AI With Us, Not To Us: A CIO’s Perspective on Building the Intelligent Organization

The organizations getting AI right aren’t just deploying better tools—they’re making a deliberate choice about the relationship between their people and the systems they’re building alongside them. At Innovation Summit 2026, Robert Sheesley called it simply: AI should do it with us, not to us. That principle, and the practical frameworks he shared for living up to it, were the heart of his presentation.

Sheesley, a former CIO who scaled an IT team from three people to over 45 while navigating two private equity transactions, brought a practitioner’s candor to questions that often get abstracted in strategy discussions. What does it actually mean to operationalize AI—not just experiment with it? Who owns the AI agenda? And what happens to the people whose roles are inevitably changing?

The takeaways below are the ones that translate most directly across industries—whether you’re a financial services firm still building foundational data infrastructure, a law firm navigating the risk and reward of generative AI, or a healthcare organization trying to move from use case experiments to enterprise-wide value.

About the Session

Robert Sheesley is a former Chief Information Officer with experience spanning UPMC, Mylan, and Wrench Group. He is also co-author of a recently published book on AI strategy and an active member of the Chief Architect Network. His session drew on years of hands-on AI deployment experience and the frameworks he has developed working with organizations to build AI strategies from the ground up.

Know Where You Are on the Maturity Curve—Before You Plan Your Next Move

Sheesley offered a clear-eyed view of where most organizations actually stand right now. He estimates that roughly 40% of organizations in 2026 are genuinely operationalizing AI—embedding it into real business processes beyond the experimentation stage. A meaningful number are still building foundations. And very few can credibly claim measurable, demonstrable value at scale.

His forecast is that widespread, provable AI value creation won’t become common until 2030 to 2032. That’s not pessimism—it’s a realistic calibration that has direct implications for how organizations should set expectations with boards, leadership teams, and employees right now.

The practical implication: if you’re still in the foundations phase, the priority is getting architecture and governance right, not racing to stand up use cases that sit on top of a data infrastructure that isn’t ready to support them. Organizations that skip the foundational work to chase visible wins end up with disconnected deployments that don’t compound into durable capability.

“Begin with the end in mind. Start with the outcomes you want to see and work your way backwards to determine where AI fits into the architecture of the organization.” — Robert Sheesley

Use Case Harvesting: Tie AI to the Capabilities That Actually Move the Business

One of the most immediately applicable frameworks Sheesley shared was what he calls use case harvesting—a structured approach to identifying where AI will generate the most meaningful value for a specific organization, rather than deploying it where it’s easiest or most visible.

The process starts with capability modeling: mapping the organization’s core business functions and working with executive stakeholders to identify which capabilities, if matured, would most directly advance strategic goals. From there, you examine the data architectures underpinning those capabilities and ask a focused question: where can AI meaningfully accelerate their maturation?

This does two things that ad hoc AI deployment doesn’t. It ensures AI investments are traceable to business outcomes—making them defensible to boards and measurable against real KPIs. And it prevents sprawl, where organizations accumulate AI tools across functions without any of them connecting to a coherent capability model. Every use case, in this approach, feeds the broader intelligence layer of the organization rather than existing as a standalone initiative.

AI Does It With Us, Not To Us: Designing for Human-AI Co-Work

The most resonant idea in the session was the simplest to state: the goal is always for AI to work with us, not to us. It’s a distinction that sounds obvious until you watch an organization deploy automation that bypasses the people it was supposed to support, or implement AI tools that change how work gets done without involving those doing the work in the design.

“We always want AI to do it with us, not to us. Coworking with AI tools would advance humanity in ways we are only dreaming about today.” — Robert Sheesley

Sheesley described three emerging workforce roles organizations need to plan for: AI users, who leverage AI tools within their existing work; AI makers, who build and configure AI capabilities; and AI developers, who embed AI into organizational processes and systems. Most organizations today are heavily concentrated in the user layer and under-invested in the maker and developer layers—which caps how quickly AI capability can actually scale.

On the question of ownership, he was direct: the CEO owns the strategy, and the CIO navigates its execution. But the CHRO has an equally critical role, particularly as AI reshapes skill requirements, learning paths, and what entry-level roles look like. AI strategy and people strategy need to be developed together—not in parallel silos—because the organizations that treat workforce design with the same rigor they bring to technology selection are the ones that will build lasting advantage.

Watch the Full Session

Robert Sheesley’s full session covers AI architecture frameworks, the cybersecurity risk of unsanctioned AI tool use across the workforce, how to build a risk register that makes the investment case to boards and CFOs, and a practical Q&A on managing the transition between running today’s operations and building tomorrow’s capabilities at the same time.

Building Your AI Strategy Around What Your Business Actually Needs?

Whether you’re mapping your organization’s capability model for the first time, connecting scattered AI use cases to a coherent architecture, or designing a workforce strategy that brings people along rather than leaving them behind—HIKE2’s advisory team works at exactly this intersection of strategy, technology, and people.

If you’re ready to move from experimentation to operationalization—or from operationalization to measurable value—let’s talk.

Contact HIKE2 to start the conversation →