How to evaluate AI marketing tools: a practical scorecard.

Describe the job in one sentence

Start with an outcome such as “produce a sourced account brief that a salesperson can check in five minutes.” Avoid defining the requirement as a long list of AI features.

Record your existing process, the people involved and the points where it fails. That gives you a useful baseline for evaluating an alternative.

Write the scorecard before the trial

Agree on what acceptable work looks like before inspecting outputs. Use the same inputs across candidates and retain the raw outputs alongside your review.

• Accuracy: are the important claims supported?
• Completeness: does the output answer the brief?
• Usability: how much correction is required?
• Workflow fit: can the output move into the next step?
• Cost and limits: which plan, usage allowance and human effort does the task require?

Include awkward cases

Try a typical case, a sparse-input case and one that is likely to expose a limitation. Record how the tool handles uncertainty and missing information. A plausible-looking answer can still be an unsuitable output.

Record the product, plan, date, settings and time spent reviewing. A trial on one plan or a handful of examples cannot establish performance for all users.

Write a conditional recommendation

Your conclusion should say which task the tool suits, where it falls short and what still needs checking. If the evidence is limited, keep the recommendation limited too.

This is an evaluation framework, not a review or ranking of a specific product. Google’s review guidance likewise encourages evidence, useful comparisons and explanations of benefits and drawbacks.