Author: Alexis

Leadership looks different when you’re a first-time founder.

There’s no handbook for the moments that matter most — the ones where you have to show up as a human, make a decision as a leader, and keep the company alive at the same time.

In this episode of Le Podcast on Emerging Leadership, I welcomed Héloïse Rozès and Nikolai Fomm, co-founders of Corma, a startup helping companies regain control over their software tools and licenses. Corma’s mission is deeply practical: bring clarity to the SaaS “black box”, improve employee experience, and help IT teams manage access, cost, and risk — especially as new tools (including AI tools) keep multiplying.

What I loved in this conversation is that it’s not theory. It’s leadership learned in motion.

Here are a few ideas that stayed with me.

Communication is not one skill — it’s many

Héloïse describes communication as her biggest challenge — not because she dislikes it, but because it constantly changes depending on who is in front of you.

Co-founders. Employees. Interns. Freelancers. Investors. Clients. People you meet at an event.

Same company. Same reality. Different language every time.

And as Nikolai adds, the CEO role amplifies this even more: you’re often “the voice” externally, and the internal team watches how you represent the company outside.

Leadership forces an uncomfortable question:
are we consistent across all the rooms we enter?

People are the challenge — and the point

Both founders put people at the top of the leadership challenge list.

Not in an abstract way. In a very real, operational way.

Héloïse shares a moment many founders face for the first time: an employee announcing a maternity leave. The human reaction is joy. The leadership reaction is also: how do we adapt?

The tension is real and constant: you can be empathetic and still compute the consequences. That doesn’t make you less human. It makes you responsible.

I appreciated how they name it clearly: balancing care and survival is part of the job.

Prioritization is the art of saying “no”

One of the most concrete leadership lessons Nikolai shares is the difficulty of saying “no”.

In startups, ideas are everywhere — especially with creative, ambitious people. Many ideas are good. The problem is that resources are limited.

If you do everything, you do nothing.

Saying no is not rejecting creativity. It’s protecting focus.

And Héloïse adds an important filter she uses: if an idea doesn’t connect to market value and impact (including revenue), it may be interesting — but not now.

That clarity is leadership.

Culture doesn’t happen — it is built

Héloïse and Nikolai insist on something many teams forget: culture is not a poster. It is practice.

They talk about culture in very concrete ways:

• being on time (or even early)
• being available to talk when someone needs it
• celebrating small wins
• avoiding “two companies” inside one (sales vs tech)
• intentionally creating cohesion

They also created a program called Cormacolindor — an ambassador-style ritual inspired by the origin story of “Corma” (a nod to the One Ring), designed to help new hires collaborate, build spirit, and shine individually.

I like this because it’s not vague. It’s designed.

Radical Candor: avoid ruinous empathy

They use Radical Candor (Kim Scott) as a feedback foundation.

And Nikolai makes a point that is worth repeating: for teams that genuinely care about each other, the biggest risk is not aggression — it’s ruinous empathy.

When you care personally, you might hesitate to challenge directly.

But not giving the feedback doesn’t protect the person. It delays the learning and increases the cost.

Leadership is not being nice. Leadership is being helpful.

Advice for first-time founders: don’t do it alone

Their closing advice is simple and strong.

You don’t learn leadership from a textbook. You learn it by doing — and by talking to people who have done it before.

Héloïse suggests a practical move: list the leaders you admire and reach out. Nikolai adds that many experienced leaders are surprisingly generous with their time when the request is genuine.

This is a great reminder: mentorship is often closer than we think.

Listen to the Full Episode

Tune in to learn more about the Corma journey, leadership insights, and practical advice for emerging leaders.

Here is the transcript of the episode

Alexis: [00:00:00] Welcome to the podcast on Emerging Leadership. I’m your host, Alexis Monville. Today, we have both Héloïse Rosas and Nikolai Faume on the show. They are both co founders of Corma, and they will explain a little bit what it is. Héloïse and Nikolai, it’s great to have you both on the show. Let’s start with some introductions.

How do you typically introduce yourself to someone you just met? It’s 

Héloïse: a good question. When I meet someone. So if this is like in an informal setting, I’m going to say, hi, my name is Héloïse. I’m working in Paris as a, as a founder. And then I’ll just see if the person is in a startup ecosystem or not before knowing how to introduce myself better.

Alexis: Okay. Nikolai, how about you? 

Nikolai: Yeah. So I would say that I’m also Nikolai, one of the founders of Corma and then yeah, maybe say a little [00:01:00] bit more about what I’m doing in life or at the company. And then usually it gets quite quickly to what we’re doing as a company. But yeah, I do it a bit like Héloïse, see a little bit, who do I have in front of me, so I don’t give like a minute monologue that the other person doesn’t really understand 

Alexis: or wants to know. 

Let’s say now I’m interested and I’m saying, okay, what is Corma about? What would you say? To keep it 

Nikolai: short and spicy. I mean, usually people work with a lot of different software tools nowadays and it’s become so many, I mean, just think of all the new AI tools that are coming in. It’s becoming a mess for all the employees to understand, okay, where do I have access?

How do I get access to a new tool? But it’s It’s messy for the company who needs to make sure you don’t pay for seats. Nobody is using your people are not signing up to dangerous AI tools and load up their entire company secret data to some weird AI company in China. It’s also a mess for the it team.

And basically as Karma, we want to solve that by bringing everything together in one place, [00:02:00] providing a great employee experience. And at the same time, making the life easier for the company. 

Alexis: Excellent. I love it. Héloïse, can you do it better? Just to check that, can you make it shorter just to put some competition between the two of you?

Héloïse: Good. I mean, I’m lucky I got a bit of time to think about the question. Corma is the co pilot of your IT for handling licenses. So that’s how I put it in a sentence. People are like, okay, but what does it really mean? I simply tell them you’re a head of IT of 1000 people. You have to manage computers, Wi Fi, VPNs, and the little mouse of the, of the computers too, but also the softwares and what’s happening in the cloud.

But when you look at the application park, that’s your software stack represents. It represents a budget of three million dollars. It’s a black box where you don’t know what the ROI is, and you have no clue of who has access to what at what hour and if it complains. So Corma solves that problem by being the cockpit of truth of your licenses.

Alexis: Excellent. I love it. So [00:03:00] your boss, co founders, if I understood well, there’s a third one who is not in the room. 

Nikolai: Yeah, it’s CTO. You know, they don’t like to talk to people too much. No, but he’s, it’s, it’s, no, that’s not true. It’s he’s called Samuel. He’s also very nice and sociable, but actually right now he’s on his well deserved honeymoon.

So we try to leave him as much alone as possible. That’s why it’s only the two of us. 

Héloïse: And 

Nikolai: I 

Alexis: love that you’re taking care of that and you’re, you’re paying attention to that. And that’s, that’s very cool. Okay. Your first time founders, how do you experience leadership? Nikolai, could you share your perspective?

For me, it 

Nikolai: was interesting because before I worked for two and a half years. in another startup and saw like some good growth there. And I always look at the founder, like whenever I didn’t know something, you obviously ask the founder and expect it’s like a bit of a wizard who knows the answer to everything and know that I I’m in this position myself.

I know this is not necessarily true. So I see it as a challenge that you need to figure stuff [00:04:00] out that nobody did before you. Obviously you have advisors, but in the end, You are the founder or you are free founders. You can ask each other. This already, this already helps at the start. And it still is a big challenge that a lot of people rely on you.

And obviously yourself, you might have some doubts. Of course you have your vision, but you might doubt it at some times. It’s a process as well to become a founder. Then you never did it before in your life. 

Alexis: Okay. And Héloïse, I’d love to hear your thoughts as well. 

Héloïse: So to be a leader in a, as, as a young founder in a startup, I think is absolutely thrilling.

I’ve always looked at leaders by leading by example, and I hope I share a good example with what I’m doing in my day and, and how I act in my life. But I think the biggest challenge for me is communication because we have so much information in our heads. We have to communicate it in one way to our co founders.

We have to not communicate some things as well because we don’t want to disturb them at some times of the day where we’re focused on deep work, but then you have to [00:05:00] communicate at different times. You start to communicate differently to employees, to interns and to freelancers, but also to investors, clients, prospects, and people you just meet at a random cocktail for networking events.

Communication for me is very, very important, and it’s a challenge that I’m not ready to have completely tackled yet, for sure. I think it gets even tougher when you go. 

Nikolai: To jump in immediately and you have the extra challenge that you are the CEO, so you’re also by role in charge of the communication with the external world.

I mean, you mentioned investors, but like people will always look to you first. So it’s. Internal different types of communications and then the whole external world. And obviously the employees see what you say outside your investors have some insights into the company as well. So I think it’s already also interesting to balance those different types of requirements of communication where you are in the spotlight.

