As AI matures, leaders are shifting their focus from experimentation to impact. Many organizations launched early pilots to “do something” with AI, but those efforts often lacked a clear purpose or connection to business priorities. Some delivered results. Many did not.
The difference often comes down to use case selection. Choosing the correct use cases sets the tone for adoption, builds internal momentum, and helps secure long-term investment. The wrong choices, by contrast, can stall progress and erode confidence.
Here are four key principles to guide leadership teams in selecting AI use cases that move the needle:
Focus on areas where value is already measured
AI is most effective when it enhances existing processes the business tracks. These areas are where teams know what success looks like, whether it’s speed, accuracy, cost, or output quality. Adding AI to a process with known metrics makes it easier to measure improvement and demonstrate impact.
Leaders should avoid jumping into vague or untested ideas just because they sound promising. Without a clear baseline, it’s hard to know if AI is helping or just adding complexity. Starting with visible, high-value processes provides clarity and makes it easier to communicate wins.
Solve problems that teams already feel
Adoption tends to be higher when AI is used to improve pain points that employees already experience. Whether reducing errors, shortening cycle times, or eliminating repetitive tasks, people are more likely to support AI when they feel the improvement in their day-to-day work.
The most effective use cases reduce friction without overhauling workflows. In some cases, AI may enable teams to rethink how work gets done. But for early success, small improvements in familiar areas often lead to faster buy-in and broader support.
Make sure your data is ready
Even strong use cases fall short when the underlying data is fragmented or incomplete. Before investing in a new AI initiative, McKinsey recommends leaders should assess whether the data supporting that use case is reliable, accessible, and structured in a way that AI models can use.
Companies that succeed with AI typically address data issues early. This doesn’t mean waiting for a perfect data environment, but it does mean knowing where gaps exist and making sure foundational elements are in place.
Think beyond the pilot
A successful AI pilot can generate excitement and open doors to broader adoption. But scaling requires more than enthusiasm. Leaders need a plan for what comes next. This includes identifying follow-on use cases, ensuring infrastructure can support more activity, and building systems to track results over time.
When teams see that early wins lead to meaningful investment and growth, they’re more likely to stay engaged. Clear next steps also make it easier for leadership to allocate resources and maintain momentum.
Setting the Foundation for Long-Term Value
AI can deliver real value when applied thoughtfully. For leadership teams, the goal is not to use AI everywhere. It’s to use it where it counts. By choosing use cases tied to measurable outcomes, current challenges, and data readiness, organizations create a foundation for sustainable progress.
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