The CIO, the CTO: Two Jobs That AI Rewrites

A short history of the two roles, and why the real story isn’t whether they merge. It’s how AI transforms both, pulling one toward the CFO and forcing the other to change its middle letter.

A couple of times a month, somebody asks me a version of the same question. With AI changing everything, should the CIO and the CTO become one job? Or should they split further apart? In an AI Native world, do you still need both roles?

It is the wrong question. It argues about boxes on a chart when the thing actually moving is the work. The more interesting question is how AI transforms each role.

Where the CIO actually came from

Nobody set out to invent the CIO. The role backed into existence.

In the 1950s through the 1970s, a company that owned a computer (yes kids some companies had “a computer”) had a Data Processing Manager. That person ran the mainframe, made sure payroll and inventory came out the other end, and kept the machine in the cold room humming. It was a deeply technical and operational job. It wasn’t strategic, it was closer to running the boiler room than running the business.

As the systems sprawled, the title grew up into Management Information Systems Director. Same core job, wider scope. You were not just keeping the machine alive; you were implementing business applications and occasionally handing management a report they could not have produced themselves.

The term “Chief Information Officer” showed up in 1981 in a book by William Synnott and William Gruber. Their idea was radical for the time: that information itself was a corporate resource, on par with capital and people, and it deserved a seat next to the CFO and the COO. The CIO was the person who managed information as an asset.

Then four decades of forcing functions did the rest. ERP. Y2K. The internet. Email. Security becoming existential. Cloud. Every one of those moved the CIO further from the cold room toward the boardroom. “What great looks like” for a CIO went from “the batch job finished on time” to “the systems are reliable and secure and cost what they should” to eventually, “technology is making the business measurably better.” The job kept getting more strategic, but its core never changed: the CIO is accountable for the machine running. Reliable, secure, governed, affordable. Run the business.

Where the CTO came from, and why it was different

The CTO arrived later and from a different direction.

It grew up in the 1990s, mostly inside technology and product companies, and it started as the chief engineer or chief architect. The person who owned the technology vision and the R&D bet. In a software or tech hardware company, the CTO owned the thing you sold. They lived next to engineering and product, and their job was to be right about where technology was going.

When the CTO title migrated into ordinary enterprises (banks, retailers, manufacturers), it kept that outward, forward lean. In a bank, the CIO keeps the core banking systems stable and secure. The CTO is the one pushing the mobile app, the new platform, the AI bet. One is pointed inward at operations and employees. The other is pointed outward at products and customers.

That is the cleanest way to differentiate the two: the CIO runs the business, the CTO changes the business. Reliability versus reinvention. Operate versus explore. The drawer where the tools are organized, versus the workbench where the new thing gets built.

The reason these were two seats was never the titles; it was because the two motions pull against each other. One optimizes for “nothing breaks.” The other optimizes for “we break things to drive change.” Put both functions in one place with one budget and one set of incentives, and someone always loses. Usually invention loses, because the pager goes off for outages, not for missed opportunities.

What happens next

I’ve thought about this a lot; there are many ways to navigate what’s coming next, but the most effective model is:

The CIO focuses on run – operations at scale is critical, and that has to be their success metric. With almost all infrastructure available through cloud and AI providers, the CIO’s best partner is the CFO.
The CTO changes their “T” – they must understand technology, but their role is to lead the Transformation to AI Native. The CTO has to pivot, their new best partner is the CPO, the Chief People Officer. This isn’t about choosing a technology stack anymore, this is about your people transforming.
Everyone has to embrace the change to AI Native – you either lead the change or it happens to you.

Start with the CIO, because the easy mistake is to read “shrinking footprint” as “shrinking importance.” Cloud did not make the CIO less important, it made the role more manageable. Those are different words. The surface area shrank, but importance held. When the machine is something you rent rather than something you build, the hard questions become consumption and cost: what are we paying, what are we actually using, are the vendors earning what we’re paying them. That is why the CIO’s center of gravity drifts toward the CFO. Run becomes a financial discipline.

The CTO is the opposite shape. It was always the forward-looking seat, and here is the part we never said out loud: a lot of what made that work was that change used to be slow. You could see a trend coming, and you had the time to aim at it. The great CTOs aimed a little faster, and made high value bets. You could be early, be a little wrong, and reposition before it cost you. Vision paid off because the future arrived at a walking pace. The CTO got to be the person who saw it first and had time to act.

