Technology 4 min read By Bethany Hadley
AI Agents Shift Business Decision-Making From Insight to Action
Business leaders are moving beyond using AI to surface insights, deploying autonomous agents that make and execute decisions, according to ZS's Aaron Mitchell.
Business leaders are no longer asking for more artificial intelligence. They want better outcomes, and that shift is forcing companies to rethink how people and AI systems work together, according to Aaron Mitchell of the consulting firm ZS.
Mitchell argues that the most significant change in corporate AI is not the quality of insights it can generate but the move towards agents that help make decisions and then carry them out. That represents a step change from the established pattern of using AI to analyse data and present findings for a human to act on.
He describes decision systems as operating across four stages: sensing, modelling, deciding and acting. The first two stages are already familiar territory for many large organisations, which use analytics to detect patterns and forecast outcomes. The decisive shift, in Mitchell's account, is the fourth stage, where autonomous agents take on the execution of decisions rather than merely informing them.
That distinction matters for how businesses design their operations. If an AI system only surfaces a recommendation, the burden of acting on it still falls on managers and their teams. When an agent is authorised to act, the speed of response can increase, but so does the need for clear rules about what the system is permitted to do and when human oversight is required.
Mitchell frames this as a question of design rather than technology alone. Companies that treat AI as a tool for generating reports will capture only part of its value. Those that build decision systems in which people and agents share responsibility for outcomes are better positioned to become what he calls an intelligent enterprise.
The commercial context is a familiar one. Businesses have spent years investing in data infrastructure and analytics capabilities, often with mixed results. Executives have grown wary of pilot projects that produce dashboards but no measurable improvement in performance. The appeal of agents lies in their promise to close that gap by connecting analysis directly to action.
That promise also brings risk. Delegating decisions to software raises questions about accountability, particularly in regulated industries where firms must be able to explain how a choice was made. Mitchell's emphasis on designing decision systems suggests that governance needs to be built into the architecture from the start rather than added afterwards.
For business leaders, the practical implication is that the debate has moved on from whether to adopt AI. The more pressing question is how to reorganise workflows so that human judgement and machine execution complement each other. That requires changes to roles, processes and oversight that go well beyond installing a new platform.
Mitchell's argument reflects a broader shift in corporate technology strategy. After an initial wave of experimentation with generative tools, many boards are now looking for applications that affect the bottom line. Agents that can act, not just advise, are emerging as the clearest answer to that demand, even as companies work out how much autonomy to grant them.
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