Alexis: What I observed so far, you are doing really [00:06:00] great. So I would say, don’t be too worried about that. Maybe that you are doing great, but I’m glad you’re taking care of that. And you’re, you’re finding that very important. So That’s very cool. There’s always challenges in early stage startups. Can you tell me what are the typical leadership challenges you face?

Héloïse, do 

Héloïse: you want to take that first? Yes. The first challenge is people. Okay. And I think it’s the most important challenge of all, because a company is laughing about its team. The main challenge, for example, that one of us, we had to face was our first maternity leave. It was happened during, uh, the life of a startup.

It was a surprise for everyone, the person included. And we’re all very happy that it’s, it’s happening. It’s going well. However, it’s of course like, no one tells you as a young founder, how to react when your employee tells you that we’re going to go on maternity leave in the next six months. And you have to react to the right words in a culture setting that’s very international and with [00:07:00] an age gap that’s quite present.

So yeah, there’s a lot of key elements to put into context for your first reaction to this type of news, which for me was quite naturally because I think Life is a Miracle was very warm. But at the end of the day, it’s also a challenge for the whole team to make sure that everything goes well for everyone professionally.

Alexis: I have to admit that the first time it happened to me, I believe my face showed something completely different from what I wanted to say. And I saw the person, the face of the person in front of me. And I realized that my face was telling off what we will do now. And what I wanted to say is, of course, congratulations, because that’s what you want to say.

But I was already starting to compute what, what we will do. That’s an important person. And when I saw the face of the person in front of me. [00:08:00] I 

Nikolai: think this can summarize many things quite up. You have like really happy moments and still lots of concerns at the same time. So you obviously, we are very happy for her, but still, we still feel the responsibility as a founder, because this person also has a leadership role, you think, okay, who will cover that?

Okay, we also have a CTO who goes on maternity leave on his honeymoon. At the same time, you have someone who has a visa needs to travel for it. So we always have A lot of things in the back of our head that we need to consider. And just because we consider, I don’t think that makes us less humane. I would say we try to be very empathetic, but still we also need to make sure that the company survives because in the end that’s the goal here.

So I think yeah, balancing those thoughts is quite important. And then maybe to add to your point, so I would agree people challenge at the top. For me, It is the challenge of challenges because [00:09:00] there’s always so much stuff happening at the same time and you need to prioritize stuff. And this means, which I find quite difficult is sometimes you have to say no.

Like we, by definition, a startup has very limited resources. So people will have ideas. That might be great, but still, sometimes you have to say, no, you need to prioritize because if you do everything at once, you do nothing. And that’s quite important to set like a clear guideline for yourself, for the founding team, but also for the employees.

Héloïse: I completely agree. Especially in a tech startup where people are like tech wizards, project geniuses, call it whatever you want, but people are very creative. They always have this cool idea to do this new research or that cool new feature. But at the end of the day, maybe it’s my stage hat, but I have me.

For me, if it doesn’t go on the front line and there’s no impact on the revenue, it’s not a good idea. It’s not good. If I cannot see where it’s going to bring more value to the market that we’re addressing. 

Nikolai: And sometimes it’s a bit brutal. And like, because, you know, you don’t want to be [00:10:00] the no person. And I mean, we don’t say no most of the time, but it happens.

And it can be kind of like, because you know, you want people to have ideas because maybe the idea is actually something nobody thought about and it sparks something great. But I mean, the minimum we have to do is like really challenge it. And then, yeah, sometimes we have to be a bit tough and say, yeah, okay, cool.

But honestly, Maybe in two years, if everything goes well and that’s not always easy. 

Héloïse: And actually the challenge to make sure that we, uh, not just challenge that, but channel that we channel the energy of the people that joined the team by creating this a new ambassador program. That’s called the Cormacolindor.

It’s literally, so, you know, the name Corma comes from the name, the ring, where the sass of sass, so one ring from the Lord of the Rings, basically. And, um, uh, Cormacolindor is literally an elfish. Yeah. The ring bearer, the person that bears the ring, because like Frodo, when you’re Corma, you’re a Cormacollindor in Elvish.

So we did this program quite [00:11:00] recently with the new hires to help them step by step collaborate with each other to reach the objective key results, make sure that they do things outside of work that build up the team, uh, team spirits and make sure that individually they shine because everyone is unique.

Everyone can not, no, They cannot be replaced, someone cannot, because you’re not here, I replace you. So it’s really a very interesting program to show that, to really leverage also and make them shine as people. Because it’s not just the challenge, it’s also the main channel of how Corma is going to do great.

Alexis: I love it. That’s very, very interesting. So you put people first as a challenge, and I can see that you are really taking care of I’ll People contribute to the company, but also how they develop themselves, how they grow into their role and grow with each other. So that’s very cool. Are there other challenges as managing people as young founders?

I mean, 

Nikolai: for like an important thing, it’s people, but [00:12:00] you know, as a startup, you’re in survival mode all the time until you get I mean, technically every company is survival mode, but I would say in startups is the strongest because they are young, they didn’t prove themselves yet. They don’t have as much money as they want.

They don’t have like their product market fit yet. That’s generating profitable revenues every month. So, so it’s really. Tough also on the, on the commercial side to manage people and data founders are obviously heavily involved as well because we have investors, we have some funding, but we need to show to get revenue to prove obviously that our product, that our idea is needed, but at the same time it also pays our bills.

And I think balancing a little bit of this financial need to just push on the commercial expansion, but at the same time, not get lost. On it. And remember, you try to sell your product because you believe in it. And if you have more clients, you get more user [00:13:00] feedback. It’s a bit difficult to balance this sometimes the need for commercial expansion or with the internal need to understand what you actually want to bid.

If the client asks you, okay, can you do this? You say, obviously yes. And if, if it’s. Not there at all. You say it’s on the roadmap and if you know, it’s like humanly technically impossible to do it. You say, okay, we’re going to look into it for the next quarter. But obviously this has a limit. You cannot oversell all the time and you need to take a step back then and know how to balance this.

I would say this is also a challenge. 

Alexis: Hmm. Very good point. So when the leadership team embodies the values and principles they want to see in the organization, then I believe the organization can scale, can grow and can become something very beautiful. Do you agree with that statement? 

Héloïse: Yeah. Culture eats strategy for breakfast.

So like, it’s a very basic sentence to say, but really summarizes the whole feeling that we see, not [00:14:00] just at Corma as a company that has, is very strong in the values that it upheld, but also within Station F, we see the startups that are very united. Where the people are already, the cement of the whole building, of the, basically the cathedral that they’re going to build.

It beats any competition. It goes, it just shines quick pass. 

Alexis: So what are you doing to create such a culture? 

Nikolai: You mentioned it’s a lot of leadership by example. You need to lift the company values and I think it’s also something we learned. You need to actively nurture it. Like, okay, people. Probably have the tendency to copy behavior, but you need to encourage it.

And it was something that was not always super easy because sometimes people feel the founder has their unique role and they always share direction. But you know, it’s part of our DNA that we want people to lead the way as well. So there’s sometimes you need to actively encourage fine programs. Like, for example, what you said with this ambassador program, but it can be [00:15:00] small stuff to how you give praise, how you give feedback.

So for example, this radical Canada methodology, we follow it, tried to implement it and how we do feedback and how we do development and like personal career development. It’s an active process. It takes active management. Even if we try to. Live as the best example. I think it’s still, yeah, we still need to be active to do it.

Héloïse: Yeah. Some examples are typically by your life on time, if not on time, like this in advance, being on time is always being late. It being present for the others. Like if someone wants to talk about something, they can pick a lunch for you very easily as a founder is something that you do. I mean, sometimes it’s a career coaching.

Sometimes I, and we talk about other things than work, which is like, what is like next five years, you know, how, how can we, how can we get you there? It’s about creating an alumni, uh, alumni group. And the alumni also inspire the current people that are present at Cuomo. And so they are inspired together and it’s about giving them the voice to be heard so that they [00:16:00] embody this leadership position that Nikolai was just explaining now.

It’s part of not just the Ambassador program, which is. Basically setting a more formal setting to what was happening before it’s really just a mix of how you celebrate the little wins or you close the deal. That’s really, really good. Okay. How can I help you close your deal today? Collaboration on different topics and putting everyone in the same team, like there’s not a tech team and a safety mask on my team.

That’s just one. That’s just not possible to not talk to each other. Even if you don’t understand what JavaScript is, at some point, you’re going to have 

Alexis: to. I love what you’re saying there. So you mentioned Radical Condor for the audience. The idea, if I summarize it, is if you care personally about people, then you can challenge them directly.

So that’s the important part of it. If people can feel that you care about them, then you can challenge them. basically give a feedback. If you don’t feel you care about them, it’s like if you were trying to put a big truck on a rope [00:17:00] bridge. It will not really work. Your feedback will not go through. So that’s basically useless.

