
What does a good AI use case look like?
With AI fever in overdrive, everyone is searching for winning AI use cases. However, the process feels like solving a giant jigsaw puzzle without a picture on the box to guide you....

by Konstantinos Trantopoulos , Yash Raj Shrestha, Amit M. Joshi, Michael R. Wade, Jingqi Liu Published August 26, 2025 in Brain Circuits • 3 min read
You move first, think big, and aim to reshape your industry.
Think: Heidelberg Materials simulating and optimizing low-carbon cement production with GenAI.
You accept higher risk for potential breakthroughs. Innovation labs, rapid pilots, and mixed proprietary-external tech are your launchpads.
Execution focus: Fast data integration, flexible cloud architecture, lightweight governance, cross-functional labs, and hands-on skill-building.
Watch out: Moving fast without ethical guardrails or scalable infrastructure can backfire.
You value trust, compliance, and control.
Think: Roche using GenAI in clinical trial monitoring under strict governance.
Your focus is steady adoption: structured data, hybrid architecture, and rigorous oversight.
Execution focus: Strong data governance, secure infrastructure, role-specific training, and alignment with regulatory requirements.
Watch out: Going too slow may erode momentum or make GenAI feel disconnected from day-to-day work.
You want quick wins with minimal disruption.
Think: CarMax summarizing customer reviews on their website with GenAI APIs.
You buy more than build, act fast, and focus on use cases with fast ROI.
Execution focus: Off-the-shelf tools, agile integration, basic governance templates, and prompt-level upskilling.
Watch out: Surface-level success can stall without deeper internal capability building.
You’re playing the long game and building your own IP.
Think: Allianz developing a GenAI stack for claims assessment and fraud detection.
You invest in data lakes, proprietary models, and enterprise-wide enablement.
Execution focus: Enterprise data integration, secure modular architecture, deep governance systems, and broad organizational transformation.
Watch out: High investment needs clarity on ROI, change management, and technical maturity.
There’s no “best” persona, only what best fits your goals, risk appetite, and capabilities. But strategy without execution is theater. Ground your archetype in five pillars: data, architecture, governance, readiness, and skills. That’s how GenAI becomes not just a tool but a transformation.

Advisor and Research Fellow at IMD
Konstantinos Trantopoulos is an Advisor and Fellow at IMD. He collaborates with senior executives across global markets to shape strategies and guide investments that drive growth and profitability. His current focus is on how companies can leverage AI to create and capture value. His insights have been featured in leading outlets such as Harvard Business Review, MIT Sloan Management Review, California Management Review, MIS Quarterly, Το Βήμα, and Forbes. Konstantinos is also the co-author of Twin Transformation, available on Amazon.

Yash Raj Shrestha is Assistant Professor and the Group Head at the Applied Artificial Intelligence Lab at the University of Lausanne.

Professor of AI, Analytics and Marketing Strategy at IMD
Amit Joshi is Professor of AI, Analytics, and Marketing Strategy at IMD and Program Director of the AI Strategy and Implementation program, Generative AI for Business Sprint, and the Business Analytics for Leaders course. He specializes in helping organizations use artificial intelligence and develop their big data, analytics, and AI capabilities. An award-winning professor and researcher, he has extensive experience of AI and analytics-driven transformations in industries such as banking, fintech, retail, automotive, telecoms, and pharma.

TONOMUS Professor of Strategy and Digital
Michael R Wade is TONOMUS Professor of Strategy and Digital at IMD and Director of the TONOMUS Global Center for Digital and AI Transformation. He directs a number of open programs such as Leading Digital and AI Transformation, Digital Transformation for Boards, Leading Digital Execution, Digital Transformation Sprint, Digital Transformation in Practice, Business Creativity and Innovation Sprint. He has written 10 books, hundreds of articles, and hosted popular management podcasts including Mike & Amit Talk Tech. In 2021, he was inducted into the Swiss Digital Shapers Hall of Fame.

Jingqi Liu is a PhD student at ETH Zurich.

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