In partnership withthe Deloitte Microsoft Technology Practice
In contrast to fixed, rules-based frameworks, AI agents possess the capability to learn, adapt, and enhance processes in a dynamic manner. As they engage with data, systems, individuals, and other agents in real-time, AI agents can autonomously carry out complete workflows.
However, realizing their capabilities necessitates reimagining processes to center around agents rather than merely attaching them to disjointed legacy workflows utilizing conventional optimization techniques. Organizations must adopt an agent-first approach.
In an agent-first organization, AI systems manage processes while humans establish goals, outline policy limitations, and address exceptions.
“You must transition the operating model to make humans the governors and agents the operators,” states Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice.
The necessity of an agent-first approach
As technology investment for AI is projected to rise over 70% in the coming two years, AI agents, driven by generative AI, are set to revolutionize organizations and deliver outcomes that exceed conventional automation. These initiatives hold the potential for substantial performance improvements while directing human effort towards higher-value tasks.
AI advancements are occurring so rapidly that traditional task automation methods are likely to yield only marginal improvements. Since legacy procedures are not designed for autonomous agents, AI agents necessitate machine-readable process specifications, clear policy constraints, and organized data flows, according to Rodgers.
Complicating matters, numerous organizations lack a comprehensive understanding of the full economic factors influencing their business, such as service costs and per-transaction expenses. Consequently, they struggle to prioritize agents that can generate the highest value, focusing instead on eye-catching pilot projects. To realize substantive change, executives should adopt a new mindset.
Companies must instead facilitate outcomes more rapidly than their competitors. “The true danger is not that AI will fail—it’s that rivals will overhaul their operational models while you remain focused on piloting agents and copilots,” warns Rodgers. “Nonlinear improvements arise when organizations establish agent-centered workflows with human oversight and adaptable orchestration.”
Automated handling of routine and repetitive tasks is increasingly prevalent, allowing employees to concentrate on more valuable, creative, and strategic initiatives. This transformation enhances operational efficiency, promotes stronger collaboration, and accelerates decision-making—enabling organizations to modernize the workplace while ensuring enterprise security.
This content was produced by Insights, the custom content division of MIT Technology Review. It was not authored by MIT Technology Review’s editorial team. It was researched, crafted, and created by human writers, editors, analysts, and illustrators. This includes writing surveys and gathering data for surveys. Any AI technologies involved were restricted to secondary production processes that underwent thorough human oversight.
