How AI agents rewire the organization

The unbundling and rebundling of organizations

Feel Free to Share

1. Scope of the role

Let’s start by looking at individual roles within an organization.

Consider a role which performs three goals, each of which constitute smaller underlying tasks.

As we’ve seen before, tasks get bundled into goals and multiple goals are bundled into a role.

When technology substitutes underlying tasks (red boxes below), the scope of the role remains largely unaffected as long as goal-seeking is critical to the performance of the role.

Let’s take the travel planning example again. As new tools come in – travel booking tools, calendar management tools, payment tools etc, – specific tasks get simplified and even substituted by technology, but the goals within which these tasks sit are still managed by humans.

AI agents are different.

AI agents attack the goal. If an agent effectively takes over a goal, that goal no longer needs to be performed by the worker and no longer needs to be bundled into the human-managed role.

Effectively, a goal-seeking AI agent can unbundle a goal from the role.

As a result, the scope of the role is reduced.

This is the first, most obvious effect of goal-seeking technologies entering the organization of the future.

But wait, there’s more…

2. Scope of the team

What is a team?

You might say a team is a collection of individuals and every individual in the team has a role within the organization. Hence, the team is a bundle of roles.

But that’s not entirely true.

A team is a goal-seeking unit. It comes together to achieve a certain goal.

Every individual within the team performs one or more goals that ladder up to the team’s goal.

So, a team is not really a bundle of individuals or roles. It is a bundle of goals. Every member’s contribution to the team is limited to the goals that are relevant to the team.

As an example, consider an individual’s role and its relationship with the individual’s position in a team.

Let’s say an individual with Role A in the organization is responsible for achieving Goal A (greyed out boxes) as part of her role. Team 1 needs a similar capability to perform Goal 1 (greyed out boxes).

Hence, Role A performs Goal 1 in Team 1.

When goal-seeking technologies – AI agents – come in, and effectively perform Goal A for Role A (greyed out boxes) in the illustration above, Role A’s position in Team 1 is challenged.

Goal 1 is unbundled from Role A by an AI agent. Role A no longer retains the right to deliver this goal into Team 1.

Team 1 can now effectively deploy an AI agent to perform Goal 1.

Hence, AI agents affect individual roles in two ways:

First, they reduce the scope of the role. Role A retains Goal B and Goal C and rebundles around that reduced scope. Goal 1 is unbundled from Role A.

Second, they displace the role entirely in a team if the team can now achieve the same goal using an AI agent.

While Role A remains with the organization (i.e. the job isn’t displaced), its position in Team 1 has been effectively displaced

3. Rebundling of roles

Let’s look at the second order effects of reduction in the scope of the role.

Consider two roles in an organization: Role A and Role B.

Let’s assume agents impact these two roles to different extents:

The scope of Role B is reduced to a far greater extent. Eventually, it may not make sense for the organization to retain Role B at all.

What typically ends up happening in such scenarios is a rebundling of roles. As most goals are unbundled away from Role B, it doesn’t make sense to retain an entire role in the organization. The remaining goals that Role B may still perform are now unbundled and rebundled into another role in the organization (Role A, which in this case is also seeing a reduction in scope simultaneously).

Role B is eliminated not because its tasks were fully substituted by technology, nor because its goals were fully substituted by technology, but because the scope of the role no longer justified a separate role.

As AI agents increasingly unbundle goals away from organizational roles, role scopes are reduced and these smaller roles get increasingly rebundled.

4. Reworking power structures

This is where the impact of AI agents becomes most telling.

There’s a curious power relationship between Teams and Roles.

Teams have voting rights on the relevance of Roles.

A particular role in the organization may achieve different goals for different teams. During quarterly or annual reviews, these teams cast their votes in favour (or not) of that particular role based on their contributions to the team.

Let’s look at the earlier example again:

In this example, Role A’s position within Team 1 is displaced once an AI agent takes over Goal 1.

At the next quarterly or annual review, Role A has one less cheerleader supporting their contributions.

Now, imagine Role A also contributes Goal B to another team and Goal B also gets substituted by an AI agent.

The fewer teams speaking to a role’s contributions, the lower the negotiating power for that role within the organization.

AI agents and organizational power and mobility

Effectively, as more goals get taken over by AI agents across an organization, several effects start to play out:

The scope of a role progressively reduces as agents take over goals.

