The economic rationale behind AI deployment
Why AI-driven capacity growth doesn’t always create value.
A shift in capacity
There is a lot of excitement around AI, and for good reason. It is one of the few technologies that can meaningfully change the capacity of an organization in a very short period of time. Engineering teams produce more code, commercial teams reach more prospects, and operations accelerate across the board. The sense of progress is real.
But beneath that momentum, there is a more fundamental question that is often left unaddressed: what is the economic rationale behind it?
Because without that structure, what looks like progress can quickly become a more expensive way of operating.
Capacity is not the same as value
AI increases capacity, that is its core promise we all can agree on. What is less clear in most organizations is how that additional capacity is being translated into outcomes. In many cases, it simply results in more activity: more output, more iterations, more parallel work.
But activity is not a measure of performance. And capacity, without a clear economic framework, does not guarantee value creation.
There is a Spanish expression I often think about in this context: “para este viaje no necesitamos alforja”. It loosely translates to for this journey, we didn’t need to carry extra weight. It captures a simple idea: adding cost or complexity without a clear reason does not improve the outcome.
This is the risk with AI. If additional capacity is not anchored to a clear economic result, the system becomes heavier rather than more efficient.
The simple math
At its core, the logic is straightforward:
If efficiency increases by 20%, output should increase proportionally
Or costs should decrease accordingly
Anything else requires explanation. If costs grow while output does not improve in a comparable way, then the system is not becoming more efficient. It is simply becoming more expensive.
Where it breaks in practice
This is where many organizations struggle. AI is introduced into existing workflows, but the underlying cost structure remains unchanged. Teams continue operating in the same way, while tooling costs increase and output expands without a clear link to business impact.
The result is a system that appears more productive but is harder to evaluate and, in many cases, less efficient than it seems.
The difficulty is not in understanding the principle. It is in measuring the impact. AI does not operate in isolation, and its effects are rarely immediate or linear. It changes how work is created, how it flows, and how often it needs to be revisited.
Most organizations are not set up to observe these dynamics clearly, so they fall back on what is easy to track: adoption, usage, and output volume. These signals are useful, but they do not explain whether the system is actually improving.
A more grounded approach
A more disciplined approach starts by anchoring AI to the existing structure of the organization. If capacity increases, there needs to be clarity on what that increase is expected to deliver and how it will be measured.
Only when that relationship is understood can decisions be made about scaling, reallocating, or adjusting the system.
AI is often described as a 10x technology, and in terms of potential capacity, that may be true. But from an economic perspective, the question is not how much more can be produced. It is whether what is produced translates into measurable improvement.
Without that link, increased capacity can create as many problems as it solves.
What leaders should be asking
The conversations we are having with leaders across large organizations increasingly reflect this shift. The focus is moving away from adoption and toward accountability. Not whether AI is being used, but whether it is actually improving performance in a way that can be demonstrated.
That ultimately comes down to a small set of questions:
Is AI increasing delivery speed?
Is quality improving or degrading?
Is rework increasing over time?
Are costs scaling efficiently with output?
These are not easy questions to answer, but they define whether AI is functioning as a true efficiency driver or simply as an additional layer of cost.
In our case, we have focused on making these questions answerable by connecting AI usage directly to the work itself—how it is produced, how it moves, and what is ultimately delivered. That is what allows organizations to move beyond activity and understand impact in concrete terms.
Because in the end, the equation is simple. If efficiency improves, the economics of the system must improve with it. If they do not, then the strategy is not yet defined.
And without that discipline, it is very easy to carry more than is needed for the journey.
A shift in capacity
There is a lot of excitement around AI, and for good reason. It is one of the few technologies that can meaningfully change the capacity of an organization in a very short period of time. Engineering teams produce more code, commercial teams reach more prospects, and operations accelerate across the board. The sense of progress is real.
But beneath that momentum, there is a more fundamental question that is often left unaddressed: what is the economic rationale behind it?
Because without that structure, what looks like progress can quickly become a more expensive way of operating.
Capacity is not the same as value
AI increases capacity, that is its core promise we all can agree on. What is less clear in most organizations is how that additional capacity is being translated into outcomes. In many cases, it simply results in more activity: more output, more iterations, more parallel work.
But activity is not a measure of performance. And capacity, without a clear economic framework, does not guarantee value creation.
There is a Spanish expression I often think about in this context: “para este viaje no necesitamos alforja”. It loosely translates to for this journey, we didn’t need to carry extra weight. It captures a simple idea: adding cost or complexity without a clear reason does not improve the outcome.
This is the risk with AI. If additional capacity is not anchored to a clear economic result, the system becomes heavier rather than more efficient.
The simple math
At its core, the logic is straightforward:
If efficiency increases by 20%, output should increase proportionally
Or costs should decrease accordingly
Anything else requires explanation. If costs grow while output does not improve in a comparable way, then the system is not becoming more efficient. It is simply becoming more expensive.
Where it breaks in practice
This is where many organizations struggle. AI is introduced into existing workflows, but the underlying cost structure remains unchanged. Teams continue operating in the same way, while tooling costs increase and output expands without a clear link to business impact.
The result is a system that appears more productive but is harder to evaluate and, in many cases, less efficient than it seems.
The difficulty is not in understanding the principle. It is in measuring the impact. AI does not operate in isolation, and its effects are rarely immediate or linear. It changes how work is created, how it flows, and how often it needs to be revisited.
Most organizations are not set up to observe these dynamics clearly, so they fall back on what is easy to track: adoption, usage, and output volume. These signals are useful, but they do not explain whether the system is actually improving.
A more grounded approach
A more disciplined approach starts by anchoring AI to the existing structure of the organization. If capacity increases, there needs to be clarity on what that increase is expected to deliver and how it will be measured.
Only when that relationship is understood can decisions be made about scaling, reallocating, or adjusting the system.
AI is often described as a 10x technology, and in terms of potential capacity, that may be true. But from an economic perspective, the question is not how much more can be produced. It is whether what is produced translates into measurable improvement.
Without that link, increased capacity can create as many problems as it solves.
What leaders should be asking
The conversations we are having with leaders across large organizations increasingly reflect this shift. The focus is moving away from adoption and toward accountability. Not whether AI is being used, but whether it is actually improving performance in a way that can be demonstrated.
That ultimately comes down to a small set of questions:
Is AI increasing delivery speed?
Is quality improving or degrading?
Is rework increasing over time?
Are costs scaling efficiently with output?
These are not easy questions to answer, but they define whether AI is functioning as a true efficiency driver or simply as an additional layer of cost.
In our case, we have focused on making these questions answerable by connecting AI usage directly to the work itself—how it is produced, how it moves, and what is ultimately delivered. That is what allows organizations to move beyond activity and understand impact in concrete terms.
Because in the end, the equation is simple. If efficiency improves, the economics of the system must improve with it. If they do not, then the strategy is not yet defined.
And without that discipline, it is very easy to carry more than is needed for the journey.
