Six Levels of Autonomous Work: How AI Will Complement and Replace

The AI ​​revolution is on the horizon, but it remains extremely challenging for business leaders to set direction, vision, and develop solid plans. However, we can provide some relatively indisputable insights into current and future capabilities that can help you begin to build a complete picture of this revolution. These include:

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• AI’s generative and predictive capabilities are already impressive and will continue to improve.
• There’s a lot of investment and enthusiasm going around in this space, and it doesn’t look like it’s going to slow down anytime soon.
• CEOs are always looking for ways to achieve more (growth and profits) with fewer resources.
• Many jobs, or parts of jobs, are routine, procedural, or algorithmic in nature, making them candidates for reallocation to AI resources. According to an article by H. James Wilson and Paul Daugherty in the Harvard Business Review (September-October 2024), most business functions and more than 40% of all work activities in the United States could be augmented by AI.
• New companies will quickly become AI-native, meaning they won’t hire humans in the first place unless necessary. These companies will likely show the rest of us where humans are still valuable and where they aren’t, and we’ll follow suit (some companies will follow sooner than others).

Building on this imperfect but relatively stable foundation, we were inspired by the “Six Levels of Driving Automation” created by the Society of Automotive Engineers to develop a framework that reflects the evolution of AI capabilities and how they will impact enterprises over the next decade or so.

Over the next decade, a series of continuously improving AI resources will have a two-fold impact on business and the human workforce: First, AI will have a far-reaching augmentation effect, taking over low-value tasks and allowing humans to focus on more strategic and creative work.

But perhaps in five years or so, AI will begin to take over entire work roles, starting with the most “procedural” or rule-based jobs, and eventually gain enough decision-making and orchestration capabilities to take over entire teams and even business units.

These two distinct effects, known as the augmentation and displacement phases, are expected to occur gradually at first and then rapidly, although the speed and depth of adoption will vary across industries, functions, teams, and individuals.

Six levels of autonomous work

Below is a row-by-row explanation of the graph above.

level: Each autonomous work level is given a number (0-6) and a title. The title indicates the amount and complexity of work that the AI ​​can do at that level. It’s essentially a general breakdown of work, starting with the smallest and simplest chunk of work, or task (Level 1). The next level up from task is subprocess (Level 2), which refers to a group of tasks that are typically performed sequentially to complete a discrete part of a business process, such as ensuring that all relevant information is accurately and completely collected to open a customer case.
At Level 3, the AI ​​has the ability to complete business processes such as taking customer orders, managing customer cases from start to finish, qualifying leads, etc. At Level 4, the AI ​​completes multiple processes from start to finish, performing most of the work traditionally assigned to roles such as sales representatives, marketing specialists, and service agents. We focus here on general commercial operations, but the same is true for manufacturing and all other types of operations.

And to help your company get the most out of AI, here are four things to tell your board:

At level 5, AI or AIs can perform most of the roles associated with any commercial team, including a “manager” and his or her direct reports running one or more complex business processes. At level 6, AI can coordinate the work of multiple teams, functions, and processes traditionally organized as a business or business unit. Ultimately, this will include all small and medium-sized businesses, and in the long term, large enterprises (although “large” refers only to the complexity of the business and the size of revenue, not the number of employees).

step: The six levels of autonomous work outlined above don’t represent a linear trajectory for AI. AI won’t advance to more senior roles within an organization like traditional career progression. Instead, there are two very distinct stages in its progression. The first, levels 1-3, are an augmented phase where digital assistants enable human employees to do what they do best while also creating new opportunities.

The second, levels 4-6, is the replacement phase, where digital agents take on increasingly larger and more complex responsibilities from humans and begin to replace them over time.

The role of AI: Here I will explain the main functions of AI and its relationship with its human colleagues by level. This is from a non-technical perspective. If you are interested, I will follow up with a deeper technical perspective on each level, but for now I wanted to focus on the relationships.

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Human role: This is the flip side of the role of AI, again focusing on the relationship between humans and AI, and their relative responsibilities and capabilities.

Adopted: These are the dates by which mainstream adopters (broadly including both early and late adopter categories) are expected to start applying AI at each level, with innovators and early adopters even sooner and late movers probably even slower unless the crisis changes their trajectory.

