CHANGE TALK

Does the following sound familiar? You ask AI to analyze a 40-page strategy document. Within seconds, it identifies patterns, compares competing arguments, and produces a remarkably useful synthesis. Then you ask it to make one seemingly minor change to a table. It removes a row. Or changes two numbers. Or confidently tells you the change has been made when nothing has changed at all.  You try again. It makes the same mistake. That is AI jaggedness: the peculiar reality that AI can outperform humans in some areas while struggling, sometimes headscratchingly, in others.

By 2027, global digital transformation spending is expected to approach $4 trillion, with AI and generative AI among the primary forces driving that investment. According to McKinsey's 2025 State of AI survey, 78% of organizations already use AI in at least one business function, up from 55% only two years earlier. Similarly, Gartner predicts that within the next two years, 33% of enterprise software applications will incorporate agentic AI capabilities and that 15% of day-to-day business decisions will be made autonomously. More than half of enterprise GenAI models are also expected to become tailored to a specific industry or business function, signaling a shift from generic experimentation to embedded business capabilities.

For a long time, AI adoption in organizations largely meant introducing tools such as ChatGPT, Gemini, or Copilot into the workplace. Organizations focused on access, experimentation, governance, and enablement. Individuals integrated AI into parts of their daily work, and companies tried to understand where productivity gains could be realized. In many ways, AI initially entered organizations as an additional layer within existing workflows rather than as a catalyst for broader organizational change.