Why optimized models with a smaller footprint will define the next phase of enterprise AI

Is your AI strategy scaling at the expense of sustainability?

As organizations accelerate from pilots to enterprise-wide AI adoption, many are facing rising energy consumption, increasing infrastructure demands, and growing regulatory scrutiny. Larger models are often deployed by default, yet they frequently exceed what use cases actually require, driving unnecessary cost, complexity, and environmental impact.

A different approach is emerging. Instead of scaling infrastructure indefinitely, leading organizations are rethinking the models themselves.

Rightsizing large language models enables enterprises to reduce compute requirements, lower emissions, and maintain strong performance. This shift is becoming essential to achieving sustainable, cost-effective AI at scale.

In this point of view, you will discover:

  • Why AI economics become unsustainable at scale
  • How model compression reduces cost, energy use, and resource demand
  • Practical ways to balance performance, sustainability, and data sovereignty
  • How Capgemini and its ecosystem are enabling efficient, production-ready AI

Explore the full PoV or connect with the authors to start building sustainable AI at scale.