Is AI right for this problem?
Define the customer or business decision AI should support, who it affects, what could improve, and whether AI is the right tool.
Diagnostic 04
Know if your AI use case is ready before you build it.
The AI Service Readiness Review tests the use case, workflow, data, human oversight, governance, adoption needs, and success measures. You’ll get a clear decision to proceed, fix the gaps first, narrow the idea, or stop.
Questions answered
Turn AI interest into a clear decision.
We start with the customer and business problem. Then we check whether the right conditions exist before you compare platforms or plan automation.
Questions this diagnostic answersView questions
Is the workflow ready?
Review the current work, ownership, exceptions, data, and decision rules to see whether the process is clear enough for AI support.
Where do people stay in control?
Define where people must review, approve, correct, override, or escalate AI-supported work and who remains accountable for the outcome.
How will you know it works?
Set the evidence, adoption needs, performance measures, risk signals, and review points required before the work expands.
Scope and evidence
Check the work before you automate it.
The evidence depends on the decision and what your team can share. I compare the proposed AI behavior with the real workflow, available context, human judgment, risks, and measures needed to run it well.
Evidence we reviewView evidence
Use-case and decision fit
The customer problem, business decision, intended users, expected value, other options, and whether AI fits the level of risk.
Workflow and process readiness
Current steps, handoffs, decision rights, exceptions, service rules, recovery paths, and places where the work is still unclear or inconsistent.
Data and knowledge dependencies
Required records, knowledge sources, data quality, access, age, source, privacy limits, and the context people use to judge a response.
Human oversight and exceptions
Review points, approval limits, escalation paths, override authority, quality checks, and the work people still own.
Governance, risk, and trust
Acceptable-use limits, security and privacy needs, customer disclosure, audit records, accountability, failure impact, and stakeholder concerns.
Adoption and measurement
User readiness, training, workflow fit, success measures, risk indicators, feedback paths, and the evidence needed to continue, change, or stop.
Deliverables and decisions
Get a readiness decision and a responsible path forward.
You’ll get more than a list of AI risks. Each deliverable shows whether the use case should proceed, what must shape it, and which evidence should guide any expansion.
Readiness decision
An evidence-based answer on whether the use case is ready, needs operating changes first, should be narrowed, or should stop.
Supports the decisionWhether to proceed, fix the missing conditions, reshape the use case, or choose another approach.
Human-in-the-loop map
A clear view of where AI can help, where human judgment is essential, how exceptions move, and who has authority and accountability.
Supports the decisionWhich decisions AI can support, which need approval, and how people respond when the system is unsure or wrong.
Operating constraints and guardrails
Documented workflow, data, knowledge, governance, privacy, quality, and trust needs that must shape the implementation.
Supports the decisionWhich boundaries and controls must be in place before the use case can operate responsibly.
Sequenced implementation priorities
A practical order for operating changes, a focused pilot, adoption support, measurement, review, and any justified expansion.
Supports the decisionWhat to change first, what the first release should include, and what evidence should guide the next investment.
Engagement fit
Use this review before AI becomes a build commitment.
A useful review needs access to the people, workflow, data, knowledge, and risks around the use case. Your organization must also be open to the answer that AI is not the right move yet.
Strong fit
This is likely a good fit when
- An AI proposal has support, but the workflow, governance, adoption needs, or success measures are still unclear.
- The use case affects customer service, success, or operations across teams, systems, or decisions.
- A sponsor can involve process owners, frontline users, data and knowledge owners, risk partners, and technical teams.
- Your organization is willing to delay, narrow, or reshape the use case when important conditions are missing.
Limited fit
Another path may work better when
- You only need vendor selection or implementation of a chosen solution, with no readiness decision.
- AI is expected to cover for a broken workflow without changing ownership, process, or decision rules.
- Process owners, frontline users, data or knowledge owners, risk partners, or accountable leaders cannot take part.
- Success is measured only by launch, use, or automation volume, not by useful work and customer outcomes.