Is it a good summary? I think 

Nikolai: like the other thing is even more dangerous that because you care to, because I would say we all care deeply about the, the, the team, the humans, the people behind it, that you, because of that don’t challenge directly. I think in the concept it’s called ruinous empathy, and I think it’s a big risk that you’re trying to be too nice, too cushy.

You know, it’s like a bit of the example after lunch, you have some food stuck in your face. You don’t want to tell the person because you don’t want to embarrass them, but imagine then they go off and spend all their day with food in their face and they would have been so much more grateful to have this.

uncomfortable moment where you say, yes, sorry, maybe clean your mouth a little bit. There will be so much that you were created this little uncomfortable moment, but overall, because you care personally, you gave some, yeah, let’s say negative or in the sense, constructive criticism. This is better. Like, so for me, just being like toxic, not caring about [00:18:00] people giving meaning.

Feedback. Obviously there are toxic people. I don’t think we are at risk of it. So for me, it’s more the thing to avoid the ruinous empathy and to challenge directly because we have the best intention behind it. 

Alexis: I love it. Thank you for the example. That will make it very clear to people. They will all try to look if they don’t have anything left.

So lastly, what advice would you like to share with our audience, especially those who are aspiring leaders or early stage founders? 

Héloïse: That’s a good question. 

Nikolai: I think there are a lot of answers to 

Héloïse: it. Yeah, there’s so many. I mean, it depends on what context you’re in, but do you want to start or? 

Nikolai: I mean, for me, there are some basic things.

You throw yourself into the cold water. I know some people that hesitated away from leadership positions because they are scared to manage others, be it because they’re still a bit young. They’re a bit shy. You have to do it to learn it. It’s not something you learn in the textbooks. So you. First, you have to bring yourself in the position to [00:19:00] lead.

And then the next thing is you will see, you don’t need to figure everything on your own. Find yourself some mentors, find yourself some friends. Ideally. I mean, we have us three co founders, I would say we are really, really tightly linked. Then you can like with people that have similar experiences or similar learnings.

Try to like, once you bring yourself out there, try to exchange with people that live in the same or lived in the past in the same situation and try to learn from there what worked for them, from what doesn’t. And for me, something that I usually do, like try to follow your intuition. If you feel something doesn’t feel right, maybe, yeah, reflect if it’s good.

And if you have a good feeling of something, you also need to have to act, like have the courage to act. Don’t be like too scared in moments, even if it might seem a bit scary. Like sometimes you have to push yourself a little. I think that’s the uncomfortable part of the leadership. Sometimes you need to do things that are not super pleasant, but you have to do it because nobody else will.

Héloïse: I mean, I can [00:20:00] only second what Nicolas just said. One thing in addition came into my mind. So when you’re young as a founder, there’s a lot of topics. But you’re going to realize that exists first in life, like managing new employees, like having a specific type of client to manage or stuff like that. It’s called like zones of hurtful ignorance that are very difficult to not difficult to observe because they’re quite come quite fast, but very difficult to, you know, on your own, if you really isolate yourself, it’s going to be a harsh on you, on your life, on your mental health.

To actually overcome and because you, you have like 15, uh, ignorance bits to, to master at the same time. Like, uh, it’s like, just life gets in the way. What I really would recommend to a young founder, how founder starting out is you just get your phone, build out a list of the top 50 people. And so maybe five in every category, or 10 in every category that you most admire in the world.

in your region, in your industry, and get [00:21:00] them on the phone, book a meeting with them. It might take you a year, but at the end of the day, you’re going to make it happen. Actually, I would disagree. 

Nikolai: It won’t take you a year. Like one thing that surprised me, how happy many leaders are actually are to share their knowledge.

I mean, if you think of some people that we spoke, like we, I mean, yeah, it’s, We’re not out of school for that long and the amount of senior people we speak to just because we reach out because, okay, they maybe get spammed by sales people, but just people asking for like founder to founder advice. They are usually really happy to share.

So like really go out there and try to get some advice, some mentorship. I would say it’s easier than you think. I don’t think you need a year for it, but it’s super valuable to do that. 

Alexis: I love that. That’s really beautiful. Thank you for being here. Join the podcast today. I’m sure it will be already uploaded.

Héloïse: Thank [00:22:00] you.

When we talk about the future of User Experience, the conversation often jumps straight to AI models, automation, and performance.

But what if the real challenge isn’t prediction — but judgment?

In this episode of Le Podcast on Emerging Leadership, I had the pleasure of discussing this question with Sebastian Cao, a UX and technology leader who has worked at Red Hat and Tesla, and who recently taught a Stanford course on the future of User Experience.

His perspective is refreshingly clear:
technology only creates value when it strengthens human capability.

Prediction is easy. Judgment is human.

Sebastian draws a crucial distinction between two forms of intelligence:

• Prediction, where machines excel by identifying patterns in massive datasets
• Judgment, where humans rely on experience, context, and intuition

At Tesla, this distinction became very concrete. Machine learning models could predict likely failures based on historical data. But technicians — by touching, seeing, and sensing the vehicle — often detected signals no model could capture.

The best systems were not fully automated.
They were guided systems, where AI informed decisions, but humans remained accountable.

From automation to augmentation

One of Sebastian’s most telling stories is surprisingly simple.

When his team was called Service Automation, frontline technicians immediately feared job loss. The name alone created resistance.

Renaming the team Service Augmentation changed everything.

Words matter.
Framing matters.

By explicitly positioning AI as a tool to amplify human skill, rather than replace it, adoption became possible. Productivity improved, trust increased, and the technology actually delivered value.

The “ghost in the machine” problem

Sebastian uses the metaphor of the ghost in the machine to describe what happens when AI systems behave like black boxes.

When users don’t understand:

• where data comes from
• how predictions are made
• how confident the system really is

they stop trusting the tool — or actively work against it.

Transparency is not a “nice to have” UX feature.
It is the foundation of trust.

Explaining reasoning, showing confidence levels, and making decision logic visible turns AI from something magical and frightening into something usable and credible.

Empathy is not optional anymore

One of the strongest messages of the episode is that empathy has become an engineering requirement.

Designing AI-driven systems without understanding:

• user incentives
• fears around automation
• real-world decision-making

almost guarantees failure.

Sebastian insists on something deceptively simple:
go where users work, observe them, and learn how decisions are really made.

No amount of code can replace that.

Open source, ethics, and trust

Finally, Sebastian makes a strong case for openness.

When AI systems influence frontline decisions — impacting customers, safety, or livelihoods — leaders must be able to explain who built the model, how it was trained, and what biases may exist.

For him, open source is not an ideology.
It is a practical condition for trust and accountability.

Leadership in a human-centered AI world

Sebastian’s advice to leaders is clear:

Understand the technology.
But never forget the human on the other side.

The future of User Experience will not be decided by the biggest model or the fastest deployment.
It will be shaped by leaders who combine technical literacy, empathy, and responsibility.

That is where Emerging Leadership truly begins.

Here is the transcript of the episode

Alexis: [00:00:00] Welcome to the podcast on emerging leadership. I’m your host, Alexis Monville. Today, we have a fascinating conversation with our guest, Sebastian Cao. Sebastian is a visionary leader with a wealth of experience at the intersection of technology and user experience, having held pivotal roles at Red Hat, Tesla, among others.

He recently delivered an insightful course at Stanford on the future of user experience, where he explored critical topics like AI’s role in augmenting human capabilities. So we are thrilled to dive into these topics with him today. Sebastian, welcome to the podcast. How do you typically introduce yourself to someone you just met?

Sebastian: Thank you Alexis.  I love to be here. I usually speak that I’m an engineer that can talk about [00:01:00] Problems that could be solved with technology. I like to talk about problems. that are worth to be solved, like real problems talking about as a species worldwide, globally, problems that we have and what are good options and good ideas to solve with technology.

So that’s my, how I introduce myself. 

Alexis: I love those kinds of introduction where I need to ask more questions to know a little bit more about your background and all those things. But we will go through that at some point. You did a Stanford course. about the future of user experiences, and you emphasize the balance between prediction and judgment.

Can you tell us more about that? Because I’m very curious about that thing. 

Sebastian: Yeah, as I mentioned, I’m an engineer, computer science engineer, but I’m certainly not the kind of guy that would go and hack and make a model, an LLM model better, or just make a little bit more incremental performance out of a model.

I’m more [00:02:00] concerned and interested about how that model could really solve human problems. We can go into that and move into Silicon Valley, like a few years ago. And you always read about all this story and the heritage around this area. And you see all this company, but it’s still a really engineering led culture.

So everyone right now is like competing about it’s an arms race, right? Who has the biggest, boldest, more expensive model in a way. But I was concerned and we can certainly touch into that. My experience of 2 years at Tesla about how we can solve real human everyday problems for frontline workers that.

