Is Your Business Ready for AI? A Practical Readiness Assessment
AI readiness is the ability to test and operate a defined use case responsibly. It depends on the business question, workflow, evidence, systems, people, governance and operating ownership rather than one organisation-wide maturity score.
Assess readiness against a specific use case
A business may be ready for one bounded AI application and unready for another. A low-consequence retrieval assistant has different evidence, integration and control requirements from automated credit decisions or production changes. Define the intended use before rating readiness.
Business readiness
The organisation needs a clear problem, intended user, outcome, baseline and accountable business owner. Leaders should understand what action will follow the AI output and why the change matters. A use case with no owner or measurable result is not ready for a pilot.
Workflow readiness
The present process should be observable enough to identify inputs, decisions, handoffs, exceptions and the completed outcome. It need not be perfect. The project may redesign it. However, the organisation must know where the AI task fits and how work continues when the system is uncertain or unavailable.
Evidence and data readiness
The required evidence should be accessible, sufficiently representative and permitted for the intended use. Definitions, lineage and outcome data matter more than raw volume. For generative AI, readiness also includes current source material, access control and a method for checking whether retrieval supports the answer.
Technology and integration readiness
The business needs a secure route to required systems, an environment for testing, identity and access controls, logging, monitoring and recovery. A pilot can use limited integration, but the team should understand what production operation would require before treating the pilot economics as representative.
People and capability readiness
Users need the time, authority and capability to participate in design, evaluate outputs and change their work. Technical teams need responsibility for data, integration and service operation. Risk, legal, security and domain specialists should enter early enough to shape the design rather than approve it at the end.
Governance readiness
The organisation should define intended and prohibited use, decision rights, risk tolerance, human oversight, documentation, incident response, vendor responsibility and change control. Governance can be proportionate to consequence. It should make accountable experimentation possible rather than reduce every use case to the same checklist.
Operating readiness
Someone must own performance after launch. The business needs measures, review frequency, escalation thresholds, rollback, continuity and a route for user or customer feedback. A solution is not ready for production if its project team disappears at deployment.
Turn gaps into an action plan
Classify each gap as required before testing, repairable during the pilot or required before production. Some gaps should stop the use case. Others define the work. The readiness assessment should conclude with owners, evidence and decisions, not a maturity label.
AI Capability and Team Design addresses roles and organisational arrangements. AI Evaluation and Assurance addresses evidence for scoped reliance.