Redesign Workflows: Move beyond individual AI tasks and rebuild end-to-end processes around AI agents and human decision-making.
Build Control and Resilience: Establish governance, cybersecurity, cost visibility, regulatory readiness, and technology fallback plans.
Focus on Business Value: Measure AI through productivity, costs, revenue, outcomes, and operational performance, rather than adoption alone.
AI has moved past the pilot stage, yet many companies still lack a clear path from AI use to business value. Nearly 9 in 10 organizations now use AI in at least one business function, while 44% report enterprise-level AI scale. Still, only 37% report a positive effect on organizational earnings before interest and taxes (EBIT). This gap gives the chief operating officer (COO) a clear mandate: Turn AI from a collection of tools into a core part of how the business runs.
It is easy to look at AI one task at a time: write this email, summarize that report, review this document. The bigger opportunity is to rethink the workflow itself. AI agents can now handle multiple steps, coordinate tasks, and support decisions. That makes processes such as order-to-cash, customer service, procurement, claims, and demand planning worth revisiting from end to end.
The COO does not need to hand everything over to AI. Instead, the question should be: Which parts should AI handle, where does a person need to step in, and how will we know if the new process is actually better?
AI growth can quickly create gaps in oversight. IBM reports that 70% of technology executives say business teams deploy technology faster than IT can track. The number of AI agents could rise 38% by 2027, while 77% say AI use now moves faster than governance. A central control system should give every model and agent a clear owner, access rules, activity logs, review standards, and a shutdown process. This approach can reduce the risk from unknown or poorly controlled AI tools.
AI costs extend well beyond software fees or model tokens. Infrastructure, data, security, oversight, support, and failure recovery can add major costs. IBM reports that 84% of surveyed technology executives have not fully put AI financial management into daily use, while 85% lack full real-time AI cost visibility. COOs need clear measures such as AI cost per transaction, cost per successful outcome, labor-hour savings, revenue impact, and return on investment for each major AI system.
AI cannot deliver reliable results when critical business data lacks quality or clear ownership. PwC reports that 87% of operations leaders say poor data quality has hurt digital value. Only 30% report major gains in data quality and reliability. A better approach starts with decision-critical data rather than a huge cleanup project. Each key data set needs a business owner, quality standards, clear source records, and fast access for approved AI systems.
AI is already helping people get more done. But individual productivity gains do not automatically translate into better business performance. The harder question is what happens to the job itself. Some tasks may disappear. Others may become faster. New responsibilities may emerge. Managers may spend less time checking routine work and more time making decisions.
The COO should help redesign roles around this new reality. That means thinking about skills, incentives, management practices, and career development, not just headcount. The objective should be better work, rather than simply fewer people.
Also Read - CFO 2027 Checklist: 10 Financial Priorities for Managing AI-Driven Business Growth
Cyber risk now reaches deep into business continuity. PwC reports that 50% of security leaders see attacks on AI systems among the threats for which their companies feel least prepared. Only 39% of security, risk, and operations leaders have formal continuity plans for cyber risks. A COO should test what happens when a critical AI system, cloud service, vendor, or data source fails. Each major process needs a clear backup route and recovery target.
AI regulation is becoming part of normal business planning, particularly for companies operating across multiple markets. The EU AI Act is one example. Some of its requirements affect high-risk applications, including certain uses in employment, education, infrastructure, biometrics, and other sensitive areas.
The COO does not need to become the company's AI lawyer. But operations leaders do need to know where AI is being used, which systems carry higher risk, who is responsible for them, and how oversight works in practice. Compliance should be built into the process rather than added after the system is already running.
AI can shorten the gap between a market signal and a business response. Supply chain, pricing, inventory, workforce capacity, and procurement can all benefit from faster forecasts and scenario tests. The goal is a simple cycle: sense a change, predict the effect, test options, choose an action, execute it, and learn from the result. That model can give the COO faster control over volatile business conditions.
One department may have a great AI tool for sales while another has built something for supply chain and finance has its own system. Each tool may work well on its own, but the business can still end up with a collection of disconnected solutions. That is a problem because customers and business processes do not operate inside organizational charts.
A better approach is to look at the journey across functions. A customer order, for example, touches sales, inventory, finance, fulfillment, and customer service. AI becomes much more useful when those parts of the process can work together. The COO is often in the best position to make that happen because the role naturally cuts across functions.
The modern enterprise depends on models, cloud platforms, software, data providers, APIs, and outside suppliers. PwC reports that 54% of organizations use multi-cloud or hybrid strategies, while 47% have strengthened regional technology and data redundancy.
Another 37% have localized infrastructure within specific regions. The COO must know where critical dependencies sit and what happens if one fails. Supplier concentration, cloud outages, model access, and data loss all need clear fallback plans.
Why this MattersAI now affects core business decisions, costs, workforce design, security, and customer operations. A clear COO strategy helps companies capture AI’s value without losing control of risk or resources. The 2027 priorities matter as businesses shift from small AI projects toward enterprise-wide systems that can reshape how work gets done.
The strongest companies will not win through AI tools alone. They will win through better workflows, trusted data, clear controls, resilient technology, and a workforce built for human and AI collaboration. The COO now sits at the center of that shift. The real test for 2027 will not ask how much AI a company owns. It will ask how much better the company performs when AI becomes part of its core operating system.
1. What is the COO’s role in AI transformation?
The COO should integrate AI into core operations, redesign workflows, manage risks, measure ROI, and align people, processes, and technology.
2. Why is AI ROI difficult to achieve?
AI costs extend beyond software and models to infrastructure, data, security, oversight, integration, and recovery. Clear operational metrics are essential.
3. How can companies prepare for AI agents?
Companies should establish ownership, access controls, activity monitoring, review standards, governance processes, and clear human intervention points.
4. Why is data quality important for AI?
Poor-quality or fragmented data can undermine AI outputs. Critical data needs clear ownership, quality standards, trusted sources, and reliable access.
5. What should businesses prioritize for 2027?
Businesses should focus on AI-enabled workflows, governance, ROI, data quality, workforce redesign, cyber resilience, regulatory readiness, and cross-functional operations.