Council Post: AI Is Redrawing The Boundaries Of Leadership

Milan Shetti, President and CEO, Rocket Software.

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Enterprises have traditionally been organized around specialization. Teams across the business developed their own expertise and leadership structures, creating clarity about who was responsible for what.

AI is putting new pressure on that structure because its opportunities and consequences cut across those functional lines. A single use case designed to improve customer service with an AI chatbot, for example, can raise multiple questions about technology, operations, data, compliance, risk and the customer experience. No single function has all the expertise required to make those decisions well.

Organizations are responding in part by creating new leadership roles, including the Chief AI Officer (CAIO). Particularly in regulated industries, dedicated AI leadership can bring greater consistency to enterprise-wide capabilities.

However, creating another specialized function only addresses part of this undertaking. AI is increasingly requiring leaders across the enterprise to work toward shared outcomes, combining expertise that traditionally sat in separate parts of the organization.

New AI Leadership Roles Reveal A Changing Landscape

The emergence of the CAIO reflects a real need for greater coordination as AI adoption accelerates. Leadership teams are creating CAIO roles to establish common standards and capabilities across the enterprise, but the danger lies in allowing a single role to assume responsibility for every AI-related decision.

That responsibility still sits with the leaders who understand the outcome that the technology is intended to change. If AI is expected to improve sales productivity, sales leadership needs to define success and remain accountable for the result. If it changes the customer experience, the leaders responsible for that experience need to shape its deployment.

The value of AI leadership, then, comes from connecting those responsibilities rather than consolidating them.

Technology Leaders Are Becoming Business Leaders (And Vice Versa)

The strongest CIOs and CTOs I've worked with understand the business as deeply as they understand technology. Some came through technical ranks, whereas others followed very different paths. What distinguishes them is their ability to translate between the two.

Rocket’s CIO, Darlene Williams, is one example. Her background is in business, and she recently completed a master’s degree in computer science to deepen her technical understanding and strengthen her ability to connect technology decisions with business priorities. Leaders like Darlene can recognize where technology can change the outcome and know which business expertise needs to come together to make it work.​

The Best AI Opportunities Can Come From Anywhere

Employees closest to customers and day-to-day business processes often see problems and opportunities that a centralized innovation team can't. They know where repetitive work or inaccessible data creates friction and where a different approach could materially improve a process.

Leadership, therefore, needs to create ways for those ideas to surface. Rocket.Build, our annual innovation program, offers one example. In 2024, more than 500 employees across 18 countries submitted 167 projects. Some of those ideas continue beyond the competition. The winning Rocket.Build project in 2025, Rocket Security Patch Auditor, moved into full production.

Organizations can allow ideas to emerge from across the enterprise while leadership teams provide the capabilities, governance and support needed to develop the strongest ones.

Turn Connected Leadership Into An Operating Model

If AI opportunities and expertise are distributed across the organization, leadership needs a way to connect them without losing accountability. That starts with a shared business outcome. Every significant AI use case should have a leader accountable for what it's meant to achieve, whether that's improving productivity, reducing risk, accelerating a process or changing the customer experience. Around that outcome, organizations can bring together the functions whose expertise is required to make the right decisions while the approach can still be shaped.

Governance is part of that same process. Before an initiative moves into production, teams need clear governance around its use, including where human oversight is required. Those guardrails allow teams to innovate with a clearer understanding of where they have freedom to act and where broader review is required.

Once AI becomes part of a core workflow, leaders should manage it against the business outcome it was designed to achieve, adjusting their approach as its impact and risks evolve. Cross-functional leadership works when accountability remains clear while expertise is shared.

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