AI content tools: selection checklist explained

What to take away

For AI content tools, a selection checklist is a written set of pass or fail tests that a tool or provider must clear before you shortlist it.
Score each candidate against your own criteria, then keep the evidence in one file so the decision survives a later review.
Check data protection, electronic marketing rules and AI standards before you sign anything.
Use a decision table to match the buying situation to the right option, and record what you deliberately avoided.
Re-run the checklist when pricing, ownership or model access changes, and keep the old version on file.

Set the pass or fail tests

Start with the tests that end a conversation. A tool that cannot export your content in a usable format fails, however good the demo looks.

Pass or Fail Tests

Can editors see who approved a draft?
Can you delete client data on request?
Does the supplier publish a change log?
Capabilitycovers the work itself
Evidenceproof the tool performs as claimed
Compliancedata protection and consent
Commercial termspricing and contract

Group the tests into four areas: capability, evidence, compliance and commercial terms. Capability covers the work itself. Evidence covers proof that the tool performs as claimed.

Use these 15 pass or fail tests. Set any required thresholds before suppliers respond, then record the evidence for each result.

Core workflow:Run the required content task using your own material. Pass only if the output and approval steps meet your stated requirements.
Export:Test an export. Pass only if it produces a usable format and retains the metadata you need.
Integration:Test the relevant CMS, taxonomy and metadata workflow. Pass only if it works without losing required information.
Access and approvals:Check user roles and approval controls. Pass only if they support your access and sign-off requirements.
Trial:Use a sandbox or trial account. Pass only if your team can test the relevant workflow on representative content.
Performance evidence:Ask how the supplier evaluated the tool and what its limitations are. Pass only if the method and limitations are documented clearly enough for your team to assess.
References:Request two named references you can contact directly. Pass only if the supplier provides them and you can verify relevant experience.
Reporting:Inspect usage, cost and quality reporting. Pass only if the supplier can demonstrate the reporting you need.
Processing roles:Request the data-processing terms and identify the parties handling your data, including subprocessors. Pass only if the responsibilities are clear.
Data location and transfers:Ask where data is stored and whether it is transferred internationally. Pass only if the supplier explains the applicable safeguards.
Retention and rights:Get retention, deletion and data-subject request processes in writing. Pass only if they meet your requirements.
Marketing and cookies:Where relevant, check how the tool handles consent records for electronic marketing and cookies. Pass only if the process supports your compliance duties.
AI framework evidence:Ask which recognised framework or standard the supplier uses and request evidence of how its controls map to it. Pass only if the claims are specific and substantiated.
Commercial terms:Check the licence basis, usage limits, overage charges, price-change provisions, renewal and termination terms. Pass only if the terms are clear and acceptable; record the actual figures in your comparison.
Ownership and exit:Check rights to inputs and outputs, whether your data may be used for model training, support commitments, and export or deletion at termination. Pass only if the contract answers each point to your satisfaction.

Name one owner for the checklist. A single named owner keeps the tests consistent when two teams score the same supplier against different expectations.

Keep the list short. Twelve to fifteen tests is enough for most teams. A longer list slows the shortlist without improving it.

Evidence you should ask for

Ask for a sandbox, not a slide deck. A trial account on your own content shows more than a recorded walkthrough.

Evidence to Request

Sandbox trial on your own content
Two named references you can contact
Handling of existing taxonomy and metadata
Monthly usage, cost and quality reporting
Fail if references cannot be provided

Request two named references you can contact directly. If the supplier cannot provide them, treat that as a fail on the evidence test.

Check how the tool handles your existing taxonomy and metadata. Most migration problems appear here, not in the drafting features.

Ask how the tool reports usage, cost and quality over a month. A supplier who cannot show you the reporting view before purchase is unlikely to provide one afterwards.

For a fuller scoring model across several suppliers, read AI content operations: reviews and comparison methods, which sets out how to weight criteria before you score anything.

Compliance checks before shortlisting

In the UK, personal data processing is governed by the UK GDPR and the Data Protection Act 2018, with the Information Commissioner's Office (ICO) as the data protection regulator. The Privacy and Electronic Communications (EC Directive) Regulations 2003 (PECR) cover electronic marketing and rules on cookies and similar technologies. Check which rules apply to your use case, and ask where relevant consent records live and how they can be exported.

For personal data, check the supplier's processing terms, the purpose of processing, retention period, deletion route and arrangements for handling data-subject requests. Ask where data is stored and how any international transfers are safeguarded. Keep the answers in writing; a verbal assurance is not evidence.

Ask how the supplier evaluated its model and which recognised framework or standard it uses. Examples include the NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001 and ISO/IEC 23894. Ask for evidence of how the supplier's controls map to the framework it names; alignment is not the same as certification. The NIST AI Standards page is a reference point when assessing a supplier's claims.

For marketing practice benchmarks, the CIM articles and reports library carries practitioner guidance you can cite in your own selection paper.

Score and document the choice

Score every shortlisted option against the same tests, using the same scale. A 1 to 5 scale can work if you define it: 1 means fails the requirement; 2 means major gaps remain; 3 means the minimum requirement is met, with documented caveats; 4 means the requirement is fully met with evidence; and 5 means the tool exceeds the requirement with strong evidence. A failed mandatory pass or fail test disqualifies a candidate regardless of its total score. Weight the tests where some matter more than others, and note the weighting in the same file.

Record the reason for each score in one line. A score without a reason cannot be defended six months later.

Keep a decision log with dates, names and the evidence reviewed. If you use a formal review process, AI content operations review methodology explains how to structure the scoring and the audit trail.

Match the option to the situation

Use this table to pick the right route rather than defaulting to the biggest platform.

Choose

Small team, one workflow, tight budget: A focused tool with a clear export path
Regulated content or personal data: A supplier with documented consent and deletion processes
Multi-brand operation: A platform with role permissions and shared taxonomy
Fast pilot before renewal: A short paid trial with success measures agreed
Existing CMS you intend to keep: A tool with a tested integration

Avoid

Small team, one workflow, tight budget: Suites priced per seat with unused modules
Regulated content or personal data: Any tool that cannot show where data is stored
Multi-brand operation: Tools with a single admin account
Fast pilot before renewal: Annual contracts signed before the pilot ends
Existing CMS you intend to keep: Providers who insist on replacing the CMS

ChatGPT, Microsoft Copilot and Jasper are examples of named AI content tools you can assess; they are not recommendations. Apply the checklist to the exact product, plan and configuration you are considering, since features and terms can differ.

Common questions

How many suppliers should reach the shortlist?

Three to five is workable. Fewer than three gives you no comparison. More than five makes scoring inconsistent.

What kills a candidate fastest?

A failed data protection answer or a refusal to provide references. Both are hard to fix later, so treat them as pass or fail tests.

When should the checklist be updated?

When pricing changes, when a model provider updates terms, or when your own data protection duties shift. Review it at least once a year.

Do we need a legal review before signing?

For any contract involving personal data or marketing consent, yes. The checklist prepares the questions; it does not replace advice. Shortlist legal advice early if the contract runs for more than a year.