Priority No. 2: Orchestrating shifts in work and human-agent responsibilities
Unlike many previous technology-driven implementations that focused on specific functions or processes, agentic AI can deliver the most value when organizations rework end-to-end workflows, not simply automate existing tasks.
Deploying AI agents into existing roles, workflows, and processes without redesigning the work itself is unlikely to yield the benefits COOs and their organizations are seeking.7 The transition requires a shift from fixed task completion to fluid human-agent collaboration.8
To unlock value across the organization, COOs should approach process transformation as a redesign challenge rather than an optimization exercise. This involves rebuilding operating models so that humans and agents can each play to their strengths, with clear accountability measures built into every step.
Getting that right requires a holistic view of the organization. Alessio Marras, cofounder and head of organization for AideXa, an Italian digital bank serving small- and medium-sized businesses, says: “We’re developing a broader strategy. It’s based on a clear vision for where and how we want to use AI agents, then implementing them progressively. The challenge is coordination–moving from one use case to multiple orchestrated agents requires an end-to-end view. Agents that are very efficient in single use cases can become much more difficult to coordinate and govern when they operate as part of an orchestrated system. That’s why we believe in continuously monitoring outcomes and being ready to adjust the balance between automation and human oversight as processes evolve.”
AI can help reduce the time employees spend on execution, analysis, and routine tasks, allowing them to focus more on strategic and value-creating priorities and responsibilities. But in the near term, organizations may face challenges in developing the internal agentic AI expertise required for scaling.9 Employees who have worked alongside external teams may need to be reskilled to operate effectively in an agent-based environment. That transition will require COOs and other senior leaders to carefully manage accountability, risk, and performance. They will need to establish guardrails that account for multi-agent systems and the dynamic relationship between humans and machines.10
As agent networks grow, the complexity of coordinating them—across business units, systems, and decision points—could increase rapidly. COOs may need to manage that complexity by defining how work should flow between humans and agents; where escalation points should be placed; how to monitor performance across the full system rather than within individual use cases; and how to maintain visibility and control as the network scales. Getting these orchestration layers right is what may likely set organizations that build coherent, enterprisewide operational capabilities apart from those that are only able to implement a collection of AI experiments.
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