Scoring AI Use Cases for Maximum Success
Enterprises, associations, and government agencies can apply a rigorous evaluation model to identify their high potential use cases and move beyond AI hype.
CognitivePath’s proprietary use case scoring framework ensures organizations focus on strategic AI projects that are operationally achievable.
AI Use Case Scoring Framework
Though AI presents organizations with countless opportunities for innovation and operational improvement, many struggle to determine which AI projects are worth pursuing. This confusion often stems from unclear distinctions between actual use cases and vendor claims about AI capabilities. Vendors frequently present their own product features as “use cases”, but features are irrelevant unless they solve a real business need.
CognitivePath’s use case scoring methodology helps organizations navigate this complexity. Our methodology assists companies in evaluating AI projects based on alignment, outcomes, resources and readiness, ensuring that AI’s impact is quantifiable and its implementation is achievable.
This framework shifts the focus from hype to practical solutions, providing a clear path forward for organizations seeking to harness AI.
Fit and Feasibility: Scoring AI Use Cases
We use four scoring metric categories to help organizations determine a use case’s – Fit and Feasibility within the organization’s larger strategic goals. Determining Fit ensures AI use cases align with the organization’s mission and delivers measurable business outcomes. Feasibility assesses whether the AI solution can be realistically implemented, considering the organization’s technical and operational capabilities.
The four primary scoring categories used to determine Fit and Feasibility are:
- Mission Alignment
- Measurable Outcomes
- Technology Assessment
- Organizational Impact
By analyzing these categories, organizations can prioritize use cases with the highest likelihood of success and the greatest potential impact based on their strategic needs and organizational realities.
Aligning AI with Strategic Objectives and Outcomes
The first two dimensions – loosely aligned with determining Fit – analyze use cases within the lens of the broader mission of the organization and their ability to deliver measurable value.
Mission Alignment
Mission alignment is a must-have for any use case to move to implementation. If a use case doesn’t serve the larger organizational strategy, then the AI project simply should not be implemented. The key questions to consider for mission alignment include:
- Does the AI model serve the organization’s larger strategy?
- How does it serve the strategy?
- Will it resolve a specific challenge?
- Will it provide the organization with a new capability or opportunity?
- Or serve an important customer or stakeholder community?
Measurable Outcomes
Many AI concepts sound great, but the rubber hits the road with measurement. Even if an AI initiative aligns with your mission, it must also deliver tangible results. Measurable outcomes quantify the value that the AI project will bring to your organization. This might include:
- Productivity increases
- Process optimization
- Return on investment
- Time saved
- New capabilities and stakeholder benefit
When we work with clients, we quantify key outcomes so that the AI use case can be valued based on measurable financial upside.
Organizational realities focus on pragmatic views of the organization’s ability to deploy the use case from a technological perspective and whether policies and the actual workforce are “AI ready.” These next categories examine if it is really feasible for the organization’s current infrastructure and culture.
Technology Assessment
A technology assessment evaluates the complexity, cost, and timeline required to bring an AI use case to life. What may seem like a simple project could grow in scope when data strategy and governance, AI model training, and deployment demands are fully considered. The critical factors to assess include:
- Technical complexity
- Time to deploy
- Total cost (including labor)
- Data demands
- Model training needs
- Cost of ownership/maintenance
When AI projects promise favorable outcomes or strong strategic alignment, a thorough technology assessment sets realistic expectations. The process of scoring this quadrant will often lead organizations to begin their AI journey with smaller, less complex projects. This allows organizations to reserve higher-cost, more complex AI implementations for times when they can be strategically aligned with broader corporate goals and supported by appropriate budget cycles.
Organizational Impact
This category is the “submarine quadrant” because it’s often the least considered or valued by organizations—and yet it’s the one most likely to “sink” an AI project. From legal policies and cybersecurity issues to interdepartmental rivalries and hesitant employees, many of the biggest barriers to AI success stem from unforeseen organizational and operational challenges.
Taking the time to assess the potential impact of these “underwater” concerns can help better prepare for an AI initiative. Evaluating organizational impact can help prioritize simpler, more achievable AI wins while filtering out projects that look good on paper but lack the necessary internal buy-in and adoption for new technology. Key organizational impact criteria include:
- Cross department demands
- Governance
- Change management
- HR/legal requirements
- Workforce upskilling
A Measured Approach to AI Success
Evaluating AI use cases along with these criteria provides a comprehensive framework for selecting implementations that are not only aligned with strategic goals but also practical for the organization. This holistic approach helps AI decision makers prioritize projects that are likely to succeed while avoiding those that may drain resources or fall short of delivering value.
Our use case scoring methodology is a proven agile framework. Each of the four categories and their determining factors can be weighted based on any given organization’s unique situation and needs. Specific criteria can be changed, dropped, or added. This allows organizations to use the methodology in a way that is always relevant to specific missions, objectives, resources, and readiness.
Are you interested in identifying, scoring, and prioritizing your highest potential AI use cases?
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