Chief operating officers are emerging as the key leaders who can translate ambitious AI plans into real operational results, but most organizations lack the governance structures to manage the risks that come with autonomous AI agents. While 74% of companies expect to use AI agents within the next two years, only 21% have established mature governance models to oversee them, creating a significant gap between AI ambition and organizational readiness.

What's Driving the COO's New Role in AI Transformation?

Agentic AI, which refers to autonomous AI systems that can make decisions and take actions with minimal human intervention, represents a fundamental shift in how work gets done. Unlike earlier AI tools that required constant human oversight, these agents can operate independently across business processes. This capability is powerful, but it also demands a level of coordination and strategic oversight that goes beyond traditional technology management.

The challenge is that many organizations are rushing to adopt agentic AI without the foundational structures in place. Deloitte research shows that leaders feel compelled to move quickly, but few are actually prepared for large-scale deployment. This is where COOs come in. Unlike Chief Information Officers who focus on technology infrastructure, or Chief Financial Officers who manage budgets, COOs oversee the entire operating model, making them uniquely positioned to orchestrate AI adoption across the enterprise.

How Can COOs Successfully Lead Agentic AI Adoption?

Deloitte's research identifies four core priorities that COOs should focus on to bridge the gap between AI aspirations and operational reality:

• Business Alignment: Start by collaborating with C-suite leaders to define clear business outcomes before deploying AI agents. This ensures that AI investments directly support strategic goals rather than becoming expensive experiments that generate no measurable value.
• Orchestration of Workflows: Design systems that coordinate how AI agents and human workers interact. This requires rethinking organizational structures and decision-making processes to leverage agent capabilities while maintaining human oversight where it matters most.
• Operational Flexibility: Build processes that can adapt as technology evolves and the organization learns. Rigid implementations often fail because they cannot accommodate new capabilities or changing business needs.
• Disciplined Scaling: Track value delivery against real success criteria rather than simply counting the number of agents deployed. This prevents organizations from scaling ineffective implementations.

As Sameer Shetty, group head of digital business and transformation at Axis Bank, one of India's largest private sector banks, explained:

"You can spend a lot of money on agentic AI without achieving real, valuable outcomes. You need the end-to-end view."

Sameer Shetty, Group Head, Digital Business and Transformation, Axis Bank

This perspective underscores a critical insight: technology alone does not drive transformation. The COO's role is to ensure that AI agents are deployed strategically, with clear business justification and measurable outcomes.

Why Is Governance the Hidden Bottleneck?

The governance gap is particularly concerning. While 74% of companies plan to adopt AI agents, only 21% have mature governance models in place. Governance in this context means having clear policies, risk management frameworks, and decision-making authority structures that define how AI agents operate, what decisions they can make autonomously, and when human review is required.

Without strong governance, organizations face several risks. AI agents operating without clear boundaries can make costly mistakes, create compliance violations, or damage customer relationships. In regulated industries like banking, healthcare, and government services, the stakes are even higher. This is why companies like Conduent are investing in leadership talent with deep expertise in both AI and operational infrastructure. Conduent recently appointed Narayanan Sundaresan as Chief Information and Technology Officer, bringing 28 years of experience in enterprise AI and digital transformation. His mandate explicitly includes accelerating AI-powered capabilities while strengthening the secure, reliable technology foundation that underpins operations.

Sundaresan's appointment signals a broader market shift. Among channel partners surveyed, 78.3% of AI software sellers expect AI to drive business growth in 2026, and 86.7% of AI consulting sellers rank AI consulting as a top growth service. However, the competitive bar is rising. More than half of channel partners, 55.6%, already claim deep subject-matter expertise in AI. This means that simply deploying AI is no longer a differentiator; the real competitive advantage comes from deploying it responsibly and at scale.

What Role Does Data Intelligence Play in Agentic AI Success?

As organizations scale agentic AI, the quality and governance of underlying data becomes increasingly critical. AI agents make decisions based on the data they access, so poor data quality, outdated information, or biased datasets can lead to poor agent decisions at scale. This is why data intelligence, which includes data quality management, governance, and security, is becoming a core component of agentic AI strategies.

The security dimension is particularly important. AI model inversion attacks, which attempt to reverse-engineer sensitive information from trained AI models, now cost an average of 6 million dollars to address. This underscores the need for robust data protection and governance frameworks that prevent sensitive information from being exposed through AI systems.

Organizations that successfully implement agentic AI are those that treat data intelligence as a foundational layer, not an afterthought. This means investing in data governance, security infrastructure, and quality assurance processes before deploying agents at scale.

What Should Organizations Do Right Now?

The window for getting agentic AI adoption right is narrowing. As more competitors move forward with implementations, organizations that lack clear strategies and governance frameworks risk falling behind. However, rushing into deployment without proper planning is equally risky. The most successful organizations will be those that take a measured, strategic approach guided by experienced operational leaders.

For many organizations, this means elevating the COO's role in AI strategy and giving them the authority and resources to coordinate adoption across the enterprise. It also means investing in governance frameworks, data intelligence capabilities, and change management programs that help employees adapt to working alongside AI agents. The companies that get this balance right will unlock significant competitive advantages in the years ahead.