So, when I was discussing that we engineers that were getting coding these big models back inside Tesla, we’re always. Computers will always be better than us and doing prediction made by that we mean getting a huge amount of data historical data and see patterns things that [00:03:00] are repetitive over there and say, OK, this is with a high.

That’s a probability, right? With a high degree of a chance, 90 percent 80%. So this will happen because data show us that happened before. And that’s great. We shouldn’t be doing that. So in the case of a car company, any company, you get all the historical repairs and you know that certain type of cars in certain weather, when driven by certain patterns, you usually break this part of this subsystem X amount of kilometers or miles or whatever.

So that’s great. We were doing that. We predicted diagnostic to Manchell learning. What I wanted to add to the equation, because it’s the part that’s easy to forget is What about the judgment? What about the human judgment? The mechanic or technician will see the car coming, and we might see by touching it, by feeling, by looking at the car, might see that something else is off.

Maybe there was a, I don’t know, heartbreak or there was actually a crash or something that happened before that we’re getting all [00:04:00] this data and all these signals coming from the car that might also come into play. And that’s what we humans are good at. We remember seeing that before. We made a decision back in the day that the outcome was a particular outcome, and that kind of is part of your knowledge base and your experience.

So how we can merge both how we can merge the cold data coming from the car in this case, but leaving the opportunity for the technician to make. a judgment call. So I was pushing not for a automated kind of result, but like a guided, guided diagnosis process where the machine will provide all the probabilities looking at the car and getting all the data coming from the sensor.

But the technician will actually use that to make their own judgment and say, yes, I will do that. Or maybe now we’ll do the other thing. So I think that is a, that’s a great concept that I’ve been talking and I’m In love right now, and I think it’s really important because we’re seeing this much development in a it’s [00:05:00] thinking about a not only as artificial intelligence, not even as artificial intelligence, but more like augmented intelligent or amplify intelligent where we make humans better, they can do more because we’re feeding them with data pre analyze data with a lot of prediction by your living room for judgment.

Alexis: Okay, so a large place for human, but not only human, the experience they have in a particular field, and that could be any field. 

Sebastian: In this case, we’re talking about mechanics technicians, people that do wrenches, they take wrenches, I mean, with their hands, they’re not coding, they’re not engineer, they don’t care about at all about machine learning.

How you can give them More especially there for example compensated and then we get into compensation and kind of incentives that’s a cold economical kind of analysis and there’s a lot of research about that they were incentivized by the number of cars so why don’t we tell them a story that they [00:06:00] will be able to use him.

Augmented. Tools or machine learning or whatever, it’s not about machine learning. They can actually go through more cars through the day, through the week. So they’re more productive. They get a better paycheck. So we’re all happy. But sometimes I feel as an industry coming from any, again, as a software engineer all my life, and we met in a software company, we tend to fail to explain that we go into, okay, this is cool.

This is the latest, this is the latest model. LLMs, but we understand who is the customer who’s on the other side consuming that technology, how they’re incentivized and what is that they’re trying to solve. And we certainly fail at telling that story sometimes. 

Alexis: I feel there’s something deeper that we can grasp there.

And so AI is not a replacement for human intelligence and definitely human intelligence, their experience and how they understand the world is something important. So they could be augmented. How do you do that? Practically. 

Sebastian: [00:07:00] That’s where I learned a lot and made a lot of mistakes doing that. And I think that’s what I’m looking at.

What is the company or yeah, a company, a software provider actually going to crack that code. I think right now everyone is fighting about releasing the biggest model and spending a lot of billions of dollars in training, but no one is certainly there might be companies doing that. But we haven’t seen that and the headlines and the stories in the media.

It’s all about the biggest model and the competition between the providers. No one is okay. This model, whether it’s the biggest or not, it’s actually increasing productivity for it. Frontline workers, technicians, insurance clerks, customer support operators, airlines, whatever, we’re still not seeing that because we’re failing at deploying those models along human beings, working side to side, the whole copilot idea, whatever we would like to call it.

So when I’m seeing failings first, as an engineer, we tend to, it’s too [00:08:00] complicated. We throw a lot of technology in a lot of. Explanation and a lot of, we tend to use automation and artificial intelligence a lot. And if on the other side you have someone that doesn’t come from our industry, the first thing that comes to mind is, okay, this is automation, this is going to replace me.

No matter your intention is the first, because it’s the human reaction to that. One of the first things that I did at Tesla’s team that I inherited when I joined was called service automation. And we were supposedly, we were tasked to, okay, let’s create more tools. For internal customers to employees, more tools for them to be become better.

I say, okay, the first thing I want you to change is the name because every time I present myself as service automation, they say, okay, you’re coming for my job. So it was super quick and everyone say, okay, that’s cool. That’s a good idea. So we change it to service augmentation and I started sharing a lot of like research, not papers, but just.

Headlines and professors kind of [00:09:00] analysis at both sides of the aisle, as they say here in the US, both the engineers are building the software and the consumers on the other side, the technicians say, Hey, this is what we’re trying to build. We’re trying to build something that will help you go through your day that are going to augment you.

Augmentation is still like a 20 word. As I say here, it’s like too complicated. Maybe the amplification. But just change the name because words carry a lot of weight. Right now in the media, it’s much more interesting to publish. story about automation or AI taking out jobs than talking about AI making people better.

Alexis: It’s very interesting how we oscillate between a 1 world and 5, 000 world, and we are mixing them in one sentence and it’s scaring everybody. 

Sebastian: It’s a human behavior and we go from, this is going to be a great feature, to Skynet and Terminator and we’re going all like the matrix. So, and that sells. So I think it’s for people like us, like you to understand the technology, but I think you need to [00:10:00] go further and explain the technology, explain what’s going on, explain why you’re using 

Alexis: it.

And it’s a very good point. You need to care about the users themselves, the people who will really use the technology and go a little bit further in understanding how they work and what they are trying to achieve. And it’s not a game about feature or that’s not only that it’s really about. what they need to accomplish, even if they don’t really know what kind of feature they would need on pantyhose with users, I feel is very important.

Absolutely. You picked an example about the ghost in the machine. And I was very curious about that because yeah, I’m probably old enough to know about that album from a police, uh, from the police, 

Sebastian: 1981 great songs that I was doing, but I always heard, I mean, I always listened to music that is. Yeah, but yeah, I got a t shirt and I used that t shirt that’s about that album that is called the policy of ghost in the machine.

And I used that t shirt when I went to a meeting that I want to explain the [00:11:00] concept and say, the ghost in the machine is that idea. It’s a phrase that had been going on forever. It’s just. You can also talk about the Turing test and all that. Okay. If it is a machine, if it is new enough or strange enough, and I think most people got that experience with JGPD like two years ago, you do feel that there’s something else about a program there.

That is the ghost. There’s a soul, there’s a human touch. At the end of the day now, if you delve into it and you get into the research and you do, you understand the transformer model and all that, okay, it’s a pretty big program choosing what’s the next word to use. But at the beginning, it feels magical.

I think that is the idea is people will tend to think, okay, this is actually sentient. This is actually thinking by itself. So with the ghost in the machine, I tell the people, if we don’t explain them what’s going on, if it is a black box, that is a concept that we also use a lot in software, you’re not explaining where’s the data coming from for you to make the decision, the prediction, where it’s coming from, who selected the data, [00:12:00] who labeled the data, and then you don’t explain how you use that data to make a decision.

And then you explain. In simple terms, kind of the, like the confidence interval, I say, okay, we’re pretty sure up to 80 percent you don’t need to use percentage worth. I was pushing a lot for like graphical representation, easy to understand that this is a recommendation based on all this data. I think we also need to get better at that with sending all these models that you ask a question, you get a response, but there’s nothing that will explain you.

How that response got constructed, how that response came to be, and then we get into a lot of and we all we saw that already a lot of crazy stuff on really dangerous stuff about labeling and who’s bias data and all that. So I think that is an also an important concept. We’re dealing with a frontline workers say sharing with them.

Okay, we’re giving you this recommendation because A, B and C or D happened before in my case, I was pushing, but it was a pretty simple concept to fix [00:13:00] an issue. You’re relying on millions and millions of rows of data, lines of data coming from previous repairs. The repairs, historical repairs you have done 10 years of experience, those repairs were done by other technicians just by sharing to the technicians.

Hey, this is actually recommending you what to do, but it’s trained in a way or based on what your peers have done in the past. It’s like this shared knowledge of all your peers that you look up to. It’s not a machine that the machine is just sorting the data and just going through that really fast.

That was a good example of how I was pushing for the ghost in the machine. We will need to explain because they will embrace it much. There will be much more open. That is just again, a black box. They’ll say, Hey, the machine is telling you to do this. Then they will know, you know what I’m doing the other way around.

