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. 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"
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, data is inconsistent, workarounds replace standards, or decisions are unclear. AI needs clarity. It needs clean data, stable processes, and consistent execution.
That puts COOs in a dual role:
- Running the business today with all its urgency
- 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.
AI and Automation Require Stable Processes
AI doesn't fix broken processes — it amplifies whatever already exists. That means a clear, standardized process becomes an automation success story, while 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, and 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, 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, and constantly changing product definitions.
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
In many organizations, decisions across Product, Sales, Marketing, Finance, or IT are still made in isolation and then handed off to Operations. AI has revealed how unsustainable that model is. Poor alignment becomes visible immediately: inconsistent promises break forecasting; product complexity breaks planning; fragmented processes break AI models.
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
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 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 promote Lean — not as a cost initiative, but as an operating system that unlocks AI's full potential.
The Factory Becomes an Intelligent, Connected System
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, and human-machine collaboration.
The job is no longer to "run a plant." It is to design and orchestrate a cyber-physical system where humans, machines, and AI interact seamlessly.
Workforce Transformation Is a Strategic Leadership Duty
AI is not eliminating the workforce, but it is reshaping it. In the near term, most roles will not disappear — instead, AI will handle lower-value, repetitive tasks, and humans move up the value chain. People interact with AI tools, interpret insights, and focus on the parts of their roles that require nuance, context, and judgement.
But there is a second scenario we must acknowledge. Over time, as AI capabilities continue to advance, there is no inherent stopping point. Once robots and autonomous systems become more capable, AI-powered physical work will expand rapidly — 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 increasingly take over not just routine work, but many tasks currently considered "human-only."
COOs Must Continuously Identify AI-Eligible 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 AI-enabled.
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. 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.
Scaling AI Requires Operating Model Transformation
95% of AI pilots never scale. Why? Because scaling AI isn't a technical problem — it's an operating model problem. Unclear ownership, inconsistent processes, fragmented data, siloed functions, and outdated decision structures all prevent AI from moving from pilot to production.
COOs must redesign accountability structures, workflows, data standards, governance mechanisms, team roles, and cross-functional forums. AI becomes powerful only when the operating model evolves with it.
The COO of the Future
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 — 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.
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.
Frank Anisits, Founder — NEXT STEP AIThis article was created with the assistance of AI tools and reviewed by our editorial team for accuracy and clarity.
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