The Moment the Question Changes
In February 2024, a Canadian tribunal ruled against an airline whose chatbot had provided incorrect information to a customer. The company argued, in effect, that the bot was responsible for its own mistake. The tribunal disagreed. The airline was ordered to compensate the customer, reaffirming a simple legal truth: if it acts on your website, it is your responsibility.
On the surface, it was a minor consumer dispute. But strategically, it signalled something larger. AI systems are no longer passive tools. They are active participants in business processes. They communicate, decide, escalate, and transact. And as autonomy increases, responsibility becomes harder to locate.
The question facing boards is no longer “Can we deploy AI?” It is “Who owns the agent?”
Autonomy Without a Clear Owner
AI systems are evolving from advisory copilots into operational actors. They initiate workflows, coordinate across systems, prioritise cases, and make decisions at speed. In many enterprises, agents now operate across CRM, finance, HR, support, and analytics environments with minimal human intervention.
Technically, this is impressive. Organisationally, it is destabilising.
Traditional governance assumes a human decision owner at each critical juncture. Responsibility is attached to a role. Accountability sits at a named individual or executive level. When a loan is approved, a clinician alerted, or a payment released, someone is ultimately answerable.
Agentic systems compress those decision points. They sequence actions automatically. They combine data sources. They interpret context. They learn.
The academic literature anticipated this dynamic. The “responsibility gap” describes how learning systems can behave in ways not fully foreseeable by any single designer or operator. Meanwhile, the concept of the “moral crumple zone” captures how blame often collapses onto the nearest human operator, even when that individual has little practical control over system behaviour.
In enterprise environments, this translates into a governance vacuum. Product teams configure the agent. Data teams own inputs. Platform teams manage runtime. Legal defines compliance. Business leaders own outcomes. When something goes wrong, accountability fragments.
Autonomy increases. Responsibility diffuses.
Regulation Is Catching Up, Slowly
Regulators are responding, but not from a unified doctrine.
The EU AI Act introduces operational obligations for high-risk AI systems, including requirements for logging, traceability, and assigned human oversight. Deployers must monitor system performance and designate competent natural persons with authority to oversee its use. The message is clear: autonomy must be supervised.
Yet the Act deliberately defers many civil liability questions to existing national law. It creates compliance duties without fully harmonising who pays when harm occurs.
In the UK, policymakers acknowledge that allocating accountability across AI supply chains is difficult, especially where highly capable general-purpose systems are involved. Governance principles are articulated, but responsibility across vendors, integrators, and deployers remains context dependent.
Meanwhile, courts are offering practical clarity. In the Air Canada case, the tribunal rejected the idea that a chatbot could be treated as an independent actor. The deploying organisation remained liable.
The emerging pattern is pragmatic. Externally, accountability defaults to the deployer. Internally, governance often remains ambiguous.
The Strategic Shift: From Deployment to Designated Ownership
For boards and executive teams, this is not a philosophical issue. It is a structural one.
AI adoption is accelerating. A major global survey reported that 88% of organisations use AI in at least one business function. Yet governance maturity lags. Only 39% of Fortune 100 companies disclosed any board oversight of AI as of 2024. Many directors report limited knowledge of AI risks.
The gap matters more when systems move from advisory to autonomous.
Consider autonomous vehicle testing. Following a fatal 2018 collision involving an automated driving system, investigators focused not only on technical performance but on organisational safety culture and risk management practices. The failure was interpreted as a governance failure, not merely a software malfunction.
The pattern repeats across domains. In financial services, supervisory guidance already requires model inventories, assigned responsibilities, and ongoing monitoring. In healthcare, regulators emphasise post-market monitoring of AI-enabled devices.
Agentic AI compresses these expectations into mainstream enterprise systems. When an agent can initiate actions across ERP, CRM, and finance platforms, it behaves less like a feature and more like a delegated executive assistant with system access.
The strategic shift is clear: organisations must treat agents as governed actors, not experimental tools.
This means explicit ownership at four levels:
- Ownership of business intent, what the agent is authorised to optimise.
- Ownership of runtime behaviour, logging, monitoring, escalation.
- Ownership of data and permissions, what the agent can access and trigger.
- Ownership of compliance and risk, regulatory, reputational, financial exposure.
Without this, enterprises recreate internal moral crumple zones. Frontline users are blamed for outcomes shaped upstream by model design, data constraints, or incentive structures.
The Human Dimension: When Responsibility Becomes Abstract
Autonomy changes behaviour.
When an agent operates reliably and efficiently, humans step back. Oversight becomes passive. Decision authority shifts quietly from named individuals to distributed systems.
You see it in customer service when agents auto-resolve tickets. In finance when payment approvals are pre-validated by models. In HR when screening is automated. The system appears to “just work.”
But when a decision triggers harm, a denied claim, a discriminatory outcome, a financial loss, the instinct is to ask: who signed this off?
If no one can answer clearly, trust erodes.
Autonomy does not eliminate responsibility. It obscures it.
For employees, this creates uncertainty. For customers, opacity. For regulators, suspicion. For boards, exposure.
The accountability question is not about whether AI should be used. It is about whether governance has kept pace with delegation.
If your agent can act, you must know who answers for its actions.
What Happens Next
The accountability gap will not close through rhetoric. It will close through structure.
Regulation is already moving towards lifecycle logging, designated oversight roles, and explicit monitoring obligations. Courts are signalling that deployers remain responsible. Insurance markets are beginning to price AI-related risk.
The organisations that navigate this transition successfully will do three things.
First, they will inventory every agent as a governed system, not a feature. If you cannot list it, you cannot own it.
Second, they will assign named executive accountability. Not “the AI team.” Not “the platform.” A role with authority and reporting lines.
Third, they will build incident playbooks that treat agent misfires like operational events, not technical bugs.
Autonomous systems are becoming operational actors inside the enterprise. They do not remove responsibility. They redistribute it.
The question is not whether AI agents will act. They already do.
The real question is whether your organisation knows who owns the consequences when they do.
