Conduct workshops with cross-functional teams: Bring together stakeholders from different departments to brainstorm ideas. Different perspectives will help uncover the most promising opportunities.

Focus on enhancing human capabilities: Look for ways AI can augment human skills rather than replace them. This builds trust in AI and focuses its power where it shines.

Prioritize opportunities to optimize data: AI thrives when applied to data analysis, processing, and insight generation. Identify areas where better data utilization would be impactful.

Evaluate customer pain points: Look at your customer journey and determine where AI could delight customers by improving experiences or offerings. Highly customer-centric use cases often provide significant value.

Assess repetitive and time-consuming tasks: AI excels at automating rote work. Analyze workflows to determine where automation could free up employees for higher-level work.

Start small, think big: Look for contained, low-risk pilots that can demonstrate quick wins and build confidence in AI's potential before pursuing larger initiatives.

Define clear success metrics: Tie use cases to tangible business results like cost savings, revenue increases, or operational efficiency gains. This focuses efforts and simplifies ROI projections.

Prioritize integration readiness: Consider the level of effort required to integrate potential AI applications into existing infrastructure. Pursue use cases with more seamless integration paths first.

Evaluate required data assets: Factor in the availability, quality, and accessibility of data needed to train AI models. Use cases relying solely on existing ready data have faster paths to deployment.

Assign an AI champion: Appoint someone to shepherd AI explorations and use cases. Their cross-functional authority and mandate to drive adoption streamlines identifying high-potential opportunities.