That slack is gone. AI is not a new tool on the bench, it is a whole new workshop. It is an accelerant on the rate of change itself, and the rate is compounding. The future is no longer arriving at a walking pace; it is arriving faster every quarter, soon to be every month, then every week. In that world the value of the CTO stops being “pick the right technology” and becomes “build a company that can absorb a new future on repeat.” Vision stops being a telescope you look through occasionally and becomes flight controls you never take your hands off.

The best CTO today needs to see a little further down the road than everyone else and get the company ready for change. That is the whole job. Nobody sees past the event horizon. Anyone selling you a five-year AI roadmap right now is selling you snake oil, because we are wildly early. My first cell phone was a handset bolted to the console of my Chevy S-10 with a cord running into the dash, and I was sure I had arrived. We are at that phone-in-the-truck stage with AI, and the road past the next curve does not exist yet. So the job is not prophecy, it is keeping a fast-moving, low-visibility vehicle pointed in the right direction, and building an organization that can take the next turn without spinning out.

The CTO has to raise their view and widen their aperture. They have to walk fully out of the research lab they were born in. The job is no longer to own the technology, it is to own what the technology does to the company: not the resident expert on the new tech, but the person accountable for the whole organization metabolizing a new future on repeat, faster than it is comfortable doing it.

A different job needs a different partner. This is the tell that the role has changed, not just relabeled. The old CTO was wired into the technical core. In the enterprise that meant standing shoulder to shoulder with the CIO and engineering, vision next to operations and product. The CTO’s closest alliance was with other technologists.

Becoming AI Native is not a technology problem; it is an organizational one. The new alignment is CTO and CPO, vision next to organizational design. One side reads the road and decides where the company has to go. The other side rewires the org so it can get there, reshaping teams, retraining people, changing how the work is divided so the whole company can accelerate with AI. Org charts, span of control, promotions, incentives, budgets – it all has to change. You have to build a company that can absorb change at pace, and you build it through people. A transformation seat that stays glued to the CIO and the engineering org is solving the old problem, stuck in the past. The one that pairs with the CPO is solving the new one.

How both roles transform

So, back to the question: Merge or Split? Neither. That was always the wrong frame, because both options try to bolt AI onto the org you already have, keeping the boxes on the chart and adding “…with AI” to each one. AI is an “empty the cup” moment.

The CIO does not disappear, it tightens. Run becomes a sharper, smaller, more financial discipline that holds the guardrails and watches the meters, shoulder to shoulder with the CFO. The CTO does not disappear, but thrives when they walk out of the lab, partner with the CPO, and transform how the whole company works.

The companies that win the next decade will not be the ones that picked the cleverest org chart or the coolest technology today. They will be the ones that understood, early, that AI Native is not a tool you adopt but a change you lead. You either drive the change, or it happens to you.

Step 5: The Day Anxiety Became Curiosity

📍 Part 5 of 8 · Becoming Agent-Native
An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · Proof of Value · → The Pivot · Co-Creation · The Garage · The Flywheel

There isn’t a single moment. It’s more like a temperature change.

Gradual. And then all at once. Exactly like Hemingway described bankruptcy.

The signal: someone stops asking “is this going to replace me?” and starts asking “what else could they do for me?”

That question – unsolicited, forward-looking, a little excited – is the pivot. And everything after it is different.

What caused it.

Not a single thing. An accumulation.

The email draft that was perfect. The research that came back before they’d finished their coffee. The weekly summary that was just there without anyone asking for it.

When those moments pile up, the mental model flips. The agent stops being a threat and starts being an asset.

And once it’s an asset, a very natural question follows: how do I get a better one?

That question is the whole game. Because it means your delivery team has become an active participant in the quality of their own AI teammates. They want them to improve. They have opinions about how. They’re invested.

The frame that accelerated it.

Our team always has more work than capacity. There are always more customers to serve, more research to run, more value we haven’t gotten to yet.

We are not, and have never been, trying to reduce headcount.

What we’re trying to do is amplify the headcount we have. Get more high-value work. Free people from the repetitive work that agents handle better anyway. Work on the hard stuff. Grow your career.

It’s like the tractor replacing the hand plow. You didn’t lose the farm. The farm got bigger.

When people understood that frame, agents as multipliers, the math became obvious. More impact, same team, better work.

That’s not a threat. That’s a competitive advantage for every person on the team.

What the pivot looked like in practice.