Alexis: What I really like there, it’s not just trying to explain how the feature work, but basically showing the work that is done, explaining all the reasoning [00:14:00] that got us to the conclusion. So you need to explain a few things. You did it very well to say, okay, that’s basically the model is just trying to predict what is the right word to use after the previous one based on historical data.

That’s probably a rough explanation, but that’s pretty cool because then, okay, I understand that this is the data, this is how it works, and I can trust. that thing. In addition to that, I have a kind of confidence level that is shown to me. I can really rely on it or I can say, ah, okay, the confidence is very low.

There’s maybe not a lot of historical data on my current situation. You probably need to pay attention a little bit more. That’s very interesting, I 

Sebastian: feel. It’s spot on, and you mentioned such a key word, Alexis, that is trust. And the other word that I kept on using in all my meetings with product managers and engineers is empathy, too.

You need to increase the empathy for them to say, Hey, this is actually helping me. And [00:15:00] I’m actually rooting for the software, rooting for this solution because it becomes better, the solution becomes better or machine learning, whatever the AI becomes better, I become better. We’re all peers. We’re all partners.

If I think you’re trying to replace me, then I will do all my best to actually hijack and just kill your project. 

Alexis: You have quite a fascinating career trajectory funding companies in Latin America, working with RADAT in Latin America and in the U. S., working in the Silicon Valley for Tesla. How do you see the role of technology in customer experience?

in the future. And how have you seen that evolve? And how do you see that for the, in the future? 

Sebastian: That’s a good question. You know, Alex, one thing that I keep repeating myself, just not to forget, and I keep telling friends that I have in Latin America, they go, okay, Tesla, Silicon Valley is great. And you can relate to that being in France is at the end of the [00:16:00] day here, you will see probably bigger, Ammunition, bigger weapons, or bigger things that they’re building for a global scale, we’re solving the same kind of problems.

Cultural change, resistance to change, human behavior is the same in Silicon Valley, in Paris or France, in Buenos Aires, in Argentina, in Brazil, or in Africa. This problem of, let’s say, for Tesla, but if you’re throwing a fully automation machine learning, whatever, diagnostic to a technician. In Tesla and Silicon Valley, without explanation, they will resist to it.

You do that in France, they will resist to it. Also, you do that in Turkey, they will resist to it. And the same in Latin America. That’s again, that was an insight that was a realization for me. I finally understood that, okay, I’m here because you get exposed to global scale of solving problems. You probably have bigger resources and tools to solve that problem.

But at the end of the day, the problem that you’re solving is still a human problem that is the same, no [00:17:00] matter what language you speak or the color of your skin or whatever, to be honest, this is amazing because even with everything that we’re discussing about AI and all that, at the end of the day, human beings at the core, we are still the same and we fear the same things and we need the same kind of help.

So that’s probably what I think it’s the biggest. Outcome of my journey so far, but yeah, as I mentioned here in Silicon Valley, you see that we go really fast and sometimes too fast. So I like being here and seeing everything that’s going on with AI and everything that we’re thinking about building at the same time.

I’m super interested in how we are going to build all of that with a good adoption and with empathy. So this is a great 

Alexis: place to try all of that. So that’s the right balance of technology and human touch. That’s the empathy that you build with the users and to foster the adoption of technology or foster the idea of innovation itself.

Sebastian: I read as many psychology books as [00:18:00] Coding or AI machine learning algorithm books. I think we need both, especially with AI right now. Any, any you on your, what you’re working on your consultancy and we need people that talk technical because you’re going to be exposed to technical discussion or code or a solution or diagram.

Okay. This is what we’re building, but I think we need more people that can understand, okay, we build this and we ship this product. This is going to happen. And if you don’t know, at least you’re going to, Catching a bus or a taxi and you go there with your user and you sit with them, you sit with them and see them in action.

In my case, it was going to the Places where they were actually branching car and working with them. You have to work with them, understand what they’re doing. So if you’re just shipping code, pushing code into production without ever talking and touching and feeling your customers, it’s going to be hard.

Alexis: I had a, I had a conversation with a really high performing team. I was looking at what they were doing every week to have a sense of what they are, the [00:19:00] things that were important to them. I noticed that. All the team members had user, real users, interviews every week. Not all of them. Every week there was a contact with a user.

at least one. And that was different people on the team. And they had a user interview guide that was constantly evolving because they were testing their assumption with different users. And I was looking at it and say, Oh, okay. So probably a successful team needs to be in contact in touch with their users at least weekly that showed up in their work.

Of course, 

Sebastian: I agree with you. I think they’re really successful like B2C consumer companies. The product management team had been doing the, they know that, and they’ve been doing for AI, we’re trying to hopefully not replace, but augment decision. It’s even more that you need to be there and understand how that person is making decisions.

If [00:20:00] you trying to build something. That person was going to use on their day to day. Now you end up with Clippy from office in the 90s. They’ll say, Hey, what do you need to do? Do you need to print? Hopefully we’ll become better than that. 

Alexis: Yeah. That’s the first question everybody asked was how to turn off that thing.

Absolutely. Finally, as a leader who worked in different high tech environments, what advice would you give to a new leader who want to effectively evolve in that world? 

Sebastian: Advice. Okay. For leaders, I would say maybe what we’ve been discussing, it’s, I think today you need to have exposure to the technical part of things, understand everything that is going on, how it’s been created, why it’s been created and by who, and there’s a lot of, we know.

Political things at stake and companies competing against each other. So you need to understand them. We probably need another podcast to discuss open source versus closed source for [00:21:00] things like AI and all that. But you need to understand where everything is coming from. But those again, those are tools in your tool belt.

What I would like leaders. I think it’s very important. We’ve been discussing, understand, be empathetic, understand who’s on the other side. Who’s your customer? Who’s consuming that? It’s a B2C, it’s a B2B. Are your users experienced with AI or whatever technology you’re using, or they are not? Do they trust it or not?

And if not, and if you’d make those questions and you get answers, work with those answers, I think one thing that I see a lot here is, again, we are shipping code without asking any questions and we think that code is the best and that option will follow. And I think we need a little more human touch on that.

So that will be my recommendation for leaders. Again, human touch and empathy. 

Alexis: Excellent. Oh, I cannot resist. People will not see that on video. I can see it on your wrist. You have an interesting message. Tell me more about that. 

Sebastian: All right. Yeah, I am. I just I was it was lying around. [00:22:00] This is a wristband that I got from one of my favorite places in the U.

S. That is the Air and Space Museum in Washington, D. C. That you have all these. Historical planes and the Apollo mission, all that. And this is a wristband that says failure is not an option. And there was a wristband that was created for the Apollo team before sending someone to the moon. And then you see how much was achieved in collaboration between private and public sector, different political views and all that, how much was achieved in like six years, that’s amazing.

Alexis: I like the story, I like the message and at the same time you mentioned open source a second ago, so don’t we say fail often, fail fast or something like that? 

Sebastian: Oh, you got me there. Okay, we have another hour and a half to keep the discussion. I don’t like the idea of with AI and everything that’s going on that fail often, fail fast.

I mean, just releasing whatever it is because now this is something that is talking at you and many people are making decisions based on the responses that [00:23:00] they got, the answers that they have. So if it’s not curated, if it’s biased, a lot of things can go wrong. And we have seen examples. So I think for the ethos of just move fast and break things, I’ve never liked that much.

And especially here right now with AI. And the other part that you asked me, we share that background together is I think there’s need to be a much more open source involved. And again, ghosting the machine and the black box. If that model is answering me questions, I want to understand who built the model and who made those initial training, initial answers.

And that’s what I love. What a lot of companies are doing in France are taking the more human approach and they’re mostly based in open source. If we go and this is a personal opinion, so I don’t know, I don’t care about all the comments that we may have. This is handled by one big corporation with all the data and it’s closed.

We have seen that before, and it’s never a good story. So I will push for [00:24:00] maybe we have seen you and I were competing against other companies. Maybe you have your closed source. That’s good. And you have your open source of that is good enough to and it kind of the similar. It’s your choice, but at least you have an open source choice.

I wouldn’t trust my frontline workers to make decisions that will affect customers based on a model that I don’t know exactly how it was built. Personal opinion, 120%. 

Alexis: Totally agree. And that we are back to the trust aspect and transparency is the foundation to build trust. So I love that. I’m happy that I asked the question about failure.

Thank you for joining the podcast, Sebastian. Thank Alex. You have been great. Let’s do this. 

Sebastian: Once again, in the future, okay? Pleasure. Take 

care.

Some episodes give you a framework.
This one gives you a practice.

I sat down with Jeffrey Fredrick, VP of Engineering at Ion Analytics and co-author of Agile Conversations, to explore what happens in the moments that matter: when tension rises, stakes feel high, and the conversation goes off the rails.

