By Gagan Bihari Satapathy, Chief Information Security Officer (CISO), iServeU
Artificial intelligence has moved beyond the experimental stage. Now, it is influencing product design, customer experience, risk management, and decision-making within the organization. As AI adoption accelerates across organizations, companies face an important question: Who should own AI?
Is it the CIO, considering that it is technology? Is it the CTO, since it drives innovation? Is it the CMO because AI drives customer engagement? Or should it have its own office?
The reality is that AI is too pervasive to be confined to a single department. Restricting it within the confines of one department usually restricts its capacity. Companies which gain the most out of AI are the ones who understand the fact that AI is a cross-departmental capability with shared ownership and governance.
AI Is a Business Capability, Not Just a Technology Initiative
One of the major misunderstandings about AI is that it is purely a technological endeavour. While technology supports AI, it finds its application in a functional setting. Each functional area has its own goals, performance metrics, and issues to solve. AI solves them all differently: product teams employ AI to speed up documentation and feature planning; operations teams employ AI to automate redundant processes; risk and compliance teams utilise AI to build robust monitoring and governance; and customer-orientated teams utilise AI to increase efficiency and quality of services delivered.
Since all functions use AI differently, accountability for its outcomes must stay with the teams responsible for those functions.
The responsibility for delivering results does not change simply because AI is involved.
AI Changes the Way Work Happens, Not Who Owns It
Think about product development.
Traditionally, product managers used to spend a lot of time creating Product Requirement Documents (PRDs), creating user stories, and describing functional requirements. Today, this kind of content can be created by AI in minutes.
But content creation is a small fraction of what product management is about.
The real value is in validation of assumptions, refinement of requirements, alignment of business priorities, anticipation of customers’ needs, and decision-making. AI may speed up the first draft creation, but only human judgement can determine whether the output is correct, comprehensive, and aligned with business goals.
In other words, AI shifts the way things are done, not the ownership of the process.
The ownership stays with product managers, just like ownership of tech decisions belongs to engineering teams, compliance decisions belong to risk teams, and customer decisions belong to business people.
Distributed Ownership Requires Centralized Governance
As the process of execution continues to be decentralized, governance must not be.
Without a unified governance structure, companies run the risk of forming disjointed AI programs, incompatible standards, duplicate spending, and unchecked risk exposure.
The benefits of central governance are that it:
Enables responsible AI implementation
Supports security and compliance
Fosters data governance
Manages risks
Encourages ethical application
Prioritises spending
Assesses results
Centralised governance provides a level of consistency throughout the company while giving each individual department the opportunity to innovate within established boundaries.
When Teams Compete, Business Priorities Must Decide
With the increasing use of AI, organisations often anticipate friction among technology, data, and business teams.
Often, there is no need for conflict in most AI projects, as they tend to have inherent alignments with business units. Marketing focuses on customer engagement , finance with efficiency, the product team with innovation, and the risk group with governance.
Conflicts, however, cannot always be avoided.
In such situations, the decision process should not be biased by organisation structures or power dynamics within departments. Rather, it needs to take into consideration certain business principles:
For example, does the project increase regulatory compliance?
Does it minimise risks associated with operations?
Does it produce some form of tangible customer value?
Does it directly help business growth?
This helps take the focus off any ownership issues.
Accountability Cannot Be Delegated to AI
Undoubtedly, the most essential thing for organisations to consider when it comes to AI is that:
AI can provide recommendations.
But AI cannot make the decision.
Whether AI writes a software program, a business report, responses to customers, or even compiles documents, someone must validate AI-generated output before it reaches customers or stakeholders .
The issue of accountability does not vanish just because AI is involved in the procedure. On the contrary, AI allows professionals to focus on judgment, creativity, and decision-making instead of routine output rather than wasting time on producing routine output.
Building the Right AI Operating Model
If companies were to design AI ownership starting from scratch, the ideal structure would involve centralised governance combined with decentralised delivery.
Central leadership must set the standards, governance policies, security considerations, and priorities.
The individual business units need to be responsible for delivering, adopting, and executing on them.
Many companies also have AI champions in place in every department — people who know both the business strategy and what AI is all about.
These AI champions serve as enablers to bridge the gap between enterprise strategy and implementation.
The Real Question Isn’t Who Owns AI
Perhaps the debate over which executive should own AI is the wrong question altogether. AI is not yet another enterprise application which needs to find a home in a department.
It is fast evolving into a basic capability that cuts across almost all the areas in an organisation. The organizations that succeed will not be the companies who will centralize all decisions on AI. They will be the organisations that have established governance, enable business functions to innovate in a responsible manner, and keep the ownership of decisions as close to the action as possible.
After all, AI is supposed to supplement human intelligence rather than take over human ownership.
In the coming days, as AI matures, the real advantage will not lie in having the best AI applications but the most appropriate governance and ownership models for them.