Using AI to become more productive is just the tip of the iceberg. What we are beginning to experience goes much deeper: integrating our capabilities with AI, extending what we can do, and discovering an augmented version of ourselves.
The first step is to build a relationship with AI. To learn how to interact, think together, and develop conversations with purpose and direction. From there, we begin to expand our ability to understand, decide, and act in the world.
Gradually, we discover that the focus is dynamic co-creation. We stop simply asking AI to do things. Together, we build more precise solutions, smarter processes, and better-developed ideas.
The final leap is symbiotic. We begin to approach our environment, challenges, and decisions with expanded capabilities. This is not about technical expertise. It is a new way of thinking, with a greater capacity to understand, connect, and create.
What is disruptive is not the technology. It is the relationship we choose to build with it. How much we allow it to transform and augment us. How willing we are to change. How much we choose to grow.
When we use AI only to execute tasks, we limit its potential. When we co-create with it, we extend our capabilities.
What Is Augmented Intelligence?
Augmented intelligence means expanding our capabilities with AI. Reaching a new quality of thinking.
In this article, I share the first steps to put it into practice.
What matters most? Starting with a clear objective—or asking AI to help us define one—and developing cycles of execution, reflection, and shared learning.
It is not about delegating tasks. It is not about automating without judgment.
It is about combining our human capabilities—judgment, prioritization, intuition, and context—with AI’s capacity for synthesis, breadth of analysis, speed, and contextual memory to design solutions together.
This involves:
* Formulating clear objectives.
* Understanding the data, models, and assumptions we are using.
* Asking, receiving, refining, and building through continuous interaction.
* Evaluating what we have created and iterating with what we have learned.
The primary aim is not efficiency. It is structured, strategic, actionable thinking, with AI as an active participant.
This is not a software revolution. It is a revolution in critical thinking.
We are not facing a technical change. We are facing a structural change in how we learn, analyze, and decide.
The differentiator is not knowing how to code. It is being able to think with openness and clarity.
Access and technical capabilities are rapidly becoming more widely available. What makes the difference is using them with purpose, judgment, and strategic vision: augmented intelligence.
Technology provides leverage. But only when critical thinking supports it.
Automating non-repeatable processes means scaling errors. Optimizing without purpose means reinforcing what is unnecessary.
This new stage favors those who develop critical thinking and embrace learning. Those who explore augmented intelligence with curiosity, initiative, and judgment, whatever their starting knowledge.
How Do We Put It into Practice?
What changes?
* The logic: no longer simply input → output, but iteration and continuous improvement.
* The human role: instead of giving orders, we facilitate a learning process for both participants.
* The result: not an isolated answer, but something built together that opens new possibilities as it improves.
Co-creating with AI means providing context and setting objectives—or inviting AI to help formulate them.
It means asking questions, clarifying, exploring initial approaches, and doing the work. Reaching incomplete results. Refining the process, data, and frameworks. Correcting and going deeper, together.
Then iterating again, as many times as necessary. There is always room for further improvement.
We repeat the cycle until the incremental gain no longer justifies the effort.
The Scalabl® Augmented Intelligence Cycle
This process is part of everything I do with AI. It is not a rigid formula, but it enables us to benefit from augmented intelligence.
The five steps:
Let’s look at each step.
Before starting, I define what I want to achieve. When that is unclear, I ask AI to help me articulate it.
We talk. It asks me questions. I provide context. I set the objectives or invite it to propose them. We exchange ideas.
We agree on a clear objective and an expected result.
I ask AI to write an initial prompt aligned with the objective.
If I have relevant documents, audio, images, data, frameworks, or references, I bring them in from the start.
If not, I ask AI to identify the information it needs, draw on its existing knowledge or search online, or tell me which files or sources to include and where to find them.
The aim is to build a solid foundation together so we can work through the problem with sound judgment.
We run the prompt. AI responds. But this is not about accepting whatever it produces.
The objective is clear: reach the desired result for the first time, even if the process is inefficient or the result incomplete.
We build it together. At any point, we can ask AI to change its approach, propose alternatives, suggest frameworks, or request additional data.
We push. We clarify. We challenge assumptions. We adjust.
That is the foundation of the work.
With that first version in front of us, I ask AI to propose the prompt, process, and data that would have allowed us to reach the same result more directly and efficiently.
That revised approach becomes the starting point for the next attempt.
We run the optimized prompt.
The solution improves. So does the process.
We repeat the cycle until the incremental contribution no longer justifies another iteration. Then we stop and move forward.
This iterative process also allows us to start with something entirely incomplete and reach extraordinary results.
I present it as a linear model, but many people are already operating at step 3 without having gone through the earlier steps. There is considerable value in returning to steps 1 and 2, and applying the reflection and iteration of steps 4 and 5 to work you have already done.
A Simple Example of the Process in Action
Steps 1 and 2:
Give me a prompt to achieve [X: context, objective, expected result]. Include how to obtain the data or frameworks you need: whether you should ask me for them, draw on your existing knowledge or search online, or give me links so I can download the files and share them with you.
Step 3:
I run the prompt and work with AI to reach the desired result for the first time. Don’t worry if it feels tedious or inefficient.
Step 4:
What would have been the ideal prompt to reach this result efficiently from the start? What data, approaches, or frameworks would have helped? What do you need me to provide alongside that initial prompt when I run it?
Step 5:
I repeat step 3 with the new prompt. The process can keep improving. So can the solution. We repeat as many times as necessary.
This is a minimal example. My intention is to simplify the starting point and encourage more people to begin a process that can become deep and systemic.
The logic is the same whether you are designing a business strategy, building a financial model, developing a product hypothesis, or preparing an executive presentation.
The complexity may increase. The process does not change.
Tools That Extend This Approach
With this logic in place, tools multiply the impact. Without it, we miss enormous value, however powerful the tool may be.
We can build functional, useful systems that evolve, and achieve remarkable results with tools as accessible as ChatGPT—custom GPTs and Projects—research in NotebookLM, and simple workflows in Make or n8n.
But it all starts with the same sequence:
Clear objectives → Co-creation to reach the first result → Iteration with a revised prompt to improve it.
The tool does not replace thinking. It amplifies it only when it is well directed.
Thinking with AI Requires Structure, Judgment, and Method
Progress does not depend on boundless creativity, knowledge of the latest tools, or technical brilliance.
It does not require software engineers, outsourced automation, or hiring someone to implement multiple agents.
It means embracing augmented intelligence and choosing to develop it. Allowing ourselves to change, evolve, and grow.
For me, it means moving from Francisco to Francisco with augmented intelligence. My achievements are no longer mine alone. They belong to the inseparable team I form with AI.
What is the focus? What matters most?
Developing the habit of continuous learning, with initiative, perseverance, consistency, and resilience.
The present and the future are about learning and applying what we learn to make things possible.
An ongoing collaboration between humans and AI:
* Knowing what we want to achieve.
* Building through the relationship.
* Learning continuously.
* Applying what we have learned with judgment in the next attempt.
This is augmented intelligence.
What did this article spark for you? Did it move you to act? Will you put it to the test?
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