From the course: Generative AI for Business Leaders

Identifying high‑value GenAI opportunities

From the course: Generative AI for Business Leaders

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Identifying high‑value GenAI opportunities

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Generative AI creates the most value when it's focused on actual business problems. That sounds obvious but this is where lots of organizations get it wrong. They start with the technology and ask what can we do with generative AI. Then they launch pilots that are interesting but not always important. A better starting point is the business. Where are you trying to grow? Where are you losing margin? Where are customers experiencing friction? Where are employees spending too much time on low-value work? Where are decisions too slow, too inconsistent, and too dependent on how to access knowledge? Those questions help you find opportunities that matter. But there is an even bigger point here. The highest value opportunities rarely come from simply overlaying AI onto existing processes. That might make a process faster, it might make it cheaper, it might remove some friction. But the highest value opportunity is transformation. How could AI change the product itself? How could you build intelligence into the services you offer? How could you give customers a smarter, more personalized experience? How could you redesign the way work happens rather than simply automating the way it happens today? This is where leaders need to think bigger. In most organizations, valuable Gen. AI opportunities sit in a few common places. The first one is knowledge work. Look for teams that spend a lot of time reading, summarizing, drafting, searching, reporting or preparing information for decisions. The second is workflow friction. Look for processes where people move between too many systems, copy information from one place to another or wait for someone else to complete a routine step. This is where an AI agent can become especially useful. The third is customer experience. Where are customers waiting too long, receiving generic responses, struggling to find information or getting inconsistent service? The fourth is experience at scale. Many organizations have deep expertise locked inside documents, systems, or a small number of experienced people. Generative AI can make that expertise more accessible across the business and your customers. But the real test is value. Can it reduce Can it increase revenue? Can it improve customer satisfaction? Can it reduce risks? And can it speed up decision-making? Can it create a capability competitors find hard to copy? You should also assess feasibility. Do we have the right data? Can we integrate it with the systems involved? Is the process ready to change? Are people likely to adopt it? Can we manage the risks? The strongest opportunities sit at the intersection of business value and practical feasibility. So my advice is simple. Don't start with the coolest use case. Start with the most valuable problem and then ask how generative AI can help you solve it in a better, smarter, or completely new way. That discipline is what separates AI experiment from AI strategy.

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