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AI experiments are usually simple to launch and often produce promising results in controlled settings. But translating those successes into scaled, enterprise-wide impact can be much harder.\u00a0<\/p>\n<\/div>\n
As Chair and CEO of Deloitte Consulting LLP, I have counseled many senior leaders on AI implementation, and this has become a recurring theme in my conversations with clients. Many of them turn to us to help them move beyond what I\u2019d call \u201cpilot fatigue.\u201d Our latest State of AI in the Enterprise<\/a> research points to the same trend: companies are launching numerous pilots but are scaling fewer than 30% of them.\u00a0<\/p>\n The pace of AI innovation is extraordinary. New models, tools, and capabilities arrive almost weekly. It\u2019s easy to focus on the newest breakthrough and assume that\u2019s where progress will come from.\u00a0<\/p>\n But in most organizations, the limiting factor isn\u2019t the technology. It\u2019s the foundation around it: Data architecture. Integration through APIs. Governance. Process redesign. Performance. These are not the headlines in AI, but essentials for scaling AI across a business. Without them, even the most advanced models can remain isolated experiments.\u00a0<\/p>\n<\/div>\n And AI transformation is not just technical. It changes how people work together and how decisions are made. Judgment, creativity, and accountability remain human responsibilities. That means leaders must think just as carefully about operating models, ethics, and workforce design as they do about model selection.\u00a0<\/p>\n Organizations that succeed tend to approach AI from this broader perspective. They see it as a shift in how the enterprise works, not just a new set of tools.\u00a0<\/p>\n Building an organization around AI is not a single initiative. It\u2019s a series of deliberate shifts.\u00a0<\/p>\n<\/div>\nSeven principles for moving beyond pilots\u00a0<\/h2>\n