AI tools often handle basic issues well but miss nuanced problems, contextual risks, and subtle inconsistencies that experienced professionals catch immediately. Understanding these limitations helps you supervise AI output appropriately.
Strategy #4 - Use AI to Collect Information From Public Sources
How to implement: You can ask your AI platform to do the administrative work to better focus your time. For example, ask it to:
Review vendor websites, privacy policies, and public documentation
Use AI to populate standard forms like DPAs with vendor-specific information
Create initial drafts of technical documentation requirements
Generate questions based on gaps in public information
Develop comparison documents when evaluating multiple vendors
Why this works: AI tools often handle basic issues well but miss nuanced problems, contextual risks, and subtle inconsistencies that experienced professionals catch immediately. Understanding these limitations helps you supervise AI output appropriately.
What other ways have worked for your team? Leave a comment below.