Sopra Steria Next Outlines Blueprint for Scaling Generative AI

• Sopra Steria Next unveils the first publication from its CIO Compass, focused on scaling generative AI

• Despite widespread adoption, fewer than one-third of projects reach a stable production level, highlighting a key challenge: moving from experimentation to sustainable, secure and governed performance

• With this first focus, Sopra Steria Next reinforces its position as a leading player in structuring AI at scale, offering a practical perspective and tangible levers to turn innovation into business outcomes

Sopra Steria Next unveils the first publication from its CIO Compass, focused on scaling generative AI. While organisations have invested heavily in the topic, a gap remains between ambition and reality: most initiatives are still confined to experimentation, without delivering sustainable value. In response, Sopra Steria Next sets out a clear and practical framework for turning AI into a core performance driver.

This publication forms part of the CIO Compass, a thought leadership platform launched by Sopra Steria Next to support CIOs in accelerating technological transformation. It identifies ten priority actions to be undertaken over the next 18 to 24 months, structured around four key pillars — AI, data, infrastructure and performance — to help organisations secure their investments and maximise business impact.

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Generative AI: moving beyond the hype towards industrial performance

As generative AI has moved to the top of executive agendas, Sopra Steria Next highlights a major paradox: while adoption is widespread, industrialisation remains limited. Fewer than one-third of projects currently reach a stable production stage, with most organisations still stuck in fragmented experimentation. To help CIOs move beyond this stage, Sopra Steria sets out key recommendations to turn this technological challenge into a structural transformation.

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These recommendations are structured around three main priorities:

• Laying the foundations for AI at scale – This includes implementing an appropriate architecture that combines governance, technology and change management:
  – Making governance a lever for value creation and security
  – Adapting to different AI models and knowing when to deploy SLMs to promote efficiency where relevant
  – Driving adoption beyond training by building bridges between business and technology
• Moving beyond isolated use cases to redesign end-to-end processes – Rather than automating individual tasks, AI and its orchestration offer an opportunity to fundamentally rethink business processes and create value.

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