Recasting, Not Retooling

At The Future Of conference in Chicago, I moderated a panel with Anthony Wolf from Canadian Tire, Carly Price from ServiceNow and Roger Rohatgi from Chai. They came at AI from different directions, but kept returning to the same challenge: changing the work around the technology.

John Gleason and the team behind The Future Of had brought enterprise leaders into the same room to work through those questions. The hallway conversations were as useful as the sessions.

These three kept arriving at the same place from different doors. And the place is something most companies are getting wrong.

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 strategies, roadmaps, and governance frameworks, someone running AI activation at one of Canada's largest companies just said they don't have one.

AI is in service of their corporate strategy. That's it. And the more I've sat with it, the more I'm convinced the act of creating a separate AI strategy is itself the mistake. The moment you separate it, you've turned it into a project. A thing to manage. A workstream with a sponsor and a timeline and a definition of done. That's exactly what most companies did with digital transformation. Separate team. Separate budget. Separate initiative. Most of them never finished.

An AI roadmap needs to connect to the business it is meant to serve. Otherwise it can become another workstream with a sponsor, a timeline and too little effect on the work around it.

Roger described finding disconnected processes, difficult data and unfinished transformation work inside companies preparing to use AI. Those conditions shape what a new system can actually do.

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.

AI was already woven into everything at ServiceNow when she arrived. 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 produced 20 prototypes, using AI to support synthesis across portals. These were results she reported from her team’s exercise.

She compared the exercise with six to eight weeks under the prior process. That is a comparison for synthesis and prototyping, with further testing and delivery still required. It made me ask which parts of a familiar timeline are necessary, and which were built around costs that are changing.

That question travels well. Look for a slow process, identify what consumes the time, and test whether a different way of working improves the result. Keep the comparison tied to the work you actually measured.

Her team has since built custom Claude skills for heuristic evaluation. Junior designers running mini usability audits before they 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.

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 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.

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.

This is the recasting.

Canadian Tire has millions of loyalty program users. Deep first-party data on what customers are buying, when, and in what combinations. They could have used AI to optimize how they sell products. Faster recommendations, better targeting, the obvious stuff. Instead, they reimagined what they're selling around. Not products. Occasions. What's the weekend going to look like for a store operator near a lake during fishing season versus an urban store near a music festival?

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

But here's the part of the recasting 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) is 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? You don't lose your job. You lose the part of the work that made you feel competent. That's a harder thing to manage than headcount.

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. Every change management playbook that assumes you're moving from state A to state B breaks when there is no state B.

Roger pulled the camera back.

Roger described work moving from chains of human handoffs toward combinations of people and agents. The operating question is how those handoffs should work, and who remains responsible.

The question stops being about tools and starts being about decisions. Which ones are you willing to hand over? Roger believes that's the biggest decision 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, will move fastest.

A group of agents becomes a useful service only when the handoffs, exceptions and human decisions make sense together. That is experience design as much as a technology choice.

Which brings us to the most important argument Roger made, and the one I think most companies are going to miss.

Jensen Huang's AI stack has five layers. Energy. Infrastructure. Chips. Models. Applications. Roger argues there's a missing sixth: experience. Everything else is being built. Everyone has access to roughly the same models. The chips are commoditizing. The applications are converging. But people aren't adopting any of it. They're confused by it. The experience layer is what's missing.

Infrastructure and models matter. Roger’s argument pushed attention toward the experience people actually use: how the work flows, how decisions are handed over and how a person understands what the system is doing.

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. Most of them are still waiting for someone to hand them a strategy.

Don't wait. Build the experience layer. That's where the advantage compounds.

An audience member asked a question that proved Roger's point. He's inside an enterprise where the internal AI tools aren't as capable as what's available externally. Use the company tools and fall behind. Experiment on your own and create risk. That gray space is the experience layer breakdown in real time.

Anthony's answer stuck with me. Governance should be guardrails, not speed bumps. Guardrails let you go faster because the rules of engagement are clear. Speed bumps just slow everyone down equally. Most companies are building speed bumps and calling it governance.

I told the room to get hands-on with the tools. Build something within your organization’s data and access rules, then examine both what it can do and where it falls short. That experience helps you ask better questions about the systems you buy or build.

Nobody in that room is as far ahead or behind as they think. The maturity curve doesn't end. If you approach it with curiosity, to learn from each other instead of measuring yourself against each other, we all move faster.

Access to AI tools alone is unlikely to be a durable advantage. What matters is how well your organization turns that access into useful work.

The advantage is how fast your organization can absorb it.

Absorption is a function of culture, trust, and leadership. Not technology procurement. Speed of decision. Speed of synthesis. Speed of handoff between humans and agents. Speed of recasting the processes that no longer match the new unit cost of intelligence.

Speed compounds. The companies that internalize that first will end up running a different business than the ones still building AI strategies.

There is no other side. Just the next casting.