The number of teams which a role can contribute to and develop power relations with decreases.

Roles may get rebundled if their constituent goals are increasingly unbundled by AI agents.

A role that is displaced from multiple teams loses organizational power and mobility:

1. Organizational power is a function of voting power of teams. Teams that have outsized voting power have the power to drive upward mobility for a role on the basis of its contribution. If the role’s contribution to the team (one or more goals) is substituted by an AI agent, the role loses that voting power.

2. Lateral mobility (the ability to move horizontally) is a function of visibility and participation across diverse teams. If a role is increasingly moved out of teams because AI agents can perform those specific goals effectively, its lateral mobility gets adversely impacted.

And that is how AI lets you keep your job, but it undermines the power and mobility associated with your role.

We’ve seen this before, just not with tech

None of the four effects laid out above are entirely new. We’ve seen this before, with offshoring, outsourcing, on-demand gigs etc. Goals that were previously performed with organizational resources could now be performed using external resources.

But these resources were always human resources. Goal-orientation was a defensible human advantage.

And that’s what’s different this time around. Goal-seeking, for the first time, can be performed by technology.

And with that, the effects above get accelerated.

The rate at which AI agents learn will be much faster than the rate at which an external freelancer or outsourcing agent would.

AI agents can also be embedded into organizations in a way that external human resources can’t. AI agents – constantly learning from conversations, documents, and other workflows – can more effectively achieve organizational fit over the course of interactions with organizational resources.

The cost vs organizational fit bargain in outsourcing work to external human agents may no longer apply in the case of AI agents which can progressively learn and adapt.

Roles unbundle, teams rebundle

Let’s take a break from the scenarios above and look at some anecdotal parallels.

At one of the companies that I’ve advised over the past decade, the corporate development team was looking to hire a head of strategy to aid our expansion into the Chinese market. We ended up hiring a McKinsey partner – born in China, educated in the US – for that role. It sounded like a fancy role and we needed someone fancy to come on board.

The role was a bundle of three goals:

• Goal #1: Gain market insight
• Goal #2: Develop go-to-market strategy for our global products entering China
• Goal #3: Develop market channels relevant to these products

A few months down, someone pointed out that a junior sales manager brought on board was bringing in a lot of valuable insight on the channels that would work best for our products.

Essentially, a junior sales manager drawing a fraction of the salary, was delivering ‘good-enough’ results on Goal #1 and Goal #3.

Suddenly, the seven figure US dollar salary didn’t make sense. The company realised that much of the value created by the strategy role was attributable to Goal #1 and Goal #3 but the role into which these goals had been bundled was being priced based on Goal #2.

Those three goals were now rebundled across (1) A two-member team comprising the in-country junior sales manager (Goal #1, #3) and a corporate HQ strategy manager (Goal #2), with (2) some of the strategic leadership of the role also being rolled into the China country manager.

This unbundling and rebundling of roles and teams is constantly at play in organizations. Roles may get unbundled and specific goals may be taken away from a role bundle because of underperformance within that role bundle. They may then be rebundled into teams which bring together the best specialists.

Conversely, task forces (teams) set up to address a bundle of goals may soon realize that some specific goals may need a dedicated resource (role).

All this is to say that this cycle of unbundling and rebundling across roles and teams is inherent to the organization of work.

AI isn’t fundamentally changing goal-seeking and resource allocation. It is merely inserting itself into the organization and re-organization of work.

Reimagining the AI-led organization

We’re at the dawn of the rise of goal-seeking technologies.

Future AI-led organizations will organize themselves as fleets of AI agents. Hierarchies of agents will achieve complex goal-seeking to further organizational goals.

AI agents will need to interact with each other and such interaction will require the emergence of new protocols to streamline agent communication.

Agents will specialize by vertical. Here’s an example of an agent-led organization for education, for instance.

Marketplaces will emerge to organize a growing market of autonomous AI agents, allowing you to hire AI agents much like you hire freelance agents on online work platforms like Upwork or Fiverr today.

Agent compliance and liability will emerge as a huge opportunity. When AI agents run amok, the few players who take over liability and compliance management will benefit from outsized gains.

The future is already here. Check out alphakit.ai or Relevance AI, for instance.

It’s just not evenly distributed.