The AI paradox: Why 95% of enterprises are scaling spend, but stalling on value

The data is startling: 97% of large enterprises have committed budgets to AI, yet only a tiny minority, roughly 5%, are generating significant value at scale. The remaining 95% are trapped in a cycle of "isolated use cases" that simply do not scale. 

We are moving from simple chatbots to complex, agentic systems. However, this transition comes with a hidden "tax." A single agent-driven workflow can cost 5–10x as much as a standard prompt-response interaction. 

Because agentic systems trigger a chain of retrieval, reasoning, and validation calls for every "business action," the infrastructure costs are ballooning. In fact, AI infrastructure spend grew by 166% year-over-year as of Q2 2025. For many organisations, these workloads have become the leading cause of unplanned cloud spend. 

The strategies that felt successful 18 months ago are failing today. Early wins were built on: 

• Isolated use cases and simple copilots.
• Non-critical data where mistakes were low-risk. 
• Human-in-the-loop processes that limited runtime exposure. 

As we try to scale, we hit a wall of fragmented workflows and duplicated logic. When every SaaS platform ships its own proprietary AI, you end up with conflicting answers across systems and a complete lack of "semantic truth". 

The difference between the "Early Value Leaders" and the majority isn't about ambition; it's about architecture. Leaders are pulling ahead by focusing on: 

1. End-to-end workflows rather than disconnected tools. 
2. Shared data definitions to prevent inconsistent "truths". 
3. Governance designed-in, not retrofitted after a crisis. 

This is especially critical in our region, where the EU AI Act and shifting data residency rules mean that what works today may be legally unacceptable tomorrow. 

To survive this "Coming Wave," enterprises must decouple their business logic from the underlying AI tools. We need a Universal Semantic Layer, a governed, portable, and open foundation that ensures every agent and every user is working from the same playbook. 

At Strategy, we call this "Freedom as a Service". It allows you to orchestrate AI across your entire ecosystem, from Snowflake and Salesforce to Power BI and beyond, without falling into the trap of vendor lock-in or runaway costs. 

The era of "AI experimentation" is over. As we look toward the rest of 2026, the winners won't be those who spend the most on compute, but those who master their data's meaning.