Leaders from Canadian Tire, ServiceNow, and Chai all said the same thing.

AI and design leaders from Canadian Tire, ServiceNow, and Chai kept arriving at the same place from completely different directions.

A retail AI leader, an enterprise design leader and an operator building AI-enabled services shared a stage in Chicago. They brought different experience to the same question: what has to change inside an organization for the technology to become useful?

I was moderating a panel at The Future Of conference with Anthony Wolf from Canadian Tire, Carly Price from ServiceNow, and Roger Rohatgi, Chief AI Officer at Chai. John Gleason and the team behind The Future Of had built a room worth being in: P&G, IBM, Whirlpool, KraftHeinz, Colgate, Mondelez, 3M, HauerX, BrandRank.AI all working through the same questions on the same day. Phil Gilbert opened the morning. Phil Duncan from P&G did a fireside moderated by Bob Jennings from 3D Color. The hallway conversations alone were worth the trip.

But something happened on stage that I didn't expect. These three kept arriving at the same place from completely different directions. And the place they kept arriving at is something I think most companies are getting wrong.

Canadian Tire Doesn't Have an AI Strategy

Anthony told the room that Canadian Tire doesn't have an AI strategy.

I watched people’s faces shift. In a room full of leaders building AI roadmaps and governance frameworks, Anthony had put the corporate strategy first.

AI is in service of their corporate strategy. That's it. And the more I've sat with it, the more I think the act of creating a separate AI strategy might be the mistake itself. The moment you separate it, you've turned it into a project. A thing to manage. And that's exactly what most companies did with digital transformation. Separate team. Separate budget. Separate initiative. And most of them never finished.

Roger Sees the Evidence

Roger described arriving at companies that wanted to use AI and finding disconnected data, legacy systems and unfinished process work. AI inherits that operating environment. It does not make the unfinished work disappear.

Two people. Two completely different vantage points. Same diagnosis.

The problem isn't AI. It's everything you didn't finish before AI showed up.

Then Carly Complicated Things in the Best Possible Way

She joined ServiceNow about ten months ago. AI was already woven into everything. The question wasn't whether to use it. It was how fast. And the breakthroughs she's seeing aren't coming from a strategy. They're coming from people.

Carly described a two-day workshop that used AI to help synthesize work across portals and produced 20 prototypes. These were figures she shared from her team’s experience on the panel.

She compared that workshop with a process she said would otherwise have taken six to eight weeks. The example concerns synthesis and prototyping, rather than a completed product launch. It raises a useful question: which of your timelines reflect the work itself, and which reflect the process built around it?

Her team has since built custom Claude skills for heuristic evaluation. Junior designers running mini usability audits before they even share their work. Demo scripts built from PRDs, turned into coded components, connected through to engineering. The whole concept-to-testable-product pipeline compressed in ways that would have been hard to imagine a year ago.

And Carly isn't overseeing this from a conference room. She uses voice memos on walks for meeting notes. Builds prototypes herself. When a designer showed her something impressive, her first question was "show me the steps." Then she started doing it.

Here's why that matters in the context of what Roger and Anthony were saying. Roger says companies aren't ready. Anthony says the readiness problem is organizational. Carly is showing what actual readiness looks like, and it looks nothing like a transformation program. It looks like a leader who's in the tools creating space for people to figure it out. No roadmap. No separate AI strategy. Just conditions for experimentation and a leader who goes first.

Lessons from the stage

The Future Of Conference · Canadian Tire · ServiceNow · Chai · 2026

The Die Casting

Anthony's analogy keeps rattling around in my head. AI isn't like bringing a power drill to replace a screwdriver. It's like a die casting that replaces the entire need to screw something together in the first place.

Anthony described using first-party customer data to think about shopping occasions, rather than products alone. His example was how the relevant occasion could differ for a store near a lake during fishing season and an urban store near a music festival. That shifts the planning question toward what customers are trying to do.

That's not a faster version of Canadian Tire. That's a different company.

But here's the part of the die casting that nobody said out loud. If AI replaces the need to screw things together, then the expertise people built around screwing things together is suddenly worth less. The 70% people problem Anthony talks about, 10% models, 20% data and tech, 70% people, it's not just about adoption and training. It's about identity. What happens when the thing that made you an expert is the thing AI just collapsed?

There Is No Other Side

Anthony named the emotional weight of this more honestly than I've heard anyone do it. There's a maturity curve with AI, he said. And it doesn't have an ending. It just keeps going. You can't cross a bridge and tell your board you're on the other side.

There is no other side.

Every leader going through this is feeling the same excitement, the same fear, the same apprehension, all at once. That's just what leading in the age of AI feels like. And every change management playbook that assumes you're moving from state A to state B breaks when there is no state B.

The Trust Architecture

Roger pulled the camera back furthest. He shared a framework from his work with MIT: the way work has always been done is human to human to human. H, H, H, H in the chain. Now you introduce an agent. H, H, H, A. Then another. The question stops being about tools and starts being about decisions. Which ones are you willing to hand over? And he believes that's the biggest question organizations will face. Not which AI to buy. Which decisions to trust it with.

Most companies don't have a framework for that. They're trusting AI with everything or trusting it with nothing. The ones who build trust architecture first, with clear principles for what goes to humans and what goes to agents, are going to move fastest.

The Missing Sixth Layer

Roger also made an argument that connects everything I heard on that stage. Jensen Huang's AI stack has five layers: energy, infrastructure, chips, models, applications. Roger argues there's a missing sixth layer: experience. Everything else is built. But people aren't adopting it. They're confused by it. The experience layer is what's missing.

And I think that's where all three of these perspectives crash into each other in a way that matters. Carly's team is building the experience layer inside ServiceNow one prototype and one Claude skill at a time. Anthony's team is building it at Canadian Tire by completely redesigning the relationship between data and the customer. Roger is arguing it's the most important layer in the entire AI stack and most companies don't even see it.

If the first five layers are commoditizing, the only place left to build something defensible is the layer closest to humans. Design leaders, the people who understand experience, who understand how humans actually interact with systems, are sitting on the most valuable real estate in the AI landscape. And most of them are still waiting for someone to hand them a strategy.