Most of us take org charts for granted. We rarely question how they are structured or how we work within them. However, the underlying principles are already changing. The rise of AI has profound consequences for organizational structures, management, and the capabilities enterprises require to stay ahead.
Why Organizations Look The Way They Do
Frederick Taylor, seen by many as the inventor of the corporate hierarchy, in his 1911 “Principles of Scientific Management” established a clear division of labor with distinct responsibilities for managers and their subordinated employees. Managers were responsible for planning, training, and overseeing work. Employees were expected to follow directions and complete delegated tasks in specialized roles. His approach aimed to maximize efficiency and productivity by distributing labor and authority, breaking down complex value creation into standardized work processes and functions—all while providing clear instructions.
Ronald Coase, in turn, explored a different dimension of how organizations function. In his 1937 essay “The Nature of the Firm” ( which later earned him a Nobel Prize in Economics), he described companies as entities formed to reduce transaction costs. These include the costs of coordinating work, talent, and resources to create value. Coase argued that firms arise when transaction costs (e.g., for information gathering, bargaining, enforcing policies, and collaborating) are lower internally than on the market. According to his theory, organizations keep growing until the increasing overhead costs, like resource misallocation by overwhelmed managers, outweigh the savings.
While organizational theories and models have certainly evolved, these two thinking schools are still defining how we have been building and running organizations over the last decades.
Despite the increasing criticism of the Tayloristic pyramid, it has mostly outlasted other approaches. Organizations have found that steep hierarchies and strict division of labor come at the cost of reduced customer centricity, local optimization, and poor adaptability; Nevertheless, the model remains the best for ensuring accountability and cost control.
Ronald Coase already anticipated how technology could fundamentally bring transaction costs down, indicating the conclusion that organizations could get smaller again over time. Yet, even fully digital, successful enterprises have significantly grown their headcount in the last decades, while still experiencing significant transaction costs (particularly around the coordination of labor, e.g., for hiring and retaining talent). Although finding and coordinating talent has become more efficient, the costly limitations of human managers and individual contributors in today’s hybrid working world have outweighed the benefits.
Some argue that the current rise of Artificial Intelligence is just the continuation of increasing automation. However, given the pace of innovation and the growing adoption of autonomous AI use cases in enterprises, mostly through increasingly autonomous AI Agents, there is a lot of evidence that the way organizations look and operate is about to change profoundly.
Why AI Is a Unique Catalyst For Redefining Org Structures
AI introduces new patterns of coordination and execution. These shifts challenge traditional hierarchies and reopen fundamental questions about how organizations should be structured. The next sections explore different ways these tensions are playing out.
Will AI Replace or Reinforce Taylorism?
According to McKinsey, until 2030 alone, about 30% of the work done by today’s workforce will be automated by AI — and up to 40% in some markets. If you look at examples what AI already delivers today, these projections are easy to believe: it automates the work of software engineers and turns them into architects who rather instruct and monitor AI development (e.g., using Cursor, Workpath already sees 20-25% productivity gains with still room for improvement). AI Agents automate customer service (many of our customers report productivity gains between 15-20% in areas like insurance, logistics, and commerce), as well as top-of-funnel sales teams (e.g., AI doing account research, suggesting outreach tactics and texts to sales reps).
There is a pattern emerging in all of this: human professionals who automate their work can become significantly more productive and impactful by becoming great managers for these agents (and for mixed Human-AI teams), or they face the risk of being replaced. This brings us to the key concepts of Taylor’s division of work in an organization: management and execution.
Executing work
With AI Agents, the available workforce to get things done will grow exponentially. For every task, role, and job, there will be an army of Agents ready to be “hired” on internal and public marketplaces. Execution capacity will expand, and mixed groups of humans and Agents will be significantly bigger than today‘s high-performing teams. Organizations will start exploring, experimenting, and generally doing more work that was once too expensive for humans, yet too complex to be automated just a few years ago.
Managing workers
In order to set goals, make plans, break down tasks, train, guide, and monitor the work of autonomous AI workers, organizations will need a new type of manager. As indicated above, these will be former subject matter experts and early adopters of working with AI—the ones best equipped to train, guide, and monitor new AI colleagues. And while individual contributors are already competing with AI Agents and AI-empowered human colleagues, the role of traditional team leads and middle managers will evolve significantly.
Even a decade from now, human managers will still lead other people, especially at higher levels, but there will also be a new type of middle management. Instead of overseeing predefined tasks, they’ll be responsible for guiding and supervising AI Agents and hybrid teams.
