How AI Is Redefining the COO’s Role

A strategic perspective for modern operations leaders

I’ve spent more than 20 years in operations, including many years as COO of multinational organizations, and I’ve been following the rise of AI closely — studying its implications for how businesses function, how decisions get made, and how work actually flows. Combining that hands-on experience with the shifts we’re now seeing, I can say with confidence: AI is fundamentally redefining what it means to lead operations well.

A recent McKinsey report captures many of these shifts, and in my own work with companies, I see the same patterns emerging everywhere: the COO role is expanding, accelerating, and becoming more central to the future of the enterprise.

AI is not simply a new tool. It is a catalyst that is reshaping the COO agenda from end to end.

The “Two-Speed COO”: Balancing today’s operational demands with tomorrow’s AI-enabled capabilities

Operations teams often excel at firefighting. Most companies still reward “heroic” managers who fix broken processes at the last moment. What they often don’t reward enough are the leaders who design processes so well that nothing breaks in the first place.

AI makes this tension impossible to ignore.

AI cannot function in environments where:

• processes vary by team, shift, or location

• data is inconsistent

• workarounds replace standards

• decisions are unclear

• workflows change daily

AI needs clarity. It needs clean data, stable processes, and consistent execution.

That puts COOs in a dual role:

1. Running the business today with all its urgency, and

2. Building the stable foundations AI requires for tomorrow.

Automation behaves like a multiplier: A stable process becomes faster and better with AI; an unstable one becomes worse and more chaotic. This is the operational reality the modern COO must master.

AI and Automation Require Stable Processes. Why process maturity matters more than ever

AI doesn’t fix broken processes; it amplifies whatever already exists.

That means:

• a clear, standardized process becomes an automation success story

• a messy, inconsistent process becomes an automated disaster

This is why COOs must elevate process stability to a strategic priority:

• define processes clearly

• eliminate unnecessary variation

• simplify decision chains

• build consistent data structures

• enforce standards across sites

AI becomes powerful only when the operational environment is designed for consistency.

Many Productivity Levers Are Set Before Operations Begin

Most people assume productivity is created on the shop floor. In reality, some of the biggest cost and complexity drivers are determined much earlier:

• which features a product includes

• how much variation is allowed

• what service levels are promised

• what lead times Sales commits to

• which materials are chosen

• what the volume and mix strategy looks like

If these choices create unnecessary complexity, operations inherits a system that is already inefficient.

AI makes this extremely visible.

AI models struggle with:

• high variation

• inconsistent inputs

• constantly changing product definitions

• unrealistic service promises

This is why Operations needs to be involved from the very beginning of commercial and product decisions – not after they’re finalized.

The productivity ceiling must be set far earlier in the value chain, and the COO must increasingly help shape the decisions that define it.

Cross-Functional Alignment Must Start Early – Not at the Handover Stage

In many organizations, decisions across Product, Sales, Marketing, Finance, or IT are still made in isolation and then handed off to Operations with the expectation that they “make it work.”

AI has revealed how unsustainable that model is.

Poor alignment becomes visible immediately:

• inconsistent promises break forecasting

• product complexity breaks planning

• ambiguous service levels break routing

• fragmented processes break AI models

• poor data discipline breaks automation

For AI and automation to succeed, these functions must integrate Operations early – as a shaping force, not an afterthought.

Large organizations especially tend to skip this step, and AI exposes the consequences quickly. COOs must therefore champion a more collaborative operating model – one where critical decisions are co-designed rather than sequentially handed off.

Lean Management Remains Essential and AI Amplifies Its Power

Why Lean is more important than ever in an AI-driven world

While AI introduces new capabilities, it does not replace the core philosophy of operational excellence. If anything, AI makes Lean Management more important, not less.

Lean is built on principles that align perfectly with AI:

✔ Standardized work

AI requires consistent processes to learn, predict, and automate effectively.

✔ Elimination of waste

AI makes hidden waste visible — inefficient flows, rework, waiting times, unnecessary movements, and excess variation.

✔ Flow and pull systems

AI enhances flow by predicting demand, optimizing sequencing, and adjusting capacity dynamically.

✔ Visual management and transparency

AI generates real-time insights that elevate transparency to a new level.

✔ Continuous improvement (Kaizen)

AI assists problem-solving by identifying root causes, providing suggestions, and detecting patterns humans would miss.

✔ Gemba mindset (“go to the place where value is created”)

AI enriches the Gemba with data, helping leaders focus their time on the right issues.

Lean and AI are not competing philosophies. They are mutually reinforcing.

Lean gives AI the stable foundation it needs. AI gives Lean the analytical power it has always deserved.

The modern COO must therefore understand Lean deeply and promote it consistently –not as a cost initiative, but as an operating system that unlocks AI’s full potential.

The Factory Becomes an Intelligent, Connected System

From isolated processes to real-time, adaptive operations

AI is reshaping production and supply chain operations in profound ways. The factory is transforming from a sequence of machines into a connected, learning system.

Modern operations now rely on real-time sensor data, predictive maintenance, AI-supported quality inspection, autonomous material handling, advanced scheduling algorithms, digital twins for simulation, human–machine collaboration, just to name a few.

The job is no longer to “run a plant” (or multiple plants), but to design and orchestrate a cyber-physical system where humans, machines, and AI interact seamlessly.

Workforce Transformation Becomes a Strategic Leadership Duty

AI is not eliminating the workforce, but it is reshaping it.

