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COO 2027 Priorities for a New AI-Driven Enterprise

Description: AI now affects cost, risk, data, talent and core operations. Ten COO priorities can help firms scale AI with tighter control and clear business value.

Why This Matters

AI has moved from a specialist tool to a factor in core business work. McKinsey’s latest 2026 survey reports that nearly nine in ten respondents say their organizations use AI in at least one business function. The share that reports enterprise-wide scale has reached 44%, up from 38% a year earlier, while 37% report a positive effect on earnings before interest and taxes (EBIT). The same survey says 40% of respondents from large organizations report scale of AI agents in one or more functions, up from 27% a year earlier. For context, McKinsey’s 2025 agent survey found 23% of respondents at the scale stage and another 39% at the experiment stage. The data point to a clear gap: AI reach can rise faster than business value, control, and process design.

PwC adds a sharper operations view. Its 2026 Digital Trends in Operations Survey found that 89% of 767 operations and supply chain leaders said technology investment had not fully delivered expected results. A further 87% said poor data quality had hurt digital value. The same study found that only 27% had fully embedded an AI strategy across business units, while 37% felt comfortable giving AI agents responsibility for full end-to-end processes. These results do not prove what every company will face. They do show a broad execution gap across a large survey sample.

The COO now has a central role in that gap. The task is not to buy more AI tools. The task is to decide where AI can improve a process, where human judgment must remain, how risk should work, and how value should reach the income statement.

A Practical Data View

Priority One: Redesign Workflows Around AI Agents

AI agents can handle a series of tasks rather than a single prompt. That distinction matters for the COO. A firm may gain little value if an agent writes a draft yet leaves the rest of the process unchanged. A larger gain may come from a full process redesign across customer service, claims, procurement, order management, or demand work.

PwC reports that 83% of operations leaders expect AI agents and automation to speed the breakdown of traditional functional silos. Yet only 37% feel comfortable with agents that execute full end-to-end processes. That gap suggests a practical rule: autonomy should rise only after the firm defines decision rights, exception rules, audit records, and human escalation.

The right measure should focus on process results. Cycle time, error rates, customer outcomes, cost per case, and exception volume offer more useful evidence than the number of agents a firm owns. This approach also limits legal and operational risk. A system can support a decision without holding final authority over a high-impact outcome.

Priority Two: Create an Enterprise AI Control System

AI control must sit close to the systems that perform the work. IBM reports that 59% of surveyed technology executives cite security and compliance concerns as top barriers to wider AI agent use. Its 2026 study also found an average of 54 AI agent incidents per surveyed organization in the prior year. Of reported high-severity incidents, 37% involved data exposure or security breaches, 33% caused cascading system failures, and 17% triggered compliance issues.

These figures do not predict incident rates for any specific company. They show why a COO needs a clear control model. Each production AI system should have a named owner, approved data access, defined authority, activity records, escalation rules, and a method to stop or limit its actions.

A useful control model should also separate low-risk tasks from high-impact decisions. An agent may have broad authority for routine internal tasks yet require human approval for employment, credit, safety, legal, or customer-rights decisions. That distinction can make AI adoption more practical and more defensible.

Priority Three: Put AI Economics at the Core

AI can alter the cost base of digital operations. Model calls, data services, cloud capacity, security, audit controls, vendor fees, and support all affect total cost. IBM reports that 84% of technology leaders have not fully operationalized AI financial management, while 85% lack full visibility into real-time AI spend.

A COO should therefore treat AI as a portfolio of business services rather than a single technology budget. Cost per transaction can show whether a process has a sound economic case. Cost per successful outcome can show whether high AI use also produces useful results. A value dashboard can compare savings, revenue effects, quality gains, and risk costs.

This model also gives leaders a basis for stop, scale, or redesign decisions. An AI project should not continue solely from past expenditure. Future resources should follow evidence of value, subject to risk and legal constraints.

Priority Four: Make Data Quality an Operating Discipline

Poor data can limit the value of even a strong AI model. PwC found that 87% of operations leaders said poor data quality had hampered digital value, while only 30% reported a significant improvement in data quality and reliability over the prior two to three years.

The answer does not require perfect enterprise data. A more practical route starts with data that supports high-value decisions. Product records, customer data, location data, cost data, supplier terms, service levels, and key business assumptions need clear ownership and quality standards.

A COO can set data service levels for critical processes. Those standards can cover accuracy, freshness, access, source traceability, and error resolution. This gives AI systems a stronger base and also improves ordinary business decisions. Data quality then becomes part of operational performance rather than a separate technology concern.

Priority Five: Reshape Roles Around Human and AI Work

AI can change the value of time inside a company. McKinsey reports that many firms have begun workflow redesign and new AI governance roles, yet enterprise-level financial impact remains limited. The practical issue is not only headcount. It is the design of work.

