You’ve hit on a massive truth, Karine. The CAIO appointment is often just box-ticking to avoid structural redesign. And you're exactly right: when an undesigned system gets an efficiency boost, its default move is to shrink and cut heads rather than build value.
But your premise that leaders should rebuild the organization starting with AI is where the theory hits a wall. Designing around a tool might work for a brand-new startup with a blank canvas, or a low-risk digital service. But for complex, established companies, that’s an incredibly dangerous, outside-in approach.
Having led high-risk transformation programs in the trenches, I’ve learned that a successful redesign requires a very specific sequence. You cannot rethink how to organize your people or deploy resources around AI until you have done the hard work of cleaning up your data and figuring out what actually needs to be measured.
Without that operational ground truth, a tech-first redesign triggers a domino effect of blind spots:
The Reality vs. Margin Gap: If you don't first map exactly how input costs drive value at the product or process level, a redesign is just a blind guess on whether you're actually saving money.
The Blame Game: When a human and a machine co-produce a decision, you need absolute clarity on who owns the consequences and practical safety nets to catch errors before they scale.
The Friction Domino Effect: Optimizing one department usually breaks another. If an automated process saves operations money but kills the customer experience, the financial gains are wiped out instantly.
CEOs absolutely need to be systems architects, but they can't build from 30,000 feet. We have to anchor the architecture in operational logic first. Otherwise, even a CEO-led redesign risks becoming just another high-level corporate hallucination.
Jane, thank you for such a thoughtful response. I actually agree with much of what you’ve written.
The distinction I would make is that I’m not advocating for designing around a tool. In fact, I believe that’s exactly where many organizations are getting stuck.
Data quality, measurement, governance, accountability, process clarity, and operational logic are foundational. Without them, AI simply accelerates dysfunction.
Where I would challenge the traditional sequencing is that AI is not merely another technology implementation. It changes the economics of knowledge work, decision-making, coordination, and value creation.
Because of that, leaders need to rethink both the foundation and the future-state architecture in parallel.
If we wait until data is perfect, processes are fully optimized, and every metric is agreed upon before redesigning the organization, we risk optimizing for a world that no longer exists.
The questions I believe CEOs need to ask are not “Where do we deploy AI?” but:
• What work should humans uniquely do?
• What decisions should be automated, augmented, or reserved for people?
• Where does accountability sit when intelligence becomes distributed?
• How do we create more value, not simply lower cost?
To me, that’s why this is fundamentally an organizational design challenge rather than a technology challenge.
The operational ground truth matters enormously. My argument is simply that the destination architecture can no longer be designed independently of AI’s capabilities.
I completely buy into a design-forward look, Karine. The destination architecture must look ahead so we don't just optimize for yesterday's constraints.🚀
In a perfect, AI-native future, a company runs on a Unified Optimization Engine. Take the critical CEO question: 'What decisions should be automated, and what actions are reserved for people?
In a future state, if a product variant drops below a margin threshold, the AI autonomously makes the total-business decision to sunset it. That choice instantly sets off an automated chain reaction: it sends a notice to Supply Chain to stop vendor orders, adjusts Finance's margin forecast, and pushes a CRM alert to Sales to transition the customer to a better variant. The machine handles the decision; the humans handle the relationships.
But today, nobody is starting from scratch.
If you tried to let an AI trigger that decision right now, every department would pull the emergency brake. In a complex reality where companies juggle thousands of SKUs, custom workflows, and regulatory rules, Sales, Supply Chain, and Product Management don't agree on the math.
Right now, roughly 80% of enterprise AI projects stall because companies try to automate these complex chain reactions over an unreconciled operational reality.
To actually build the bridge to that future state, the CEO needs a parallel track. While designing the destination, we must simultaneously use an operational ground-truth framework to get the leadership team into a room, face the raw data, and align on how those trade-off decisions are actually made. It’s the essential engineering work required to turn a whiteboard design into an operational reality.
