What the CBA Appointment Signals About the Future of Organisational Operations
The AI Role that Could Own the CEO Coherence Problem
Let’s start here. When it comes to costing AI there is a growing disconnect. Customers are increasingly looking for a way to understand what it actually costs to execute work using AI (something close to derived pricing) while vendors, by contrast, are comfortably positioned at the other end of the spectrum, leaning into value-based models that emphasise outcomes over cost structure. Both positions are rational but leave a very wide gap currently being filled with demos and promises of ROI.
The thing is, when organisations ask questions about ROI and total cost of ownership, they aren’t just asking abstract questions about value. They are actually trying to understand what it costs them to run the business that way. And right now, no one has the answer. But more importantly, we can’t wait for vendors to solve this. We are unlikely to meet in the middle. So the work of deriving cost doesn’t disappear. It shifts to the customer. Which means rolling up our sleeves, making work visible, understanding how it actually gets done, and translating that into cost.
In many ways, this may reintroduce consultative services back into the centre of enterprise strategy. Because adopting AI at scale does not remove operational complexity. It has to expose it. So as organisations attempt to make execution measurable, governable, and economically visible, a new executive function increasingly appears to be emerging alongside it. Not simply to oversee AI, but to coordinate how work itself executes across the enterprise. What does that role become? What does it own? And could it ultimately define the path to sustainable AI ROI?
Enterprise technology has long been framed as a transformation problem. Whether that starts with a conversation to review an existing program of work, or more analytically to get straight to upgrading systems or modernise into more flexible hybrid cloud environments better aligned to address the modern reality of disruptive business, it has always been an unwieldy problem. Now we’re being asked again to modernise the stack, and introduce AI.
And yet, despite unprecedented tactical investment in tech, business leaders are struggling to explain how their organisations are executing the real work of delivering products and services to customers in a better way today than just a few years ago. If anything, I’ve observed a decline.
I think what CEOs are actually facing is not a technology problem in isolation, but one of business cohesion and alignment. And they are looking to bring the divergent parts of their organisation back together into a single, coherent way of operating.
Yet at the operational level, many of those flows remain fragmented, inconsistent, and largely invisible outside the people directly involved. You can throw a rock in most organisations and hit a process held together by workarounds, tribal knowledge, disconnected systems, and manual intervention. The challenge emerging in the AI era is not simply automating systems, but understanding how work actually moves through the organisation in the first place.
That is why so many symptoms of our legacy operating models persist. For years, organisations have pursued stronger performance within individual functions, believing optimisation at the local level would naturally improve the whole. In reality, it has often increased the friction between functions, creating a breakdown in organisational coherence. Not just coherence around how work gets done, but around what the organisation is ultimately trying to achieve and how value is created across the enterprise.
If value itself were more visible, would the coherence problem be less severe? Would rising operational costs be easier to explain? There is a strong argument that the current race to apply AI is amplifying both problems by accelerating execution without a corresponding ability to measure how that execution translates into meaningful organisational value.
The world of business technology is clearly at an inflection point. The AI era is creating an unusual concentration of authority. What we are witnessing is a handful of individuals increasingly shaping the systems through which organisations think, decide, and execute, while most organisations are still struggling to operationalise what that actually means for work, governance, coordination, and value creation.
At the same time, many of our measurement models remain anchored in an earlier era. As analysts, we still tend to evaluate organisations largely as collections of systems, licenses, infrastructure, projects, and headcount assets, strategically aligned to business vision. Over time, organisations themselves have aligned to the technology industry built to serve that model.
Yet business value was never truly created in those assets alone. It has always emerged through the outcomes, decisions, services, and execution they enable. The systems absolutely matter. But they are enablers of value, not value itself.
The challenge is that AI is accelerating execution faster than organisations can rethink the models they use to measure productivity, performance, and value creation. Yet the contours of the new model are beginning to emerge. Increasingly, enterprise economics are being shaped by four things. How work flows, how decisions are made, how often execution breaks, and how much of that execution should be automated. In that world, cost is no longer simply systems plus people. It becomes a function of flow, decisions, exceptions, coordination, and automation.
