Thank you so much.

Introduction

Good afternoon, everybody. Welcome.

I brought a few games for you.

Audience Roles and Backgrounds

So this will be a theoretical talk, maybe, but from a practical perspective. I wonder how many of you are doing development with AI or have developed AI. Can you raise your hand, please?

Okay, how many of you are from the business side? Entrepreneurs, founders, great.

How many of you are end users? Everybody, right?

Speaker Background and Roles

So actually, I myself play a little bit different roles. I'm a business leader, but also a coach, facilitator, and also a developer.

I develop myself. play a lot with citizen development, also helping customer adopt that kind of roles.

The Emerging Role of the Chief AI Officer (CAIO)

And I wonder if any of you know what a CAIO is? Sounds any well? Rings any bell?

It's usually referred to, please? Chief AI Officer. Chief AI Officer, okay.

So this is a role that is somewhat emerging. For the last couple of years, there have been more and more CAIOs out there, let's say. But it's a role open for reinterpretation.

Even if the role is young, Already there are some alternative views of this role, and I want to encourage you to reflect on this.

Innovation Beyond Technology

So it's maybe theoretical talk from that point of view, but it's more from the organizational perspective.

Where Innovation Comes From

So as facilitator or innovation coach, a lot of time the innovation doesn't come only from the technology side. Sometimes it comes from the business model, or it comes from a combination from a business model, a new technology, and maybe a new way of organizing the teams and the way they collaborate.

So more or less, those are some of the points we are going to discuss.

Personal Journey in AI and Standards

I myself have been engaged with AI since 1985. says to my father, who was developing these symbolic AI systems in the 80s. And I got across, I have a long library, part inherited from him and part from my own, of books and artifacts related to AI from the last three, four decades. And it's a fantastic journey.

I am very much engaged as well with standards development. So I am a little bit of a weird person. individual, because being a mathematician, I love regulations. I love norms.

I love all these rules that you have to put into a system. And actually, a little bit of this gadget relates to that idea of a system. I will go a little bit in detail into those in a moment.

Actually, this mask is very powerful. After, during the networking or whatever, you can try it. You will gain superpowers. If you try this one, you can become anything you want. You have later to work a little bit, but at least the intention is there.

Agenda and Historical Context of AI

So actually, it's kind of a brief agenda.

When Did AI Begin?

Let's discuss a little bit when this AI story starts. when this started.

This is a project I have been researching into for quite a few years now. And even though some people, more and more, are aware that this didn't start in 2021 or 22, we shot GPT, of course.

The Dartmouth Proposal and the Birth of AI

But many, many, many decades, or a few decades earlier, actually 70, a few weeks ago, it was 70 years exactly of the first appearance of the term artificial intelligence, which is in this document.

Well, that's not the document. This is the document. which is a project proposal for researching into the problem of AI.

This was a theoretical, let's say, approach to it. They call it the problem of AI.

It actually started in 1955 as a formal field.

And they talk about seven main problems or problem areas, you could say.

Seven Problem Areas and Early Questions

Some of them related to automatic computers, so we have this all around us. Can computers be programmed to use language? Actually, we are right now at that point when the usage of language with computers is so extensive and so powerful, neural networks, size of calculations.

I especially like this one, randomness and creativity. This is such a hot discussion right now. Is the AI creative? I'm sure there will be different opinions.

So I don't expect you to right now answer this, but we will talk maybe in the discussion.

Creativity, Randomness, and AI in the Arts

Actually, this LP, this is music that has been made with AI. not by me, by some musicians, if anyone knows, is called anarchic AI. I think a lot of this idea of creative AI maybe relates to this. By the way, this is 2021, 2020, sorry.

Earlier Mentions: La Pensée Artificielle (1952/1953)

Actually, if you move along, is not really the first appearance in print. That's the first appearance of artificial intelligence, the term in English.

If you research a little bit more, I have this book here. I only take it with me in very scarce occasions. This is a book published in French in 1953. It's called La Pensee Artificielle.

And this book was actually written in 1952, because they also signed the prologue in 1952. And it's so interesting that those ideas were already there, especially with the perspective of cybernetics, that idea of systems.

And actually, there is an appendix which describes a roadmap into how the effectors could evolve. And basically, say in 1952, what we are seeing now with agents and this craziness around agentic AI and AI agents, it was more or less described there. more than 70 years ago.

Amplifying Performance with AI

So this is just a little bit of maybe an opportunity to rethink how mature or how fast or slow have these matured. I always speak about amplifying, and this will be maybe a moment I will connect with these artifacts.

From Individual to Team to Organization

How can we amplify performance? When you want a team to achieve GBO in other organizations, you probably want to generate more results, cheaper, with more impact, or with more scope. And a lot of time, this is kind of a stir for me, or at least when I work with teams.

I need to start personally, individually, at the individual level. So what can I do?

How can I take that to a team? And how can I take that to the whole organization?

This amplification.

Prior Work on Team Amplification

Actually, this could be a kind of deja vu. I'm not sure if anyone was in the MindStone meetup in April last year. Anyone here? No?

I had the opportunity to share that presentation, which was about amplifying team performance. At that time, we shot GPT team, the team subscription. Anyone is using the team subscription? Please, raise your hand.

Yeah. Three? Okay. Did you know that there is a team subscription?

Yeah. Some of you know. Okay.

Collaborating as a Team with AI

Actually, at that time, the discussion, more than a year ago, was about can these tools help you collaborate as a team? Because a lot of time, the discussions right now, what you see most often is some individuals working with AI, and then working with another individual who is working with AI. 1But working as a team with AI is a completely different paradigm.

