Case Study — Performing AI Use Case Discovery and Prioritisation
- Jan 28
- 6 min read
Updated: May 15
1. Organisational Problem
As interest in artificial intelligence continues to grow, many organisations recognise that AI may offer opportunities to improve productivity, enhance services and support better decision-making.
However, leadership teams often face an important challenge before committing to AI initiatives:
"Which AI opportunities are actually worth pursuing?"
Organisations may identify a wide range of potential AI opportunities, but without a structured way to assess and prioritise them, they can easily invest time and effort in ideas that are poorly aligned to strategy, difficult to deliver or unlikely to create meaningful value.
This case study describes a situation faced by Orr Consulting, a specialist advisory consultancy focused on helping organisations navigate AI transformation.
As part of its own internal development, the firm wanted to explore how artificial intelligence could support its operational activities while remaining aligned with its strategic priorities.
In the Orr Consulting AI Transformation Process, this type of engagement typically sits within the Discover stage, helping organisations identify and prioritise the most valuable AI opportunities before moving into strategy development and implementation.
2. Situation
Orr Consulting operates as a specialist advisory consultancy focused on supporting leaders and decision makers navigating AI transformation. Across content development, advisory delivery, knowledge management and business development, the firm recognised a growing range of potential AI opportunities. However, without a structured prioritisation process, there was a risk of pursuing ideas that were disconnected from strategy, difficult to deliver or unlikely to create meaningful value.
The leadership question therefore became:
"Which AI opportunities would most effectively support the organisation’s strategic goals while remaining practical to deliver?"
3. Background
Orr Consulting’s strategy focuses on building recognised authority in AI transformation, converting thought leadership into advisory engagements and developing a scalable intellectual property platform. Within that context, artificial intelligence could potentially support a wide range of operational activities.
4. Action Taken
Rather than pursue ideas opportunistically, the firm applied its AI Use Case Discovery methodology to identify and prioritise the opportunities most aligned to strategy.
The exercise followed three stages.
Stage 1 — The Long List
Initial business-led discovery discussions explored operational functions and activities across the organisation, generating an unbounded long list of 56 candidate AI use cases. A second pass using the AI Universe capability framework identified a further 20 opportunities, bringing the total long list to 76 potential use cases.
Stage 2 — Assess and Prioritise
The long list included both potential AI use cases and wider improvement opportunities. It was assessed to distinguish genuine AI use cases from wider business improvement opportunities.
Genuine AI use cases were defined as those where a system learns patterns from data and uses those learned patterns to make predictions, classifications, recommendations or generate outputs that cannot be fully determined in advance through explicit human-written rules.
This ensured that conventional automation, analytics or improvement opportunities were not incorrectly treated as AI use cases.
Each AI use case was then assessed using the Orr Consulting prioritisation criteria:
Alignment to Business Strategy
Cost, Complexity and Risk
Impact and Benefits
Data Readiness
Cost, Complexity and Risk considered the inherent complexity of the AI capability, the level of adaptiveness or autonomy required, delivery dependencies, organisational readiness, assurance implications and likely mode of delivery.
Each criterion was scored on a 1–3 scale, with High = 3, Medium = 2 and Low = 1. For Cost, Complexity and Risk, the scoring was inverted, with High = 1, Medium = 2 and Low = 3, to favour lower-complexity opportunities. Where a use case failed a basic feasibility or risk gate, it could be scored as 0 — Not viable now.
Scores were then totalled to produce an overall viability score indicating how suitable each use case was for structured pilot planning, with a credible path to scale if successful.
0 — Not viable now
4–6 — Low viability
7–9 — Moderate viability
10–12 — High viability
Valuable non-AI opportunities were not discarded. They were separated from the AI short list and retained for potential routing through wider digital, automation, analytics or business improvement activity.