Given these developments, it is likely that a hierarchy between managers and workers will persist and potentially even steepen. A hierarchy will also remain between human managers and AI contributors, but one between AI-enhanced humans and those who fall behind in adopting the new technology.
While at first sight it seems like managing an AI Agent and Agent teams is much simpler than managing human co-workers (imagine, for example, no human needs or interpersonal issues), new challenges in coordinating work between humans and autonomous technology with agency will arise. These will require new organizational capabilities and significantly influence the transaction costs of creating value.
Did Ronald Coase Predict The “One-Person Billion-Dollar Company”?
We already have fully digitized marketplaces: on the one hand, for professionals who offer their work (think LinkedIn, but also Stepstone and others); on the other, for tasks and projects that need to be done—ranging from basic gigs to complex white collar work— across platforms like Taskrabbit, Fiverr, Upwork, and Consultport. Both types have significantly decreased the transaction costs of search and matchmaking.
However, as described before, a new category of marketplaces is emerging right now: platforms like Agent.ai (“The #1 Professional Network for AI Agents”), started by a co-founder of Hubspot, now also offer the services of thousands of Agents that can be hired and deployed into existing software tools with just a few clicks.
What will happen next is the rise of similar internal marketplaces within larger enterprises.
Under the theme of a more “liquid, on-demand workforce”, global corporations have already started to establish more market-like structures within their companies. Those platforms and their related operating models (Bayer’s Dynamic Shared Ownership model, DSO, being one of the most prominent examples) aim for more entrepreneurial and dynamic teams that find exactly the capabilities they need to achieve a certain goal. At the same time, they promise to better serve the needs of a workforce that increasingly views their career as a sequence of shorter job sprints, while keeping employees in the organization and reducing the transaction costs of finding and onboarding new talent.
These already adopted mechanisms for matching goals and projects with (professionals’) skills and capacity are now additionally getting populated with AI Agents that offer their capabilities just like their human colleagues. However, with Agents that can autonomously pursue certain goals and execute a quickly growing range of tasks, these internal and public marketplaces will soon dominate and change the way we have to think about work.
Proven AI Agents can be scaled (imagine replicating your best employee a thousand times), they don’t need any breaks, nor do they have any other human needs that sometimes make work in a team more complicated. Looking at the current speed of development, within the next 5-8 years, nearly any kind of repetitive task will be done better and faster by an AI Agent. Especially as AI now also takes over more complex responsibilities, human workers will face superhuman competition, while demand will grow for a new generation of leaders capable of managing teams of AI Agents and hybrid Human-AI teams.
Will AI drive transaction costs of getting work done toward zero?
Through the lens of Ronald Coase, there are strong arguments to answer this with yes. Compared to human workers, there will be significantly more data available on AI Agents and their output. As a result, the AI-enhanced search and matchmaking technologies behind the marketplace mechanisms will become very powerful, fast, and reliable. AI Agents don’t need to be interviewed or onboarded—they can be tested with a click and at no cost, then autonomously brief and onboard themselves.
AI Agents have clear, consistent, and predictable needs—such as data, guidance (prompts), workflow access, and feedback—that are generally easier to meet than human needs in an organization. As a result, related transaction costs will decrease rapidly.
However, these developments may also introduce new transaction costs. Like with remote work or more diverse teams, the promoted business benefits dominate the debate initially, but new challenges also emerge. Indeed, for the rise of AI Agents in the workforce, there are interesting parallels to these two examples.
Remote workers might not need an office anymore, but they still require new approaches to communication and engagement, as well as financial support to upgrade their home office. Similarly, AI Agents will perform even worse than disengaged humans if organizations fail to communicate and integrate them properly into the workforce. AI Agents might not require a salary, but despite increasingly efficient LLMs (large language models) and infrastructure, the energy costs of a powerful and active Agent can still exceed the salary of entry-level workers in many parts of the world.
Diverse teams, on the other hand, have proven to deliver better results in many ways, but they also create trade-offs, requiring more communication as well as higher levels of interpersonal skills and awareness. In that sense, mixed teams of human professionals and AI Agents open a completely new chapter in the evolution of diverse teams. Just like fully remote teams usually outperform mixed teams of remote-office setups, homogeneous teams of AI Agents have their own protocols of hyperefficient communication and coordination.
Although staged for a hackathon project (GibberLink), the video shows a realistic outlook: two AI agents interacting through their own language.
However, organizations and their human teams still have to establish capabilities and protocols to help these new kinds of mixed teams excel. AI Agents may work 24/7, but they still require human feedback and regularly experience that as a bottleneck. Some human behaviors, influenced, for example, by impatience, laziness, or mood changes, are already challenging for other humans to navigate. So it’s understandable that AI Agents find it even harder to filter, interpret, and deal with these dynamics. Accordingly, misinterpretations, slow and flawed feedback loops, as well as a lack of accountability, might drive new kinds of transaction costs in Human-AI teams.