Feedback volume jumped. People who had never commented on an agent suddenly had opinions. Feature requests started flowing. Someone said “could Reese do this if we gave him this additional context?” and “I think George would be even better if he also pulled from this system.”

That’s not tool usage. That’s coaching. And you can’t coach something you’re afraid of.

When you see this shift starting, lean in fast. Turn that spark into a fire. Prioritize the feature requests that come from delivery. Make it visible that their input is landing in the roadmap. Create the fastest possible feedback loop.

The pivot is fragile at first. Feed it.

The moment your team starts coaching their agents instead of tolerating them, the phase change is real.

*Next: What happens when delivery stops requesting agents and starts building them.

Step 4: The Agent Dashboard

📍 Part 4 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization – lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · → Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel

Early in Phase 2, before we knew if people would even use these agents, we built a usage dashboard and a small component that every agent had to include – Power Automate, Copilot Studio, M365 Copilot – it was table stakes to onboard.

It felt a bit like overhead at the time.

It became the foundation of everything.

What the dashboard tracked: which agents were being used, how often, by whom, and with what outcome.

Simple. But surprisingly revealing.

Some agents were hits. Usage climbed. Feedback was positive. The team became genuinely dependent on them. These got investment: more features, deeper integration, wider rollout.

Some were mediocre. Usage below expectation but not zero. The dashboard made us ask the right question: is the agent underperforming, or is there an onboarding gap? Is there a better design? Those are different problems. You can’t diagnose without the data.

Some just didn’t work out. And the dashboard gave us permission to retire them. No politics, no ego, just “the numbers say this isn’t earning its place.”

The data showed us insight about people, not just agents.

We saw a clear split emerge: pro users and skeptics.

Some team members were all in. They used agents daily, sending feedback all the time, acting like internal product managers for the agents they’d adopted. Others were lukewarm.

That visibility mattered. It let us find the right internal champions. It let us understand the gap between those two groups. It let us have a business conversation, with real numbers, about what was working.

ROI reporting doesn’t only justify the investment. It shows your team you’re taking this seriously. And them.

But the most important proof never showed up in the dashboard.

It was the moments.

The team member who realized they hadn’t manually changed that case status in weeks. Not because they forgot, but because Theo handled it.

The person who got their Friday afternoon back because George was doing the weekly summary.

The quiet relief of: oh, that’s just handled now.

When those moments accumulate, something shifts. The agent stops being an experiment and starts being infrastructure. The dashboard tracks the what. The moments explain why it matters.

If I were advising someone starting this today, I’d say: Build the measurement layer early, at the business group level, not deep in IT.

Once you have 10 agents and a skeptic asking “what’s the ROI on all this?” you’ll be very glad you have an answer.

“Measure early. The dashboard will make decisions for you that would otherwise become arguments.”

Next: The inflection point, when the team stopped worrying about agents and started wanting more of them.

The AI Revolution is here – and it is the savior

I saw some of the fallout of the interview with Dario Amodie yesterday and one of his key attention grabbers was:

“unemployment will spike to 20% in the near future”

On my team, we’re driving hard and fast into onboarding agents (more on that soon), and in doing so, we’re building earned wisdom, not just hypothetical or philosophical views.

His statement made me pause—not because it’s dramatic, but because it’s directionally right and emotionally wrong. There’s a better thought exercise to pursue:

AI won’t just disrupt jobs – it will accelerate the creation of their replacement.

The uncertainty around AI is due to the rate of change—and how fast that rate is itself accelerating.

If you consider past paradigm shifts, they all disrupted the existing workforce massively, but slowly:

The Mainframe
The PC
The Internet
Mobile
The wheel, fire, electricity…

These changes all transformed industries. They put people out of work—but not forever. No one today is training to be a switchboard operator. People adapted.

The fear with AI, especially Agentic AI, is that those changes are happening in days or weeks instead of years or decades. But this isn’t like past tech waves where new roles emerged slowly.

The technology that is disrupting everything is part of everything.

This means that AI will help design, build, and onboard the future of work in real time. It will empower people to adapt faster, create faster, and solve problems from every angle—not just the top down.

This is the key difference that gives me great hope from working in real time with AI ; the disruptor is the savior all in one, and it brings the power to help those that are disrupted.

AI is driving disruption centrally within organizations, but we are adapting in a decentralized way – AI is enabling those that are getting on board to create systems, training, opportunities, and resiliency for the new future.