Conversion Experiment Governance for AI product companies: Risk Register Template

Conversion experiments in an AI product can change more than a button or headline. A new onboarding prompt may alter what users disclose, how a model is evaluated, which outputs are trusted, how support volume arrives, and which customers enter a paid plan. A risk register gives the team a shared place to record those dependencies before an experiment is exposed to a larger cohort.

What decision does the register control?

State whether the team is deciding to design, instrument, launch, expand, hold, modify, or roll back an experiment. Name the product surface, user cohort, geography, model or workflow involved, exposure window, budget owner, and decision date. “Increase conversion” does not specify what can be changed safely.

Write the success and stop conditions together. A treatment can improve a click rate while increasing unsafe disclosures, support burden, refund requests, or unqualified activation. Preserve the ability to return to the previous experience and identify who can make that call.

What is the experiment hypothesis?

Separate user problem, intervention, expected mechanism, measurable outcome, and decision rule. A hypothesis might say that clearer task examples help qualified evaluators reach a useful first result; it should not quietly assume model accuracy, user authority, or commercial readiness.

Record the counterfactual and excluded outcomes. Decide which user groups are ineligible, which model responses are out of scope, and what evidence would disconfirm the mechanism. Avoid a post-hoc story built from whichever metric moved first.

Which AI boundary is affected?

Name model, version, provider, retrieval source, tool call, moderation layer, prompt, human review, and fallback. Record whether the experiment changes input collection, output display, ranking, pricing, access, or escalation. The same interface change can have different risk depending on whether a human verifies the result.

The NIST AI Risk Management Framework offers a useful vocabulary for trustworthy design, development, use, and evaluation. It is voluntary guidance, not a certification, product guarantee, legal conclusion, or evidence that a particular model is safe for every context. Map the relevant questions to the actual experiment.

Who may be affected?

List direct users, reviewers, administrators, support staff, customers whose data appears in context, downstream recipients, and people who may be excluded or misclassified. Add accessibility, language, age, geography, role, and account-plan boundaries where material.

Describe the user-visible consequence of a failure. It may be a confusing answer, a misleading confidence cue, an unintended disclosure, a blocked task, a repeated charge, a missed escalation, or a support queue that cannot respond. Keep hypothetical harm separate from an observed incident.

How are likelihood and impact made comparable?

Define likelihood over a named exposure and time window. Define impact across user harm, privacy, security, financial loss, reliability, trust, support effort, and recovery cost. Keep a confidence field so a high score based on sparse evidence does not look identical to a high score supported by a monitored pilot.

The NIST Information Quality Standards can help reviewers ask whether evidence is useful, objective, integral, contextual, and correctable. It does not score an experiment or establish causal impact. Store source, population, reviewer, timestamp, exclusions, and correction route beside each claim.

What evidence is required before exposure?

Specify unit of analysis, assignment rule, sample boundary, instrumentation, logging, exclusion, data retention, reviewer, minimum evidence, and decision date. Test eligible, ineligible, duplicate, missing-context, error, abuse, opt-out, and rollback paths with synthetic records before using real traffic.

Separate product analytics from user-reported usefulness, human quality review, safety incidents, commercial progression, and support outcomes. A positive metric cannot erase a severe incident or an unresolved measurement break.

Which claims can appear in the experiment?

Maintain a claims ledger for model capability, accuracy, speed, privacy, security, integrations, savings, productivity, customer results, and suitability. For each claim capture source, scope, date, permission, reviewer, limitation, and expiry. Label illustrative examples so they cannot be mistaken for observed performance.

Use the FTC advertising and marketing guidance as a general prompt for truthful, supportable copy. It is not global legal advice, an AI safety standard, or proof of a conversion result. A product team still needs the relevant legal, privacy, security, and commercial review.

How are privacy and consent protected?

Map every input, output, event, identifier, audience, vendor, retention rule, access role, deletion path, correction path, and regional condition. Distinguish product telemetry from model training, marketing audience use, customer support, and abuse investigation. Do not collect a sensitive field merely because a treatment makes it easy.

The NIST Privacy Framework helps structure questions about purpose, control, communication, protection, and correction. It does not grant permission or replace a data-protection assessment. Test withdrawal, deletion, redaction, wrong-account access, stale suppression, and an operator with excessive rights.

Which operational handoffs can break?

Trace experiment assignment, model event, alert, human review, support ticket, billing state, and product rollback. Name the first responder, backup, response window, escalation path, user notification owner, and handback condition. Keep model version and treatment ID visible after the record leaves analytics.

The GOV.UK Service Standard is a useful prompt for user needs, joined channels, privacy, success measures, and reliable operation. It is not an AI experimentation standard. Adapt the questions to the product’s actual support and delivery model.

What capacity limit should pause rollout?

Estimate review volume, support contacts, trust-and-safety escalations, refund work, latency, inference cost, human labeling, and incident response. Name the maximum queue, age, and specialist dependency that can be accepted without harming existing users.

Create automatic or manual holds for missing logs, rising error rates, unreviewed sensitive inputs, unsupported claims, payment anomalies, model drift, a growing exception list, or an unavailable rollback owner. Scaling exposure should be a decision, not a default after a timer expires.

How should review and rollback work?

Set daily monitoring for critical guardrails, weekly review for quality and support, and a defined decision checkpoint for expansion. Record what evidence closes, reduces, transfers, accepts, or escalates a risk. Preserve treatment configuration, assignment data, model version, prompt, event definitions, and the previous stable experience.

When a stop rule fires, pause exposure, protect affected users, capture the evidence, notify owners, and restore the known-good path. Do not delete the experiment record; the decision history is part of the learning and correction trail.

What belongs in the template?

Use fields for risk ID, experiment ID, hypothesis, treatment, user cohort, AI boundary, input and output data, trigger, likelihood, impact, confidence, evidence, owner, approver, mitigation, guardrail, privacy boundary, capacity limit, review date, status, escalation path, and rollback condition.

Keep this article as a local noindex draft until product, source, privacy, security, accessibility, overlap, canonical, implementation, and owner checks are complete. The register supports a reversible decision; it does not guarantee conversion, model quality, safety, compliance, or revenue.