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Brain Circuits

How to scale your AI use cases: A checklist 

Published November 27, 2025 in Brain Circuits • 3 min read

The business case for AI is realized when the application is scaled, not in the use cases of the pilot phase. Use the checklist below at the outset to ensure the transition from a successful use case to a transformative project.

 

Checklist

  • Strategic objectives

Will the scaled use case contribute significantly to our strategic objectives (e.g., customer experience, operational excellence, employee experience, business model, etc.)?

  • Repeatability

Is the use case solving a repeatable business issue (i.e., it’s not a one-off) that merits a long-term AI solution?

  • Resources and budget

Do we have the resources and budget to scale (e.g., financial planning, vendor and partner management, etc.)?

  • Technical foundation and standards

Do we have a scalable technical foundation? What underlying standards across the use case portfolio should be applied (e.g., common data and technology layer)?

  • Data sources

Do we have high-quality and sustainable data sources for the long term (e.g., data governance and quality controls, flexible data partnerships, etc.)?

  • Security, compliance, and ethics

Can we manage security, compliance, and ethics at scale (e.g., regulations, data privacy, ethical guardrails, etc.)?

  • Organizational readiness

Do we have the skills and organizational readiness to deploy at scale (e.g., cross-functional collaboration, talent, and training plans)?

  • Budgetary responsibility and ownership

Will there be clear budgetary responsibility and a business owner who will be accountable for embedding the AI solution into the business process landscape?

 

Key learning

By treating scalability early as a core objective rather than an afterthought, AI use cases can transition from exploration to production and pave the way for AI deployment that will deliver meaningful business value over the long run.

Authors

Didier Bonnet

Professor of Strategy and Digital Transformation

Didier Bonnet is Professor of Strategy and Digital Transformation at IMD and program co-director for Digital Transformation in Practice (DTIP). He also teaches strategy and digital transformation in several open programs such as Leading Digital Business Transformation (LDBT), Digital Execution (DE) and Digital Transformation for Boards (DTB). He has more than 30 years’ experience in strategy development and business transformation for a range of global clients.

Achim Plueckebaum

Achim Plueckebaum is an Executive-in-Residence at IMD. He is a global, entrepreneurial senior executive with strong experience in the life sciences industry, combining a highly successful CIO and business-leader digital/data career track, with additional experience in management and startup consulting and finance/M&A. Achim holds a master’s degree in information systems from Stevens Institute of Technology, USA and an MBA from the University of Giessen, Germany, and Napier University, Edinburgh, Scotland.

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