A process may need fewer manual steps but more review, judgment, or exception control. A customer service role may shift from routine replies toward complex cases. A planner may spend less time on data collection and more time on trade-offs. A manager may need new skills to review AI output and challenge weak recommendations.

The COO should link AI plans with job design, skills, incentives, and career paths. Any workforce action should follow local law, contract terms, and fair employment practice. AI output should not become the sole basis for a high-impact employment decision without proper human review and legal checks.

Priority Six: Strengthen Cyber-Resilient Operations

Cyber risk now reaches the core of business continuity. PwC’s 2027 Global Digital Trust Insights survey, released in October 2026, found that 50% of security leaders rank attacks on AI systems among the cyber threats for which their organizations feel least prepared. The same survey found that only 39% of security, risk, and operations leaders have a fully formalized operational continuity plan that addresses cyber risk.

The gap matters for the COO. A cyber event can affect orders, payroll, customer service, finance, logistics, and data access at the same time. A firm therefore needs more than a security response plan. It needs a business recovery plan.

Critical processes should have defined recovery targets, backup routes, manual fallbacks, supplier alternatives, and clear authority for emergency action. These plans should cover AI failure as well as traditional cyber events. The goal is not zero risk. The goal is a known response path when a key digital service fails.

Priority Seven: Prepare for AI Regulation

Regulation adds another operational layer. The European Commission states that rules for high-risk AI systems under Annex III of the EU AI Act apply from December 2, 2027. The rules call for risk assessment, high-quality data, activity logs, documentation, human oversight, cybersecurity, robustness, and accuracy.

A legal team can explain the rule. Operations must make the rule work inside a process. That means a firm needs an inventory of relevant AI systems, a clear risk class, named accountability, records of system use, and evidence of human oversight where required.

The legal position can vary by jurisdiction, sector, system role, and use case. No single checklist can confirm compliance for every company. The safer approach is to treat regulatory requirements as control requirements and obtain local legal advice for material use cases.

Priority Eight: Move From Periodic Plans to Faster Decisions

AI can reduce the time between a business signal and a management response. PwC’s 2026 supply chain research points toward a model where firms define which decisions should rely on automation, which should use AI support, and which should remain human-led.

This model can apply beyond supply chains. Price, inventory, workforce capacity, procurement, cash management, and customer service can all benefit from faster analysis. The key issue is decision authority. A fast recommendation has little value if a process still needs several manual approvals before action.

The COO can set clear decision classes. Routine, low-risk choices may receive greater AI authority. High-impact choices may need human review. Each class can have a response target, evidence standard, and escalation path. This structure can improve speed without treating every AI output as fact.

Priority Nine: Break Functional Silos

PwC reports that 94% of firms with siloed or partly integrated structures expect a shift toward a more horizontal, networked model, while only 41% say their firms operate that way today. The gap shows why separate AI projects can produce limited enterprise value.

A customer order may cross sales, finance, inventory, procurement, logistics, and service. If each function has its own data and rules, an AI tool may improve one step while the full customer journey remains slow.

The COO can place core workflows above functional boundaries. That does not remove functional expertise. It gives one process a shared owner, shared measures, and shared data rules. Such a model can also reduce duplicate technology and clarify accountability.

Priority Ten: Build a Resilient Technology and Supplier Ecosystem

AI adds new dependencies across models, cloud platforms, data providers, software firms, application interfaces, and specialist suppliers. PwC’s 2026 operations research shows that technology complexity and segmented structures remain barriers to enterprise AI value.

Resilience therefore needs a wider scope. A COO should know which external services support critical processes, how many alternatives exist, how fast a substitute can arrive, and what data or contract limits could delay a switch.

A simple risk table can help set priorities.

What the Ten Priorities Mean for the COO

These ten priorities point to one central conclusion. AI strategy cannot sit apart from the operating model. McKinsey reports that 44% of respondents now report enterprise-wide AI scale, up from 38% a year earlier, while only 37% report any positive EBIT contribution. The same research reports that 80% say AI has improved personal productivity. The gap between personal benefit and enterprise financial impact remains material.

PwC finds that only 4% of surveyed operations leaders report success across four areas at once: full enterprise AI integration, few barriers to agent scale, a horizontal operating structure, and technology investments that fully deliver expected results. That figure does not represent a universal market benchmark. It reflects the survey sample and the specific conditions used in the study.

The figures show a pattern rather than a guaranteed outcome. AI access has become common, while full operational value remains less common. That distinction matters for legal, financial, and board-level claims. Survey data can show market direction, but they cannot establish that one company will receive the same result.

The strongest COO agenda therefore starts with control and evidence. Each AI use case should have a business purpose, a measurable result, a risk class, an accountable owner, and a clear path for human review. Each major process should also have a recovery route when AI, data, cloud, or supplier access fails.

The central test for 2027 is simple. A company should know which processes AI improves, which decisions AI can support, which actions AI can take, and where human authority must remain. That clarity can turn AI from a technology purchase into a disciplined operating capability.