The gap between AI ambition and AI delivery has become one of the defining challenges of enterprise technology in 2026. Companies have spent billions on generative AI initiatives. Pilots have launched. Demos have impressed. But the hard data on what actually reaches production is sobering. Sopra Steria Next, the consulting division of European IT group Sopra Steria, has published the first edition of its CIO Compass series, focused specifically on scaling generative AI from experimentation to sustainable business performance. Its headline finding: fewer than one-third of generative AI projects currently reach stable production.
That statistic is not surprising to anyone inside large organizations trying to deploy AI. It is, however, useful to see it quantified and named as the central challenge of this phase of the AI cycle.
The production gap is real and it has a specific set of causes
The pattern is consistent across industries. A business unit identifies an AI use case. A pilot runs successfully in controlled conditions. The results are impressive enough to justify further investment. Then the project stalls somewhere between pilot and production, unable to scale for reasons that have nothing to do with the quality of the underlying AI model.
The causes are well documented. Data quality issues that were manageable at pilot scale become blocking problems at production scale. Governance frameworks that did not exist during experimentation become required before security and compliance teams will approve deployment. Integration with legacy systems proves far more complex than anticipated. Change management for users who were not involved in the pilot fails to generate adoption. The AI works. Everything around it does not.
According to McKinsey’s State of AI 2025, organizations that have successfully scaled AI beyond pilots consistently share three characteristics: they invested in data infrastructure before scaling models, they established governance frameworks early rather than retrofitting them, and they treated change management as a technical deliverable rather than a soft skill. Organizations stuck in pilot mode consistently lack all three.
The architectural decisions made during experimentation determine whether scaling is possible
One of the most common reasons AI projects fail to reach production is that the architecture used during experimentation was never designed to scale. Proof-of-concept deployments often use simplified data pipelines, manually curated datasets, and infrastructure that cannot handle production-level load or security requirements. When the time comes to scale, the entire technical foundation needs to be rebuilt rather than extended.
Sopra Steria Next’s framework addresses this directly, emphasizing that governance, technology, and change management need to be built into the architecture from the beginning rather than added afterward. That includes decisions about whether to use large frontier models or smaller, more efficient small language models for specific use cases, a distinction that has significant implications for both cost and deployment complexity.
The SLM versus LLM question has become one of the most practically important decisions in enterprise AI deployment. According to Gartner’s enterprise AI deployment research, many enterprise use cases that were initially deployed using large general-purpose models are being migrated to smaller, fine-tuned models that deliver equivalent performance for specific tasks at dramatically lower inference cost and latency. Getting that architectural decision right during initial deployment avoids expensive rearchitecting later.
Process redesign is where AI actually creates business value
The second major theme in Sopra Steria Next’s framework is the distinction between automating individual tasks and redesigning end-to-end processes. Most early enterprise AI deployments fall into the task automation category: an AI generates a first draft, summarizes a document, or answers a customer query. These use cases deliver value but do not fundamentally change business economics.
The organizations generating the largest returns from AI are those that have used it as an opportunity to rethink how entire processes work rather than applying AI as a layer on top of existing workflows. A customer service operation that uses AI to help agents respond faster is improving efficiency. A customer service operation that uses AI to redesign the entire resolution workflow, predicting customer needs before they escalate and routing issues to the right resource automatically, is creating structural advantage.
The World Economic Forum’s Future of Jobs 2025 report documented that companies reporting the highest AI-driven productivity gains were consistently those that had paired AI deployment with process redesign rather than using AI to automate processes that already existed. That finding aligns with Sopra Steria Next’s emphasis on moving beyond isolated use cases.
What CIOs actually need to do in the next 18 to 24 months
Sopra Steria Next’s CIO Compass framework identifies ten priority actions across four pillars: AI, data, infrastructure, and performance. The 18 to 24 month timeframe is significant. It reflects the consensus among enterprise technology advisors that the current window is when organizations either build the foundation for scaled AI deployment or fall permanently behind competitors who do.
The data pillar is arguably the most foundational. AI models are only as good as the data they are trained on and the data pipelines that feed them in production. Organizations that have invested in data quality, data governance, and real-time data infrastructure over the past three years are finding that AI deployment is significantly more straightforward than for organizations still working with fragmented, inconsistent data estates.
The infrastructure pillar reflects the reality that production-scale AI workloads have different requirements from conventional enterprise applications. Low latency, high availability, security isolation for sensitive data, and cost management for inference at scale all require infrastructure decisions that cannot be retrofitted from pilot architectures.
The performance pillar, measuring whether AI is actually delivering business outcomes, is where many organizations have the most significant gap. Without clear metrics defined before deployment, organizations cannot distinguish between AI that is being used and AI that is generating value.
Sources
- McKinsey — State of AI 2025
- Gartner — Enterprise AI Deployment Research
- World Economic Forum — Future of Jobs Report 2025
- Sopra Steria Next — CIO Compass
Editorial disclosure
This article uses a thought leadership publication from Sopra Steria Next as a starting point and has been independently expanded with broader industry research and editorial analysis. It covers the challenge of scaling generative AI from experimentation to production in enterprise environments. Market context is sourced from McKinsey, Gartner, and the World Economic Forum. Commentary reflects the author’s own assessment. The information provided on this website is for informational and educational purposes only. Our content is derived strictly from verified online sources to ensure accuracy and objectivity. This analysis does not constitute financial, investment, or professional advice. Readers are encouraged to consult with qualified professionals before making decisions based on this information. For more information, please see our full DISCLAIMER.
