Evaluate an AI tool
AI Tool Evaluation Framework
The AI Tool Evaluation Framework is a method for assessing any AI marketing product on twelve criteria, from use-case fit and output quality to privacy, scalability and total cost, so that tools are compared on the same scale.
The model
- Use-case fit
- Output quality
- Reliability
- Ease of use
- Integrations
- Automation
- Data and privacy
- Team collaboration
- Scalability
- Support
- Pricing structure
- Total cost
Developed by Markaigen · Updated
What each part means
- Use-case fit
- Does the tool solve your specific task, or a general version of it?
- Output quality
- Accuracy, usefulness and consistency of what it produces for your real inputs.
- Reliability
- Uptime, speed and how often results vary without reason.
- Ease of use
- How fast a colleague becomes productive without training.
- Integrations
- Native connections to the systems you already use, and an API if you need one.
- Automation
- Whether it can run steps without a person, with limits and approvals.
- Data and privacy
- Where data goes, whether it trains models, and whether a data processing agreement is available.
- Team collaboration
- Shared workspaces, roles, prompts and assets.
- Scalability
- Whether it still works at ten times the volume, users or markets.
- Support
- Documentation, response times and a human when something breaks.
- Pricing structure
- Seats, usage credits or outcomes, and how predictable the bill is.
- Total cost
- Licence plus set-up, training and the time spent fixing output.
How to use it
- Test each tool with the same three real tasks before you score it.
- Weight the criteria before you see the scores, so the weights reflect your needs and not the winner.
- Keep the matrix: it becomes your record when the contract comes up for renewal.
Build an evaluation matrix
Name up to three tools, set a weight per criterion and score each tool from 1 to 5. The matrix shows weighted totals and the strongest and weakest criteria per tool.
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Download or e-mail this framework
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