1. The CIO–CPO partnership is becoming mission-critical

For years, CIOs partnered most closely with CFOs (ERP) or CROs (CRM). But AI is different — it’s not just a system upgrade, it’s a redefinition of how work happens.

That shift is forcing a much tighter partnership between technology and people leaders. Because now, you’re not just implementing tools — you’re redesigning how humans and machines collaborate.

“We’re architecting now more than ever how humans and machines collaborate.”

And that means culture, skills, workflows, and systems all have to evolve together. You can’t treat AI like a software rollout — it’s a full organizational transformation.

2. Don’t collapse roles — deepen collaboration

There’s a growing narrative that AI will collapse executive roles (CIO + CHRO = “Chief Work Officer”). Trevor strongly pushed back on this.

Technology and people leadership are distinct crafts — and both are getting more complex, not less.

“Blending them into a single role risks losing depth in both.”

We’ve seen this before. When e-commerce exploded, companies predicted CMO + CIO would merge. That didn’t last.

The takeaway:
Don’t consolidate — collaborate more deeply. The companies that win will have strong partnerships, not overloaded generalists.

3. The biggest shift isn’t tools — it’s how we define success

One of the most subtle but important insights: AI is changing what “good” looks like.

Leaders often start with initiatives and KPIs. But as workflows evolve, those metrics quickly become outdated.

“Success actually looks a little different this year.”

Instead of locking in rigid metrics, leaders need to stay flexible and humble:

• Old productivity ratios may no longer apply
• Outputs may increase without increasing headcount
• Value shifts toward creativity, speed, and decision-making

The real work is redefining success as the system evolves — not before.

4. Treat AI as a human-centered cultural transformation

This came up again and again: AI adoption fails when it’s treated as a tech rollout instead of a people transformation.

At Genesys, they started with literacy — not tools.

“We need to handle this as a human-centric cultural transformation.”

That meant:

• Starting with the basics (what AI is, how it works)
• Creating space for experimentation
• Reducing fear by building understanding
• Meeting employees where they are

They even launched an internal AI use case contest — and saw massive engagement.

The goal isn’t just adoption. It’s confidence + curiosity at scale.

5. Don’t “boil the ocean” — focus on signal, not noise

With all the hype, it’s easy to feel like you need a full AI strategy yesterday.

That’s where most companies go wrong.

“Don’t launch a hundred ships. Be thoughtful.”

Instead:

• Start with a few high-impact areas
• Test and learn before scaling
• Avoid layering AI onto broken processes
• Focus on “strong wins” that can expand

There’s real FOMO in the market — but reacting too fast often leads to failure.

Calm, steady progress beats chaotic transformation.

6. Reskilling is real — and it’s already happening

This wasn’t framed as doomsday — but it was framed as inevitable.

There will be:

• Responsibility destruction (tasks disappear)
• Skill shifts (new capabilities required)
• Job creation (new roles emerging)

“The people who think they can do their job the same way in a year… are the ones who will be left behind.”

The skills that matter most going forward:

• Curiosity
• Judgment
• Empathy
• Creativity
• Ability to orchestrate across systems

And here’s the twist: the most valuable employees may not be the most technical — they may be the most adaptable.

7. Your job as a leader: reduce fear and create clarity

AI is creating real emotional load inside organizations.

People are uncertain. They’re overwhelmed. They’re questioning their future.

That’s why leadership tone matters more than ever.

“How do we create an environment that’s safe for people to experiment in?”

The best leaders are:

• Slowing things down (strategically)
• Communicating clearly and often
• Building psychological safety
• Encouraging experimentation over perfection

Because transformation doesn’t fail due to lack of tools — it fails due to lack of trust.

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