Stage 3 — Prioritised Short List
The scoring exercise reduced the long list of 76 candidate opportunities to a prioritised short list of ten high-viability AI use cases aligned with Orr Consulting’s strategic objectives. The short list provided a clear basis for deciding which opportunities should move forward into pilot planning and which should be considered later as part of future AI Strategy and Roadmap development.
5. Outcomes
The discovery exercise produced a prioritised short list of ten high-viability AI use cases, together with a number of broader strategic insights.
5.1 Top 10 Prioritised AI Use Cases
The Top 10 prioritised AI use cases were grouped into three broad areas.
Knowledge and Thought Leadership Acceleration
AI-assisted research and drafting support for new Insights (Generative AI)
AI summarisation and adaptation of Insights into LinkedIn posts, newsletters and briefing notes (Generative AI)
AI semantic search across the AI Insights Library (Conversational AI / Decision Support AI)
AI generation of executive briefing packs combining multiple Insights (Generative AI)
Client Engagement and Advisory Delivery
AI-assisted generation of client proposals and engagement documents (Generative AI)
AI generation of structured workshop outputs and maturity assessment reports (Generative AI)
AI prospect intelligence reports automatically prepared before client meetings (Autonomous AI Agents / Generative AI)
Knowledge Platform Development
An AI assistant trained on the AI Insights Library capable of answering questions about AI transformation (Conversational AI / Autonomous AI Agents)
A conversational AI assistant guiding website visitors to relevant Insights and frameworks (Conversational AI)
Automated generation of executive briefings from library content (Generative AI)
5.2 Discovery Before Technology
The exercise showed the value of starting with business-led, function-focused discovery rather than with predefined technologies or solutions. By generating an intentionally unbounded long list before applying prioritisation criteria, Orr Consulting was able to explore a broader range of opportunities and avoid narrowing the conversation too early around fashionable tools or assumptions.
5.3 Strategic Alignment
Overall, the prioritisation exercise showed that the highest-value AI opportunities were those that strengthened Orr Consulting’s knowledge assets and advisory delivery capabilities. The most valuable opportunities were not the most technically advanced, but those most closely aligned to strategy and practical delivery.
5.4 Generative AI Opportunity
A further insight was that many of the highest-priority opportunities sat within Generative AI. For many organisations, this suggests that some of the most practical early opportunities may lie in accessible Generative AI use cases that improve knowledge work, communication and service delivery.
5.5 Early Benefits Identification
The prioritisation exercise also enabled early identification of the benefits associated with each short listed use case. This helped clarify where value was expected to arise and provided a practical foundation for subsequent structured benefits realisation as delivery activity progressed.
6. Recommended Next Steps
Following completion of the discovery exercise, the prioritised short list provided a clear foundation for future AI development. Lower-complexity opportunities could now be explored through small-scale pilots, particularly in generative AI support for content creation and proposal development.
The firm also identified opportunities to strengthen knowledge management, including semantic search, executive briefing generation and a potential AI knowledge assistant trained on the AI Insights Library. Together, these next steps would support operational efficiency and provide an important input into future AI Strategy and Roadmap development.
7. Final Thoughts
Artificial intelligence can present organisations with a wide range of potential opportunities.
However, without structured discovery and prioritisation, it can be difficult to determine which opportunities are most valuable and feasible.
AI Use Case Discovery provides a disciplined way to address poor AI use case selection and move from broad curiosity about AI to a focused short list of opportunities aligned with organisational strategy.
For Orr Consulting, this exercise demonstrated how structured discovery methods can identify meaningful AI opportunities while reinforcing the organisation’s strategic priorities.
For larger organisations, the same approach can help leadership teams move beyond experimentation and towards structured, strategically aligned AI adoption.
This Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
8. Call to Action
If your organisation is exploring artificial intelligence but is uncertain where the most valuable opportunities lie, AI Use Case Discovery can provide a practical starting point.
If this case study reflects questions your organisation is currently considering, Orr Consulting would be pleased to discuss your next AI steps.
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