Stop Retrofitting the Future: Why Agentic AI Forces an Operating Model Reset

Organizations are not struggling to capture value from AI because the models are immature. They are struggling because their operating models are. Yet, the industry conversation remains fixated on frontier capabilities, benchmarks, plugins, and the next wave of agent libraries. That debate is a distraction. If that is not enough, just throw phenomena like Moltbot/OpenClaw into the equation. The point of structural failure is not model intelligence. It is the inability to scale autonomy within deterministic operating models and legacy governance constructs.

We have seen this before. Cloud was not an infrastructure story; it was an operating model story. Agentic AI compresses that lesson into a much shorter innovation cycle. You cannot bolt agents onto linear swim lanes and expect a structural advantage to emerge. Engineering maturity does not equate to economic impact. Agentic AI fundamentally redistributes authority and decision rights among humans, systems, and governance layers. That redistribution becomes visible in the last mile: the gap between what algorithms recommend and what leaders are willing to be accountable for. This is where value leaks.

The question is no longer whether agents can work alongside humans. The question is whether organizations are prepared to redesign how decisions, accountability, and work itself are orchestrated. In this blog, we are building on the first part of a series on Agentic Orchestration, which concluded with the call to Stop Chasing Agents, Start Delivering Outcomes. How can organizations design workflows and operating models that generate measurable impact and build a structural advantage resilient to AI-driven disruption? And how should we reframe the Agentic AI narrative so enterprises can scale autonomy and translate investment into durable economic returns?

A real AI operating model starts at the last mile and works backward

The journey toward Agentic AI must begin with changing an organization’s mindset. That mindset must embrace the non-deterministic nature of Agentic AI, which, over time, can move beyond the standardization paradigm. Yet all too often, organizations chase tools and cognitive capabilities in the vain hope of productivity and efficiency gains without changing process flows or ways of working. Starting with productivity gains is a pragmatic and sensible entry point; it helps secure early buy-in and demonstrate tangible ROI. However, it should be viewed as a stepping stone rather than the destination. The uncomfortable truth is that productivity gains are politically convenient. Yet they are strategically insufficient. The real ambition needs to be operating model transformation. Leaders must recognize that value does not come from isolated automation but from coordinated autonomy.

Furthermore, current adoption patterns are largely focused on consuming capabilities within ISV platforms rather than on progress toward proprietary buildouts or reimagined workflows. Most of the time, these efforts are guided workflows with bounded autonomy at best, rather than progress toward multi-agent orchestration that enables greater autonomy. Currently, successful agents are engineered systems, not autonomous actors.

Unsurprisingly, most current deployments miss the fundamental change Agentic AI enables: the primary impact is not efficiency. It is the redistribution of authority and decision rights among humans, systems, and governance layers. Tackling the last mile is the cornerstone of operationalizing Agentic AI and central to reimagining operating models. An effective way to think about the implications is to describe the last mile as the gap between what algorithms recommend and what people are willing to be held accountable for. This gap needs to be shaped and reduced through incentives, culture, governance, and effective change management, not by code or models. And it is here that the value leaks. Therefore, these issues must move to the center stage of broader narratives.

Agentic orchestration changes the fundamentals of process flows, provided that technology, process, and cultural debt can be written down

To ground these narratives in actionable steps, organizations need to frame agentic orchestration as a strategic North Star, one that steadily shifts the operating paradigm toward goal-directed autonomy. In this model, algorithms do not merely execute predefined tasks; they progressively decompose objectives into executable components and, over time, reconfigure entire processes within clearly defined governance boundaries. Most importantly, we must stop pointing to sandbox environments and AI-native startups if we want to understand the challenges of the transformation journey toward agentic orchestration. AI won’t overcome technology, process, and cultural debt. It will mercilessly expose it. Therefore, organizations need to cut through the market noise and design their own path toward agentic orchestration.

Three topics that are critical at the starting point of this decision-making process:

• Avoid strategic ambiguity. Don’t talk transformation while designing operating models optimized for augmentation: The most common failure mode in Agentic AI is not over-hype or immature technology. It is strategic ambiguity. Organizations talk transformation while designing operating models optimized for augmentation. That contradiction shows up immediately in how agency is framed. Most organizations frame augmentation versus transformation as a question of ambition or maturity. That’s a convenient fiction. In reality, it’s a last-mile decision that determines how much authority leaders are actually willing to cede. Unlike in previous secular shifts, such as cloud and automation, AI-driven innovation cycles are so compressed that organizations can no longer afford to sit on the fence and hope that agentification will provide landing zones, as with cloud-native capabilities. Their moat might dry up before they can make up their minds.
• Assess your moat dispassionately. Zero in on the intersection of proprietary data and your operating model. Discussions about disruption through Agentic AI are emotionally charged, often driven by hustlers and self-anointed influencers. The furore over SaaS-pocalypse and Anthropic’s launch of industry-led services is a potent reminder of that. Yet the reality is that if you haven’t rewired your operating model around your proprietary data, you don’t have a moat; you have a pilot. Agentic AI does not create defensibility on its own. Your moat emerges (or grows) only when your unique data is embedded in a reimagined operating model that competitors cannot easily replicate. Proprietary data is the only real moat. Therefore, focus on a “good enough” foundational model and your unique, proprietary data, regardless of how messy they are. When you encode your organization’s intellectual property and proprietary data into every product, service, and process, you can create entirely new markets and revenue streams. If your agents, drawn from an agent library, run on generic data, you are building someone else’s moat.
• Rebuild the operating model around agency, not platforms. Focus on the last mile of AI: To a large degree, current narratives about Agentic AI are rewriting the RPA playbook. Cognitive capabilities are enhancing traditional workflows rather than reimagining them. Not only that, but just as with bots for RPA, we are boasting the number of agents as a proxy for maturity. Instead, organizations must embrace a non-deterministic mindset that decomposes goals and process steps to avoid getting stuck in incrementalism. A critical milestone on the journey toward agentic orchestration is organizational design. The inconvenient truth is that centralization kills the last mile. Most AI operating models over-centralize by default, not because it works, but because it feels safe. Thus, central teams (be it a CoE or something else) hog model ownership, data access, and change authority. Meanwhile, the last mile requires the opposite, namely local autonomy, domain judgment, and rapid iteration. What it boils down to is that you cannot centralize intelligence and decentralize accountability. Yet that’s exactly what most AI strategies attempt.

This leads to the broader point that most Agentic AI strategies often collapse too quickly into platform choices: frameworks, AI stacks, and marketplaces. Yet agency and autonomy challenge traditional management orthodoxy. Once decisions are distributed, architecture and organization become inseparable.

The Seven Pillars of the PAC Agentic AI Operating Model

To help more organizations finally capture value from their investments in (Agentic) AI, we need to refocus discussions on business outcomes and operating model change. Providing clearer operational objectives is a key part of this. Therefore, we have identified 7 pillars of the PAC Agentic AI Operating Model (see Exhibit 1). While these topics should not be viewed as a linear progression, we have aligned them to reflect the transformation lifecycle. These pillars build on cloud-native narratives but must deliver reimagined workflows and ways of working.

Agentic operating models must be underpinned by context engineering to achieve goal-directed autonomy across the enterprise and in external ecosystems. Mature organizations will progress from automation projects to an interaction of machine-led, reusable capabilities. The shift from prompt to context engineering is more than semantics or dinner-table conversations about our kids’ job prospects. It is a shift from interaction to system-level operations.

We spoke at length about the non-deterministic mindset and the need to redesign workflows. One of the most essential reflections on agentic orchestration is that it is not just a technology challenge but a workforce transformation. Shared agency pushes organizations to stop managing work as a sequence of tasks and start managing it by outcome intent, with agent and human roles allocated by an algorithm. Handoffs are no longer conceptualized as a linear process swim lane but as a dynamic routing process based on confidence/risk thresholds, exception types, and cost-to-serve signals. The human-in-the-loop is a useful transitional model, but if human approvals remain embedded at every step, you’ll bottleneck at enterprise scale. Therefore, the operating model must move humans toward policy, exception handling, and auditing, not perpetual clicking to approve.

Most enterprises still govern AI through capability validation, technical risk controls,  and compliance processes inherited from deterministic software. That governance model collapses under agentic conditions. To overcome this, adaptive, outcome-centric governance must pivot to a non-deterministic mindset by answering three pivotal questions. What outcome is this agent system accountable for? What variance is acceptable? And who intervenes, and on what signal? To enable that pivot, organizations must turn observability into intervention, not dashboards. This reiterates the point we made at the outset: narratives must move beyond models and capabilities. Governance must evolve toward business assurance, not technology feasibility, as organizations progress toward the North Star of Autonomous Services.

Exhibit 1:  To enable Agentic Orchestration, enterprise leaders must drive cultural change effectively throughout their organizations

Bottom line: Agentic AI exposes weak operating models. It does not fix them

Agentic AI will not fail because the models underperform; it will fail because leaders refuse to reimagine operating models and redistribute authority. If you design for augmentation, you will get incremental gains at best. Yet if your competitor designs for autonomy, they will redesign your margin structure. This is not a technology decision but a leadership decision, and hesitation will compound the disadvantage. These are the issues the narratives around Agentic AI must address. Tools and capabilities aren’t the differentiator; how quickly leadership adapts to innovation and change is.