What struck me is that Jeffrey does not talk about communication as “soft skills”. He talks about it as deliberate practice.

The moment that changed everything: “You’re good at advocacy, but where is your inquiry?”

Jeffrey traces the origin of his journey to a conversation at CitCon. Someone he had just met told him something precise and unsettling: he sounded skilled at making the case for his ideas, but not very curious about others’ reasoning.

That single observation pointed him toward Chris Argyris’ models:

• Unilateral control: I’m right, the answer is obvious, and my job is to make my idea win
• Mutual learning: I share my reasoning and I’m genuinely curious about yours, so we can learn our way to a better decision

The key move is deceptively simple: balance advocacy and inquiry.

The Four Rs: a method to practice, not just understand

Jeffrey introduces a simple cycle that turns good intentions into actual behavior:

1. Record the conversation (in a two-column format)
2. Reflect using a model (Argyris, NVC, LEAP, etc.)
3. Revise by writing an improved version
4. Role play to make the new behavior real, out loud

What matters is not the elegance of the model. It’s the repetition.

Jeffrey’s point is sharp: these ideas make so much sense that we assume we already do them. Recording exposes the gap between what we value and what we actually do under pressure.

Why we can’t recall what we said

One of the most useful insights is about memory.

Jeffrey explains that we don’t remember the exact words we used because we’re not hearing our own “tapping”. We’re hearing the “music in our head”. In other words, our internal story is richer than the actual signals we send.

That gap is exactly why deliberate practice matters. If you can’t accurately perceive your behavior, you can’t reliably improve it.

Trust first, fear second, then why

Jeffrey challenges a popular leadership reflex: starting with purpose.

He argues the sequence often needs to be:

1. Build trust
2. Surface fear
3. Then talk about why and direction

Why? Because without trust, “why” becomes a debate weapon. And without acknowledging fear, teams act out of loss aversion while pretending they’re being rational.

I especially liked one framing: if you hide your concerns, you’re not being neutral. You’re withholding relevant information. That changes the emotional meaning of vulnerability. It becomes part of doing the work well.

Triggers, tells, twitches: spotting your patterns

Over time, practicing the Four Rs helps you identify repeat patterns in yourself:

• what situations trigger you
• what your “tells” look like
• what your body does (“twitches”) when you’re about to slip into control mode

Then you can do the adult thing: plan your moves in advance, instead of improvising under stress.

Can this work at scale?

Jeffrey has seen it spread in organizations in two ways:

• explicitly, through shared practice and vocabulary
• implicitly, by embedding mutual learning into rituals like incident reviews and postmortems

His reminder is both hopeful and pragmatic: it spreads one person at a time. You don’t need everyone trained for the conversation to improve. But when more people practice together, everything accelerates.

The simplest takeaway

If I had to keep one idea from this episode, it would be this:

When stakes rise, stop trying to “win”.
Return to mutual learning: be transparent about your reasoning and curious about others’.

It’s not a slogan. It’s a practice.

Transcript:

Alexis: [00:00:00] Welcome to Le Podcast on Emerging Leadership. I’m your host, Alexis Monville. Today, we are diving into the world of agile leadership with a special guest, Jeffrey Fredrick. Jeffrey is the Vice President of Engineering at Ion Analytics and the co author of the book Agile Conversations. With a wealth of experience in the tech industry, Jeffrey has a unique perspective on how agile can transform teams and drive innovation.

Welcome Welcome to the podcast, Jeffrey. How do you typically introduce yourself to someone you just met? 

Jeffrey: Well, I had lots of practice that last couple weekends being at a couple of conferences. So I got to refine my pitch of it. The usual way that I put it is I work four days a week as a software executive, currently VP of engineering and then one day a week as an executive coach, both to individuals and to executive teams.

Alexis: Wow, that’s a perfect pitch. I love it. And I [00:01:00] like the balance between the four days a week doing the job for real and one day a week helping others do it. That reminds me something. You are also the, the co-author of the book Agile Conversations. Can you tell us about the pivotal moment that led you to the book?

Jeffrey: Oh, well, in a sense, I think the pivotal moment came for me actually at a, at a different conference, at CitCon, the conference that I organized and have organized for the past 19 years. I was there and I was talking to someone who I just met, a gentleman named Benjamin Mitchell, and we were talking and he said to me, something very strange.

I didn’t understand what he meant at first. He said well, you seem very, very familiar. Practice very skilled making the case. For what you’re saying, you know, he says you’re, you’re very good at advocacy, but I’m not hearing much inquiry. And I was very confused by what he said. He clearly meant something very particular but I didn’t know what it [00:02:00] was, and that really was my very, very first taste of the model from Chris Argyris what he would call model one and model two where it’s easily understood as the unilateral control model and.

The mutual learning model and the idea. One of the one of the ideas in it is that you should be balancing advocacy inquiry. There’s you should be both making the case for what you believe and sharing the reasons why you believe it. And you should also be curious about the other person’s beliefs and why they believe what they do.

it was, so it was actually that conversation that ended up setting me down the path that led to the book. The gap between that conversation and the book was about nine years. So it wasn’t a quick path, but it was actually fairly direct.

Alexis: I love the story about it and I can understand why in a way, in our current timeframe or mindset [00:03:00] it could seem long, but at the same time When I read the book I was thinking, okay, it sounds very simple at first. And you’re talking about the four R’s and I will ask you to explain that a little bit, but if you want to practice it, suddenly you’ll realize that you will need a lot of practice, but can you give us a taste of what the four R’s are?

Jeffrey: Sure. The four Rs actually came about from trying to teach other this model and w what happened is we, we, I started learning Chris Argyris material. I started practicing it myself and I was very fortunate to be in a study group with aforementioned Benjamin Mitchell, Douglas Squirrel, my co author.

And we would have long weekly sessions where we would study it. And then I tried to bring the same material into the workplace and have other people learn it as well. We weren’t though going to be able to have multi hour [00:04:00] conversations. So what I needed was a format that would allow us to get the same, a lot of the same value of practice, but in a shorter timeframe.

And so what the four R’s are is a, process for studying really any kind of conversational technique that nice. And I make this point because there are lots of different models of what good communication looks like. There’s the Chris Argyris model that we just, we’re discussing the mutual learning model, but there’s also models like nonviolent communication.

Xavier Armador’s leap model. Many different types of things that people might want to check to see if they’re doing and the four Rs in a sense is kind of separate from all of those. It’s a process really of studying conversations and it’s the four steps involved are first you record Your conversation, you do this in a, in a two column format and the two column part is important and you write it down and this is important.

We can come back to why. [00:05:00] So there’s the record. And then once you have it written down, then you reflect. This is where you bring whatever model you’re using to bear. You evaluate your conversation that you’ve recorded according to the model you have in mind. And then having reflected. On it and seeing some things that you might improve.

You then revise, you, you try creating an alternate version of the conversation. And this, these two steps are really where the practice is, you know, up until now, you’ve been kind of evaluating what you’ve done, but it’s really in the work of trying to improve as the element of deliberate practice. And once you’ve written down a version of the dialogue that you.

Prefer then you can go to the, the final step of practice, which is to role play which is to actually say things out loud that you have written down and and having done this of course, there’s kind of a loop here. You’re, you might not like your first revision. You might not like it, what it sounds like when you role play.

And that’s the point is that you will then revise or repeat rather, you’ll go back and re [00:06:00] revise and re reflect and come up with better versions. And even In the role play, you might change sides with the person you’re practicing with, and that’s role reversal. 

Alexis: It’s very powerful. And just the recording phase is very interesting. I usually take. notes when I have a conversation with people but I realized when I was reading the book that I usually take notes about what the other person says, not so much about what I say. And when I recall the conversation, usually I don’t really remember what I said.

There’s a few things that I note, but it,, that’s basically things that I want to remember. I said that to that person. I give that advice or I asked for a particular thing, but all the rest I am, I forget about it and I am able to remember it. And, and I was stunned by that. That’s, I, I, I just can’t is it something you observed? 

Jeffrey: Absolutely. it’s [00:07:00] not surprising that we don’t. Remember what we said exactly because in practice, actually, we aren’t aware of what we say in a conversation this is kind of why people have difficulty improving their conversational skills without this kind of deliberate practice and writing things down.

Now, when I say we don’t know what we see in the conversation, this may sound , hyperbolic, but it’s, but it’s actually true that one of the, one of my favorite experiments about this is they asked a number of people to tap out a tune, one that was very familiar, that everyone would know.

And so for example, the song, happy birthday to you. Right? So they would tap on the table, you know, tap, tap, tap, tap, tap, you know, and then, and then they would ask the people, what are the odds that the person listening to it will be able to guess what it was that you were tapping out. Right.