You’ve hit on a massive truth, Karine. The CAIO appointment is often just box-ticking to avoid structural redesign. And you're exactly right: when an undesigned system gets an efficiency boost, its default move is to shrink and cut heads rather than build value.
But your premise that leaders should rebuild the organization starting with AI is where the theory hits a wall. Designing around a tool might work for a brand-new startup with a blank canvas, or a low-risk digital service. But for complex, established companies, that’s an incredibly dangerous, outside-in approach.
Having led high-risk transformation programs in the trenches, I’ve learned that a successful redesign requires a very specific sequence. You cannot rethink how to organize your people or deploy resources around AI until you have done the hard work of cleaning up your data and figuring out what actually needs to be measured.
Without that operational ground truth, a tech-first redesign triggers a domino effect of blind spots:
The Reality vs. Margin Gap: If you don't first map exactly how input costs drive value at the product or process level, a redesign is just a blind guess on whether you're actually saving money.
The Blame Game: When a human and a machine co-produce a decision, you need absolute clarity on who owns the consequences and practical safety nets to catch errors before they scale.
The Friction Domino Effect: Optimizing one department usually breaks another. If an automated process saves operations money but kills the customer experience, the financial gains are wiped out instantly.
CEOs absolutely need to be systems architects, but they can't build from 30,000 feet. We have to anchor the architecture in operational logic first. Otherwise, even a CEO-led redesign risks becoming just another high-level corporate hallucination.
Jane, thank you for such a thoughtful response. I actually agree with much of what you’ve written.
The distinction I would make is that I’m not advocating for designing around a tool. In fact, I believe that’s exactly where many organizations are getting stuck.
Data quality, measurement, governance, accountability, process clarity, and operational logic are foundational. Without them, AI simply accelerates dysfunction.
Where I would challenge the traditional sequencing is that AI is not merely another technology implementation. It changes the economics of knowledge work, decision-making, coordination, and value creation.
Because of that, leaders need to rethink both the foundation and the future-state architecture in parallel.
If we wait until data is perfect, processes are fully optimized, and every metric is agreed upon before redesigning the organization, we risk optimizing for a world that no longer exists.
The questions I believe CEOs need to ask are not “Where do we deploy AI?” but:
• What work should humans uniquely do?
• What decisions should be automated, augmented, or reserved for people?
• Where does accountability sit when intelligence becomes distributed?
• How do we create more value, not simply lower cost?
To me, that’s why this is fundamentally an organizational design challenge rather than a technology challenge.
The operational ground truth matters enormously. My argument is simply that the destination architecture can no longer be designed independently of AI’s capabilities.
I completely buy into a design-forward look, Karine. The destination architecture must look ahead so we don't just optimize for yesterday's constraints.🚀
In a perfect, AI-native future, a company runs on a Unified Optimization Engine. Take the critical CEO question: 'What decisions should be automated, and what actions are reserved for people?
In a future state, if a product variant drops below a margin threshold, the AI autonomously makes the total-business decision to sunset it. That choice instantly sets off an automated chain reaction: it sends a notice to Supply Chain to stop vendor orders, adjusts Finance's margin forecast, and pushes a CRM alert to Sales to transition the customer to a better variant. The machine handles the decision; the humans handle the relationships.
But today, nobody is starting from scratch.
If you tried to let an AI trigger that decision right now, every department would pull the emergency brake. In a complex reality where companies juggle thousands of SKUs, custom workflows, and regulatory rules, Sales, Supply Chain, and Product Management don't agree on the math.
Right now, roughly 80% of enterprise AI projects stall because companies try to automate these complex chain reactions over an unreconciled operational reality.
To actually build the bridge to that future state, the CEO needs a parallel track. While designing the destination, we must simultaneously use an operational ground-truth framework to get the leadership team into a room, face the raw data, and align on how those trade-off decisions are actually made. It’s the essential engineering work required to turn a whiteboard design into an operational reality.