“Traditionally, CEOs set the company’s vision and COOs executed it with great efficiency”, said Edgeverse. But the role of the Chief Operations Officer (COO) is changing, as AI transforms industries. The COO can become a pivotal lever for transformation. With overall responsibility for operations, the COO is uniquely positioned to lead a comprehensive strategy for organizational change.
A closer examination of the reality reveals that on the one hand, COOs and the C-suites view AI through rose-colored lenses; but on the other hand, they have not comprehensively grasped exactly where and how to implement AI transformation.
A 2025 Deloitte survey, for example, found significant gaps between C-suite and non-C-suite views of AI risks, benefits, and implementation. The C-suite was substantially more optimistic than the rest of the workforce, showing a need for greater alignment and a comprehensive strategy to turn AI implementation into real transformation.
“The value of AI comes from rewiring how companies run,” according to a recent McKinsey study, which also states that redesigning workflows has a major impact on an organization’s “ability to see EBIT impact from its use of gen AI.” But only one in five executives said that gen AI increased revenue by more than 5%, according to another survey.
The COO can change these dynamics by leading a comprehensive AI strategy, and bringing greater alignment among functional areas, resulting in greater ROI from AI investments. This article will examine three areas in which the COO can lead transformation in the AI age, together with the rest of the C-suite and even the board of an organization.
Before launching any AI initiative, the COO must make sure the organization is prepared for it with an AI data strategy. The effort should include three broad areas:
AI governance: AI governance includes, among other things, the ethical and legal standards that act as “guardrails” for the use of AI, such as compliance with legal and regulatory regimes that are already in place in the U.S., the E.U., and elsewhere. The COO must also ensure that the organization’s internal standards build confidence among customers, suppliers, and other stakeholders, that its AI solutions are reliable, and that their data is secure and won’t be misused. Both the C-suite and the board of directors need to be on top of other aspects of AI Governance, such as defining and refining purpose, principle, policy, and risk management throughout the life cycle of model development, validation, data management, monitoring, reviews and model retirement and/or upgrading.
Cybersecurity: Cybersecurity is another critical concern for the COO. AI applications, network systems, connected devices, and cloud resources vastly increase the number of contact points between internal systems and the outside world. Constant communication with the Chief Technology Officer (CTO) or Chief Information Officer (CIO), Chief Information Security Officer (CISO) is essential. Equally important, organizational culture, policy, and procedures must emphasize proactive measures to protect sensitive data and privacy.
Data Management: Data management has been identified as a challenge by 70% of AI “high performers,” according to McKinsey. One challenge is to ensure the availability of data from legacy systems for AI applications. Another is to compile data into a consistent whole, since different functional areas may collect incomplete or contradictory information. Creating a centralized hub to collect data and eliminate discrepancies can be one of the useful approaches. Keeping humans in the loop to maintain oversight, check for errors, and come to a consensus about AI mistakes overlaps AI governance.
The COO is best positioned to identify high value “domains” for AI transformation, avoiding a piecemeal approach. Without an overarching vision and strategic oversight, individual business units or functions may implement AI tools that only solve local problems but fail at the enterprise level. The results can be wasted investment and lost opportunities.
The challenge, per McKinsey, is to find a balance between designing solutions large enough “to achieve meaningful end-to-end impact”, and yet small enough to be completed in a “reasonable period of time.”
One approach is to address immediate “pain points” while looking for root causes and systemic issues that can be transformed over time as part of a comprehensive solution.
COOs can prepare for change through a program of growth and development.
COOs can meet the challenge by embracing a “learn it all” culture, in the words of Microsoft CEO Satya Nadella, based on a growth mindset, which holds that skill, talent, and intelligence can all be developed.
Accelerate learning and development by making sure employees have meaningful opportunities to gain skills and knowledge in the workplace. Long term success will come from building a learning culture that encourages curiosity and exploration, more than task-focused skills alone.
The COO can lead transformation in the AI age by creating a coordinated strategy for the organization. With a strong data strategy, strategic implementation, and developing a learning culture, the COO can be the focal point for change.
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