AI Use Case Prioritisation Examples
Frameworks and methods for identifying, evaluating, and prioritising AI opportunities — from initial brainstorming to rigorous scoring and portfolio balancing.
AI Opportunity Scoring Matrix
beginnerA weighted scoring matrix that evaluates potential AI use cases across business impact, feasibility, data readiness, strategic alignment, and risk to produce a prioritised ranking.
Key takeaway: Scoring matrices with explicit weights force transparent trade-off discussions — the process of agreeing on weights is as valuable as the final scores.
Impact-Effort Quadrant Analysis
beginnerA visual framework that plots AI opportunities on an impact-effort matrix to identify quick wins (high impact, low effort), strategic bets (high impact, high effort), and time wasters (low impact, high effort).
Key takeaway: Start with the quick wins quadrant — these build credibility and funding for the strategic bets in the next phase.
AI Value Chain Mapping
intermediateMaps AI opportunities against the company's value chain to identify where AI can add the most value at each stage, from supply chain to customer experience, ensuring comprehensive coverage.
Key takeaway: Value chain mapping reveals AI opportunities in back-office operations that are often overlooked in favour of customer-facing applications — but back-office ROI is often faster.
AI Feasibility Assessment Framework
intermediateA structured assessment of whether specific AI use cases are technically feasible given current data assets, infrastructure, skills, and available AI capabilities, with go/no-go recommendations.
Key takeaway: Feasibility assessments that check data quality before technology capability save months of wasted effort on projects that fail due to insufficient data.
AI Portfolio Balancing Model
advancedA portfolio management approach that balances AI investments across risk levels, time horizons, and business areas to ensure a mix of quick wins, medium-term growth projects, and long-term transformation initiatives.
Key takeaway: A balanced AI portfolio prevents the common failure mode of betting everything on one large project — diversification of AI investments reduces overall programme risk.
Patterns
Key patterns to follow
- Explicit scoring criteria force transparent discussion about trade-offs and priorities
- Start with quick wins to build credibility before pursuing strategic bets
- Data readiness is typically the strongest predictor of AI project success
- Portfolio approaches to AI investment diversify risk and maintain momentum even when individual projects struggle
FAQ
Frequently asked questions
Three approaches work well together: workshop with business leaders to identify pain points and opportunities, analyse process data for high-volume manual tasks, and review industry case studies for proven AI applications in your sector.
Start with 1-3 depending on team capacity. Running too many pilots in parallel dilutes focus and resources. Better to do 2 well than 5 poorly. Scale the number as you build internal capability and processes.
Good AI use cases have: a clear business problem, sufficient quality data, measurable success criteria, a willing internal champion, manageable risk if it fails, and alignment with business strategy. The best initial use cases are high-volume, repetitive tasks with clear right/wrong answers.
Start with business problems, not AI capabilities. Ask 'what would make the biggest difference to our business?' before asking 'where can we use AI?'. Some high-priority problems may have better non-AI solutions, and that is fine.
Start with internal use cases — they are lower risk, easier to iterate on, and build internal capability. Customer-facing AI carries higher reputational risk and should come after you have built confidence with internal applications.
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