Evaluation
how to measure if you have met your goals
This card covers how you measure whether the tool met its goals. Without a way to check, it is easy to assume a tool works because it was built. Good evaluation looks at real use and real output, not just whether the tool runs without errors.
Questions to explore
- What would you measure to know if the tool is actually helping?
- How will you compare the work with the tool against the work without it?
- What does success look like in numbers, and what only shows up in conversation?
- How often will you check, and who looks at the results?
- What result would tell you to change or retire the tool?
Expert voices
“Assess whether AI tools actually improve journalism with metrics beyond efficiency: quality, accuracy, and audience impact.”
“Decide at what point you will review and evaluate the impact of AI on your work.”
“Set realistic testing periods, typically one to three months, with clear success metrics. Give the evaluation enough time to get past the learning curve and show real production impact.”
“Test AI systems before newsroom adoption, not after. A shared testing grid turns scattered impressions into comparable evaluations.”
Things to consider
- A tool that runs is not the same as a tool that helps.
- Decide how you will measure success before you launch.
- Some effects show up in how people work, not in the metrics.
Pull Evaluation when it is relevant and set it aside when it is not. Pair it with the other AI Solutions cards, lay them out on a table, and use the questions above to get everyone on the same page. Capture what you discuss on sticky notes or in a shared doc.
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