Maximizing Operational Impact from AI

Generative AI (gen AI) and the agents built on it promise a lot: faster processes, lower costs, and new revenue streams. And for some early adopters, that's already happening — they're using gen AI to write code, maintain equipment, and even unlock millions in new earnings. Analysts believe AI could eventually drive trillions in productivity growth.

But many businesses aren't seeing those results yet. Most are still stuck in pilot mode, unsure how to scale AI or tie it to real revenue. Surveys show only a small number of companies are getting strong returns from AI, and many leaders say progress feels slow.

This gap between promise and reality is where the COO plays a critical role.

1. Set Up the Right Structure

AI impacts every part of the business, so it needs to be managed carefully. Without the right structure, teams work in silos and miss out on bigger wins.

• Start with a centralized approach: In the beginning, create a Center of Excellence (COE) or "AI factory" where operations, IT, and business units can work together. This helps set standards, avoid duplicated efforts, and focus on use cases that matter at the enterprise level.
• Think in domains, not just tasks: Don't just automate small tasks — look at entire business areas (like customer onboarding or procurement) to find bigger opportunities.
• Let the model evolve: As the company matures, business units can take more ownership of AI projects, while still getting support and guardrails from a central team.

2. Get Data Governance Right

AI only works if the data is accurate, available, and well-managed. Many companies struggle here, especially with older systems and siloed data.

• Create a single source of truth: COOs should back centralized data platforms that pull together information from across the business.
• Keep humans in the loop: AI still needs oversight. Teams should regularly check outputs, validate results, and fix issues.
• Build governance into the process: Set up clear rules for how data is cleaned, reviewed, and updated. This helps reduce risks tied to bad data or unreliable models.

3. Lead the Change

Adopting AI isn't just about new tools — it's about changing how people work. That takes leadership, communication, and support.

• Have a clear vision: Don't just chase automation. Focus on solving big, cross-functional problems that AI can help fix.
• Encourage collaboration: Bring together teams from ops, engineering, and change management to design better workflows with AI in mind.
• Support your people: Start training early. Use "change champions" in each team to spread skills, encourage adoption, and gather feedback.
• Manage the risks: AI comes with new risks — like privacy issues, bias, and unpredictable outputs. These need to be monitored and managed as the tools scale.

Work Closely with IT

For AI to deliver real results, COOs and CIOs need to work as partners. The COO brings the business challenges and opportunities. The CIO brings the tech expertise. Together, they can make sure AI investments are aligned with real operational needs and deliver actual outcomes.

Final Thought

Running lots of small AI pilots won't transform your business. The real value comes from rethinking how your business works — and building the systems, data, and culture to support it. With the right setup and leadership, COOs can help make AI not just possible, but profitable.