Readiness is not a single score
AI readiness is often treated as a technical assessment. Technology matters, but operational adoption depends on a wider set of conditions: a valuable use case, accountable leadership, usable data, clear risk decisions, prepared people and a way to measure change.
Canadian organizations also operate across different policy, privacy, security, procurement and accountability environments. A private company, public body and post-secondary institution will not apply identical controls. This checklist therefore does not prescribe legal conclusions. It helps leaders identify the questions that must be answered with their own privacy, security, legal, procurement and policy specialists.
Use it to compare candidate initiatives and expose work that must happen before—or alongside—a first release.
1. Business value and use case
- The problem is specific. Name the workflow, users, current constraint and desired outcome. “Use AI in customer service” is not specific enough.
- A baseline exists. Record current time, volume, backlog, rework, quality or effort so impact can be assessed.
- AI has a clear role. Identify why interpretation, generation or contextual reasoning is needed rather than conventional software alone.
- The consequence is understood. Classify what could happen if the system is wrong, delayed, unavailable or misused.
- The scope is bounded. A first release should address one end-to-end workflow, not an organization-wide platform.
2. Leadership and ownership
- An executive sponsor is accountable. The sponsor connects the initiative to an organizational priority and resolves cross-functional barriers.
- A business owner owns the outcome. The function responsible for the work must be able to redesign it and accept operational responsibility.
- Decision rights are explicit. Identify who can approve scope, data use, risk treatment, release and expansion.
- A cross-functional team is available. Include operational users, technology, data, privacy, security, policy and procurement expertise as the context requires.
3. Data, privacy and security
Readiness does not require perfect enterprise data. It requires clarity about the information a use case needs and whether the organization can use it appropriately.
- Sources and owners are known. Document where required information lives, its quality, currency and accountable owner.
- Access is proportionate. Users and systems receive only the data and capabilities required for the task.
- Data handling is reviewed. Confirm what may enter a service, where it is processed or retained, and what contractual and organizational controls apply.
- Sensitive cases are identified. Set separate handling, review or exclusion rules where information or outcomes carry greater consequence.
- Security review begins early. Threats, identity, logging, vendor access and incident response should influence design before procurement or launch.
4. Governance and human accountability
- The use case has a risk classification. Controls should reflect consequence rather than applying one rule to every AI use.
- Human review is specific. Define who reviews which outputs, with what evidence, and what happens when they disagree.
- Activity is auditable. Maintain an appropriate record of sources, system actions, approvals, overrides and outcomes.
- Change is controlled. Model, prompt, data and workflow changes can alter behaviour and should be evaluated before release.
- Policies are operational. Turn broad principles into instructions people can apply during real work.
5. Workforce and operating model
- Role changes are described. Show what the system prepares, what people decide and which tasks disappear or emerge.
- Users participate in design. Frontline knowledge is essential for discovering exceptions and earning trust.
- Training covers judgment. People need more than feature instruction; they need to understand limitations, escalation and accountability.
- Feedback has an owner. Corrections and exceptions should reach the product team and influence priorities.
- Support is planned. Name who answers questions, monitors the workflow and responds when it fails.
6. Integration and technical operation
- Systems of record are identified. Decide where context comes from and where approved results belong.
- The workflow avoids duplicate entry. A separate AI interface that adds copying may move effort rather than remove it.
- Fallbacks exist. The organization can continue important work when the AI or an upstream service is unavailable.
- Performance is evaluated. Use representative cases, including difficult and uncommon ones, before expansion.
- Cost and monitoring are visible. Understand usage, service dependencies, operational alerts and who responds.
7. Measurement, procurement and vendors
- Outcome measures are agreed. Track business and service results alongside technical quality and appropriate use.
- Expansion criteria are explicit. Define what evidence is required to move from prototype to pilot and from pilot to broader operation.
- Vendor claims can be tested. Require demonstrations against representative scenarios and document assumptions.
- Contract questions match the use case. Review data use, retention, service levels, change notification, exit, portability and subcontractors with the right internal specialists.
- Lock-in is considered. Understand what can be exported, replaced or operated differently if needs or vendors change.
How to use the checklist
Rate each area as clear, partially clear or unresolved for a specific use case. Do not average the results into a reassuring number. One unresolved issue—such as authority to use the data or ownership of a high-consequence decision—may be enough to pause a release.
Turn unresolved items into a readiness backlog with owners and dates. Some can be solved while a low-risk prototype is built. Others must be settled first. Reassess at each expansion point because wider use changes both value and risk.
For organizations beginning this work, AI Strategy & Readiness can establish the portfolio, governance and delivery pattern without requiring an enterprise-wide program first.