AI Org Charts: Beneath, Beside, and Between Workers

Introduction

As artificial intelligence becomes more capable and accessible, organizations are beginning to rethink the fundamental structure of work. Rather than simply automating discrete tasks, AI is being positioned to interact with the org chart itself—beneath, beside, and between human employees. This shift raises critical questions for IT, security, and engineering professionals who must design, deploy, and govern these systems at scale. The future of the firm is not just about adopting AI, but about integrating it into the very fabric of how teams operate, coordinate, and deliver value.

Drawing from observations at leading AI labs and early adopter enterprises, three illustrative scenarios are emerging. Each presents distinct implications for organizational hierarchy, worker agency, firm size, and compensation structures. Understanding these models is essential for leaders who want to stay ahead of the curve while maintaining operational integrity and workforce trust.

AI Beneath Us: The Managerial Layer

In the first scenario, AI operates beneath existing employees, functioning as a new basement of labor that reports up to each individual contributor. This model promotes every worker into a managerial role, with AI agents handling routine tasks such as drafting emails, scheduling meetings, and managing data entry. As these systems improve, they begin to coordinate with one another, forming mini-firms under the direction of human supervisors.

For engineers and IT teams, this means building platforms that support agentic workflows, real-time monitoring, and cross-agent communication. Security considerations include ensuring that AI agents do not inadvertently expose sensitive data or violate compliance protocols when executing delegated tasks. The rise of AI as a direct report also demands new governance frameworks to define accountability, access control, and error handling.

Importantly, this model does not imply layoffs. Instead, it automates past versions of ourselves, allowing workers to move up the value chain. However, it also introduces complexity in performance evaluation, resource allocation, and team dynamics—all areas where engineering and DevOps teams must provide tooling and infrastructure support.

AI Beside Us: The Colleague Model

The second scenario places AI alongside human colleagues, integrated directly into existing reporting structures. These AI systems act as full-fledged team members, taking on responsibilities traditionally held by analysts, coordinators, or specialists. Over time, they may assume broader functions and report through the same channels as their human counterparts.

This approach requires robust identity and access management (IAM) systems capable of distinguishing between human and machine actors. It also demands clear policies around decision-making authority, especially in high-stakes environments such as finance, healthcare, or critical infrastructure. Engineering teams must ensure that AI colleagues are auditable, explainable, and capable of seamless handoff when human intervention is required.

From an organizational perspective, the colleague model can flatten hierarchies by enabling faster execution and reducing bottlenecks. But it also blurs the lines between augmentation and replacement, creating potential friction among workers who may feel displaced or undervalued.

AI Between Us: The Coordination Layer

The most unintuitive of the three, the “between us” model positions AI as an intermediary layer that coordinates and monitors work across teams. Humans remain at the top and bottom of the hierarchy, while AI enforces executive vision, tracks progress, and identifies contributions from enterprising employees. This model resembles a digital middle management layer, optimizing workflows and ensuring alignment with strategic goals.

For IT and security professionals, this scenario introduces new challenges in visibility and control. AI systems acting as coordinators must be trusted to make routing decisions, allocate resources, and escalate issues appropriately. They must also be resilient to manipulation or adversarial inputs that could disrupt operations or compromise data integrity.

Additionally, this model has significant implications for privacy and surveillance. Employees may be subject to constant monitoring, raising ethical concerns that organizations must address through transparent policies and strong governance. Engineering teams play a key role in designing systems that balance efficiency with fairness and respect for individual autonomy.