Organizations are facing significant challenges in preparing their operating models and workforces for the widespread adoption of AI agents, according to recent research from Deloitte. While there is a strong expectation that AI agents will transform work by boosting productivity and enabling employees to focus on higher-value tasks, many businesses lack the necessary processes and workflows to fully realize these benefits.
Approximately half of the surveyed leaders indicated an understanding of how AI agents will impact their future operating models. However, several key obstacles are hindering broader implementation. These include the absence of a unified and accessible data foundation, limited trust in and governance of AI agents, and the inherent cost and complexity associated with integration. Broader AI adoption is also constrained by issues related to data management, decision-making frameworks, organizational design, and overall workforce readiness.
Fewer than half of the leaders surveyed reported that their organizations are adequately prepared for agentic AI across most business areas. Workforce readiness and business processes were identified as the weakest points, suggesting a need for organizations to develop entirely new methods of collaborating with AI agents. The analysis indicates that a sustained focus on improving work outcomes, rather than isolated fixes, is crucial for successful AI transformation.
Leaders anticipate that AI agents will drive significant changes in both business processes and the workforce over the next four years. They foresee processes being redesigned to support real-time decision-making and greater autonomy, with agents performing complex, multi-step tasks across various business functions while humans primarily provide oversight.
Currently, only 16% of leaders believe their organizations' processes are ready for agentic AI, with a mere 5% describing them as highly prepared. Even among organizations that have deployed AI agents at scale, readiness remains limited, with only 46% reporting prepared business processes. Executives and AI/data science leaders attribute this lack of readiness to poorly documented processes, fragmented data and systems, entrenched work habits, and insufficient AI expertise among both leadership and employees. Only one in five organizations are prepared to redesign business processes for autonomous operation with AI agents.
Many organizations are opting to integrate AI agents into existing workflows rather than undertaking a complete redesign of processes. This layered approach may offer quicker short-term returns and help organizations gain experience and credibility with the technology. However, long-term value is expected to depend on a more fundamental redesign of business processes around AI agents, a transition that is likely to span several years. About 31% of surveyed organizations expect at least half of their business processes to be redesigned or rebuilt around AI agents within two years, a figure that rises to 74% over the next four years.
Job disruption is also expected to accelerate, with approximately 43% of leaders anticipating significant to extreme job disruption within the next 12 to 18 months. Routine and structured tasks are projected to become automated, while creative, strategic, and high-judgment work will continue to require human involvement. Employees are expected to direct AI agents, review their output, validate quality, and determine when human judgment is necessary.
Organizations are preparing their workforces for this shift through AI literacy programs and targeted training. About 71% are providing baseline AI agent literacy training, and 65% are focusing on upskilling or reskilling for roles expected to grow or change due to agentic AI. However, half of the surveyed leaders believe their organizations are not investing enough in the workforce changes needed to support AI agent adoption. Rising infrastructure and usage costs are adding pressure, as companies must allocate resources between technology and workforce investments. Governance will play a critical role in defining these new working arrangements, including the scope of agent decision-making, human intervention points, accountability for outcomes, and how tasks transition between employees and AI agents.
