April 2026
What Happens When CIOs Stop Presenting—and Start Talking About AI
Recently, we brought together a small group of CIOs from across a broad range of industries—energy, construction, distribution, and large-scale philanthropic organizations—for the inaugural session of our Augmentation Series of Executive Roundtables.
No presentations. No decks. No sales.
Just a room of people responsible for implementing this technology, comparing notes on what’s actually happening.
To anchor the conversation, we were joined by Dr. Christopher DiCarlo—philosopher, ethicist, and one of the leading voices on the real-world implications of AI. Dr. DiCarlo advises the Canadian government, the World Bank, and the World Economic Forum, among others. He didn’t come just to present. He came to listen, probe, and help the group think more carefully.
The conversation didn’t take long to get real.
At a glance: CIOs from energy, construction, distribution, and philanthropy sat down off the record with ethicist Dr. Christopher DiCarlo. No decks. No sales.
What emerged wasn’t panic or hype—it was a quieter, more unsettling pattern: AI is already in production, dependency is forming faster than governance, and the friction that used to force organizations to think is quietly disappearing.
The real risk isn’t failure. It’s drift.
Why this Conversation Matters Now
Many of the most consequential AI decisions aren’t being made in strategy sessions or policy forums.
They’re being made incrementally—inside operations—often without visibility into second- and third-order effects.
This Isn’t Experimental Anymore
One of the first things that became clear: AI has moved well past the pilot phase.
It’s in production. Not in isolated use cases, but embedded directly into operations:
- Routing and logistics are optimized in real time
- Customer behaviour is being modelled and acted on
- Quoting, onboarding, and planning cycles compressed dramatically
- Decision-making supported by systems evaluating billions of permutations
“We’ve gone from people making smart decisions to evaluating a billion permutations and picking one.”
That shift is already here. Quietly, but materially.
And in most cases, it’s not replacing existing systems. It’s sitting beside them—making them smarter, faster, and harder to unwind.
The Gains Are Real—And So Is the Trade-Off
There wasn’t much debate about whether AI improves performance. It does. The group broadly recognized what might be called the “30% effect”—AI making individuals and teams materially more productive, often dramatically so.
The more interesting part of the conversation was what happens next.
Because once individuals become materially more effective, organizations gain the ability to do more—move faster, take on more complex work, and compete in ways that weren’t previously possible. The question most leaders are working through is how to direct that capacity intentionally.
“We’ve gone from evaluating a handful of options to evaluating a billion permutations—the question is whether we’re asking the right ones.”
The organizations navigating this well are the ones pairing capability with clarity—being deliberate about where AI-driven productivity gets reinvested, and what it makes possible
The Competitive Landscape Is Shifting—Fast
Another pattern that came through clearly: the advantage is moving. Not entirely away from scale—but toward speed, curiosity, and the ability to execute without friction.
Smaller, more focused organizations are now doing things that previously required larger budgets, more specialized roles, and longer timelines. Internal bureaucracy, once a manageable cost, is becoming a structural liability.
“I can now compete with companies that used to need entire teams—with a fraction of the people and budget.”
That changes more than efficiency. It changes who can compete.
In smaller organizations, the approach is often less formal—and in its own way, more honest:
“In smaller organizations, it’s not strategy—it’s just ‘get it in and get better than yesterday.”
That urgency, it turns out, may be its own competitive advantage.
The Workforce Is Evolving—And the Pace Is Accelerating
The conversation around people and roles was nuanced. The consensus wasn’t about displacement—it was about transformation. The nature of work is changing, and organizations are actively navigating what that means for their teams.
- A few patterns are emerging consistently:
- Roles are shifting toward higher-order thinking, judgment, and oversight
- Early-career development paths are being reimagined as repetitive tasks are automated
- Cross-functional capability is becoming more valuable than narrow specialization
- Organizations are investing in the skills that help people work effectively alongside AI
“You won’t be replaced by AI—but by someone using it.”
That framing resonated in the room—not as a warning, but as a call to action. The leaders who are getting this right are the ones investing in their people’s ability to adapt, not just their technology’s ability to perform.
The Human Side Isn’t Going Away—But It Is Changing
There was also a real conversation about what this means for people inside organizations, right now.
Something unexpected surfaced: people are working more, not less. Because the barrier to execution has dropped so significantly, ideas turn into action almost immediately. That creates a different kind of pressure—cognitive load, compressed reflection time, and a growing expectation to produce at pace.
And with that comes a quieter risk:
Are we solving the right problems?