And this is part of the idea behind some of these elements, which is, how can I amplify playing with the world? not just myself, but my team, through this multidisciplinary collaboration.

Custom GPTs and Role-Based AI Advisors

Actually, this slide, which is vintage from 2024, I presented this. I have about 12 more custom GPTs. or gems in Gemini, whatever, which are, as you know, probably a customization of chat GPD. But in this case, with different roles.

And taking that idea further, we created like a team of visual advisor, which can collaborate, for example, as an executive committee, which is basically AI. You can give a problem to them, and they will be collaborating. Some people will challenge this idea that they are actually collaborating, they're exchanging information to create something new.

Citizen calls wizard. This is especially related to that idea.

Of course, this might sound fantastic for some, or for some people, something that is a deja vu. The question is, how can you take this further?

Agility, AIgility, and AI Agents in Teams

One major value proposition of AI and new ways of working is agility. It's being more flexible, more adaptable, faster.

Introducing the AIgilist Concept

And having been there with this idea of agilism, AIgilist is somehow a provocative proposal for considering what would happen if your next agile team would be then composed by AI agents.

So imagine that this is a scarce role that sometimes you don't have, I mean, in an organization like a scrum master or a coach, like myself sometimes. You can now somehow deliver that value with an agile agent, maybe with another person, yeah?

maybe there is a parallel like a citizen development, imagine like a citizen IG list. Well, that's part of the things I have been doing as well.

Human Concerns: Roles and Learning

This is part of the challenge and where human resources, not just the function, people I mean, or humans, would be maybe a little bit concerned, at least I would be.

Because the question is not just if somebody takes a role that the person is doing right now, like a scrum master or product owner. The question is not just if the Agile team is from just AI agents.

The question is also, what's happened with the learning? where does the learning goes?

Because, for example, in Agile, if anyone knows, these ceremonies like retrospective, feedback, all these ongoing conversation, if those happens within AI agents, the human team or the human side of the team is not necessarily learning from those conversations. It's only receiving the final output. And this might be a major challenge.

Prototype: Multi-Agent Workflows

This is actually a proof of concept or a prototype. using NAN, one of these automation platform, and using different nodes to take different roles.

This is a lot of fun. Any of you who have done this, you can see a kind of magic happening in front of you because in this case, I connected different nodes taking different roles.

This is a very simple version just to make it fit.

Opportunities and Challenges Ahead

So I say this idea of A-amplified intelligence or artificial intelligence, It's not really new, or is it new? Maybe for some people, but it's not really new, as you can see.

But the challenges that they bring along are quite challenging and new.

Augmented Professionals and Personal AI Toolkits

For example, you don't hire me. You hire me with all my gadgets and artifacts.

By the way, I'm not talking about this metaphoric one. I'm talking about my whole team of GPTs or whatever I have, which means in the next round of collaboration, if the people next to me is not necessarily, let's say, with that kind of teammates, personal teammates, might have a different situation.

I would say it's jammed to the power of two, to the power of infinity, whatever.

Obsolescence and Evolving Skill Sets

And this is one of my favorite gadgets. It's the first chatbot. It's a plastic robot. It's actually a toy, but it's called chatbot from the 80s.

And a lot of times, I didn't bring it today because it's a little bit big. I call it eradismo robotico.

It's like, what do you do when technology or someone is becoming obsolete? People can also become obsolete. Let's say not people, the skill set.

So what do you do with that? How can you deal with that?

Data, Fairness, and Bias Considerations

And eventually, what happens with your data? The bottom line of everything is data. That's where everything starts, that's where everything ends, let's say.

And you can play with words like this, you know, like unfair fairness, because it all depends on what data have been used to train, and whatever fair you think was the process, it might be unfair or biased or unbiased.

Toward the AI Factory: Building with Agents

And just to complete this journey, think about a near future, maybe the future is not so far, where working in a factory, oops, Yeah.

Working in a factory, in this case, AI factory, where you're building AI elements, component systems, can be done by yourself, like Miguel did. Yeah.

And that's not very far. You can build.

Instantiating Teams and Automating Delivery

Actually, the prototype you saw, the proof of concept, is actually a team that I can instantiate in any project. And I have instantiated that team. I am invoking the team by chat, by natural language.

And the team is creating user stories, definition of the board of the project, breaking down the story in Trello, then taking the stories and developing code.

I have a team. And it's just a visual team.

So probably, in this new environment,

Rethinking the CAIO: From Officer to Orchestrator

Cayo, or if you think of this Cayo, maybe it's not necessarily a chief, or he's a kind of chief. Who's a chief? Yeah, right, chief.

1Maybe I prefer to think about some kind of collaborative, creative, augmented intelligence, and maybe not even officer, but orchestrator. Because in fact, a lot of the things we do with this system is orchestrate how they interact.

Systems Thinking and Orchestration

This is a very simple artifact. It's a, let's say, it's this tube with this spring and this skin. Tube, skin, spring.

Do you see, do you know how they sound together? If they collaborate, do you want to listen? Sure? Let's see.

This is system thinking at practice working right here, right? The magic is not in the parts, in the individual parts. It's in how they relate.

And as a Caillou, or whatever you want to take the role, you're playing with system. Actually, I could just take one final step, which is, if it works, which is to make it even larger.

Literally, I am amplifying what is going to happen. You can expect. It's like a thunderstorm.

Conclusion

And I think AI is amplifying our capabilities.

Amplify Responsibly

If we use it for good, if we use it responsibly, we can amplify for good our capabilities.

Closing and Q&A

So I invite you to somehow take maybe some of these ideas and play with them further. And I will be happy to answer any questions if you have.

Yeah.