One of the more dangerous assumptions in the current AI market is that widespread adoption means meaningful adoption. it’s not. Many of what companies refer to as AI transformations are actually AI experiments that focus on the edges of the business, such as systems and workflows that support employees, and not the core of how the company actually operates. These include calendaring, scheduling, meeting summaries, employee communications, customer messaging, document creation, internal assistants, and similar productivity-focused use cases.
While these applications may be useful, they are not the core applications that directly run your business and determine whether your company is doing well or poorly. Inventory management, order entry, logistics execution, supply chain planning, procurement, warehousing, manufacturing operations, and financial transaction processing belong to this category. When these systems fail, businesses immediately feel it through delayed orders, lost revenue, increased costs, decreased customer outcomes, and weakened operational controls.
According to a McKinsey report, AI is most commonly used in IT, marketing and sales, and knowledge management, with common use cases including content support, conversational interfaces, and customer service automation. It also notes that most organizations are still experimenting or in pilot mode, with only 39% reporting an impact on company-level revenue. This supports the idea that while adoption is widespread, deep core business transformation remains limited.
