How to Evaluate Any AI Tool in One Afternoon: A Scorecard

You can evaluate an AI tool in one afternoon if you split it into five timed blocks: define the job, read the data terms, test on real work, price it at your volume, and check how you would leave. This post gives a 100-point scorecard for that. The weights are mine. The facts behind the data and change-risk sections come from vendor documentation I read on 13 November 2026.

Key Takeaways

• This is about the tool, not the model. If you want to compare models on writing quality, use the test protocol in how to evaluate AI models for marketing. A tool adds data handling, admin, pricing and lock-in questions on top.
• Data promises differ by product tier. Anthropic's data retention page says zero data retention does not cover its consumer plans or its Teams and Enterprise product interfaces. OpenAI's data controls page covers API data. Check the tier you will actually use.
• "Not used for training" and "not retained" are different promises. OpenAI's page says API data is not used for training unless you opt in, yet abuse monitoring logs are kept for up to 30 days unless an approved zero-retention arrangement applies.
• Change risk is real and documented. Anthropic's deprecations page lists a Sonnet model retiring on 30 November 2026 and sampling parameters removed in API version 2026-10-21.
• NIST's AI Risk Management Framework is voluntary, and its generative AI profile (NIST AI 600-1) was released on 26 July 2024. It is useful vocabulary for risk questions, not a certification.
• Run the afternoon with a pre-agreed kill criterion, so a flashy demo cannot overrule a failed requirement.

The afternoon plan

Before you start, pick the tool tier you would actually buy. Free, team and enterprise tiers often have different data terms, so evaluating one and buying another proves little.

Block 1: Define the job and the kill criteria

Write one sentence: "This tool will do X for Y people, replacing Z." Then three pass criteria that can be checked (for example, "summaries need no more than five minutes of editing") and two kill criteria that end the evaluation immediately (for example, "cannot opt out of training on our data", "cannot export our work").

Anthropic's success criteria guide says good criteria are specific, measurable, achievable and relevant. The same applies to tools.

Block 2: Paper checks

Read the vendor's own pages, not a reviewer's summary. For each item, save the URL and the date.

Data handling

Ask these questions and write the vendor's answer, in its words, with a link:

• Is our content used to train models by default, and can we turn that off?
• How long are prompts and outputs retained, and does that differ by feature?
• Is there a zero-retention option, who qualifies, and what does it exclude?
• Where is stored data held, and can we choose a region?

Two real examples show why this matters. Anthropic's page says conversation content is not retained by default on its API, with an exception for certain "Covered Models" that require 30-day retention, and that retained data is not used for training without express permission. It also lists what zero data retention does not cover, including the Console, Managed Agents, consumer plans, and Teams and Enterprise product interfaces. OpenAI's page says API data is not used to train models unless you opt in, abuse monitoring logs are kept up to 30 days, and zero data retention or modified abuse monitoring needs prior approval. It also says the Responses API keeps application state for 30 days by default, and that files, vector stores and other objects are stored until deleted.

A headline promise often has tiers or features outside it. Find the exclusions.

Change risk

Vendors change models and parameters. Anthropic's deprecations page says it gives at least 60 days' notice before retiring publicly released models. As of 13 November 2026 it lists claude-sonnet-4-5-20250929 as deprecated on 30 September 2026 with a retirement date of 30 November 2026, and says temperature, top_p and top_k were removed in API version 2026-10-21. Google's Gemini prompting guide says newer Gemini versions ignore custom values for the same settings and that future releases will return errors. OpenAI's prompt guide says it is deprecating reusable prompt objects and recommends keeping production prompts in your own code.

Questions to ask any tool vendor: how much notice do you give before changing a model the tool depends on? Can we pin a version? Do you tell us when the underlying model changes?

Risk vocabulary

NIST says its AI RMF is for voluntary use and was being revised as part of the White House AI Action Plan when I read the page. A marketing team does not need to adopt it, but borrowing its questions (what could go wrong, who is affected, how would we notice) gives your security or legal reviewer a common language.

