AI Use Case Discovery: Prioritising What Matters
- orrconsultingltd
- 2 days ago
- 5 min read
Updated: 2 days ago
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.
Industry best practice shows that successful AI adoption is use-case led. Clear, prioritised, business-owned use cases 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.
Within the AI transformation process, AI Use Case Discovery sits within the Discover stage, following AI Capability and Maturity Assessment.
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, 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
Structured AI Use Case Discovery provides a disciplined way to move from capability awareness to strategic intent.
While it fits naturally within a 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 – How It Works
Effective AI Use Case Discovery is business-led, collaborative, and grounded in organisational reality. The emphasis is on outcomes first, technology second.
3.1 Establishing a Shared View of AI Capability
Discovery is most effective when stakeholders share a common understanding of the different types of AI capability available.
An AI capability framework (often referred to as an “AI universe”) is used to anchor discussion across categories such as:
Generative AI
Predictive AI (Machine Learning)
Conversational AI & Virtual Assistants
Automation & Intelligent Process Automation
Computer Vision and Pattern Recognition
Recommendation and Decision Support Systems
Each category carries a different baseline level of complexity, availability, and delivery risk.This framing helps ground discussions and manage expectations from the outset.
3.2 Business-Led Use Case Identification (Creating the Long List)
Use cases are identified by the business, supported by structured facilitation rather than prescription.
Participants are guided to consider:
Where the most significant operational pain points exist
Where decision-making is slow, inconsistent, or poorly informed
Where manual, repetitive activity limits performance or quality
Where AI could realistically augment — not replace — human effort
The output of this stage is a long list of candidate AI use cases, typically spanning multiple functions and services.
At this point, breadth is encouraged. The objective is to capture opportunity, not to judge feasibility prematurely.
3.3 Assessing and Prioritising Use Cases
The long list of candidate use cases is then assessed using simple, transparent criteria.
1. Alignment to Business Strategy - How directly does the use case support stated organisational objectives?
2. 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 involved
Organisational capability and maturity
Data availability, quality, and governance
Change, adoption, and assurance implications
3. 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
Where uncertainty is high, data readiness may be called out explicitly.
3.4 Creating the Short List of Priority Use Cases
Applying these criteria enables organisations to move from a broad long list to a prioritised short list of AI use cases.
This short list 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, as well as associated business case development.
4. Benefits of Structured AI Use Case Discovery
When conducted effectively, AI Use Case Discovery delivers:
A business-owned long list of AI opportunities
A prioritised short list aligned to strategy and capability
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 If AI Use Case Discovery Is Not Addressed
When AI Use Case Discovery is overlooked or treated informally, organisations expose themselves to avoidable risk, including:
Misaligned AI initiatives with limited business value
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 not a brainstorming exercise. It is a decision-making discipline.
It sits naturally after AI Capability & Maturity Assessment, using a clear understanding of readiness and constraints to shape a long list of realistic AI opportunities. Through structured assessment, that long list is refined into a focused short list that leaders can confidently prioritise and act upon.
This 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.
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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