How Gen AI Is Redefining Operational Leadership in 2025

Generative AI is beginning to deliver tangible results across the enterprise. Leaders are deploying it to support supplier negotiations, improve product quality, and manage labor-intensive sales segments once considered too costly to serve. In a few cases, these deployments are already creating measurable top-line impact. A digital marketing platform, for example, added more than $30 million in annual revenue by expanding long-tail sales coverage using AI agents.

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These examples remain outliers. In a recent executive survey, just 19 percent of US companies reported revenue gains exceeding 5 percent from generative AI. Fewer than one in five global organizations derive more than 10 percent of EBIT from these technologies. The interest is real. The outcomes remain uneven.

Despite nearly three-quarters of companies having at least a draft of a generative AI strategy, most executives describe implementation as slow. Only a small subset—around 12 percent—have found ways to translate AI into material revenue. Less than one percent report having transformed day-to-day operations at scale.

This is where operational leadership begins to matter most. Across industries, the chief operating officer is emerging as a pivotal figure in making AI strategy actionable. The role extends beyond identifying use cases. It includes structuring cross-functional delivery, building enterprise capabilities, and ensuring technology choices support productivity, compliance, and quality outcomes.

For companies already moving forward, three capabilities are proving decisive. First, a clear operating structure supports coordination across business units and prevents fragmented development. In one global manufacturing firm, a centralized AI office provided early direction. It delivered a prioritized roadmap targeting €300 million in EBITDA improvement and created an internal steering function to manage execution across departments.

Second, strong data governance is foundational. As operational data becomes the input for generative tools, inconsistencies across business functions create risk. A global materials company resolved conflicting datasets by centralizing control and implementing human oversight across all data points feeding AI outputs. This increased the reliability of answers and removed bottlenecks caused by inconsistent interpretation.

Third, sustained change management enables adoption. When a technology company introduced a gen AI solution to automate statements of work across product lines, results depended less on engineering quality and more on how quickly teams adopted new workflows. The firm assigned a change leader to each product line, responsible for translating technical capabilities into day-to-day practices. This approach helped reduce thousands of hours in repetitive work and freed teams to focus on higher-value tasks.

The collaboration between COO and CIO is central to making these transformations successful. The technology company’s CIO helped identify new ways to adapt enterprise systems, while the COO structured delivery based on operational fit. Their coordination ensured that resource constraints, data needs, and delivery timelines were resolved without introducing organizational friction.

As gen AI matures, it becomes increasingly clear that isolated tools do not produce lasting operational change. Sustained value creation depends on leadership alignment, scalable systems, and the ability to rethink how work is structured across functions. The role of the COO is central to this shift. The path forward requires more than technical experimentation. It requires a redefinition of execution.

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