We know that adoption rates vary across industries and departments. Even at the employee level, adoption is unlikely to be smooth sailing. Some will embrace AI with enthusiasm, but they are more likely to embrace an AI that frees them from the monotony and tedium of their jobs than one that promises (or threatens!) to do the more creative and strategic parts.

Others will likely push back against all of this, especially those who fear their jobs will be completely replaced by AI, but broadly speaking, we are already seeing examples of both predictive and generative AI being applied across most industries, and we know that more sophisticated and capable bots and agents are on the way.

How autonomous work impacts business

We have identified three key business implications of this evolution in AI: We encourage leaders to recognize that these impacts are coming soon and start planning accordingly.

• Augmentation and replacement plans: First, as we have already noted, the six levels do not represent a linear trajectory of AI. Rather, there are two distinct stages in its evolution. The first is levels 1-3, which can be described as the augmented stage. Most commentators focus on this stage because it is uncontroversial and reassuring. Research suggests that AI could automate most tasks in knowledge-based professions by 2030, dramatically increasing the productivity of the average worker. Humans will be uplifted by AI, freeing them from manual, repetitive, and tedious tasks, allowing them to focus on strategic and creative activities. AI could also create new opportunities for humans at this stage.
  But this may obscure the reality of what’s coming next: When AI reaches level 4, it will enter the replacement phase. As AI becomes capable of performing roles autonomously, it will no longer follow traditional career progression. It will not be promoted into a role that supervises or manages the humans performing those roles. Sooner or later, AI will replace humans. And when that replacement happens, it will happen rapidly. Current HR and change leaders need to start planning for this now.

• Faster responsiveness: AI will help accelerate the business cycle of any company. Infinitely, We have introduced the SUDA model (Sense, Understand, Decide, Act) as a business operating model for the AI ​​era. AI will enhance any enterprise’s ability to Sense, Understand, Decide, and Act, and those that do will have an advantage over their competitors. Enterprises will be able to make more informed decisions faster, thereby gaining what the military calls a decision advantage or edge (more on this in future posts).
  What’s crucial here is that a company’s success depends on shortening the time between each stage of the SUDA model, and driving the gap between sense and act as close to zero as possible. Each level of the autonomous work model represents an improvement in AI capabilities at one of the four SUDA stages, as well as a general acceleration across the model in decisions and actions at different scales, from the minute-by-minute activities of individual employees to end-to-end business processes to strategic enterprise-wide initiatives. AI accelerates and amplifies both stages and scale. Companies that cannot shorten the gap between sense and act will lose to those that can.

• Beyond human capabilities: AI will not simply progress to a level where it is more productive than a human full-time employee (FTE) or measured in human power units (as we discussed in our previous post on AI, horses, and humans). At levels 5 and 6, AI will demonstrate the ability to handle situations beyond human capabilities. After that, it will be measured in machine power, perhaps as a function of complexity, accuracy, and speed, rather than simply GPU/CPU or transactions per second (TPS).

Leadership Call to Action

AI is coming and it’s already here. Leaders need to recognize that AI isn’t going away, even if the current level of hype is unsustainable. Even if leaders aren’t yet ready to embrace AI itself, there are some things they can do to prepare, including good business practices.

A company-wide or enterprise-wide data strategy can be designed and implemented (ideally extending to the business network). Data is and will always be a big topic, with or without AI. You can also focus on streamlining key business processes and be smart about eliminating, simplifying and standardizing them before bringing in AI to enable and drive them (again, this should be done with or without AI). HR and transformation teams should also plan for both phases of AI, before AI is introduced and it’s too late.

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Finally, while AI may seem like a problem to solve, as we discuss here, it is also a key part of the answer for navigating increasingly uncertain and volatile times. AI can play a key role in helping leaders and their teams make strategic, data-driven decisions and take effective action.

These are exciting times, and we hope our model helps provide leaders with enough structure to take action amid all the uncertainty and ambiguity.

This article is Henry KingHe is a leader and co-author of Business Innovation and Transformation Strategy. Infinite: A new way of thinking for unlimited business success.