When friction disappears, so does some of the thinking time that used to come with it. The space between idea and execution—where second-guessing, questioning, and recalibration happen—is shrinking. Dr. DiCarlo noted that this compression of deliberation time is one of the less-discussed but more consequential effects of AI on organizational decision-making.
What Nobody Planned For
Adoption Is Outpacing Structure. Shadow AI Is Already Inside.
If there was one area where the room showed real tension, it was here.
AI is being adopted faster than organizations can properly structure around it. “Shadow AI”—teams deploying tools and agents outside formal governance structures—is already a reality in most of the organizations represented. That shows up in several ways:
Inconsistent outputs and challenges standardizing results
Governance models still being designed after deployment has begun
Internal tools appearing without formal oversight or approval
Employees using personal AI tools for work—with or without policy clarity
The honest reality: organizations are trying to govern AI after it’s already inside the system. And at the institutional level, there was little confidence that governments are keeping pace. Policy is reactive, fragmented, and not aligned with the speed of change.
The Real Risk Isn’t Failure—It’s Drift
Interestingly, very little of the conversation focused on catastrophic failure. No one was worried about AI breaking everything.
What people were observing was subtler: growing trust in outputs, faster execution cycles, less time spent questioning, and teams adapting to the system rather than the other way around.
“We’ve removed the friction that used to force us to think.”
Nothing breaks. Things just… change. And those changes compound over time.
Which brought the group to a single question that shifted the tone of the entire conversation:
What would be the first thing to break if your AI outputs were quietly wrong for six months?
The room paused. Because it reframes the conversation from capability to dependency—from performance to resilience. Most people had to think about it. Some couldn’t immediately answer. That pause was itself a signal.
Leaders See the Stakes—But Feel Individually Constrained
The final and most honest part of the conversation touched on something broader.
The leaders around the table are aware—acutely—that the decisions they make have implications beyond their organizations. Workforce displacement. Concentration of power. The education system’s inability to keep pace. The compression of economic opportunity. The potential for broader social instability.
They see it. And they feel limited in what they can do about it alone.
Board expectations. Shareholder pressure. Competitive dynamics. The reality that unilateral restraint in a competitive environment isn’t a viable strategy. Even modest attempts at public engagement are often constrained by corporate risk considerations.
The group left with an unresolved question—one that Dr. DiCarlo has spent much of his career examining:
Do we have an obligation beyond our own organizations—and if so, what does that actually look like in practice?
No one had a clean answer. But the fact that leaders at this level are asking it seriously—not rhetorically—matters.
Ethics Is Understood. It Is Not Yet Driving Decisions.
This is where Dr. DiCarlo’s presence added particular weight. The ethical dimension wasn’t abstract in this room—it was engaged, informed, and at times uncomfortable.
Everyone understood the implications. The issue was operationalizing them. Because in practice, incentive structures still drive decisions. Leadership attention is focused elsewhere. Competitive pressure doesn’t pause for ethical deliberation. And there’s no clear ownership of the problem.
“We all know this matters—we’re just waiting for someone else to make the call.”
That tension didn’t get resolved. But it didn’t get dismissed either. The fact that it was named directly—in a room of people with the authority to act—was itself meaningful.
What this Roundtable Revealed
Not panic. Not hype. But a quiet shift.
Organizations aren’t asking whether to use AI anymore. They’re grappling with how quickly dependency forms—and how little structure exists to notice when drift begins.
Where This Leaves Us
Most organizations aren’t getting this wrong. They’re making reasonable decisions, quickly, in an environment changing faster than the decisions themselves. The problem isn’t recklessness. It’s that the choices being made right now — quietly, incrementally, without full visibility into downstream impact—are shaping the next decade of how organizations operate and how people fit into them.
What made this session different wasn’t the seniority in the room or the candour of the conversation. It was the combination: operational leaders living this daily, in genuine dialogue with someone who has spent his career thinking about what it means.
And the question they left with wasn’t about adoption. It was the one Dr. DiCarlo has spent much of his career examining:
Do we have an obligation beyond our own organizations — and if so, what does that actually look like in practice?
No one had a clean answer. The fact that leaders at this level are asking it seriously—not rhetorically—is where the next conversation begins.
About the Augmentation Series
This conversation continues through Systematix’s Augmentation Series of Executive Roundtables
The Augmentation Series of Executive Roundtables is a Systematix initiative bringing together senior technology leaders for closed, off-the-record conversations on the real world implications of technology adoption. Participants are not identified. Insights are shared in aggregate to advance the broader conversation—not to market to attendees.
