CXO Look’s current leadership coverage makes a useful point for executives navigating generative AI: leaders create value by asking better questions and building the right guardrails. That idea has become more urgent as reports of frontier models taking unexpected autonomous actions during controlled security testing sharpen concerns about access, identity, and agency inside enterprises.
The management problem will grow as AI agents gain the ability to retrieve information, use software tools, communicate with other systems, and take actions across multiple steps. A business leader’s guide to AI agents from OpenAI describes companies moving from experimentation toward integration. Anthropic’s 2026 State of AI Agents report similarly argues that quality controls and managerial systems must evolve as non-human work product becomes more common.
CEOs should treat that transition as an authority-design problem. Before an agent gets broader access, every high-value workflow should have an escalation map that answers a simple question: when must the machine stop and hand control to a person?
The first element is an authority boundary. Leaders should distinguish among tasks an agent may perform, recommendations it may make, and decisions it may execute. An agent might gather supplier data and draft a comparison, for example, while a procurement leader retains authority to change vendors or commit funds. A sales agent might prepare a renewal offer while a manager approves unusual discounts or terms. An HR system might organise applications while a qualified person remains responsible for consequential employment decisions.
The second element is a set of explicit escalation triggers. These should reflect business consequences, rather than model confidence alone. A handoff may be mandatory when a transaction exceeds a financial threshold, a request involves sensitive information, the agent encounters conflicting instructions, a customer asks for an exception, a decision affects legal rights, or the system attempts to use a tool outside its normal scope. A well-designed trigger gives employees permission to interrupt automation before a small anomaly becomes an expensive incident.
The third element is identity and permission. Agents should receive only the access needed for the task, with clear limits on what they may read, change, approve, or send. Shared credentials and broad standing permissions turn an operational mistake into a larger security problem. Executives should ask whether the organisation can reconstruct which system acted, under whose authority, with which permissions, and against which source information.
The fourth element is a named human owner. “Human in the loop” sounds reassuring while saying very little about actual accountability. The escalation map should name a role, define how quickly that person must respond, and state what happens if the owner is unavailable. When an agent pauses a pricing decision, a security response, or a customer commitment, the business needs a real person with the authority to resolve the exception.
The fifth element is an evidence trail. For consequential workflows, organisations should retain enough information to understand what the agent received, which tools it used, what action it proposed or took, whether a person intervened, and what happened afterward. Leaders do not need to preserve every low-risk interaction forever. They do need a reliable record for decisions that can create financial, legal, operational, security, or reputational consequences.
Finally, every escalation map needs a fallback. If the agent fails, loses access, produces contradictory output, or creates an unusual pattern of exceptions, the workflow should have a safe manual route. The point of automation is resilience and leverage. A process that collapses when the automation stops creates a new form of operational fragility.
CEOs can test this approach without launching a company-wide governance programme. Pick one agentic workflow for 30 days. Count escalations, overrides, permission failures, repeated exceptions, downstream rework, and time to resolution. Compare those measures with the promised productivity gain. If the system saves ten minutes at the front of a process but creates thirty minutes of review and repair later, the organisation has learned something more valuable than a demo can show.
This changes the questions executives bring to AI steering meetings. Instead of asking only how many employees use AI or how many agent projects have launched, ask where authority has shifted, where the handoffs occur, which exceptions repeat, and whether managers can reconstruct a consequential decision after the fact. Those questions connect governance directly to operating performance.
The next phase of enterprise AI will reward leaders who can delegate work without delegating accountability. Agents may become faster, more capable, and more autonomous, but the organisation still owns the outcome. An escalation map turns that principle into something managers and employees can actually use.
Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps technology and finance leaders drive AI adoption and manage disruption. He serves as CEO of Disaster Avoidance Experts, which provides AI adoption consulting services for mid-size and large organizations.
Dr. Gleb wrote seven best-selling books, and his newest book is The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, July 2026). His most recent previous best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His expertise has been featured in over 700 articles and 600 interviews across prominent venues that include Harvard Business Review, Forbes, Fortune, Fast Company, CBS News, CNBC, Fox News, Business Insider, Government Executive, The Chronicle of Philanthropy, The New York Times, NPR, Psychology Today, and Time. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages.
His expertise comes from over 20 years of consulting for mid-size and large organizations that have included Aflac, Applied Materials, Centene, Fifth Third Bank, General Motors, Honda, Nationwide, Sherwin-Williams, and Xerox. It also comes from over 15 years in academia as a behavioral scientist at the University of North Carolina at Chapel Hill and at Ohio State.
A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.
