AI Use Case Discovery — Prioritising What Matters
- Feb 20
- 7 min read
Updated: May 15
1. Insight
Many organisations recognise the potential of AI, yet struggle to translate that potential into sustained business value.
A common cause is starting in the wrong place. Instead of first identifying where AI can meaningfully support business outcomes, organisations jump straight to tools, pilots or technology decisions. This often results in fragmented initiatives, unclear ownership and unrealistic expectations of what AI can deliver.
Successful AI adoption is typically grounded in clear, prioritised, business-owned use cases that provide the foundation for effective AI strategy, delivery and benefits realisation.
AI Use Case Discovery bridges the gap between understanding AI capability and committing to strategic direction.
Its role is to translate understanding of organisational readiness into a prioritised view of where AI can most realistically and meaningfully be applied.
The resulting short list of AI use cases provides a critical input into AI Strategy and Roadmap development in the Design stage.
2. Why This Matters
AI Use Case Discovery sits at a critical point in the AI transformation journey.
Following AI Capability & Maturity Assessment, organisations have an informed view of their readiness, constraints, control and risks — including skills, data, governance and operating model considerations.
Preceding the development of an AI Strategy and Roadmap, leaders must decide where to focus effort, investment and ambition.
When this step is missed or rushed, organisations commonly experience:
AI strategies that are aspirational but disconnected from delivery reality
Competing initiatives with no agreed basis for prioritisation
Early delivery challenges that undermine confidence and momentum
Difficulty justifying further AI investment
Pilots and/or early adoption that start to feel like a solution looking for a problem
In the Orr Consulting AI Transformation Process, AI Use Case Discovery is the final step in the Discover stage — converting readiness insight into a prioritised set of realistic opportunities before strategy, governance and investment decisions are made.
While it fits naturally within the broader transformation approach, AI Use Case Discovery can also stand alone, providing clarity and focus before further commitments are made.
3. AI Use Case Discovery
Effective AI Use Case Discovery is business-led, collaborative and grounded in organisational reality. The emphasis is on outcomes first, technology second.
3.1 Shared View of Capability
Discovery is most effective when stakeholders share a common understanding of the different types of AI capability available and the organisational questions they are designed to address. The AI Universe details currently available AI capabilities and can be used to anchor discussion:
Autonomous AI Agents (which combine multiple AI capabilities)
Each category carries a different baseline level of complexity, availability and delivery risk. This framing becomes more powerful when combined with your Capability and Maturity Assessment, which clarifies what “simple” or “complex” actually means for your organisation today.
3.2 The Business Led Long List
A long list of use cases is identified by the business, supported by structured facilitation rather than prescription.
Workshops are typically conducted with functional teams such as finance, HR, operations, procurement, sales and logistics. The objective is not to begin with AI solutions, but to explore where meaningful improvements in performance, quality or service could be achieved.
Ideally, participants will have completed pre-workshop familiarisation with the AI Universe, establishing a shared understanding of the main forms of AI capability. This helps broaden awareness and reduces the risk that discussion becomes narrowly focused on a single category of AI.
During the workshop, teams are first invited to discuss areas where there may be opportunities to improve speed, efficiency, quality, consistency, control, customer experience or employee experience. This helps surface an initial set of business-led improvement opportunities in the language of the function itself.
Facilitators can then use a set of practical prompts to generate further use cases. For example, teams may identify scenarios where they would benefit from the ability to:
Predict what is likely to happen more accurately → Predictive AI
Create knowledge or content faster → Generative AI
Interact with systems more naturally → Conversational AI
Run activities with less manual intervention → AI-Driven Automation
Improve the quality and speed of decisions → Decision Support AI
Interpret documents, images, audio, video or aspects of the physical world → Vision & Speech AI
Integrate two or more of these capabilities into a coordinated flow of activity → AI Agents
This framing also helps teams begin thinking about AI Benefits Realisation from the outset, by linking potential use cases to the types of improvement they are expected to deliver.
Finally, the AI Universe diagram can be reviewed as a visual cross-check to identify any remaining gaps and support a final round of opportunity generation.
The output of this stage is a long list of improvement opportunities, including candidate AI use cases and potentially other valuable ideas that may be better routed through established digital, automation, analytics or business improvement channels.
At this point, breadth is encouraged. The objective is to capture opportunity, not to judge feasibility prematurely.
3.3 Assessing and Prioritising
The long list of improvement opportunities should first be reviewed to distinguish genuine AI use cases from wider improvement opportunities.
For organisational purposes, a use case should be treated as AI where it contains one or more components that:
Learn patterns from data
Use those learned patterns to make predictions, classifications, recommendations or generate outputs
Produce outputs that cannot be fully determined in advance through explicit human-written rules
Where an idea follows fixed rules, automates a known workflow or simply reports information, it may still be valuable, but it should usually be classified as automation, analytics or wider business improvement rather than an AI use case.