And, and, and and when they ask people, and I’ve done this many times, I’ve done this. And when I, if I’m doing [00:08:00] in person, I will tap it out and, you know, ask people, you know, what do you think? How often people they guess? And people give a whole range. They, you know, some people say 40%. Other people are much more.

Cautious, maybe 20 percent or even 10%, but actually it’s much smaller than that. Even it was something like less than 5 percent of the people could guess what was being tapped. So there’s huge discrepancy between what people guess, what people will be able to guess and the actual number of people can guess.

Now, why is that? And it comes down to this. The person doing the tapping isn’t hearing the tapping. They’re hearing the song playing in their head. They’re hearing the music in their head. And that’s what we are like in our conversations. We aren’t hearing the words we say. We’re hearing the music in our head.

 So there’s like, actually two completely different conversations happening. There’s what we’re hearing in our head, and the person we’re talking to, and all they hear is the tapping. And that’s the [00:09:00] that’s that gap explains a lot of our conversational differences and what the four Rs do in the recording format by having one column where you write down your thoughts and feelings and one column where you write down the actual dialogue.

Well, it makes that difference clear. So that’s that idea that we can’t remember is when you have this mental model is less surprising because what remember is the music, you know, that in our head, not the tapping that we’re making. Right.

Alexis: Mm, I, love the analogy and I’m I’m eager to try the experiment. . I’m pretty sure that all listeners Start With Why. And still, I tend to agree with you that it’s not really where to start.

Why are you saying that?

Jeffrey: it’s worth perhaps saying that , in the book when we lay out conversations that we believe , that team should be having it really comes down to what we value more than why, why is still important, but we put it third. , we, what we say is before you start getting into why.

What you’re doing, [00:10:00] the first place, the place to start is to start with trust. Because this is the, what’s going to be, what is the foundation for how you are able to improve going forward? You know, if you, if you don’t have trust, then . The future conversations are not going to bear any fruit.

 That’s our place to start is that is doing things that build trust. Now that the idea of how you build trust generically I’ve only come across one way to do it, which is in some sense to be vulnerable. The idea of being vulnerable means, in this case, it might mean, you know, sharing are really comes out of sharing our thoughts and feelings and being interested in the thoughts and feelings of other people.

And this way of, of being vulnerable is a very common thread that I’ve seen through all the different types of discussions of trust. Now, this is not the kind of trust or being vulnerable that you might have with a [00:11:00] traditional leadership game of, you know, maybe doing trust falls where you’ve leaned back and someone tries to catch you.

This is something that’s a little bit more high stakes. This is sort of like being willing to, to share differences of opinion and where You’re not sure when it’s going to agree but the idea is , that there’s not another approach to really building the relationships with your colleagues, other than to be able to be honest about Your thoughts and feelings.

I could say more about this. It’s not that you you need to be blunt. I can tell people when when they’re doing these kind of exercises, you might have the inner thought that says, That’s the stupidest idea I ever heard. And building trust doesn’t mean therefore saying, Wow, that was the stupidest idea ever heard.

If you if you say something, like, Well, I look, I have some concerns, great, you have full marks. But if you just say, Oh, Okay, then clearly you’re withholding relevant information. And so, that’s the low [00:12:00] trust building approach. And very often people are nervous about how people react.

So they’re nervous about sharing their differences and getting that point where you can say, I have some concerns. And we talk a bit more about how to do that in that chapter. So that’s why we don’t start with why is because we think building trust is essential. And the next thing we get into.

So our second. Conversation we think is important is having the fear conversation. That is, is a sense an extension of the idea , of building trust, because if we’re not sharing our fears, then we’re not really being vulnerable, we’re not sharing important elements to it, but fear is such a large element and actually can be a way to.

For people to better understand us when they understand a lot of our motivations are coming about from what things that we’re concerned about and what we’re concerned about is not going to be the same as other people. That idea that our, our inner fears and thoughts are not going to be different from other people’s actually a really important idea.

 One of the big [00:13:00] barriers to good conversations is something called naive realism. Which is a cognitive bias that works like this. It says, I see the world as it is. And so do you we’re both, and we’re both looking at the same objective reality. And therefore we have the same information.

And and of course I’m, what I’m seeing is correct. You know, the right thing. is obvious by just examining reality. So if I can see this thing, you can see it also. And so if you don’t agree with me, well, then you must be confused. In which case, my job is to explain to you how you’re confused or that you’re doing something sort of disingenuous or malicious.

You can see the truth. You’re just choosing not to share it. And in that world, we don’t need to talk about our fears because they’re obvious. And so if we, if we leave that, that mistaken world of naive realism and realism that we’re seeing different worlds, then the need to actually explain our fears become more apparent.

And we, and we start [00:14:00] with fears because people are, tend to have a negativity bias. People tend to have loss aversion, negative emotions and negative feelings have a stronger influence on us than positive ones. And therefore. We think addressing those hidden concerns, surfacing them is important first step before you start getting to the more positive side, which is why and you know what what we hope to address what we have to achieve going forward.

So. why is still very important we just don’t start there.

Alexis: Yeah. And I, and I love it. And I, I really love the way you are framing it about withholding information. I feel it’s very powerful because If you ask me to be vulnerable, I, there’s some discomfort with that. And now if you’re telling me, Oh, but if you want to make progress in that relationship, if you want to make progress in that conversation, you cannot keep [00:15:00] information to yourself.

You have to open up a little bit to help the other person. See what you think, see what you, Or see what you fear. I, believe it’s very, very powerful to frame it this way.

Jeffrey: yes. I’m glad that resonates with you. And one thing I’ll say is it resonates resonates with a lot of people. One of the dangers that our book is actually trying to address is that when you read these types of ideas, they all make sense. The challenge is that they make so much sense to people that people think that they not only do they agree with it, but they believe they actually are already doing it. And if we go back to the four Rs, the purpose of the four Rs, the purpose of writing things down is to allow us to observe the fact that we don’t. Behave the way that we espouse the way that we value we think that because we believe this to be true We believe it to be the best way to communicate that therefore that must be how we [00:16:00] communicate But when we look at the evidence unless we’ve done some very deliberate practice What we’ll find is that’s not the way we we actually behave by default.

We we are More strategic we fall into what? Chris Argerys is called, you know, model one or the unilateral control model. And we ended up withholding this information, not by deliberate strategy, but because it’s in a sense of the way we’ve been trained and the way that feels natural.

Alexis: Yeah, and I love the idea of identifying the triggers. Because they are derailing completely the conversation. It’s very interesting to reflect on that and say, okay, okay, let’s, let’s pause for a second on that one. let’s avoid reaction the, the hundred percent hundred percent reaction mode and

Jeffrey: Yeah,

Alexis: spend a little bit more time on that

Jeffrey: that’s right. That’s the triggers that we talk about triggers, tells and twitches, which are three types of patterns we can learn about ourselves through [00:17:00] doing multiple conversation analysis, right? So we use that four Rs for conversation analysis. If we do it multiple times, we’ll tend to spot these patterns and realizing the way we often behave.

And then the idea is that having discovered that we come up with preplanned strategies. Things that we’ve decided in advance that agree with our values rather than trying to improvise in the moment under pressure, in which case we’re likely to go fall back to those default model one strategies.

Alexis: So, you are VP of engineering at ION Analytics. 

Jeffrey: Yes.

Alexis: ,are you really able to use that in your day-to-Day? Life and how your teams maybe are using it if they are. 

Jeffrey: Would say this is in this scenario, not all of the teams are I use it all the time. I if nothing else, I, I do a workshop every month at a meetup where I have a meetup, the agile conversations meetup, and that we do a it’s open, it’s free to [00:18:00] everyone. And so once a month I will do the four Rs practice along with everyone else.

So minimally I actually do. this practice minimal once a month. Now I actually did something like this. Four or five times in the past week leading different workshops at the conference and quote unquote, summer camp between them at these conferences. So I’ve I’m you’re you’re catching me in a moment where I where I look especially virtuous in practice. So, but I will definitely use this techniques of practice, even though I’ve been doing it for 10 years. It’s still very valuable to keep practicing. Oh, 10 years. It’s much more now. Isn’t it? Time flies more more like 12 or 13 years. Yeah. But it’s not I’m not in it. The thing about these skills that we talked about here, these conversations is there are things that you can initiate.

These are practices you can do on your own without needing to get everyone’s buy in that these, these are things that you can do. These ideas of being more transparent of being more curious , [00:19:00] these are things that work whether or not other people are, have practiced them as well. And I think part of it is because goes back to what I said before, these ideas make a lot of sense to people and Generally, what we’re saying here is take those, those ideas that we would all agree are the best way to make decisions and then actually behave that way.

