In November 2022, when ChatGPT was released as a “research preview,” I barely noticed.
Like many CIOs at the time, my focus was elsewhere, helping my company recover from the pandemic, stabilizing operations, re-engaging teams, and resetting priorities for what we hoped would be a more predictable future.
Artificial intelligence was not on our roadmap, and it felt like a distant horizon rather than an immediate priority.
A week later, that horizon collapsed.
What began as a quiet research release turned almost overnight into something impossible to ignore. A million users in days. One hundred million in months.
Within a year, AI had moved from academic curiosity to boardroom urgency, from experimental capability to an expectation embedded in everyday work. This was more than another wave of technological innovation. It was faster, broader, and more destabilizing.
From my seat as a CIO, the experience was deeply human.
I started with intellectual curiosity, trying to understand what was truly different this time. That curiosity quickly gave way to enthusiasm as possibilities emerged around productivity, creativity, and new ways of working.
But enthusiasm did not last long.
It was followed by anxiety as the implications became clearer: data exposure, governance gaps, ethical ambiguity, and the reality that capability was advancing faster than our ability to control it. I could feel the distance growing between how quickly the technology was moving and how prepared our operating model was to absorb it.
Then came responsibility.
Unlike previous technology shifts, this moment did not wait for structured adoption plans or mature policy frameworks.
Our associates were experimenting. The vendor landscape was shifting overnight, with both new and established players claiming to be AI-enabled, some enabled by default.
I was fielding questions in executive forums that hadn’t existed a quarter earlier, while realizing we had less visibility into AI usage across the company than we were comfortable admitting. Questions from the board and CEO arrived before answers existed.
I knew this was a pivotal moment in my role as CIO to show leadership, yet there was no playbook, no precedent, and no experience to lean on. The instincts that had served me well for decades, planning, sequencing, certainty, were suddenly insufficient on their own.
And, if I’m honest, there was fatigue.
The cumulative weight of ongoing transformation, constant learning, and the need to separate hype from reality forced a deeper leadership reckoning about how we deploy AI and what this moment demands of leadership itself.
What follows are five leadership lessons shaped by that reckoning. I believe they are especially relevant for CIOs and technology leaders at this inflection point.
Lesson 1: Inflection Points Rarely Announce Themselves
The most consequential shifts often arrive quietly, at the edges of our attention, before they move to the center.
AI didn’t enter our workstreams through a planning cycle. It entered through individual curiosity, low-friction tools, and adoption that moved faster than governance could keep up.
By the time many leaders realized AI mattered, it already did.
The leadership challenge is developing the ability to sense inflection points early. Weak signals, rapid adoption patterns, default vendor behavior, and grassroots experimentation matter as much as formal strategy documents.
In our case, the first signal wasn’t a board discussion or an executive briefing. It was a handful of associates quietly experimenting with tools from Midjourney to ChatGPT in their daily work. Not flagged, not reported, simply adopted. That was the moment something shifted.
The question CIOs must learn to ask sooner is, “What would it mean if this suddenly mattered?” rather than, “Is this on our roadmap?”
Sensing the shift is only the beginning. The harder question is how to bring the organization with you once you see it.
Lesson 2: Lead the Human Curve Before You Lead the Technology Curve
AI transformations are often framed as technical journeys. They are also emotional ones.
Curiosity, enthusiasm, anxiety, responsibility, and fatigue aren’t signs of dysfunction. They’re predictable responses to rapid, ambiguous change.
When leaders fail to acknowledge them, teams fill the gap with fear, resistance, or quiet disengagement.
One of the most underappreciated aspects of CIO leadership today is emotional literacy at scale.
It means naming uncertainty, normalizing anxiety, and creating space for questions that don’t yet have answers. When leaders lead the human curve deliberately, they create the psychological safety required for responsible experimentation and learning.
Even with that safety, a tension remains. How do we move forward when there is no clarity?
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Lesson 3: Waiting for Clarity Is a Decision, and It Has a Cost
AI does not wait for certainty.
It doesn’t pause for governance frameworks, operating models, or perfectly articulated strategies.
While leaders debate, teams explore. While policies mature, capability compounds. In this environment, waiting feels prudent and often proves expensive.
I learned quickly that clarity isn’t a prerequisite for action. The leadership obligation is disciplined motion: moving forward while learning, steering while experimenting, protecting while enabling. In practice, this meant approving pilots before policies and building guardrails in parallel instead of sequentially.
Speed and stewardship go together. They are dual responsibilities.
In moments like this, inaction carries a cost. It quietly accumulates opportunity cost.
One of the clearest signals that motion was outpacing governance was the emergence of Shadow AI across the organization.
Lesson 4: Shadow AI Is a Signal for a Familiar Pattern, not a Failure
When teams adopt AI tools outside formal processes, it is tempting to frame the behavior as a failure of control. It is feedback.
Shadow AI is not new. It is the latest expression of a long-standing pattern: people routing around friction when official paths feel too slow, unclear, or misaligned with how work gets done.
What is different this time is the speed, scale, and individual accessibility of AI. Capability no longer concentrates on central platforms. It diffuses instantly to the edges of the organization.
The CIO’s role is to channel this energy. To create a path that is faster, safer, and more transparent than the workaround.
When governance lags curiosity, the organization routes around it. When governance adapts, experimentation becomes an asset instead of a liability.
It also forced a deeper personal reckoning: What did leadership mean in this new environment?
Lesson 5: This Is a Leadership Reckoning, not a Technology Shift
Over time, the accumulation of change takes its toll. Fatigue sets in from the work itself and from the constant need to recalibrate priorities, assumptions, and responsibilities.
AI accelerates this dynamic. It expands accountability faster than authority. It raises questions before it provides answers. And it forces leaders, especially CIOs, to confront a fundamental shift in their role.
This moment is about being a sense-maker, a convener, and a steward of trust. Alignment with the CEO and board around intent, risk tolerance, and learning posture matters more than early technical precision.
Leadership is defined by guiding the organization through uncertainty with clarity, integrity, and momentum.
The Quiet Risk of Hesitation
AI will not slow down to match our comfort level or governance maturity.
The cost of hesitation rarely appears neatly on a balance sheet. It compounds in lost learning, eroded credibility, and widening capability gaps.
This is a story about leadership: how our assumptions are challenged, how our responsibilities expand, and how our role must evolve as intelligence becomes a utility and experimentation outpaces control.
The technology tsunami shows no sign of receding.
The leaders who navigate it best are those who learn responsibly, visibly, and continuously while moving forward.
