Evaluating AI software well is the difference between a tool that pays for itself and one that becomes expensive shelfware. A simple scorecard keeps the decision honest and comparable across products - and stops a slick demo from doing your thinking for you.
The evaluation scorecard
Score each tool 1–5 on:
- Fit - does it solve a real task you actually have?
- Evidence - can it beat your baseline on a trial with your data?
- Accuracy - how often is it wrong, and can a human catch it?
- Data safety - where does your data go, and is it protected?
- Integration - does it fit your workflow or demand a rebuild?
- Cost (fully loaded) - licences plus people, data and oversight
- Portability - can you export your data and switch later?
- Support - is help there when it breaks?
Add them up and compare - the highest total, not the best demo, wins.
Always trial before you buy
A staged demo tells you little. Insist on a short trial on your own task, measured against a baseline - the practical test of how to know which AI tools actually work.
Tie it back to strategy
A high-scoring tool that serves no priority is still a distraction. Evaluate against what is in your AI strategy, not in isolation.
The bottom line
Evaluate AI software on a fit-and-evidence scorecard, trial it on real work, and tie it to strategy - so you buy value, not hype. Learning to run that evaluation is part of the Strategic Application of AI in Business course at London School of Business UK. Enquire today.