From AI Pilots to Production: The Operating Model Bottleneck
Across industries, leaders are discovering that their biggest AI challenge is not model performance but operationalisation. Analysis of more than 2,400 enterprise AI initiatives shows that 80.3% fail to deliver intended business value, a rate twice that of traditional IT projects. Pilots often demonstrate impressive proofs of concept, yet they stall when organisations attempt full production AI deployment. The gap lies in the operating model: how data flows are structured, how governance and compliance are enforced, and how AI systems integrate with existing workflows at scale. This is driving a shift in focus from experimentation to AI infrastructure optimization. Rather than endlessly testing new models, enterprises are investing in platforms and patterns that make AI repeatable, auditable and cost-efficient, enabling them to move confidently from AI pilot to production without exploding complexity or spend.
OnStak’s AI Portfolio: Cutting the ‘AI Tax’ in Production
OnStak’s newly expanded AI Portfolio targets the cost and complexity of running AI in production environments. The company argues that enterprises “don’t have an AI model problem” so much as an AI operating model problem, and positions its AI Correlation Fabric as a foundation for production AI deployment. Instead of feeding large models ever more raw data, the fabric correlates signals across the stack and passes only service-relevant information forward. In AIOps trials, this has reduced tokens per decision by 15–20x, lowering enterprise AI costs while improving speed and reducing hallucinations. Crucially, the same correlation layer underpins AI Assurance, which turns regulatory policies into runtime enforcement and audit-ready evidence, and Video AI Analytics, which runs on existing camera infrastructure. Together, these capabilities aim to minimise “throwaway” pilot tooling and ensure AI services remain embedded and economical after go‑live.
eDreams ODIGEO Shows What Agentic AI at Scale Looks Like
In software engineering, eDreams ODIGEO offers a live example of what AI-native operating models can deliver. The travel subscription company has built an AI-first infrastructure that it says accelerates innovation fivefold, with technical teams bringing new concepts to market at dramatically higher speed. In its most advanced teams, 100% of new code is now AI-generated under human command and design, freeing engineers to focus on higher-value work. This agentic AI productivity model has produced a 47% year-on-year surge in engineering output, effectively turning AI into a force multiplier rather than a side experiment. Behind the scenes, the firm deploys more than 100 Model Context Protocols to connect AI agents securely into its complex booking systems, and continuously ingests over 100 terabytes of high-quality information daily to keep applications and services optimised and responsive.
Mid-Market Adoption and the Rise of AI-Native Service Providers
While early AI industrialisation was led by digital giants, mid-size companies are now rapidly adopting platforms such as Claude and other enterprise AI services to streamline operations. They are using generative and agentic AI for tasks ranging from software development and support automation to analytics and compliance reporting. However, most mid-market firms cannot justify building massive in-house infrastructure. This is fuelling the rise of AI-native service providers and operating-model specialists like OnStak. These firms offer pre-built correlation fabrics, assurance layers and domain-specific analytics that plug into existing estates, reducing the need for heavy upfront investment. By standardising patterns for governance, observability and cost control, they help organisations scale AI from isolated pilots to business-critical workloads, while keeping enterprise AI costs predictable and aligning AI infrastructure optimization with tangible productivity and innovation gains.