When AI Takes Action, Who Takes Responsibility?
Generative AI has already changed how contact centers work by helping agents summarize conversations, surface knowledge and draft responses, but the next wave is likely to be far more consequential.
AI agents are beginning to execute work on their own, with the ability to approve refunds, grant policy exceptions, update customer records and complete transactions without waiting for human approval.
That promises faster service and greater efficiency, but it also forces enterprises to answer a question many have not fully considered: When an AI agent makes a business decision, who owns the outcome?
Accountability Doesn’t Change
The answer isn’t the AI vendor or the IT department. It’s the business. Giving an AI agent authority to act doesn’t transfer responsibility for its decisions.
“The business process owner owns the outcome. Full stop,” said Michael Bevilacqua, vice president of AI product management at Adeptia. “Accountability is not a thing you can delegate to software; the agent is an instrument, and the person who owns the process owns the result of using it.”
He notes the customers who get this right put one empowered person on their side of the table with authority over data access, workflow change, and governance trade-offs.
“Without that human owner, you do not have accountability; you have a diffusion of responsibility waiting for an incident to occur,” Bevilacqua said.
Harry Folloder, chief commercial officer at Krisp, says he agrees, noting the AI agent is executing a business process, and accountability follows the process, not the technology.
“The technology team builds the system to spec, but the spec is a business decision: what the agent is allowed to do, under what conditions, with what limits,” he said.
That distinction becomes increasingly important as AI moves beyond recommendations. A refund issued in error, a policy exception that creates compliance risk or a customer decision that damages trust all stem from business rules established long before the agent acted.
In this future, technology teams are responsible for building secure, reliable systems, while business leaders remain responsible for defining acceptable outcomes, escalation paths and risk tolerance.
Authority needs boundaries
Authorization is among the biggest challenges facing enterprises as they evolve AI capabilities.
“The biggest gap is what I call the authorization void,” Folloder said. “Companies have given AI agents the ability to act without defining the authority under which they act.”
Adam Markowitz, co-founder and CEO of Drata, says many organizations still can’t answer basic operational questions.
“The organization needs to know the agent’s owner, identity, permissions, policy, and decision trail,” he said. “Without that, accountability breaks down before the incident can even happen.”
Bevilacqua argues that enterprises should think about AI agents the same way they think about employees.
“Assign authority to an agent the same way you assign it to an employee — through a scoped identity,” he said. “An agent should be able to act autonomously inside its scope and be structurally unable to act outside it.”
That means limiting access through role-based permissions, defining exactly what data an agent can use and granting only the authority necessary for its assigned task.
Risk should drive autonomy
Organizations often make one critical mistake in determining autonomy based on volume rather than business risk. Instead, authority should be tied to the potential consequences of each decision.
“Start with two questions: what are the stakes, and is the decision reversible?” Folloder said. “Low stakes, reversible: autonomous is fine. High stakes, irreversible: a human approves before the agent acts, not after.”
Talia Schmerling, head of product at Swiftly, recommends applying the same framework companies already use for employees.
“Not every employee can approve a refund, waive a fee, override a policy, or access sensitive data,” Schmerling said. “AI agents should be permissioned the same way, by role, context, dollar threshold, risk level, and reversibility of the action.”
Routine actions such as routing customer inquiries or issuing small courtesy credits may be good candidates for autonomous execution. Larger refunds, pricing changes or compliance-sensitive decisions should continue to require human approval.
“The future of enterprise AI is not unchecked autonomy,” Schmerling said. “It is delegated autonomy, where agents can act quickly, but every action has clear authorization, observability, and accountability behind it.”
Auditability becomes essential
As AI agents begin acting independently, organizations need far more than traditional approval records.
“Every AI agent with execution authority needs a decision log with business context,” Folloder said. “Not just what it did. What it knew, what rules were in effect, and why it acted — in language a compliance officer can read.”
Markowitz adds point-in-time approvals quickly become obsolete as agents evolve and permissions change.
“Organizations need live agent inventories, identity mapping, permission and scope tracking, continuous monitoring, drift detection, inline policy enforcement, and tamper-evident decision logs,” he said.
Bevilacqua says every consequential action should be traceable to an agent identity, its authorization, the data it used and the business rules that informed the decision.
“That turns ‘the AI did it’ into a defensible chain of custody,” he said.
Governance becomes an operating model
The next evolution of enterprise AI may have less to do with better models than better management.
With agents taking on greater authority, organizations are expected to move beyond ad hoc oversight and establish formal operating models that define ownership, permissions and accountability across the business.
That means that rather than treating AI governance as primarily a security or IT function, enterprises will need clearly defined responsibilities spanning business leaders, security, legal, compliance and engineering.
It’s a shift that could include dedicated governance roles responsible for defining what AI agents are authorized to do, maintaining audit readiness and ensuring every production agent has documented authority and approval before it begins executing business processes.
“Enterprises that treat agent governance as an operating model now will be the ones that can scale autonomous action safely, rather than pulling it back after the first incident,” Bevilacqua said.
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