Frontline workers will see:

• up to 50% of tasks shifting

• more digital tools in daily work

• new expectations for data and problem-solving

• increased human–machine collaboration

• opportunities for higher-value tasks

At present, AI is not eliminating the workforce, but it is reshaping it in ways that are deeper and more structural than many leaders currently anticipate. Let me elaborate.

In the near term, most roles will not disappear. Instead, AI will reshape how work is done. There is broad consensus that AI will increasingly handle lower-value, repetitive, or routine tasks, i.e. the “non-sophisticated” work that often consumes a disproportionate amount of human time. In this first scenario, humans keep their jobs, and these jobs become augmented. People move up the value chain. They interact with AI tools, read digital dashboards, interpret insights, and focus on the parts of their roles that require nuance, context, and judgement.

While I agree with this perspective in principle, it cannot be universally generalized. The degree of augmentation versus displacement will vary significantly by industry, by role, and by the pace at which companies adopt digital and physical automation.

But there is a second scenario we must acknowledge – one that is discussed far less openly.

Over time, as AI capabilities continue to advance, there is no inherent stopping point. AI will not remain limited to “basic” tasks unless we artificially constrain it. Once robots and autonomous systems become more capable and enter the market at scale, AI-powered physical work will expand rapidly. At first, robots will address very specific use cases; eventually, they will be deployed for a broad range of tasks, including tasks once believed to be the exclusive domain of humans.

This means every COO must lead with a dual time horizon:

• Short-term: prepare the workforce for augmentation, higher-value tasks, and new skills.

• Long-term: prepare the organization for a world in which AI and robotics will increasingly take over not just routine work, but many tasks currently considered “human-only.”

  
  

The COO becomes not only a capability builder, but a long-term workforce strategist.

COOs Must Continuously Identify AI-Eligible and Automatable Processes

This evolving reality places a new responsibility squarely on the shoulders of operations leadership: the proactive, ongoing identification of processes that can be automated and eventually AI-enabled.

Traditional automation focuses on steps that are rules-based, repetitive, and predictable. But AI-enabled processes go beyond that. These are workflows that today require some level of thinking, interpreting, choosing, or prioritizing – activities historically reserved for humans – and can now be executed through AI models capable of reasoning, pattern recognition, and decision support.

COOs must help their teams cultivate a mindset of continuous discovery:

• Which processes are ready for automation right now?

• Which processes could be AI-enabled with modest redesign?

• Which processes should be redesigned from scratch, because AI opens entirely new possibilities?

• How will these answers evolve as AI capabilities expand?

  
  

This is not a one-time exercise. It is a new operational discipline.

As organizations deepen their understanding of AI and as AI systems become more capable, the number of AI-eligible processes will grow. Tasks once believed too nuanced or complex will shift into the realm of AI feasibility. Entire workflows will be re-imagined, not because automation is imposed from above, but because teams begin to see what becomes possible when AI is treated as both a collaborator and a capability multiplier.

The COO plays a central role in steering this shift – setting the pace, building the roadmap, eliminating barriers, and ensuring that every operational function is continually scanning for opportunities to automate, augment, or redesign.

Future-ready operations teams won’t wait for AI use cases to be suggested by outsiders. They will develop the ability to spot them internally and continuously.

Cybersecurity Becomes a Core Operating Responsibility

As operations become more digitized, the risks shift from physical disruptions to cyber disruptions.

Production today is vulnerable to cyberattacks, ransomware, industrial espionage, or compromised automation systems.

The COO must collaborate closely with IT and Security to ensure that digital continuity equals operational continuity. In the age of AI-driven operations, cybersecurity is plant safety.

Scaling AI Requires Operating Model Transformation

An important insight from McKinsey’s report: 95% of AI pilots never scale.

Why?

• unclear ownership

• inconsistent processes

• fragmented data

• siloed functions

• lack of governance

• outdated decision structures

Scaling AI isn’t a technical problem, but rather it's an operating model problem.

COOs must redesign:

• accountability structures

• workflows and interfaces

• data standards

• governance mechanisms

• team roles

• cross-functional forums

AI becomes powerful only when the operating model evolves with it.

The COO of the Future: Builder of an Intelligent Enterprise

Across industries, the role of the COO is undergoing its biggest transformation in decades.

The future COO will be:

• an end-to-end value architect

• a designer of human–machine ecosystems

• a steward of Lean and AI-enabled processes

• a champion of workforce capability building

• a guardian of digital and operational resilience

• a shaper of early business decisions

• a strategic partner across the C-suite

The COO role is not being diminished, but rather it is being elevated. Running operations is no longer about efficiency alone. It is about building an intelligent, adaptable organization that can thrive amid rapid technological change. And AI is the catalyst accelerating that evolution.

Closing Thought

After two decades in this field, the conclusion is clear: There has never been a more exciting time to work in operations. COOs who embrace this moment – combining operational discipline, Lean principles, and technological ambition – will shape the future of their companies.

Closing Thought

Based on my 20+ years in Operations, I truly believe: There has never been a more challenging, or more exciting, time to work in operations. For COOs willing to pair operational discipline with technological boldness, AI isn’t just another tool. It’s a catalyst for rethinking how value is created – and for elevating the COO role to one of the most strategically influential positions in the organization.

If you want to discuss how AI will reshape your operations or leadership team, feel free to reach out.

Note: This article was created with the assistance of AI tools and reviewed by our editorial team for accuracy and clarity.

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