This helps prevent conventional improvement opportunities being incorrectly treated as AI, while also helping identify genuine AI capability that may otherwise be hidden within existing systems, platforms or processes.
The long list of candidate AI use cases is then assessed using simple, transparent criteria:
Alignment to Business Strategy - How directly does the use case support stated organisational objectives?
Cost, Complexity and Risk - How challenging is the use case likely to be to deliver in practice, considering:
the inherent complexity of the AI capability
the level of adaptiveness required — how much the system learns, changes or improves over time
the level of autonomy required — how much the system acts without human approval or intervention
whether higher levels of adaptiveness or autonomy increase cost, complexity, assurance requirements or operational risk
organisational capability and maturity
implementation dependencies across processes, systems and teams
change, adoption and assurance implications
the likely mode of delivery required — for example custom AI development, configuration of AI-enabled vendor solutions or generative AI adoption
Impact and Benefits - What level of value could realistically be delivered, such as:
Productivity and efficiency improvements
Cost reduction or avoidance
Improved service quality or consistency
Risk reduction, compliance, or assurance benefits
Data Readiness - How available, accessible, relevant and suitable is the data required to support the use case, and how clear are ownership, quality, governance and lineage arrangements?
Each use case is scored against the criteria using a simple 1–3 scale. High = 3, Medium = 2 and Low = 1.
For Cost, Complexity and Risk, the scoring is inverted: High = 1, Medium = 2 and Low = 3.
Scores are totalled to provide an overall viability score (typically 4–12) indicating how suitable the use case is for structured pilot planning, with a credible path to scale if successful. Where a use case fails a basic feasibility or risk gate, it can be scored as 0 — Not viable now.
Use cases can then be categorised by viability as follows:
0 — Not viable now (fails a basic feasibility or risk gate)
4–6 — Low viability
7–9 — Moderate viability
10–12 — High viability
3.4 The Prioritised Short List
The scoring provides a clear rationale for which use cases should be prioritised for pilot planning and potential scale and which should be parked or revisited later.
Applying these criteria enables organisations to move from a broad long list to a prioritised short list of AI use cases.
Valuable non-AI opportunities should not be discarded. They should be classified and routed to the most appropriate established delivery route, such as digital delivery, automation, analytics, process improvement or service redesign.
The short list of AI use cases typically includes:
Near-term opportunities with lower complexity
Higher-impact strategic initiatives requiring greater investment
Clearly de-prioritised or future options
The short list provides a clear, defensible basis for decision-making and becomes a key input into developing a successful AI Strategy and Roadmap and associated business case development.
4. Benefits of Structure
When conducted effectively, AI Use Case Discovery delivers:
A business-owned long list of improvement opportunities, including candidate AI use cases
A prioritised short list aligned to strategy and capability
Clear separation of genuine AI use cases from wider improvement opportunities
Stronger leadership alignment and decision-making
Realistic expectations of complexity, cost and risk
Credible inputs into AI strategy and roadmap development
It also produces tangible outputs that can be reused across later stages of the AI lifecycle.
5. Risks
When AI Use Case Discovery is overlooked or treated informally, organisations expose themselves to avoidable risk, including:
Misaligned AI initiatives with limited business value
AI-labelled portfolios becoming inflated with automation, analytics or improvement ideas that are valuable but not genuinely AI-enabled
Underestimated delivery effort, cost and risk
Fragmented AI activity across departments
Governance, assurance and ethical issues emerging too late
Loss of confidence following early setbacks
These risks increase as AI moves from experimentation into operational use.
6. Final Thoughts
AI Use Case Discovery is a decision-making discipline. It uses readiness and constraints to shape a realistic long list, then applies transparent criteria to produce a prioritised short list leaders can act on.
A successful AI Use Case Discovery exercise does not need every good idea to become an AI use case. In many organisations, the process will identify a smaller number of genuine AI opportunities alongside a wider set of improvement ideas. That is a successful outcome: it helps leaders focus AI effort where it can add real value while routing other opportunities through the right established delivery mechanisms.
The prioritised short list also sets the direction for what comes next in the AI Transformation Process: developing a successful AI Strategy and Roadmap. This is where priority use cases are translated into clear strategic objectives, sequenced delivery plans, appropriate governance and investment decisions.
By deliberately sequencing AI Capability & Maturity Assessment, AI Use Case Discovery and AI Strategy Development, organisations significantly improve both the pace and quality of AI adoption — reducing risk while maximising value.
This Insight is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
7. Call to Action
Whether undertaken as a stand-alone engagement or as part of a wider AI transformation programme, structured AI Use Case Discovery provides clarity, focus and confidence.
Orr Consulting supports organisations with AI Capability & Maturity Assessment, AI Use Case Discovery and AI Strategy Development — helping leaders move from understanding, to prioritisation, to confident execution.
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