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
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
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.
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
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
ICE Scoring Matrix Template
The ICE Scoring Matrix helps you evaluate each potential AI use case across the three dimensions:| Department | Use Case Name | Hypothesis | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score | Rank |
|---|---|---|---|---|---|---|---|
| [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.
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%.”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
- 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
- 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
Implementation Roadmap Template
Once you’ve prioritized your use cases, create a structured implementation roadmap:| Phase | Timeline | Key Activities | Milestones | Responsible Parties | Resources 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] |
Best Practices
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
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
Example: IT Department AI Use Cases
Here’s how a completed prioritization matrix might look for an IT department:| Department | Use Case Name | Hypothesis | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score | Rank |
|---|---|---|---|---|---|---|---|
| IT | Automated Ticket Classification | 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%. | 8 | 7 | 6 | 21 | 2 |
| IT | Predictive Server Maintenance | We 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%. | 9 | 5 | 4 | 18 | 3 |
| IT | Automated Software License Optimization | We believe that an AI tool analyzing software usage patterns will identify $100K+ in annual savings from underutilized licenses. | 6 | 8 | 9 | 23 | 1 |
Next Steps
Ready to prioritize AI use cases at your organization?- Create a discovery plan to gather potential use cases
- Schedule an ICE scoring workshop with key stakeholders
- Develop your implementation roadmap for top-priority use cases
- Use Kowalah’s AI prioritization template to guide your process