But, but the important part here is we’re not, we’re not trying to do something wild and crazy. And I often illustrate this in my. Group sessions when I’m doing a workshop, I’ll ask people and I’ll say something like if, if I was going to put you in charge of coming up with a process, we’re going to make a decision and I’ll choose something trivial.

Let’s say you’re going to choose what ice cream we ordered tonight for the group. How would you suggest we go about making the decision? And it’s generally some variation of, well, I would ask everyone. For their idea what they would suggest. Cool. And then I asked you. So you’d be curious. Huh.

Now, would you also share your own [00:20:00] ideas? I feel like, well, yes. Okay. So you’d be transparent because everyone agrees that we’re going to make a decision together. We want all the information. So we should be curious about what everyone else knows and what they believe. And we should be transparent about what we know and what we believe. And we all agree that that’s the best way to make a decision, but it’s not the way people act in practice. I should be, I should clarify this. It’s not the way people act in practice. If they think something important is at stake, if they, if they think this, the question is trivial and they’re, they’re not concerned about it.

Or, and this is why, for example, people make great facilitators for other people’s problems because they don’t care. And when they don’t care, they. Naturally act in a way that’s very productive, which is they say, well, let’s get all the information on the table. Look, I don’t really know about this dispute.

Let’s hear from both sides. Let’s bring in all the, all the facts. And once we have all the facts, then we can decide. That’s what we, we know in really deeply [00:21:00] believe this is the best way to make decisions, but. If we think there’s something important at stake, suddenly we don’t behave that way. And the reason is because we come up with our own ideas about what we think is best.

And once we have our own idea of what’s best, now, suddenly we want it to win. We’re no longer trying to make the best decision. We’re trying to have the best. Our decision be the decision that approach of how do I get my ideas to win? It just leads to a completely different set of behaviors.

And I don’t think this is a conscious choice people are making. It’s just a function of how our cognition works. It kind of goes back to that fallacy of like the naive realism, like look, the, the right answer is obvious. Therefore, since everyone has the same information, there’s no need to be curious and there’s no need to be transparent.

It’s just a question of being logical. And so I will try to reason people, I will bludgeon them with reason until my way is victorious. And, [00:22:00] and that going along with this is the idea that this is the right thing to do. I should be trying to get my idea to win because my idea is best. And and this is an important decision.

This is consequential. If something else were to happen other than my best idea, well, that would be a loss for all of us. And it’s that fear of loss that I think generates these kinds of behaviors in us. It’s not that people are ill motivated, but the, the way our cognition works gets us caught in this trap, and this is why.

It’s really valuable to have people go and practice this other way of being and practice this other approach of saying, you know, rather than trying to focus on having our idea win, how can we focus on mutual learning? How can I focus on learning what other people believe and sharing what I believe? Now at the end, maybe we won’t agree.

On the best thing to do, but at least in our disagreement, we’ll have all available information. That’s why I think this [00:23:00] works without having other people be trained in it, because if I come to you and I say, look, we have this decision to make, but I really want to understand, Alexis, what is it that you believe and can you tell me what you saw and how you got there?

You’re happy to tell me you’re not going to say like, well, look, I, I think you should, my idea should win, but I’m not going to tell you why I got there. No, no. You’re quite happy to like share your chain of reasoning because you’ll be persuasive. And if having done that, I say, well, look, let me share.

What I’ve seen and how I got there, then people are generally are happy to listen after they’ve had their chance to talk, right? So this kind of reciprocation. So when we’ve done the practice and we can behave this way, we make the whole conversation better for everyone.

Because we’re not trying to mislead people where we’re actually taking them back to what they believe would be the right way to work and and the way that they would behave. If it was something they were designing in advance. It’s only something we fall out [00:24:00] of doing in the moment, in the heat of the moment.

Alexis: Yeah, that’s very interesting. So do you believe that drive way the teams are organized on, on the way the team works because you and probably others start to behave that way, engaging people and their interactions. Excellent.

Jeffrey: What, well, I mean the, the, the conversation I’m part of, I can bring these skills to better, and I’d also would say the more people who’ve done the practice, the better I’m. At ion via a series or a short series of acquisitions when I first was learning this, I was CTO and head of product at a much smaller company called Tim group and at Tim group.

We actually did bring this these, this material in, and we did have everyone in the company practice, and it was very effective to have everyone learn these skills because you start having the kind of jargon and it really did [00:25:00] accelerate things when you could be much more transparent about what your thoughts were in the sense of the metathoughts of saying, well, let’s make sure that we’re using mutual learning here.

You know, there was keywords people would apply and not only that, but people would do practice sessions together. We would have a weekly and then fort later fortnightly practice session where people would bring their conversations for group discussion. It was very valuable to have people to talk through their differences.

It’d be really interesting when two people would bring their own conversational analysis of the same conversation. What we found was that people consistently realize that if they’d been more transparent, more curious earlier in their conversations, the things would have gone better. they often were bringing cases where, they had these long, Debates with each other before eventually settling on something.

So in one sense, they didn’t really need the skills. They would work things out eventually, but having the skills, having done the practice, what would have been maybe three [00:26:00] hours of discussion became 10 minutes. So, and people felt better. So there was definite wins for having done this as a group. It’s not that there isn’t value, but it’s not.

It’s not required to develop it. But it is an accelerant if you have more people on board doing the practice.

Alexis: Excellent. What, what is the size of the, of ion analytics and ion group in general?

Jeffrey: I, Ion group is tens of thousands of people. Ion analytics, is a a couple thousand within engineering we’re talking 300 to 250, 300 ish. So that’s a, a much larger whereas at Tim Group, when I started there was 120 people. We’re now doing this much larger organization than when I was, was at Tim.

Alexis: Yeah, that gives a sense that we are not talking about 10 people or even 100. It’s way larger than that. And you can still see the impact of having better [00:27:00] conversations in the, in the organization. Is it something that can spread to , the other part of the group, or is it only in your area?

Jeffrey: I think it spreads individual by individual. So even if I think back at to Tim group, it it started very much within engineering. It was it was people within engineering who were practicing these things and then, and then product and then people in data science and then until it’s kind of all of the sort of technology side, and then it spread over to the executive team.

Actually, and then the executive team then wanted to instill it in the rest of the company and then took it to sales. So it it, it, it did spread bit by bit through Tim group. It did so because you had people who were explicitly championing it within the context of. Analytics, it’s something that I focus more on using than teaching.

 I have done some classes, [00:28:00] but we don’t have the same at the moment, same kind of weekly study group. It’s there for people who like it. And I do think there is an element to which it spreads on the other hand, even though there’s no training , if I’m with a group of people and. We practice the idea of being transparent and curious, then they begin developing those patterns and it comes out in some of even some of the ritualized elements.

So, for example, we’re focused a lot right now on post mortems or root cause analysis. And bringing in the idea of, look we’re going to focus here on first creating a timeline. We want to head of shared facts that we’re discussing before we then go and try to make meaning from those facts. And we’re going to move stepwise and have multiple people involved to sharing their perspective.

We’re kind of embedding in the process, this mutual learning model. And so in that sense, there’s an element which it spreads through experience rather than [00:29:00] through, you know a shared mental model of what’s happening. I do think that does spread in part because if we go back to, we said before about the need to build trust when we have built trust with other people and when we have psychological safety, then we are more likely to behave in the way that we believe is correct.

We’re more likely to share, be vulnerable and share those thoughts and differences. And we’re more likely. Having built trust and respect, we’re more likely to then be curious about what other people say. So there’s an element which the practice leads people into behaving in this fashion.

Alexis: I love it. Thank you very much, Jeffrey. Maybe one last question. What would be one advice you would give to your younger self?

Jeffrey: Well,, the advice I’d give to my younger self is to start this earlier. I’m now 54. I came to this in my forties and it would have made a massive difference to me if I had started this in my [00:30:00] twenties. I could have learned a lot more, a lot faster. I could have been much more.

Persuasive I could have been much of much more help to the groups. I was dealing with if I had learned these Skills earlier. I definitely was a Kind of person who would try to persuade other people about why my position was right and so I think that since Benjamin Mitchell was correct in his analysis when he said I was well practice at advocacy, that was true.

I had a couple decades of serious practice going back to actually the high school. So maybe three decades I had been on the speech and debate team. And so I had learned a lot in practice, a lot of persuasion. I think I would have benefited from a lot more curiosity. That’s my younger self that would be.

You know, become more balanced, learn the value of listening first and to understand that listening is not a weakness. It’s a strength. I think there’s that element of if I, if I’m [00:31:00] curious, then it makes it that I’m somehow losing ground. And that’s just simply not true. I’m, I’m adding value to everyone by being curious.

And then I’ll have my opportunity to be transparent. That’s, that’s advice I could have used a couple decades before I learned it. Thank

Alexis: I love it, Jeffrey. Thank you very much for having joined me on the podcast.

Jeffrey: Thank you for having me.