Despite the list of new transaction cost drivers, many of these new types of challenges can be managed and even eliminated over time. The benefits of a rapidly improving AI workforce outweigh the new challenges that might arise as side effects. For any organization, the better strategy is to proactively embrace this near future and its hurdles, instead of trying to slow down the underlying shift toward AI Agents running large parts of the business execution.
Many of the challenges, like energy costs, will be eliminated over time through technological progress. So organizations should focus on what they can directly influence, especially in the context of their information infrastructure and employee capabilities.
New Core Capabilities For Enterprises to Win in The AI Era
How can enterprises ensure they don’t just survive, but thrive in the emerging new reality? Based on the analysis above, there is a clear set of new organizational capabilities that businesses are forced to establish now if they want to rise as winners of the AI era.
Clear, high-quality goal structures
Autonomous Agents need clear goals and success metrics. Exactly as outcome and goal-oriented leadership has enabled expert teams to work more independently, AI Agents also require structured direction—what we call a professional graph of priorities: a ranked hierarchy of measurable goals with mapped dependencies.
Workpath’s Strategy Execution Steering Model helps teams (and Agents) align around outcomes.
For example, if an AI Agent is supposed to deliver a new software feature, it will do a much better and more efficient job if it understands the outcome (the value for the customer or user) and the business impact the organization hopes to achieve. In short, the level of achievable autonomy and automation—and with it, of speed and efficiency—through AI will be mostly defined by the clarity, structure, and richness of the data provided on the interdependent priorities of the business and its customers.
Interoperability for Agents
High-performing organizations today provide their employees with unrestricted access to the information and tools they need to succeed. They enable close collaboration both with customers and internally across team borders, towards the customer value they aim to create. It is exactly that culture, along with the consequential technological infrastructure, that AI Agents will also require if organizations want to successfully integrate them into their workforce. Instead of user logins and access credentials, AI Agents need the respective interfaces to pull relevant data.
Workpath helps teams align across silos by making goal dependencies and strategic priorities transparent.
New technical standards, like the Model Context Protocol (MCP), need to be adopted in order to allow AI Agents and their underlying models to integrate with and share data across systems. Protocols like MCP act as universal connectors, allowing Agents to dynamically interact with APIs, databases, business applications, and even with one another, not just processing data, but also taking actions and triggering workflows. Put simply, organizations need to establish a shared language across all data, processes, tools, and teams to enable communication with their new AI Agent colleagues.
Governance to select, onboard, manage, and oversee Agents
It will be much cheaper and faster to develop and deploy highly effective AI Agents for certain tasks or roles than it will be to find, train, and onboard comparable human talent. Hence, soon there will be hundreds of thousands of Agent designs and billions of AI Agents, ready to be deployed with just a few clicks.
HR teams are already using OKRs to measure success — a useful starting point as IT steps into new roles.
To benefit from this shift without drowning in an army of redundant, incompatible, expensive, or disobedient Agents, enterprises quickly need to establish clear rules and processes for selecting, onboarding, managing, monitoring, and offboarding them. IT departments will need to take on a role similar to HR, doing a lot of what HR does for human employees today. They’ll need to enforce objective quality and security standards, ensure that AI Agents can get access to the information they require, and evaluate their performance executing on organizational goals.
Enabled employees, willing and able to foster Human-AI collaboration
Like with many other technological revolutions, we’ve reached a point where the biggest bottleneck, AI-enabled progress, is the human being, with all the time many of us need to overcome skepticism and fear, accept change, and learn how to benefit from it. As described above, worries in this context are understandable. Many middle managers—those focused only on breaking down tasks and overseeing work—may find themselves obsolete if they don’t learn to manage AI Agents alongside human teams., Highly educated professionals, not just blue-collar workers, will also face disruption. Lawyers, engineers, researchers, and others will need to shift from doing the craft to observing, guiding, and giving feedback if they want to stay competitive.
This shift requires not just training and resources for new skills. First, most enterprises need to invest in awareness and change management to keep employees engaged and drive the change instead of blocking it. The most competitive organizations in the age of AI will attract and develop employees who are great at working with both AI Agents and human colleagues. Those who can collaborate in mixed teams and can differentiate when to deploy AI, select, train, and manage high-performing Agents, uphold quality standards, and feel comfortable in fast-moving, dynamic structures, where new mixed teams continuously form around shared goals and capabilities.