Block 3: Real-task test

Use ten real inputs, the same ten for every tool. Include two awkward ones. Have two people score each output independently on your Block 1 criteria, then compare. Record editing time with a stopwatch, because "looks good" is not a measurement. If the tool acts on its own (publishes, sends, edits), disable those actions for the test and read the proposed actions instead; the agent-safety checks are in AI agent safety: least privilege and run logs.

Block 4: Cost and exit

Work out cost at your real volume, not the plan headline. Count seats, usage limits, overage rules and any add-ons you need for data controls. Pricing changes often, so record "as of" with the date you looked.

For exit, do two things in the trial: export a real piece of work, and open the export in another program. Then answer: if we leave in a year, what do we lose (history, templates, trained settings), and what do we keep? Keep your prompts and templates in your own files, not only inside the tool.

The scorecard

Weights are my judgment. Change them to match your risks, but set them before you test.

A suggested rule: 75 or more is a candidate, 60 to 74 is a conditional pilot, under 60 is a no. Any kill criterion overrides the total.

A hypothetical example

This is an invented illustration, not a review of a real product. A team compares two imaginary tools, "Tool A" and "Tool B", for summarizing sales-call transcripts into follow-up emails.

Tool B writes slightly better emails but cannot say how long transcripts are kept and has no export. The scorecard shows why the better demo does not win. In practice, Tool B would also trip a kill criterion if "documented retention" was on the list.

What this means for marketers

• This week: score the one AI tool your team already pays for. People are often surprised by how many data and exit questions they cannot answer.
• Do the paper check before the demo. Demos show the best case, while terms show the constraints.
• Match tier to use. Do not paste client data into a tier whose data terms you have not read.
• Keep portable assets. Prompts, examples and test sets in your own storage make switching tools a few days of work instead of a rebuild. My AI marketing stack audit and solo agency tool list show the stack-level view, and are AI visibility tools worth it applies a similar skepticism to one category.
• Leave alone: tools that pass and are working. Re-score once a year or when terms change.

Common mistakes

• Evaluating the free tier and buying the enterprise one, or the reverse.
• Treating "we don't train on your data" as meaning "we don't keep your data."
• Letting a vendor's sample inputs stand in for yours.
• Skipping the export test.

Caveats

Vendor terms change, and I read only public documentation, not contracts. I have not tested any commercial marketing tool for this post, so the scorecard weights and thresholds are my conventions. The OpenAI details come from its developer page; third-party summaries I saw were less reliable and I did not use them. For regulated data, ask your legal team to read the actual agreement.

FAQ

How do I compare two AI tools quickly?

Fix the job and weights first, run ten identical real inputs on both, read each vendor's data and deprecation pages, then score both on the same sheet. Four hours is realistic for two finalists if you prepare the inputs in advance.

What should I check about an AI tool's data policy?

Whether your content trains models by default, how long prompts and outputs are retained, whether a zero-retention option exists and what it excludes, and where data is stored. Check the specific tier you will buy.

Is "not used for training" the same as zero retention?

No. OpenAI's API documentation says data is not used for training unless you opt in, but abuse monitoring logs are kept for up to 30 days unless an approved arrangement changes that. Anthropic's documentation likewise separates retention from training.

How much should I worry about vendors retiring models?

It depends on how much your workflow depends on a specific model. Anthropic documents at least 60 days' notice and lists dated retirements. If you keep prompts and tests in your own files, a retirement is a re-test task, not a rebuild.

Do I need a formal AI risk framework?

Not for most marketing tools. NIST's AI RMF is voluntary and can give your reviewers shared vocabulary. A short scorecard with documented answers is a reasonable starting point.

What is a kill criterion?

A requirement that ends the evaluation if it fails, regardless of score. Examples are "cannot opt out of training", "no export" or "cannot delete our data". Set them before you see any demos.

Want help with this?

I'm Younus Fardeen, an organic growth, SEO and AEO strategist with 4+ years of marketing experience across edtech and startup teams. If you want help choosing and governing AI tools for your marketing stack, see my work and reach me through the contact form at younusfardeen.com.