A systematic approach to identify, evaluate, and prioritize AI use cases for maximum business impact
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.
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:
The AI prioritization framework consists of four key phases:
Discovery Phase
Gather potential AI use cases from across your organization through structured interviews, workshops, and business process analysis.
Key Activities:
Outcome: A comprehensive inventory of potential AI use cases
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:
The ICE Score is calculated by adding the three individual scores, giving a total out of 30.
Prioritization Phase
Rank use cases based on ICE scores and additional strategic considerations to select your initial implementation targets.
Additional Factors to Consider:
Outcome: A prioritized list of AI use cases with clear rationale
Roadmap Development
Create an implementation timeline with key milestones, resource requirements, and success metrics.
Key Components:
Outcome: A comprehensive AI implementation roadmap with clear next steps
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] | [#] |
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%.”
Impact (1-10)
Confidence (1-10)
Ease (1-10)
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] |
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 |
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).
Ready to prioritize AI use cases at your organization?