AI is everywhere. The agentic organization isn’t—yet

The workforce is on the cusp of a major transformation, with most job roles expected to be reshaped within the next two to three years due to the rise of Agentic AI. While 80% of companies recognize this impending shift and have invested accordingly, a similar percentage has yet to see a significant impact on their bottom line. This paradox highlights the challenge of integrating Agentic AI at scale and the need for organizations to fundamentally rethink their structures, skills, and approaches to work. One key area of transformation is business models. Agentic AI can enable near-zero marginal cost of delivery, allowing for hyper-segmentation and tailoring experiences down to individual consumers. For example, a content distribution tech player could create personalized experiences for millions, opening up new avenues for commerce. However, this also introduces new risks. If individuals and small businesses have their own agents managing financial accounts and moving money frictionlessly to seek the best rates, the traditional "moat" of sticky deposits in financial services could disappear, fundamentally altering competitive landscapes. The internal functioning of organizations will also undergo significant changes. Agentic AI offers "superhuman capabilities" that can augment teams, necessitating a shift in day-to-day workflows and operating models. Companies will need to rethink how work is done, how teams engage in problem-solving, and how to implement appropriate governance and risk controls for an agent population. This is new territory for most, with broad use cases demanding simultaneous grappling across the organization. While it's too early to definitively predict the exact reshaping of organizational charts, early pioneers offer some insights. In pharma, large squads of agents are envisioned for R&D, not to replace researchers but to supercharge innovation speed. In corporate functions like HR, finance, and legal—often seen as cost centers—Agentic AI holds the promise of transforming how work is done, potentially freeing up human capacity for other business aspects. Many companies have added layers to their hierarchy over the past decade, slowing down decision-making and increasing costs. AI offers a chance to flatten structures, speed up decisions, and allow leaders to manage broader scopes of talent, thus increasing organizational agility. To navigate these changes, leaders must develop new skills. It's estimated that 70% of roles, including those in the C-suite, require fundamental reshaping. Nearly half of leaders perceive skill gaps within their organizations and themselves, indicating a strong need for more training and support. The tech function itself will also transform, with various models emerging, from dedicated SWAT teams supporting transformation to embedding expertise within each business area. Leading this change requires a different approach from leaders. They must demonstrate a radical transformation in how they spend their time, leveraging AI for their own work, and openly communicating about these changes. Overcoming a prevailing "trust gap" is crucial, as early AI experiments have sometimes resulted in faults, such as "hallucinations" or uncontrolled use creating more work. Leaders need to foster optimistic excitement about AI's possibilities while maintaining good judgment and risk management, acknowledging that the organization is in a learning phase, not a mature deployment state. Addressing fear among employees about job displacement is also critical. Leaders must craft a genuine narrative that generates positive energy around the change, emphasizing that embracing AI can lead to new skills and opportunities, even if it means transitioning to an adjacent role. New talent profiles will become essential. While AI excels at research, mathematical, and some data science skills, strategic thinking, systems orientation, and soft skills like people management are on the rise. Humans will be needed for oversight, judgment, and problem-solving, moving towards a "humans above the loop" model where agents handle most of the core process, and humans provide the critical judgment overlay. An example is the reimagining of legal arbitration, where agents can review case files and come to a summary decision, often more efficiently, but a human is still needed to provide the final judgment. A significant challenge lies in how new talent entering the workforce will develop the necessary skills to oversee AI without the traditional "grunt work" experience of the pre-Agentic era. Learning and development will need to shift from a periodic "sidecar" activity to a central part of an employee's journey. The next generation, having access to these tools from day one, will need to leverage this advantage. Organizations must also become comfortable with perpetual change, fostering a sense of curiosity and continuous learning without introducing chaos. The final shape of organizational structures is still unknown, but AI will undoubtedly influence it. While some envision fluid "pods of work" replacing traditional hierarchies, the practical challenges of evaluation and managerial support mean that most companies are still far from this model. However, increased fluidity in talent flows, supported by nimble HR functions, will become a competitive advantage. Organizational culture will also be profoundly impacted. Cultures that foster curiosity, continuous learning, joint problem-solving, and an optimistic yet judicious approach to risk-taking will thrive. The emphasis will be on iterative experimentation and two-way doors, allowing for exploration and adaptation rather than irreversible decisions. Ultimately, the value of this transformation will be realized in reimagined workflows and processes. The most impactful use cases involve rethinking entire end-to-end workflows that cut across multiple teams, where Agentic AI can connect disparate points quickly and efficiently. This shifts the focus from point solutions for individual tasks to comprehensive re-imagining of processes like insurance underwriting or HR's hire-to-onboard process. This deep, granular work requires systemic thinking and the involvement of individuals across all levels of the hierarchy, from those deep in the day-to-day work to senior leaders providing executive function, quality control, and governance. This multi-level engagement is crucial for unlocking the full potential of Agentic AI at scale.