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McKinsey’s new State of AI 2026 report highlights an interesting disconnect. AI adoption keeps accelerating: 44% of organizations are now scaling AI across the enterprise, and 80% of respondents say AI has improved their individual productivity. But McKinsey’s AI high performers still make up only 6% of respondents. Nearly 75% of them say they fundamentally redesign workflows around AI, compared with only about 25% of other organizations.
If your company already uses AI but the business impact is still difficult to see, the problem may not be the model.
You may already have copilots helping employees work faster, an internal chatbot answering questions, or a prototype agent automating part of a process. But if the same manual handoffs remain, the AI is disconnected from the data it needs, or employees still have to verify and move information between systems, the underlying workflow hasn't changed much.
This is where AI projects often become engineering projects. A support agent needs access to the right customer and product data. A forecasting system has to feed into actual planning decisions. An internal AI assistant needs permissions, reliable retrieval, and a clear path for cases it cannot handle.
The goal isn't simply to add more AI. It's to identify where AI can remove an actual business constraint, integrate it into that workflow, and measure whether it changes cost, speed, or capacity.
That’s why the most telling number in McKinsey’s report isn't the 44% scaling AI. It’s the 6% reaching significant enterprise-level value from it.
At DataObrii, we bridge the gap between an AI prototype and measurable operational impact: production systems, agentic workflows, and the engineering needed to integrate them into real business processes.
Source: McKinsey, “The State of AI in 2026: On the Road to ROI.”
Image credit: Jakub ?erdzicki / Unsplash
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