from news headlines and social media to academic research and boardroom discussions. The rapid rise of technologies such as generative AI, predictive analytics, and computer vision has fueled enormous excitement and expectation.

However, while the hype around AI continues to grow, the value realized from it often lags. Many organizations are eager to “do something with AI” but struggle to define where and how it can truly make an impact. They experiment with pilots or proofs of concept without a clear strategy, measurable outcomes, or alignment with business objectives — leading to fragmented efforts and limited returns.

The challenge, therefore, is not whether to adopt AI, but how to identify and prioritize the use cases that deliver the greatest value. To close the gap between AI’s potential and its realized impact, companies need a systematic approach — one that starts with business goals, evaluates feasibility, and focuses on where AI can make a visible, tangible difference.

Start with Clarity, Not Complexity

A good starting point is to begin where impact meets ease. Look for processes that are already well understood, repetitive, and supported by reliable data — areas where automation or prediction could quickly demonstrate visible results. As you review these workflows, ask guiding questions: Where could productivity or user experience be improved? What if AI could handle this step or decision? These questions help uncover practical opportunities that might otherwise go unnoticed. Early wins in these areas build momentum and confidence, showing teams what’s possible when AI is applied thoughtfully. By starting small and focusing on meaningful improvements, organizations create a strong foundation for scaling AI more broadly across the enterprise.

A structured framework for prioritizing AI Use Cases

To turn AI ambition into action, organizations need a structured way to identify and prioritize where AI will deliver the greatest value. The Three Circles of Agentic Opportunity framework, proposed by Pascal Bornet in his book Agentic AI, provides a practical lens for doing just that — helping teams focus on initiatives that are worth doing, possible to do, and valuable when done.

• High Impact — Will it Matter?

High-impact opportunities are those that make a visible difference to the business. They save significant time, increase revenue, reduce operational bottlenecks and human errors, or free skilled professionals to focus on higher-value work. Impact also includes enabling new capabilities — things the business couldn’t do before AI, such as personalized recommendations, real-time risk detection, or predictive maintenance.

When evaluating impact, ask: If we solved this problem with AI, how much would it improve our key metrics? Would it make a difference for our customers, employees, or bottom line?

• Low Effort — Is it Worth It?

Low-effort use cases are those that can be implemented without major disruption. They involve well-documented, repeatable processes supported by reliable data and systems. The rules are usually clear, and the consequences of automation are manageable. These characteristics make them ideal starting points — quick to implement, easy to explain, and likely to succeed.

Ask yourself: How much work will it take for me (or my organization) to implement this? Is this process straightforward enough to model? Are the data sources accessible and trustworthy? Can we show value without heavy customization or long development cycles?

• High Feasibility — Can It Be Done?

Feasibility ensures that a project can actually be delivered and sustained. High-feasibility use cases have defined success criteria, measurable outcomes, and teams prepared to adapt and scale. They can often be piloted on a small scale before full rollout, with minimal disruption to existing systems or workflows. This allows outcomes to be verified before affecting operations.

Consider: Do we have the data, tools, and skills to make this work today? How likely is the process to succeed? Can we test it safely and measure results quickly?

• The Sweet Spot: Where Impact, Ease, and Feasibility Overlap

The magic happens where impact, ease, and feasibility intersect. These are the projects that deliver visible value fast — proving what’s possible and building the momentum needed to scale AI adoption across the enterprise. By focusing on this sweet spot, organizations can achieve early success, strengthen internal confidence, and develop the capabilities required for more ambitious AI initiatives.

Ultimately, identifying high-value AI use cases is about structure, not guesswork. The most successful organizations resist the temptation to “do AI for AI’s sake.” Instead, they start with clear business goals. Across industries, high-value use cases are emerging that meet this test — from workflow automations that reduce the burden of administrative tasks, to intelligent customer support systems that resolve routine inquiries instantly, to AI-driven demand forecasting that cuts inventory waste and improves service levels. These examples share a common thread: each delivers measurable business impact while remaining technically feasible and operationally relevant.

Yet even the most promising ideas can falter without careful selection and execution. Common pitfalls include chasing trendy but low-impact applications, underestimating data quality issues, or launching pilots without clear success metrics. Remember, not everything should be automated. Avoiding these traps requires governance, cross-functional collaboration, and a commitment to continuous learning as AI capabilities mature.

The path forward lies in combining ambition with focus. Organizations that succeed with AI identify opportunities through a structured lens — balancing impact, ease, and feasibility — and learn quickly from what works. By starting small, measuring results, and scaling deliberately, they move from experimentation to enterprise-wide value creation. In doing so, AI becomes not just a buzzword, but a true business advantage. The future of AI belongs not to those who experiment the most, but to those who learn the fastest and scale what works.