From Chatbots to Agents: The New Org Chart

As we move from AI models as copilots to AI models as employees, a question remains: who manages the bots?



Since its release, Generative AI has rapidly gone mainstream, and organisations have been quick to adopt it. The tool has been used to create content, summarise text and generate images.

However, a more profound shift is underway with the introduction of Agentic AI. Generative AI necessitates a prompt and is passive in nature. Conversely, Agentic AI can observe, plan and execute workflows autonomously. Consequently, leaders must rethink their org charts from the ground up, as managing AI has gone beyond software; it is now a digital workforce.

The Shift From Right Brain Creativity to Frontal Cortex Execution

To better understand this shift, a 2025 Boston Consulting Group Study provides a compelling biological analogy:

Predictive AI is the left brain, focusing on logic, optimization and structured tasks.

Generative AI functions as the right brain, focusing on synthesis and creativity.

Finally, Agent AI is the frontal cortex, merging the two sides and turning probability into business impact via execution.

As Generative AI has spurred efficiency gains in knowledge production, Agent AI unlocks value in functions that are process-heavy. Thus, driving efficiency in processes where end-to-end execution defines performance.

Organisations adopting these AIs are already seeing results. Indeed, a shipbuilder has successfully cut effort by 40% by using AI agents to run a complicated design process, highlighting the effectiveness of these new tools.

The Zero-Based Redesign

According to the same Boston Consulting Group report, to maximise efficiency gains, leaders must reinvent their approach. Rather than automating steps that already exist within the organisation, a zero-based approach can be significantly more productive. In practice, this means reimagining an organisation's processes from the ground up with AI agents in mind.

Simultaneously, as AI agents can take on the role of execution, the role of human teams will accordingly shift. Rather than doing, they will be orchestrating, augmenting, and overseeing AI agents.

The Trust Protocol

Nevertheless, a major barrier to scaling AI agents remains: trust. No manager wants to be responsible for AI hallucinations that offer a discount, offend a client, or invent information. Hence, to manage this risk, firms require a trust protocol that grants agents “freedom within a frame”.

Studies have outlined such a “Graduated Autonomy Framework” that functions as a promotion path for bots:

In the first tier, the agent observes and suggests while the human acts. There is no operational risk due to human oversight from start to finish.

In the second tier, the AI agent stages an action, but before execution, a human must provide the go-ahead.

In the third tier, the AI agent becomes truly autonomous, executing actions within guardrails. Humans shift to exception handling, wherein they only intervene when the system flags an anomaly.

Finally, in the fourth tier, the AI agent operates with complete independence for defined low-risk workflows.

The 10/20/70 Rule

Implementing such a framework requires a resource reallocation following the 10/20/70 rule: 10% of effort goes into algorithms, 20% into the tech backbone, and 70% goes to people and processes.

Ultimately, future org charts will not just list human managers and subordinates but include outcomes managed by human-agent teams. While managing people will remain important, managing the “freedom within a frame” for agents is likely to be equally critical.  

 

Commentaires

Publier un commentaire

Messages les plus consultés de ce blogue

The Galacticos Fallacy

Image
Managers are often obsessed with hiring “A-players”. However, the data suggests this is an ineffective strategy. Introduction Linear logic often dominates in the corporate world, i.e., if one star performer is good, ten is surely better. Consequently, firms find themselves paying premiums to assemble dream teams and fighting wars for talent. Yet, research suggests that this highly sought-after talent is not additive; instead, it is curvilinear. A 2014 study by Swaab et al. reveals this through the “too-much-talent effect”. Initially, adding more talent increases performance, but a saturation point is reached relatively quickly. Once the peak is reached, more star team members do not just yield diminishing returns but cause performance to suffer. The Interdependence Caveat The extent to which such a talent saturation is a danger depends entirely on a team's underlying mechanics. To prove this, Swaab et al. compared two different sports: baseball and football.   In baseba...
Lire la suite

The Praise Paradox: Why Compliments Kill High Performance

Image
The "Sandwich Method" of feedback is failing you. Data shows that while novices need praise to confirm their commitment, experts need criticism to gauge their progress—and mixing the two satisfies neither. Introduction The “Sandwich Method” of balancing a slice of criticism with two slices of praise to soften the blow is an orthodox management tactic. It feels safe, polite, and “balanced.” However, extensive research in 1996 by Kluger & DeNisi found that feedback intervention, whether positive or negative, reduced performance in 38% of cases. This is because leaders often view feedback as a one-size-fits-all tool rather than a dial that requires calibration to the recipient’s expertise. The Expertise Split 2012 research by Finkelstein & Fishbach demonstrates that novices and experts process feedback through entirely different mental lenses. For the novice, since they are new to the role, they are in a commitment phase where they are evaluating whether the j...
Lire la suite