Business fit
Is the outcome material, measurable and owned by someone who can make a decision?
AI Readiness & Opportunity Assessment
How ready is this AI opportunity to move forward?
AI readiness is not a single company score. It is the evidence that a specific use case has a meaningful business outcome, a defined workflow, authorized data, feasible integrations, measurable acceptance criteria, appropriate human controls and an owner who can operate the system after launch.
Technical feasibility
Can the required data, models, systems and interfaces support the workflow within real constraints?
Operating readiness
Are human authority, evaluation, monitoring, support and change ownership explicit?
Where to Start
Choose one workflow, not the entire enterprise.
Describe the trigger, inputs, users, systems, decisions, exceptions and completed outcome. Then establish the current baseline and identify the evidence gaps that could change the investment decision.
How to identify high-value AI use casesWhat Comes Next
Use the result to select the smallest responsible next step.
A defined opportunity may move directly toward a proof or build. A promising idea with material evidence gaps may need readiness work. A broad backlog may benefit from structured opportunity discovery. A weak candidate should be stopped or reframed.
When to use an AI proof of conceptDirectional Self-Assessment
Score the evidence behind one AI opportunity.
Before you begin: choose one workflow, use evidence rather than aspiration, and do not enter confidential information.
FAQ
AI readiness questions.
Is our company ready for AI?
Readiness is use-case specific. A company may be ready for one bounded workflow and not another. Evaluate business value, workflow definition, data, integrations, evaluation, human authority, risk and operating ownership for the opportunity you are considering.
Which AI use cases should we prioritize?
Prioritize use cases that combine material business impact with measurable work, accessible data, feasible integrations, manageable risk and accountable ownership. Do not rank ideas by theoretical ROI alone.
Is our data ready for AI?
Data is ready when the required information is authorized, sufficiently reliable, accessible through supported processes, and can be evaluated for the intended workflow. Different AI use cases may require different data readiness.
How do we determine whether an AI project is feasible?
Define the workflow and acceptance criteria, then test the assumptions most likely to invalidate the project: data access, model behavior, integrations, latency, cost, human controls and operating constraints.
How do we estimate AI ROI before building?
Start with a measured current-state baseline and model ranges for adoption, time reduction, quality effects, implementation cost and ongoing operating cost. Treat the output as a planning range, not a guaranteed return.
Start with the problem
Want a second opinion on the opportunity?
Tell us what you are trying to improve. We can help determine whether the next step should be discovery, proof, build or no AI project at all.