Nieuwsbericht
The AI paradox: Why 95% of enterprises are scaling spend, but stalling on value
Across the global enterprise landscape, 2026 is being dubbed the year of the "AI Paradox": most organisations are dramatically increasing their investment in artificial intelligence, yet very few are generating meaningful business value at scale. While individual productivity and experimentation with AI tools have surged, tangible outcomes that move the needle on key enterprise metrics remain scarce.The disproportionate relationship between investment and value is striking. Nearly all large enterprises have committed budgets and resources to AI initiatives, yet only a small minority have achieved measurable impact at scale. The majority remain stuck in isolated use cases that fail to scale beyond individual teams or pilot programs. Experimentation is plentiful, but execution that delivers bottom-line impact is not.
One contributing factor is the rising cost and complexity of AI systems themselves. As enterprises transition from simple prompt-based applications to more advanced, agent-driven workflows, infrastructure costs have grown significantly. These agentic systems often require chains of data retrieval, reasoning and validation that multiply the underlying compute expenditure. As a result, unplanned cloud and infrastructure spending has become a leading cost driver for many organisations.
Moreover, early strategies that delivered quick wins no longer suffice. First-wave AI efforts were built on simple copilots, isolated tools and low-risk data. When companies try to scale these models across end-to-end business processes, they hit fragmentation, conflicting logic and a lack of a unified semantic foundation. This fragmentation prevents consistent insights, reduces trust in outcomes and slows enterprise adoption.
A widening value gap has emerged between early value leaders and the rest. Those ahead are focusing on integrated workflows, shared data definitions and governance models designed into the architecture rather than bolted on afterward. Without deliberate governance, coherent data strategy and clean integration, investments in AI can remain diffuse and disconnected from strategic outcomes.
One proposed solution is a universal semantic layer — a governed, portable foundation that ensures a consistent business logic across systems and tools. This approach helps organisations avoid vendor lock-in, synchronise data meaning and reduce contradictory outputs from disparate AI systems. A robust semantic foundation, combined with disciplined execution, is seen by some as a key to unlocking scalable AI value.
The AI Paradox highlights a critical inflection point for enterprises: ambition without architecture risks leaving organisations with high costs and limited impact. To move beyond experimentation and realise measurable value from AI, organisations need integrated workflows, strong data governance and a coherent strategy that ties investment to business outcomes. If you want support assessing your AI value chain and building a practical, scalable roadmap, I’m ready to help you navigate the next phase of AI transformation.
Full article: The AI paradox: Why 95% of enterprises are scaling spend, but stalling on value
(Strategy, 2026-02-13)
