Most Enterprises Scale AI Agents Before Defining Measurable Business Outcomes

A growing number of enterprises are deploying AI agents at speed without first redesigning the workflows those agents are meant to improve, according to recent industry analysis. IBM research found that 64% of surveyed CEOs cited fear of falling behind as the primary driver of AI investment, even before value is established, while only 25% of AI initiatives delivered expected returns. McKinsey's 2025 survey similarly found that while 62% of organizations were experimenting with agents, only 23% had scaled an agentic system anywhere in the enterprise. Experts argue that autonomy should be granted through a defined 'delegation envelope' covering one actor, one goal, an allowed action set, an evidence contract, and a recovery boundary. The core recommendation is that enterprises should first identify measurable outcomes — such as reducing account-opening time or detecting fraud — and then determine what authority each part of a workflow has actually earned, rather than treating agent deployment as a goal in itself.