Author: IRPA AI Senior Analyst, Kieran Gilmurray
This article explores why the question of who should own AI in the C suite is becoming harder, not easier, as systems move from passive tools to agents that can plan, decide, and act across workflows. Rather than hunting for one executive owner, leaders now need a clearer map of which AI decisions belong to which functions.
After reading this article you will understand why agentic AI creates a rational ownership fight across the leadership team, what a practical decision rights model looks like, and which controls need to be in place before autonomous systems can operate safely at scale.
What makes AI “agentic” in practical terms
The easiest way to explain agentic AI is to stop thinking about it as a smarter chatbot. OECD analysis shows interest in “agentic AI” surged as more capable models began to plan, act, and interact in agent like ways, while the UK government describes a broader shift from tools to agents. In plain English, the difference is that a tool waits for instructions, while an agent is designed to pursue a goal with some degree of autonomy.
That shift matters because it collapses the old boundary between software and work. An agent can draft, retrieve, route, prioritise, trigger follow on actions, and sometimes operate for long periods without constant human prompting. Recent product releases show this clearly, with command centres emerging for managing multiple agents and handling long running tasks.
Once systems behave more like delegated actors, the ownership question changes. The issue is no longer who bought the software licence. It is who authorised the system to act, who set its boundaries, who pays for its run time, who monitors its behaviour, and who answers when it causes harm.
Why the ownership fight is rational
Recent leadership discussions highlight that multiple C suite roles can legitimately claim ownership of AI. That is not political tension. It reflects the fact that agentic systems simultaneously create technology risk, process risk, financial exposure, workforce implications, and data governance issues.
The CIO has a claim because the systems must be integrated, secured, and operated. The COO has a claim because agents sit inside workflows and affect execution. The CFO has a claim because spend and ROI become material when agents run continuously. Risk and legal have a claim because autonomous actions raise questions of accountability and harm. CHRO and CDO also have legitimate territory when agents influence people processes or depend on sensitive data.
This is why a single owner model often breaks down. Enterprise experience shows organisations are still struggling with engineering, integration, and governance. If the operating reality is already distributed, executive accountability has to be distributed too.
Replace ownership with decision rights
The most useful replacement for “who owns AI?” is “who owns which AI decisions?” That sounds more technical, but it is actually simpler. It forces leaders to define the decisions that matter and assign them to the functions best placed to make them.
In practice, this means business leaders own outcomes, technology leaders own architecture and integration, security owns permissions and access, finance owns spend controls, and risk and legal own rules, escalation, and accountability.
This approach works because it ties governance to real controls rather than titles. It avoids the failure mode where someone is named as the AI owner but lacks authority over budgets, systems, or risk decisions.
The controls that matter before agents can act
The best way to judge whether governance is real is to inspect the controls. Recent platform updates, such as the introduction of usage tiers and spend caps in major APIs, show how governance is becoming embedded into tooling.
If an agent can access systems, trigger actions, or affect outcomes, a small set of controls should exist before deployment. These include a distinct identity, least privilege access, hard budget limits, explicit approval gates, and full logging of actions and decisions.
Standards bodies reinforce this direction. Security guidance highlights the need to treat agents as identities with permissions and potential attack surface, while research on agent autonomy shows that human oversight remains necessary even as autonomy increases.
What changes across the UK, EU, and US
The governance model must also reflect regional expectations. In the EU, the AI Act becomes fully applicable in August 2026, with earlier provisions already in force. This creates a clear timeline for transparency, accountability, and compliance obligations.
The EU’s broader compliance ecosystem shows increasing alignment among major model providers, particularly around transparency and safety artefacts. This gives organisations a clearer basis for procurement and governance decisions.
In the UK, recent government publications emphasise that increased autonomy raises the consequences of errors and requires stronger trust and accountability frameworks. Regulatory updates reinforce the importance of data protection and responsible use.
In the US, regulation is more fragmented. This makes standards based approaches more important, particularly around cybersecurity, permissions, and monitoring.
Does a Chief AI Officer help?
A Chief AI Officer can help, but only if the role owns coordination rather than trying to own all AI decisions. The role is most effective when it maintains the decision rights map, aligns functions, and prevents fragmentation.
If the role becomes a bottleneck for all AI decisions, it slows execution and creates unnecessary complexity.
The test is simple. If the role increases clarity and speed, it adds value. If it concentrates control without improving outcomes, it needs to be redefined.
How to know if the model is working
A strong governance model improves both speed and control. That means faster movement from pilot to production, fewer incidents, better cost control, and clearer evidence of oversight.
The most practical indicators are operational. Leaders should be able to see which agents exist, what they can access, what they cost, who approved them, and what actions they have taken.
If audit, legal, finance, and security teams cannot answer those questions quickly, then governance is not yet working.
Conclusion
The C suite ownership question is real, but it is also solvable. The mistake is trying to answer it with a single role.
Agentic systems are too cross functional for that approach. The practical solution is to assign ownership for specific decisions and link those decisions to real controls.
This turns governance from a debate into a system. And in a world where AI systems act, spend, and decide, that system is what actually determines whether organisations move fast and stay in control.
END
Photo by ‘Yibei Geng’ on Unsplash.
About the Author: Kieran Gilmurray
Kieran is a globally recognized authority on AI, automation, and digital transformation, having authored multiple influential books and hundreds of articles that have earned him prestigious accolades, including being named a Top 50 Global Thought Leader and Influencer on Generative AI in 2024, a Best LinkedIn Influencer for AI and Marketing, Top 50 Global Thought Leaders and Influencers on Manufacturing 2024, Top 14 people to follow in data and one of the World’s Top 200 Business and Technology Innovators.
CLICK HERE TO SCHEDULE AN ANALYST CHAT
Links:
Originally posted in the IRPA AI Network — Announcements & Updates