Prioritize AI usecases
Implementing AI successfully requires a strategic approach focused on business value rather than technology for technology’s sake. This framework helps you systematically gather, evaluate, and prioritize AI use cases across your organization to maximize ROI and strategic alignment.

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Why Prioritization Matters

According to industry research, up to 87% of AI projects never make it to production. The most common reason? A focus on technology rather than genuine business problems. Effective prioritization helps you:
  • Focus resources on use cases with the highest potential return
  • Build momentum by targeting quick wins before tackling complex initiatives
  • Align stakeholders around a shared understanding of value and feasibility
  • Reduce risk by identifying implementation challenges early
  • Create a strategic roadmap that drives measurable business outcomes

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The Four-Phase Approach

The AI prioritization framework consists of four key phases:
1

Discovery Phase

Gather potential AI use cases from across your organization through structured interviews, workshops, and business process analysis.Key Activities:
  • Conduct stakeholder interviews with department leaders
  • Document key business processes and pain points
  • Assess data quality and availability in relevant systems
  • Identify strategic objectives that could benefit from AI
Outcome: A comprehensive inventory of potential AI use cases
2

Evaluation Phase

Assess each identified use case using the ICE scoring matrix to quantify its potential value and feasibility.What is ICE Scoring? ICE stands for Impact, Confidence, and Ease – three critical dimensions for evaluating any AI initiative:
  • Impact (1-10): How significant would the benefit be if successful? Consider cost savings, revenue increase, improved customer experience, etc.
  • Confidence (1-10): How certain are you that this will succeed? Consider data availability, technical feasibility, past evidence, etc.
  • Ease (1-10): How easily can this be implemented? Consider technical complexity, integration requirements, change management needs, etc.
The ICE Score is calculated by adding the three individual scores, giving a total out of 30.
3

Prioritization Phase

Rank use cases based on ICE scores and additional strategic considerations to select your initial implementation targets.Additional Factors to Consider:
  • Strategic alignment with company objectives
  • Dependencies between different use cases
  • Resource availability (budget, talent, technology)
  • Organizational readiness and change management considerations
  • Regulatory or compliance requirements
Outcome: A prioritized list of AI use cases with clear rationale
4

Roadmap Development

Create an implementation timeline with key milestones, resource requirements, and success metrics.Key Components:
  • Phased implementation approach (start with pilots before scaling)
  • Clear milestones and success criteria for each phase
  • Resource allocation and responsibility assignment
  • Risk identification and mitigation strategies
  • Measurement framework for tracking ROI
Outcome: A comprehensive AI implementation roadmap with clear next steps

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ICE Scoring Matrix Template

The ICE Scoring Matrix helps you evaluate each potential AI use case across the three dimensions:
DepartmentUse Case NameHypothesisImpact (1-10)Confidence (1-10)Ease (1-10)ICE ScoreRank
[Department][Name]We believe that [AI solution] will [expected outcome] for [target users][Score][Score][Score][Total][#]
For more information on the ICE framework check out Sean Ellis’ Hacking Growth which popularized the concept.

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Hypothesis Format

Framing each use case as a hypothesis helps clarify thinking and ensures focus on business outcomes. Use this format: “We believe that [AI solution] will [expected outcome] for [target users]” Example: “We believe that implementing an AI solution to automatically categorize and route IT support tickets will reduce response time by 30% for end-users and decrease misrouted tickets by 50%.”

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Scoring Guidelines

Impact (1-10)
  • 10: Transformative impact on core business metrics or strategic objectives
  • 7-9: Significant improvement to major business processes or customer experience
  • 4-6: Moderate improvements to efficiency or effectiveness
  • 1-3: Minor improvements with limited scope
Confidence (1-10)
  • 10: Solution proven in similar contexts with ample data available
  • 7-9: Strong evidence of feasibility with good data quality
  • 4-6: Reasonable hypothesis with some data challenges
  • 1-3: Speculative idea with significant data gaps or quality issues
Ease (1-10)
  • 10: Can be implemented quickly with existing resources
  • 7-9: Moderate effort with minimal integration complexities
  • 4-6: Significant effort but achievable with proper planning
  • 1-3: Complex implementation with multiple dependencies

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Implementation Roadmap Template

Once you’ve prioritized your use cases, create a structured implementation roadmap:
PhaseTimelineKey ActivitiesMilestonesResponsible PartiesResources Needed
Discovery[Weeks X-Y]• Stakeholder interviews
• Process analysis
• Data assessment		• Use case inventory complete
• Initial scoring complete		[Names/Roles][List tools, people, budget]
Pilot Planning[Weeks X-Y]• Detailed requirements
• Vendor selection
• Success metrics definition		• Requirements document
• Vendor shortlist
• Approved pilot plan		[Names/Roles][List tools, people, budget]
Pilot Implementation[Weeks X-Y]• Development/configuration
• Testing
• Training		• Working prototype
• User acceptance testing
• Trained pilot users		[Names/Roles][List tools, people, budget]
Evaluation[Weeks X-Y]• Data collection
• ROI assessment
• User feedback analysis		• Results report
• Go/no-go decision		[Names/Roles][List tools, people, budget]
Scale[Weeks X-Y]• Full implementation
• Change management
• Monitoring		• Full deployment
• Adoption targets met		[Names/Roles][List tools, people, budget]

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Best Practices

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Do’s

  • Start with business problems, not technology capabilities
  • Be specific about expected outcomes and how they will be measured
  • Involve diverse stakeholders from IT, business units, and leadership
  • Consider change management alongside technical implementation
  • Begin with pilot projects before attempting large-scale deployment

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Don’ts

  • Avoid vague use cases like “Implement AI in customer service”
  • Don’t fall for technology hype or implement AI just because competitors have it
  • Don’t ignore data requirements – AI is only as good as the data that trains it
  • Don’t underestimate integration complexity with existing systems
  • Don’t skip the hypothesis stage – clear expectations are essential for success

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Example: IT Department AI Use Cases

Here’s how a completed prioritization matrix might look for an IT department:
DepartmentUse Case NameHypothesisImpact (1-10)Confidence (1-10)Ease (1-10)ICE ScoreRank
ITAutomated Ticket ClassificationWe believe that implementing an AI solution to automatically categorize and route IT support tickets will reduce response time by 30% for end-users and decrease misrouted tickets by 50%.876212
ITPredictive Server MaintenanceWe believe that using AI to predict server maintenance needs based on performance metrics will reduce unplanned downtime by 25% and extend hardware life by 15%.954183
ITAutomated Software License OptimizationWe believe that an AI tool analyzing software usage patterns will identify $100K+ in annual savings from underutilized licenses.689231
Based on ICE scores, this department would prioritize Automated Software License Optimization first (23), followed by Automated Ticket Classification (21), and then Predictive Server Maintenance (18).

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Next Steps

Ready to prioritize AI use cases at your organization?
  1. Create a discovery plan to gather potential use cases
  2. Schedule an ICE scoring workshop with key stakeholders
  3. Develop your implementation roadmap for top-priority use cases
  4. Use Kowalah’s AI prioritization template to guide your process