Trialling AI Tools Before You Commit
Run a fair, hands on trial of AI tools with your own data before you sign anything
A taste of a lesson
Two AI tools both summarised our sample contract well. How do I choose?
One sample cannot separate them. Build a set of thirty or so real documents, including the awkward ones: scanned pages, unusual clauses, very long contracts and ones where key terms are missing. Run both tools on the same set with the same instructions, and have a reviewer score each summary without knowing which tool wrote it. Record minutes needed to fix each one and note the worst failures, not just the best results. Exercise: name three difficult documents from your files that you would put in the set.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Plan a short, structured trial with clear success criteria
- Build a test set from real examples, including the hard cases
- Score tools side by side with a rubric that includes review effort
- Handle data safely during a trial
- Summarise results into a decision ready comparison
Lesson plan
- 1 Define the job and criteria Write down exactly what the tool must do and how you will judge it. Start
- 2 Building the test set Assemble real examples that represent typical and difficult cases. Start
- 3 The scoring rubric Design a simple rubric that measures quality, effort and failures. Start
- 4 Running the trial Test all tools the same way, with real users involved. Start
- 5 Data and terms during trials Know what data you may use and how trial terms differ. Start
- 6 From scores to decision Write a short comparison summary that supports a clear choice. Start
Try asking
About this tutor
For managers, IT staff and team leads choosing between AI tools for a specific job. Demos are designed to impress, and a tool that shines on a vendor's examples can stumble on your documents, your jargon and your edge cases. You will learn to plan a short, structured trial: define the job and success criteria, build a test set from your own real examples, score tools side by side with a simple rubric, include the awkward cases, involve the actual users and record review effort and failures, not just best outputs. We also cover what data you may safely use in a trial. You finish with a trial plan, a scoring sheet and a comparison summary ready for a decision.
Reviews
4.3
3 ratingsSample
- Petra N.Sample
Blind scoring changed our result. The tool everyone liked in the demo made more serious mistakes on our scanned invoices.
- Leila H.Sample
Good on data rules during free trials, which we had ignored. I would have liked a template spreadsheet, but making one was quick.
- Samuel O.Sample
Practical method that fit in three weeks. The minutes to fix measure was the most persuasive number for our finance director.
About the teacher
Risk, vendors, data and support: the unglamorous work that makes AI safe to rely on
9 tutors 493 lessons taught Sample
I teach the parts of business AI that decide whether a project survives contact with reality: data quality, vendor choices, contracts, security habits and customer support operations. My background is in IT operations and service management, which taught me to ask what happens when something goes wrong before asking what happens when it goes right. I teach with checklists, worked...
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