// 01
What is an AI impact review?
An AI impact review checks whether a proposed workflow has a clear purpose, defined users, known data risks, review gates, measurable controls, and an accountable owner.
Practical AI Impact
AIRIG evaluates AI impact through adoption readiness, workflow clarity, data risk, measurable controls, and the ability for people to review AI-assisted work.
AIRIG impact monitor
LIVE
OKAdoption readinessready
OKWorkflow claritymapped
OKData riskscoped
OKMeasurable controlsdefined
OKReviewabilityrequired
Areas
3
Practices
3
Review state
Open
// 02A - Impact evidence
Impact reviews turn adoption risk into inspectable evidence.
AIRIG reviews use cases through practical evidence rather than broad claims, so teams can decide what is ready to test or scale.
Readiness
Measured controls
User feedback
Decision record
Impact evidence board
REVIEWED
Inputs
Scoped
Review
Human
Record
Trace
// 01 - Impact review areas
Impact Review Areas
We frame AI impact around practical evidence that can be reviewed with the product, operations, legal, and risk teams adopting the system.
// 01 - READINESS
Operational Readiness
Review whether data, teams, approvals, and support processes are ready for AI-assisted work.
> readiness.check :: scoped
// 02 - QUALITY
Reviewable Outputs
Assess whether outputs include enough context, source material, and reasoning for human review.
> output.reviewable :: true
// 03 - ADOPTION
Operator Feedback
Gather user feedback on whether the workflow is useful, understandable, and appropriate.
> adoption.signal :: collected
// 02 - Responsible AI practices
Responsible AI Practices
AIRIG focuses on practical controls that make AI use easier to inspect, govern, and improve.
// 01 - REVIEW
Decision Boundaries
Define which AI outputs can inform decisions, which need human review, and which uses are out of scope.
> boundaries.set :: approved
// 02 - SUSTAINABILITY
Efficient Use
Choose model size, runtime, and workflow design based on the task instead of defaulting to heavier systems.
> runtime.rightsize :: active
// 03 - HUMAN FACTORS
Human Review
Design AI workflows so people can understand, correct, and improve the work they oversee.
> reviewer.loop :: visible
// 03 - Impact FAQ
Questions before an AI workflow scales.
Use these answers to frame impact reviews around scope, evidence, and responsible rollout decisions.
// 02
When should a team run an impact review?
Run an impact review before a pilot expands, before sensitive data is introduced, or whenever an AI output could influence operational, legal, clinical, or customer-facing decisions.
// 03
What evidence does AIRIG look for?
AIRIG looks for scoped use cases, data handling notes, test outputs, reviewer feedback, risk decisions, control owners, and records that show how the workflow will be governed.
// 04 - Review cycle
Plan an Impact Review
Work with AIRIG to define a scoped review of AI opportunities, risks, and practical adoption steps.