Artificial intelligence is no longer a tool exclusively used to execute predefined tasks. In many organizations, it has become an active participant in shaping how work gets done—and increasingly, how organizations themselves are designed. The transformation is unfolding quietly and unevenly, but its direction is clear: As AI becomes more agentic—autonomous, proactive, and self-learning—it begins to mediate the very structures through which work, collaboration, and capability evolve.
A recent article in The Wall Street Journal (Bousquette, 2025) describes how Standard Chartered Bank has reimagined its collaboration and operating model through the use of agentic AI. Employees no longer hold rigid job descriptions; instead, they function more like internal gig workers, taking on ad-hoc projects that may have little to do with the roles for which they were originally hired. Their redeployment is managed by an AI-enabled talent marketplace that dynamically matches skills to projects, creating a system in which the “job” itself becomes fluid.
According to Tanuj Kapilashrami, the bank’s Chief Strategy and Talent Officer, this internal marketplace is designed to increase adaptability and productivity, especially as new AI tools accelerate the pace of change. It represents a concrete example of agentic organization design—where the architecture of work is not managed through immediate human decision-making but mediated by intelligent systems capable of self-organization and learning.
Many organizations still approach artificial intelligence cautiously, using it as a tool to support specific tasks or replace narrowly defined human functions. Others have yet to integrate it meaningfully at all. The broader reality is that many companies still struggle to adapt to the initial AI wave, which focused on task-replacement or narrow AI systems designed to support existing processes rather than reimagine how work and collaboration unfold.
In contrast, agentic AI systems are not programmed to execute isolated tasks but to sense, decide, and learn within complex, interdependent contexts. As Hosseini and Seilani (2025) describe, such systems exhibit autonomy, proactivity, and adaptability—qualities that allow them to continuously reorganize how work is coordinated and where human judgment is most valuable. For example, AI can dynamically reroute global supply chains, as IBM has demonstrated in its adaptive logistics models (IBM, 2024), or autonomously reallocate cybersecurity resources in real time, as seen in enterprise systems like Darktrace (Moveworks, 2024). These applications show that agentic AI can reorganize not only who works where but how entire systems operate and evolve—often with a speed and precision traditional management and human decision-making cannot match.
To fully leverage their potential, agentic AI systems must be embedded into an organization’s design—i.e., the way a company organizes itself, how decisions are made, how teams form, and how learning occurs across the enterprise. This specific shift calls for moving from controlling change to co-creating it—with both humans and AI as active participants.
As Tanuj Kapilashrami observed, success depends on whether agentic AI systems enable people to engage meaningfully rather than merely execute efficiently. Consequently, within an AI-agentic organization design, the role of humans—leaders, team members, and employees alike—must become more strategic, more adaptive, more interconnected, and, paradoxically, more human.
Agentic AI systems thrive on trust, transparency, and engagement—qualities that leaders often demand from others but may hesitate to practice when their own roles are redefined. In environments where AI mediates work and team formation is fluid, leadership becomes less about directing fixed groups and more about cultivating shared understanding across changing constellations of people and projects. The leader’s role is to create clarity amid constant movement—to interpret what the system reveals, connect emerging patterns to purpose, and ensure that adaptability does not come at the expense of belonging or meaning.
Rather than controlling structure, leaders define the conditions that guide it: setting values, boundaries, and principles that keep AI-enabled collaboration aligned with human intent. Their effectiveness depends less on authority and more on their ability to help others navigate uncertainty with confidence and coherence.
In this evolving landscape, leadership is no longer about having the answers but about sustaining orientation—anchoring trust, sense, and purpose when everything else is in flux.
If agentic AI transforms the architecture of work, it equally transforms what it means to contribute. When projects and teams form and dissolve dynamically, employees can no longer rely on static roles for orientation. Instead, they must learn to navigate a continuously shifting landscape—one where collaboration is data-informed, distributed, and fluid.
In this environment, individuals become both learners and co-designers of their own work. Success depends on their ability to recognize emerging opportunities, adapt skills in real time, and engage constructively with AI recommendations. Those who thrive in agentic systems cultivate meta-skills—curiosity, digital literacy, and self-awareness—that allow them to translate algorithmic insights into meaningful human action.
At the same time, working alongside AI requires a renewed sense of agency. Rather than deferring to automated decisions, employees need to question, interpret, and contextualize them. The human contribution lies not in outperforming AI, but in providing the judgment, empathy, and ethical reasoning that machines cannot replicate.
Ultimately, the future of work in agentic organizations depends on this reciprocal relationship: AI augments decision-making, while people sustain purpose and coherence. The more fluid the system becomes, the more vital it is that humans remain active participants—not passive recipients—of intelligent coordination.
At first glance, one might assume that in an agentic organization—where AI systems continuously adjust structures, roles, and workflows—the need for an organization design team would fade. If the system can sense and reorganize itself, what remains to be designed? Yet the opposite is the case.
As organizations evolve into adaptive ecosystems, design teams become even more essential. Their work shifts from drawing static blueprints to curating living systems—defining principles, feedback loops, and boundaries that allow AI, leaders, and employees to co-evolve without losing coherence. The goal is no longer to control the structure but to sustain alignment between human purpose and AI-driven adaptation.
When done right, such systems can be deeply engaging—even fun. They can turn work into a continuously evolving space for experimentation, collaboration, and growth. But if poorly designed, they risk confusion, fragmentation, or loss of meaning. It is on the organization design team to ensure it becomes the former, not the latter.
The questions design teams must now raise extend far beyond reporting lines or spans of control:
These questions reinforce that the human dimension of organization design remains indispensable to truly connecting AI with the superpower of engagement, trust, and continuous growth.
In the coming years, more organizations will experiment with AI-mediated talent marketplaces, dynamic teaming platforms, and autonomous process orchestration. Yet sustainable success will rest on one essential design principle: AI—like any modern organization design—must enhance engagement and growth opportunities.
Agentic AI opens new possibilities for continuously learning, self-adjusting organizations. But as it becomes a co-architect of work, leaders must ensure that the systems it shapes remain grounded in trust, collaboration, and shared purpose. Without human sensemaking and genuine motivation, even the most advanced AI can create silent friction, fragmented priorities, and a loss of shared context.
The challenge is no longer whether organizations will adopt agentic AI—they already have. The real question is whether they can design at the dynamic interface where human collaboration and agentic AI meet, maintaining coherence and meaningful connection amid accelerating complexity.
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