Today’s Operating Model: How did we get here
Today’s operating models weren’t built overnight — they evolved as businesses grew in scale and complexity. Simple functions steadily expanded into specialized domains, each adapting to the nuanced needs of industry and markets. Take goods management: once a matter of counting stock, it expanded into supply chain forecasting, enterprise resource planning, warehouse logistics and distribution — each now a discipline in its own right, with dedicated systems and experts. Or consider customer engagement: what began as a basic sales ledger grew into customer relationship management platforms, and from there into specialized functions such as marketing automation, call-center systems, customer data platforms, and loyalty programs. Across the enterprise, complexity drove specialization, and specialization created operating models made up of interconnected micro-domains.
The result is an enterprise lattice that works but built on the operating realities of today — automation pushed to its highest achievable level, and humans taking on the remaining workload. It’s worth noting that remaining workload is so because it requires accessing large volumes of unstructured information, synthesizing and applying nuanced and evolving rules, and taking the right action in the enterprise workflow. And this layer of work — the synthesis, decisioning, and orchestration piece — has been out of reach for traditional automation. Rules-based systems could not cope with ambiguity at scale. RPA broke when exceptions and process changes occurred. Advanced analytics only described the past. None of these could reliably interpret context, adapt to change, or act with foresight — and so this layer of work had to remain firmly in the human domain. That constraint defined our expectations of what’s possible in productivity, velocity, scale, and coverage today.
Breaking the Ceiling: The Generative AI Inflection Point
Generative, and now agentic AI is breaking that ceiling. For the first time, technology can access unstructured information at scale, interpret context, synthesize meaning, and take the next logical action in a workflow. What had always required human judgment can now be executed by AI systems that read, reason, and respond. Traditional automation failed because it could not handle exceptions, scale with complexity, or interpret unstructured data; generative and agentic AI are built for exactly these conditions. And unlike analytics, which stops at describing what happened, AI agents can determine what to do next. This is not incremental progress; it is a massive inflection point in how enterprises must think about their operating models. As Harvard Business Review notes in “Generative AI Will Change Your Business. Here’s How to Adapt”, the shift is profound.
A Case in Point: The Service Desk
The enterprise service desk is a clear illustration of this shift. In the traditional model, thousands of agents handled IT tickets, following decision trees, escalating issues, and closing cases. Automation could help at the margins — routing requests, pre-filling forms, or suggesting knowledge-base articles — but the core work still had to be done by people. The moment a request contained nuance, required judgment, or touched unstructured information, automation hit its limit.
That’s changing. AI systems can now read tickets, interpret context, resolve issues end-to-end, and only escalate the few exceptions that truly require human intervention. The impact is not just faster resolution or lower cost — it is an operating model redesign. Governance shifts from static rules to continuous monitoring of AI decisions. Talent shifts from large sets of tier-1 agents to a smaller set of specialists focused on supervising, handling escalations, training, and improving AI systems. Measurement shifts from “cost per ticket” to customer satisfaction, predictive monitoring, and enterprise resilience. The service desk becomes not just cheaper, but fundamentally different in how it creates value.
Across the Enterprise: A Broader Pattern
The service desk isn’t the only example; the same pattern is emerging across the enterprise wherever structured processes intersect with unstructured information. In finance, we are starting to see reconciliation and compliance workflows being managed by AI agents that validate transactions and flag anomalies before they cascade into risk. In supply chains, AI is interpreting purchase orders, contracts, and shipping documents — resolving discrepancies without waiting for human intervention. Even in customer engagement, AI agents are synthesizing data across marketing, sales, and service touchpoints and triggering the right action in real time.
These are not isolated improvements. Each function that was once defined by human micro-specialization is now open to being re-architected with AI at the center. And the operating model is shifting from one where humans had to fill the gaps automation could not reach, to one where AI carries the core workflow and humans oversee exceptions, ethics, and innovation. There is a systemic redesign In the making.
Why Timing Matters: The First-Mover Advantage
This inflection point creates urgency. As automation came through the enterprise, the benefits were largely linear — incremental cost savings and efficiency gains that eventually diffused across industries. With generative and agentic AI — and because of the operating model shift it brings — the advantage is non-linear. Companies that redesign their operating models capture outsized gains, resetting expectations for cycle times, agility, foresight, and resilience in their industries.
The reason is simple: operating models are not just technical frameworks, they are the backbone of how an enterprise functions. Once an organization shifts to AI carrying the core workflow, the downstream effects compound — faster decision-making, more adaptive processes, and continuous improvement. But unlike pure automation; the sustainable advantage that comes from an operating model change with multi-agent systems developed with the company DNA makes it difficult for competitors to quickly replicate. As the World Economic Forum notes in “The Context Advantage: Why Your Company’s Collective Ethos is the New AI Frontier”, competitive edge will increasingly reside in how technology works within a company’s unique context.
Early movers not only run more efficiently, they also create barriers to entry in the form of new capabilities, customer expectations, and cost structures that late adopters will struggle to match. In this sense, waiting is not neutral. It risks entrenching disadvantage. The gap between enterprises that adapt their operating models now and those that delay will widen rapidly — and in many cases, may not close.
The CIO’s New Mandate: Orchestrating the AI-Native Enterprise
Green Artificial intelligence button on a computer keyboard.
getty
No one is better placed than the CIO to orchestrate this change in the operating model, and this moment redefines the role of the CIO. It is no longer sufficient to deliver platforms, manage vendors, or enforce compliance. The work needs to extend to helping re-architect the operating model of the enterprise itself. In essence, the CIO’s role is no longer about simply servicing business requirements with technology, but about enabling the enterprise to operate in fundamentally new ways. That requires pulling three new levers:
- Organizational redesign — as AI changes what talent is needed, how it is deployed, and where humans add the most value.
- Value measurement — shifting from cost-efficiency metrics to impact measures like customer satisfaction, predictive monitoring, and resilience.
- Governance — building the blueprint for how AI decisions are monitored, validated, and assured across business processes.
CIOs are positioned well for this role. They sit at the intersection of data, technology, and business process — and they are among the few leaders with both the remit and the vantage point to orchestrate enterprise-wide transformation. In this capacity, the CIO becomes not just a steward of technology, but a co-architect of a new operating model.
Question on the Table
The Executive Technology Board – a global technology think tank with 180 Global 500 CIO/CTO/CDO/CAIOs, concluded that AI is not an add-on to existing operating models; it requires and defines a new one. The companies that seize this moment will not just become more efficient; they will build sustainable competitive differentiation. The question, then, is not whether CIOs will adopt AI. The real question is:
Should CIOs now move beyond being technology leaders — and become the orchestrators of the AI-native enterprise?
