How to choose an AI writing assistant for a team

Test the same real briefs with prohibited-data rules and a human acceptance rubric. Measure correction time and factual defects; generation speed alone is not productivity.

Decision criteria

CriterionWhat to verifyDecision signal

Task benefitDefine the specific drafting, editing, summarizing, or classification job and compare accepted output per reviewer hour.Required for the shortlist

Data controlsVerify retention, training use, workspace access, deletion, subprocessors, administrative controls, and handling of confidential inputs.Required for the shortlist

Factual safetyRequire source checking, uncertainty labels, claim review, prompt-injection resistance, and a block on autonomous publication.Verify before purchase

Governance and exitDocument approved uses, reviewers, audit needs, budget limits, export, model changes, and a non-AI fallback.Verify before purchase

Total-cost model

Seats or tokens + retries + reviewer time + security/legal review + integration + monitoring + model-change and exit risk.

Skip this if

Skip deployment when outputs can reach customers or indexed pages without accountable human verification and claim evidence.

Proof-of-fit checklist

Choose three representative briefs
Define prohibited inputs
Score factual and editing defects
Test source verification
Measure accepted output per hour
Document review and shutdown controls

What changes the answer?

Team size, workflow volume, regulated data, integration depth, support expectations, and switching cost can change the shortlist. Re-run the decision after any of those constraints changes.

Reference standard

NIST AI Risk Management Framework

Last methodology review: 11 August 2026. No vendor claim or affiliate offer is active on this page.