Scale and Marginal Cost
Scaling a human team is a challenge.
You find an exceptional professional who delivers incredible results. Your operation grows and now you need more people like him.
But replicating human talent is not simple.
You can find someone with a similar profile, but it will hardly be an exact copy.
And even when you find the right person, you still need to invest in training and adaptation.
This consumes time and resources, not only of the new employee, but also of the experienced team members who need to assist in this process.
Growing a human team involves:
- Recruitment: finding candidates with the desired profile
- Selection: evaluating and choosing the best candidates
- Training: training new members in the necessary skills
- Adaptation: integrating new members into the team’s culture and processes
All of this process has a cost, not only financial, but also in terms of time and energy.
Now, let us look at AI agents.
Here, the logic is completely different.
When you develop an agent that performs its function well, you can replicate it as many times as you want.
You can have 10, 100, 1,000 instances of that same agent, all with the same behavior and response pattern.
And the best part: this replication is practically instantaneous.
There is no need to recruit, select, train, or adapt each new instance.
They are born ready, like perfect copies of the original agent.
What changes, in this case, is the consumption of computational resources.
More instances means more processing, more use of “artificial intelligence”.
But the marginal cost of each new instance is minimal when compared to the cost of adding a new member to the team.
This difference has a huge impact on the way you can scale your operations.
With AI agents, you can start small, test an idea with a single agent, and if it proves successful, expand quickly to hundreds or thousands of instances.
This transforms the dynamics of innovation.
Experimenting with new ideas becomes much cheaper and faster.
You can afford to make mistakes, because the cost of failure is lower.
And when you get it right, you can scale the success almost immediately.
Think, for example, of a customer support team.
In the human world, setting up and managing that team involves considerable risks and costs.
Hiring, training, and eventually downsizing the team can be an expensive and draining process.
With AI agents, you have much more flexibility.
You can create a support team, adjust its size according to demand, expand or reduce the number of agents according to business needs.
And all of this with much less friction and cost than would be possible with a 100% human team.
This does not mean that AI agents will completely replace humans.
But it certainly changes the way humans and AI can work together.
With scalable AI agents, you can build teams that are more lean, more agile, and more adaptable.
Teams that combine the creativity and empathy of humans with the efficiency and scalability of AI.
And it is here that the conversation about AI really starts to get interesting for managers.
We are leaving the realm of technical characteristics of agents and entering the world of management and strategy.
How can you use these scalable agents to transform the way your company operates?
How can you assemble hybrid teams of humans and AI that are more than the sum of their parts?
And how can you apply these concepts in practice, in the day-to-day of your business?
These are the themes we will explore next.