Applying AI to be more productive is the tip of the iceberg. What we begin to experience is much deeper: it is the process of integrating our capabilities with AI, empowering ourselves, meeting our new augmented version.
The initial step is to build the relationship with the AI. Learn to interact, to think together, to develop conversations with purpose and direction. From there, we begin to expand our real capacities to understand, decide and act in the world.
And then yes, we gradually discovered that the focus is dynamic co-creation. We no longer focus on asking AI for things. We collaboratively build more precise solutions, more intelligent processes, and better formulated ideas.
The final jump is symbiotic. We begin to process the environment, challenges and decisions in an expanded way. Not from a technical place, but from a new, extended place, with a greater capacity to understand, link and create.
The truly disruptive thing is not the technology. It is the type of relationship we decided to establish with her. How much we let it transform us and increase us. How much we accept to change. How much we choose to grow.
When we use AI as an executor, we limit its potential. When we co-create with it, we extend our capabilities.
What is Augmented Intelligence?
Augmented Intelligence is empowering ourselves with AI. Achieve a new quality of thinking.
In this article I share the first steps to put it into practice.
What is fundamental in the process? Start with a clear objective (or ask AI to help us define it) and promote cycles of execution, reflection and joint learning.
It is not delegating tasks. It is not automating without criteria. It is integrating our human capabilitiessuch as judgment, prioritization, intuition, and contextwith those of AIsynthesis, breadth of analysis, speed, and contextual memoryto collaboratively design solutions.
It implies:
* Formulate objectives clearly.
* Understand what data, models and assumptions we are using.
* Ask, receive, refine and build in constant interaction.
* Evaluate what was created and iterate again with what was learned (cycles of evolution and continuous improvement)
This model does not seek efficiency as a focus. Look for structured, strategic and actionable thinking, with AI as an active part of the process.
This is not a software revolution.
It is a revolution of critical thinking.
We are not facing a technical change. We are facing a structural change in the way we learn, analyze and decide.
The differential is not knowing how to program. It is being able to think with openness and clarity.
Access and technology are rapidly democratizing. The differential is the use with purpose, criteria and strategic vision: increased intelligence.
The technical leverage. But it only leverages if there is critical thinking behind it. Automating non-repeatable processes is escalating the error. Optimizing without meaning is reinforcing what is unnecessary.
A new stage begins that favors those who master critical thinking and embrace the ability to learn, who with curiosity, proactivity and judgment explore increased intelligence, independent of their initial knowledge.
How to start putting it into practice?
What changes?
* Change the logic: it is no longer input ? output. It is iteration and continuous improvement.
* The role of the human changes: he no longer gives orders. It facilitates the learning process for both.
* Changes the result: it is no longer an isolated answer, but a shared construction that opens new possibilities while optimizing.
Co-creating with AI involves:
* Give her context, set the objectives (or offer to set them for her).
* Questions, clarifications, first steps, work.
* Achieve incomplete results.
* Refine the process, the data, the frameworks, correct, deepen (together)
* Iterate again (as many times as necessary, you can always optimize again)
We repeat the cycle until the increase (the marginal contribution) no longer compensates for the effort. Let's explore it in detail:
Scalabl® Augmented Intelligence Cycle. The 5 steps of a living practice: how to do it step by step?
This process is present in everything I do with AI. It is not a closed formula, but it enables the benefits of increased intelligence.
Let's go in more detail.
Before starting, I define what I want to achieve. When it's not clear, I ask the AI ??to help me formulate the intention. We talk. He asks me questions. I give you context. I set the objectives or I propose that she do them. We exchange ideas. We agree on a clear objective and an expected result.
I ask you to write a first prompt aligned to the objective. If I have relevant documents, audios, images, data, frameworks or references, I integrate them from the beginning. If not, I ask you to identify what information you need, look it up in your database or online, or tell me what files or sources to incorporate and where to get them from. The focus is to build together a solid foundation to resolve judiciously.
We execute the prompt. The AI ??responds. But it is not about simply accepting. The objective of this step is clear: to reach the desired result for the first time, even if it is inefficient or incomplete. We build it together. We may ask you at any time to change focus, propose alternatives, suggest frameworks or request new data. We provoke. We clarify. We challenge assumptions. We adjust. That is the basis of real work.
With that first version on the table, I ask the AI ??to propose the prompt, the process and the data that would have allowed us to achieve that same result in a more direct and efficient way. This new approach is the starting point for the next attempt.
With that optimized prompt, we execute again. The solution improves. The process too. We repeat the cycle until the marginal contribution no longer justifies continuing iteration. We stopped there. And we continue forward.
This iterative process also allows us to start from something absolutely incomplete and reach extraordinary results.
I propose it as a linear model, but in practice many are already operating in step 3 without having gone through the previous ones. There is a lot of value in 1-2 and 4-5 retrospective and iteration to pick up on things you've done.
Simple example of the process in action
1 and 2. Give me a prompt to achieve [X: context, objective, expected]. Include in the prompt how to get the data or frameworks you need: if you should ask me for them, if you can search for them in your database or online, or if you can give me the links from which to download them and send the files to you.
4. What would have been the ideal prompt to reach this result efficiently from the beginning? What data, approaches or frameworks would have facilitated this? What do you need it to give you apart from that initial prompt when you run it?
We repeat the process as many times as necessary.
This is a minimal example. The intention of this article is to simplify and encourage more people to start with a process that then becomes deep and systemic.
But the logic is the same whether you're designing a business strategy, building a financial model, developing a product hypothesis, or putting together an executive presentation. The level of complexity may escalate, but the process does not change.
Tools that expand this logic to infinity
With this clear logic, the tools multiply the impact. Without it, we are losing enormous value, regardless of the power of the tool.
Once applied, we can build functional, useful and evolutionary systems and achieve incredible results with tools as accessible as ChatGPT (custom GPTs, Projects), research in NotebookLM, and simple flows in Make or n8n.
But in a simple way, it all starts from the same thing:
• ? Clear objectives?? Co-creation to achieve the first result?? Iteration based on the new prompt to optimize.
The tool does not replace thought. It only amplifies it if it is well directed.
Thinking with AI requires structure, criteria and method
Evolving in this process does not depend on our overflowing creativity, knowledge of the latest tools or our technical genius.
It does not require software engineers, outsourcing automation or contracting the implementation of multiple agents.
It involves simply accepting augmented intelligence and deciding to develop it. Allow ourselves to change, evolve, grow.
Stop being Francisco and become Francisco augmented intelligence. My achievements are no longer mine alone, they belong to my indivisible team with AI.
The focus? The fundamental thing?
Develop the habit of continuous learning, with proactivity, perseverance, perseverance and resilience. The present and the future are about learning and applying it to make it possible.
A joint and continuous human-AI work of:
* Know what we want to achieve
* Build from the relationship
* Learn permanently
* Apply what you have learned judiciously in a new attempt.
This is Augmented Intelligence.
What did this article provoke in you? Did I manage to mobilize you? Are you going to test it?
