In collaboration withUniphore
Today signifies a pivotal moment for the adoption of AI in enterprises. In spite of the billions allocated to generative AI, merely 5% of integrated pilots yield quantifiable business benefits, and close to half of organizations discontinue their AI projects prior to achieving production.
The obstacle is not solely the models. The key factors holding businesses back are the accompanying infrastructure: Limited data access, inflexible integration, and delicate deployment channels hinder AI projects from expanding beyond initial LLM and RAG trials. Hence, organizations are transitioning to composable and sovereign AI frameworks that reduce costs, maintain data ownership, and respond to the swift, unpredictable changes in AI—a transition that IDC predicts 75% of global enterprises will adopt by 2027.
From concept to production reality
AI pilots typically succeed, and therein lies the issue. Proofs of concept (PoCs) are designed to confirm feasibility, identify use cases, and cultivate confidence for larger investments. However, they flourish under conditions that seldom align with actual production scenarios.
“PoCs exist within a secure bubble,” notes Cristopher Kuehl, chief data officer at Continent 8 Technologies. Data is meticulously curated, integrations are minimal, and the task is frequently managed by the most experienced and dedicated teams.
The outcome, according to Gerry Murray, research director at IDC, is not merely pilot failure but a fundamental structural flaw: Numerous AI initiatives are effectively “designed to fail from the outset.”
