Use Case Discovery
Use case discovery is the process of identifying candidate applications of AI within enterprise operations. Effective discovery goes beyond collecting a wish list of automation ideas — it systematically examines workflow pain points, high-volume repetitive processes, decision-intensive tasks with available data, and areas where prediction accuracy or response speed would measurably improve outcomes.
Discovery methods include workflow observation, structured interviews with process owners and front-line staff, analysis of support ticket and inquiry volumes, and review of existing process documentation. Each discovery method surfaces different types of candidates: observation reveals tacit workflow knowledge that documentation misses; ticket analysis surfaces high-volume problems at scale; interviews reveal subjective pain points that data alone would not identify.
Scope and Boundary Definition
Translating a candidate use case from a general idea into a scoped design requires explicit definition of what the AI system will and will not do. Scope definition should specify: the input data the system will process, the output or decision the system will produce or recommend, the workflow context in which the output will be used, the human role in reviewing or acting on system outputs, and the conditions under which the system should escalate to human judgment.
Overscoped use cases — those that attempt to automate complex multi-step processes with varied inputs and outputs — are more likely to underperform during development and face adoption resistance. Breaking complex use cases into scoped sub-tasks with defined handoffs between human and AI processing increases the probability of delivering measurable value incrementally rather than creating large, late-failing development cycles. For a related discussion of how workflow integration affects scope decisions, see the Workflow Integration guide.
Defining Success Criteria
Success criteria for an AI use case should be specified before development begins and tied to business outcomes rather than only to technical performance metrics. A model that achieves 95% accuracy on a test set but does not measurably reduce review processing time, improve decision quality, or reduce error-related costs has not delivered a successful use case implementation. Connecting model performance metrics to downstream business metrics requires explicit analysis of how model outputs translate into operational effects.
Baseline measurement is essential: establishing current performance levels before AI deployment allows post-deployment outcomes to be attributed rather than asserted. Where current processes are not instrumented, baseline measurement may require a specific data collection phase before development proceeds.
Feasibility Assessment
Use case feasibility assessment evaluates whether a proposed use case can be implemented reliably given available data, technology, and organizational readiness. Technical feasibility depends on whether the task structure is appropriate for AI — whether clear input-output relationships exist, whether sufficient labeled data is available or can be created, and whether the required accuracy level is achievable given known limitations of current approaches.
Organizational feasibility assesses whether the workflows, governance structures, and human processes that the AI system needs to integrate with are sufficiently well-defined and stable to support a reliable integration. Use cases embedded in rapidly changing processes face an increased risk that the system will become misaligned with actual workflow practice before deployment is complete.
Data Requirements Assessment
Data availability, quality, and governance compliance are frequently the most significant constraints on use case feasibility in enterprise settings. Data requirements assessment should inventory what data is required, where it currently resides, what quality issues exist, whether the intended use of the data for AI training or inference is consistent with the purposes under which it was collected and any consent given, and what access controls are required to maintain compliance.
In Canadian organizations, use of personal data for AI model training requires assessment against PIPEDA's knowledge and consent principles and, where applicable, Quebec Law 25's privacy impact assessment requirements. The data requirements assessment should include legal review for any use case that involves personal data, particularly personal health information, financial information, or other sensitive personal data categories. See the Data Privacy guide for a structured overview of these requirements.
Governance and Oversight Design
The governance and oversight structure for an AI use case should be designed alongside the use case itself rather than appended after deployment. Key governance design questions include: Who is accountable for the AI system's outputs? What review and audit processes apply to model decisions? How will errors or adverse outcomes be detected and remediated? Who has authority to suspend or roll back the system? What documentation must be maintained for regulatory or audit purposes?
Use cases that affect individuals — such as screening applications, prioritizing service access, or generating communications — require more extensive governance design than internal operational use cases, particularly in sectors where Canadian privacy and consumer protection legislation imposes accountability requirements.
Use Case Prioritization
Organizations typically identify more candidate use cases than can be developed simultaneously. Prioritization frameworks weigh expected impact (value if successful and probability of success) against implementation complexity (data availability, integration effort, governance requirements, change management requirements). High-impact, lower-complexity use cases — those that address clear pain points with available, well-governed data and limited workflow disruption — are typically developed first, providing early proof points that support organizational adoption of subsequent use cases. For context on how prioritized use cases connect to rollout planning, see the Rollout Strategy guide.