Outlook: The path towards high-performing Human-AI organizations
Based on Taylor’s theory, you can see a future where we will continue to have hierarchies and a split between delegation and execution, but humans will delegate more to AI Agents and less to other humans. As AI Agents do not have human needs and are easier to manage in that sense, and because much of the middle management’s work—breaking down goals, coordinating tasks, and monitoring execution—will be automated, there will be bigger teams with fewer human managers and fewer hierarchy levels.
Accordingly, “who reports to whom becomes less relevant, while “who can get the most work done“ becomes more relevant. In most cases, even for multi-billion-dollar companies, more than three human hierarchy levels will no longer make sense. Enterprises will turn into rather flat marketplaces where the best ideas and most relevant goals must be matched with the most effective resources. Many indicators suggest that the supply side of that market will become more present and defining for an organization, while the execution side will grow more arbitrary and fluid.
Through the lens of Ronald Coase, we have analyzed how transaction costs on both internal and external markets (between employers and employees) will develop in the age of AI. While a hybrid workforce of humans and AI Agents will initially drive new kinds of transaction costs (particularly at the Human-AI interface), we have also argued that most of these are rather temporary. The costs of AI Agents working within a team or company will quickly move close to the costs of coordinating the same kind of work across organizational boundaries.
Ultimately, finding and deploying the right AI Agent, team, or even organization for a given goal will become incredibly fast and efficient. There will be billions of scalable and easily accessible AI Agents on the global marketplace, which again will make an organization‘s „supply“ of relevant, measurable goals and the respective infrastructure a dominating element for organizational structures.
As teams of AI Agents can be scaled more easily, and because the Human-AI interface will remain a weak link (a source of higher transaction costs), we will see the rise of large homogeneous AI teams on the market—operating as hyper-efficient organizations, run by just a handful of humans and with an absolute minimum of Human-AI interfaces. Human professionals who can orchestrate these kinds of teams of AI Agents around defined goals, either as employees or as service providers, will not just lead the salary ranks but also make organizational charts more fluid and outcome-oriented than they are today.
As a consequence, the boundaries of organizations will also become more blurry and the hurdles between internal and external marketplaces will decrease, but leaders must actively drive and enable that transformation. New capabilities will be required within a short period (we estimate about five years to enter the AI era as a winner): employees have to be convinced and empowered to provide AI Agents with the infrastructure they need to succeed. Responsibilities must shift, and where humans are no longer competitive, they need to evolve into new roles. Organizational context data—most importantly, information on the business‘s strategy, goals, and resources—must be made accessible in well-governed, interoperable graphs linked to the global marketplace.
Providing that kind of infrastructure and clarity will be a true competitive edge for enterprises in the next decade. Those who are best at defining their goals, evaluating whom to fund for their achievement, and how to quickly enable them with data and evaluate their performance as early as possible, will be the winners of the AI era. Those winning enterprises won‘t look at org charts of people with their goals and tasks anymore, but at org charts of goals and their responsible people and Agents mapped to them. Input, output, outcome, and impact goals will take the lead.
Where does all this really leave us as human professionals?
While organizations may have fewer hierarchy levels, the ones that remain will become more extreme. There will be stark differences in power and economic outcomes, both between companies and human professionals, based on their ability to deploy AI Agents at scale. The pressure on human professionals to move up in the delegation hierarchy will intensify. Many experts will have to accept shifting from builders to coordinators and observers, as their peers begin applying for their jobs with an entire squad of proven AI Agents at their side.
Hence, defining clear and actionable goals, informing and activating AI to work on them, monitoring their progress, and maintaining quality standards will become foundational skills for the 2030s workforce.
At the same time, these developments offer tremendous opportunities for millions. Entry barriers to learning new jobs will continue to drop, as AI Agents patiently follow amateur guidance and help users learn and iterate towards better outcomes. No-code tools like Lovable, which build software products from simple text prompts, and development copilots like Cursor are just two examples.
As AI Agents outperform their human counterparts in a growing range of areas, organizations will also experiment more, produce more, and deploy more Agents. Many enterprises will have millions of Agents that require governance, monitoring, and further development by a new generation of managers. Accordingly, this will fuel a fast-growing demand for human professionals with deep subject matter expertise, technical and interpersonal skills, and the ability to define and translate goals for AI Agents in mixed Human-AI teams.
Don’t fall behind. Learn how to build, guide, and deploy your own AI Agents in the Workpath AI Bootcamp.
References
- Ronald Coase, "The Nature of the Firm" (1937)
- McKinsey Global Institute, "The Future of Work After COVID-19" (2021)
- MIT Sloan Management Review, "How AI Is Changing Work and Organizational Design" (2023)
