Study first
Review the ideas behind the questions
Review the checks before an AI tool uses customer data or shapes real marketing work. Focus on vendor due diligence, contract terms, purpose-specific data, accuracy measures, reviewer authority, and privacy commitments.
Review The Tool Before The Team Adopts It
An AI tool review should happen before customer data, campaign processes, or decision support move into the tool. The review should cover accuracy, bias, data source, contracts, and ownership.
- Before procuring AI systems, datasets, or coding, teams should do due diligence on accuracy, bias, and design tradeoffs.
- Contracts or agreements should formally document the relationship between the organization and the AI provider or third party.
- Privacy and confidentiality commitments matter because AI data practices can be deceptive when companies do not honor what they promised.
In Practice
Ask Before Connecting Data
Before a tool is used with campaign, customer, or sales data, confirm who processes the data, why it is needed, how long it is kept, and what promises were made.
Treat Claims As Evidence Requests
If a vendor claims strong accuracy, bias controls, or privacy protections, ask for the evidence and contract terms that support the claim.
Common mistakes
Approving a tool because the demo output looks useful while contracts, data use, and accuracy evidence are still unclear.
Pause adoption until due diligence covers accuracy, bias, data source, relationship terms, and privacy commitments.
Q&A
What should an AI tool review cover before procurement?
Cover accuracy, bias, design tradeoffs, data source, processing roles, contract terms, and privacy commitments.
Why check privacy promises during tool review?
Because the team needs to know whether the actual data practice matches what users, customers, or admins were told.
Minimise Data And Document Tradeoffs
More data can make some AI systems more useful, but it can also increase privacy risk. Intermediate review means asking which data is actually needed and documenting tradeoffs instead of collecting everything.
- The key data-minimisation question is whether only the personal data needed for the purpose is processed.
- AI procurement due diligence should include how accuracy, bias, and design tradeoffs were considered.
- A DPIA should describe tradeoffs such as statistical accuracy versus data minimisation and document the methodology and rationale.
In Practice
Purpose Comes First
Ask which campaign, research, or segmentation decision the AI workflow supports before adding more customer attributes.
Document The Reason
If a team keeps a data field to improve useful accuracy, record why the field is needed and what risk controls apply.
Common mistakes
Adding every customer attribute because the model may find a pattern later.
Define the purpose, test whether less data is enough, and document any accuracy-versus-minimisation tradeoff.
Q&A
Can a team collect extra personal data just in case it improves AI output?
No. It should connect each personal-data use to the purpose and document any tradeoff with accuracy or utility.
What belongs in the governance note for a data tradeoff?
Record the purpose, the accuracy need, the minimisation risk, the method used to compare options, and the reason for the chosen data.
Make Accuracy And Human Review Meaningful
Approval is not meaningful if reviewers lack time, training, independence, or error evidence. Tool governance should define the accuracy standard and give reviewers a real way to challenge the output.
- An AI system that makes inferences about people needs to be sufficiently statistically accurate for its purpose.
- Overall statistical accuracy is usually not enough on its own; teams should measure and prioritize the right accuracy measures.
- Human reviewers should have appropriate qualifications, manageable caseloads, training, independence, and the ability to influence senior decision making.
In Practice
Pick The Right Accuracy Measure
For a churn-risk workflow, the useful question is not whether the average accuracy sounds high. Ask whether the error pattern fits the business risk and affected audience.
Reviewers Need Power
A reviewer who cannot pause the workflow, challenge the output, or influence the decision is not a strong control for risky AI use.
Common mistakes
Calling the workflow human-reviewed when one busy teammate rubber-stamps outputs after launch.
Define reviewer qualifications, time, training, independence, override logging, and a fallback path for serious issues.
Q&A
Why is one headline accuracy number weak evidence?
It may hide the errors that matter most, so the team should choose measures that fit the purpose and decision risk.
What makes human review meaningful?
Reviewers need knowledge, time, independence, training, logs, and authority to challenge or override the AI-assisted decision.