AI Services Case Study — Performing AI Use Case Discovery and Prioritisation
- Jan 28
- 7 min read
Updated: 7 days ago
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 case study demonstrates the Discover-stage role of AI Use Case Discovery in helping organisations identify and prioritise AI opportunities before committing to investment, strategy or delivery.
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, website engagement 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?"
Orr Consulting therefore undertook a structured AI Use Case Discovery and prioritisation exercise to identify the most strategically aligned, beneficial and viable opportunities.
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 including knowledge creation, advisory delivery, website engagement and business development.
The objective was not simply to generate a large list of technology ideas. It was to determine which opportunities genuinely required AI, which were most strongly aligned to strategy and which offered a credible path towards organisational value.
4. Action Taken
Rather than pursue ideas opportunistically, Orr Consulting applied its structured AI Use Case Discovery methodology to identify and prioritise the opportunities most strongly aligned to business strategy and organisational value.
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.
The long list deliberately spanned all major operational activities across the business. Candidate use cases included knowledge creation, website visitor engagement, advisory delivery, proposal generation, knowledge management, executive briefing generation and business development support. At this stage, the objective was to maximise breadth rather than prematurely judge feasibility or implementation approach.
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.
This prevented technology labels from driving prioritisation and ensured that each opportunity could be routed towards the most appropriate organisational response.
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 structured planning, which should be retained for later consideration and which should be routed through wider digital, automation, analytics or business improvement activity.
5. Outcomes
The discovery exercise created several notable outcomes, observations and lessons learned.
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)
Digital Engagement and Business Development
A conversational AI assistant trained on the AI Insights Library to answer AI transformation questions and guide website visitors to relevant Insights, services and frameworks (Conversational AI / Autonomous AI Agents)
AI-assisted market and sector intelligence to identify relevant organisations, trends and opportunity signals for business development (Decision Support AI / Generative AI)
AI-enabled campaign management supporting business development, prospect discovery and campaign preparation (Autonomous AI Agents / Decision Support AI / AI-Driven Automation)
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.
This reduced the risk of selecting use cases simply because a particular tool or capability was fashionable, readily available or already being promoted by a supplier.
5.3 Strategic Alignment
The prioritisation exercise showed that the highest-value AI opportunities were those that strengthened Orr Consulting’s knowledge assets, digital engagement, advisory delivery and business development capabilities.
This gave the organisation greater confidence that future AI activity would support the business strategy rather than become a disconnected programme of technology experimentation.
5.4 Identified AI Capabilities
Although many of the highest-priority opportunities involved Generative AI, the exercise also identified significant opportunities for Conversational AI, Decision Support AI and Autonomous AI Agents.
This demonstrated that valuable organisational opportunities can span multiple AI capability types rather than being concentrated around a single technology.
The exercise also reinforced the value of the AI Universe as a structured discovery framework. Considering the full capability landscape helped Orr Consulting identify a broader and more balanced portfolio aligned to organisational needs.
5.5 Early Benefits Identification
The prioritisation exercise enabled early identification of the expected benefits associated with each shortlisted use case.
This clarified where value was expected to arise and provided a practical foundation for defining, measuring and tracking benefits through a structured AI Benefits Realisation approach as selected opportunities progressed.
5.6 Reduced Delivery Waste
The exercise prevented all 76 candidate opportunities from competing equally for attention, planning effort and investment.
By narrowing the portfolio before delivery activity began, Orr Consulting reduced the risk of fragmented experimentation, duplicated effort and resources being committed to low-value or poorly suited initiatives.
5.7 Clear Path Forward
The prioritised short list created a clear basis for deciding which opportunities should progress into structured planning, which should remain within the future AI portfolio and which should be routed through wider improvement activity.
This also provided practical evidence to inform subsequent AI Strategy and Roadmap development.
6. Final Thoughts
Artificial intelligence can present organisations with a wide range of potential opportunities.
The difficulty is not usually generating ideas. It is determining which opportunities genuinely require AI, align with strategy, offer credible benefits and can be delivered at proportionate cost and risk.
AI Use Case Discovery provides a disciplined way to move from broad organisational curiosity to a focused and prioritised portfolio of viable opportunities.
In this case, the most important outcome was not the long list of 76 ideas or even the final short list of ten. It was the evidence-based process that enabled Orr Consulting to distinguish genuine AI opportunities from wider improvement activity and direct attention towards the opportunities most likely to create strategic value.
This AI Services Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
If your organisation would benefit from a clearer and more structured view of where AI could create value, we would be pleased to discuss your next AI steps.
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