That’s the CEO constraint. A lack of visibility into how work actually executes end-to-end. Without that, measurement is partial, and management has no other method other than to become reactive. It’s not a new problem. Deming identified it decades ago. What is new is the extent to which modern business systems have evolved faster than our ability to meaningfully observe how work actually executes through them.
This is why the market narrative has shifted so aggressively toward new platform-based architectures. To manage this opaque execution layer across fragmented systems. But this introduces the second problem.
Technology can enable coherence, but it cannot own it. Any attempt to delegate that responsibility entirely to technology already has a name. Managed cognitive dependency.
Cognition is not coherence. AI can accelerate analysis, automate decisions, and increase execution capacity, but coherence requires alignment across operating models, governance structures, systems, funding priorities, and decision rights.
And increasingly, no single role inside the enterprise is truly accountable for that outcome. Years of decentralised technology buying, fragmented digital programs, and function-led transformation have distributed execution authority across the organisation while weakening ownership of enterprise-wide coherence itself.
The CIO still owns systems. The COO still owns operations as they are understood (though I would argue that operations have fundamentaly changed). The CFO still manages cost. The CDO where they exist, still own data. But no one owns how work actually flows across all of it. So while the CEO, and everyone else, feels the problem, the organisation cannot resolve it.
The problem is not merely a leadership gap. It is a structural shift in organisational execution itself. But structural fragmentation does not eliminate the need for leadership. It makes coherence, alignment, and accountability even more dependent on it in a world where the technology stack is now multi-platform, where TCO models have moved from systems to execution and where continuous disruption has become the norm. That combination creates a new requirement.
I’ve increasingly observed calls for a new kind of executive role, and they are beginning to emerge. Last week’s appointment by the Commonwealth Bank of Professor Mary-Anne Williams as its first Chief AI Scientist is one example.
It will be interesting to watch how these positions evolve because I suspect they are not ultimately about technology ownership alone. Nor are they simply transformation or delivery roles. What organisations increasingly appear to need is a single point of accountability for enterprise execution itself.
In many ways, I think better external framing for the role is as a Head of Execution. Someone accountable for how modern work flows across the organisation, how decisions are coordinated, what execution costs, where friction accumulates, and how increasingly AI-enabled operating models scale coherently across the enterprise. That’s no small task.
Whatever the final title becomes, this is fundamentally a role about operationalising execution as a measurable discipline. It will need to redefine the “unit of work” inside the bank, instrument execution end-to-end, and expose the economics of how work actually flows through the organisation. Not just systems costs or labour costs, but the true cost-to-execute across decisions, exceptions, orchestration, coordination, and automation.
In time, that likely means establishing standardised execution patterns across functions and translating execution performance into financial terms the enterprise can govern at scale.
At an operational level, this program of work cannot remain abstract for long. It will materially shape the bank’s technology choices over the coming decade. Because once an organisation begins instrumenting execution, a fairly clear maturity path emerges. First comes making work visible. Then establishing baseline cost, flow, and performance metrics. From there comes optimisation, orchestration, and ultimately measuring ROI as a function of how work actually executes across the enterprise.
It becomes easy to see why investments in platforms such as Pegasystems would increasingly fall within scope, not simply as workflow tools, but as part of the operational architecture through which execution is standardised, measured, and scaled. Those are the kind of decisions that move markets. Not only for the platform providers themselves, but for the system integrators, advisory firms, and ecosystem partners aligned around them.
The natural progression is that as visibility improves and execution costs become measurable, AI and automation can be applied more broadly, unlocking higher ROI and creating a compounding cycle of operational improvement. In effect, this role would do what no current executive role is fully designed to do. It would connect work. Once those relationships become visible, organisational dynamics begin to change. Productivity, cost-to-serve, operating performance, and ultimately coherence itself can be tied directly to how work actually flows.
Only at that point, the promise of modern platforms and AI becomes real. Not because the technology changed, but because the organisation finally became capable of absorbing it. That is the operational signal the Commonwealth Bank has just sent to the market.