In the latest edition of the Full Stack Leader, we talked to Luisa Herrmann, Head of Product at Arch Systems.

Luisa delves into the challenges faced by women in the tech industry, addressing unconscious bias and the need for diversity and inclusion. She emphasizes the importance of acknowledging and counteracting unconscious bias, especially for women and younger professionals in the field. Luisa also discusses the impact of AI and machine learning, highlighting the potential risks and the need for responsible development and regulation.

Our conversation also touches upon the evolving role of product leaders in the age of AI, emphasizing the importance of understanding and effectively leveraging emerging technologies, a skill set that every tech leader tomorrow must have. Luisa talks about the importance of openness, receiving feedback, and embracing new ideas. She stresses the importance of actively listening and being receptive to input, cautioning against overconfidence in one’s own knowledge.


Top leadership tips from Luisa Herrmann

Below is a summary of the top Leadership tips shared during this week’s interview. Listen to the episode to learn more about the thoughts behind these tips:

  1. Be open to being wrong, receiving feedback, and new ideas
  2. Give people credit for their work
  3. Find ways to communicate that make people want to listen to you
  4. Be generous with your knowledge
  5. Understand the value that you bring

We hope you enjoy the episode. You can find more Full Stack Leader episodes here.


Part 1. On the career and experience

Ryan: Hello, everyone, and welcome to this week’s episode of the full stack leader podcast. This week. I’m here with Luisa Herrmann. She is the Head of Product for Arch Systems. It’s great to have you here, Luisa.

Luisa: Thank you for having me.

Ryan: I’m looking forward to this conversation. It’s an area I’m deeply passionate about.

I’m excited to hear how we’re going to get into the topic of AI and hear how that develops within the world of tech leadership, but also being a woman in tech leadership, especially around this area. So, I’m expecting some great conversations today. And let’s start with maybe you telling us a little bit about how you got to where you’re at.


Unconventional paths on a tech journey

Luisa: Sure. So, I actually have a background in chemical engineering. My college degree is in chemical engineering, and I expected to just be a chemical engineer, but it turns out from my last few years of college that what I really was passionate about was process optimization. And so when I graduated, and I started working in the chemical manufacturing industry, there just wasn’t enough process optimization happening there all the time.

So, I started looking for something that was more dynamic and more changing. So I could look at processes and figure out how things work and how to make them work better. By complete fluke, I ended up working in software because I worked for a company that did software for industrial manufacturing. From there, I started working in marketing and then moved into product marketing. I discovered product management and found that I thought product management was my calling in life. And so I’ve been doing that since then as an individual contributor and as a leader for about eight, nine years now.


Not something you learn at school

Ryan: Yeah, that’s amazing. It’s interesting how those paths go once you begin to explore ideas outside of what you have in school,

Luisa: Right, there’s no degree for product management.

Ryan: Yeah, I agree. And it’s funny because I’ve been doing that particular field for probably 25 years at this point. And it really was the Wild West in the early days. And even still, it feels like it’s not fully developed into something that is easily trainable at the university level.

Luisa: It’s not. Actually, one of the things that I find is that different organizations and different industries define it in different ways.

And so there’s still a lot that is unknown – as in “undefined” – but I find that’s part of the fun.

Ryan: Absolutely. What was the first job where you made that leap into product management? Where was that at?

Luisa: So I had a job at this company called Enernoc back in 2015 where I had a product marketing title, but I started dabbling in product management because the company was at a size where I could do that. There were departments full of people, and so you could go in and out. And then, after that, my next job was at Zoominfo. And that was a full product manager role. And that was back in December of 2016.


Product marketing VS product management

Ryan: And just for everybody who may be listening for the first time, this, maybe you can give us a little breakdown of the difference between product marketing and product management and the way those two things work.

Luisa: Sure. Actually, it took me a while to learn that. And that goes also with what we were saying that it’s still relatively undefined, and there’s a lot of gray area in there.

But, mostly, the job of product management, as I see it, is to understand the market, understand the user, and then understand the technology stack that the organization has and the engineers and the talent, and prioritize the work that we need to do to solve the problems that the industry has, they’re willing to pay for the solution and do that in a way that is sustainable from a development standpoint. Then, once that’s done and prioritized and communicated throughout the organization, it is the job of product marketing to take that information, to understand how that solution works, who the users are, and how they’re going to use it, and come up with; I don’t want to say it is the messaging, but it is like the value proposition.

What are the important things about this? Like, how are we selling it? How are we going out to market and explaining to people what it does? Honestly, if we just have ” this is a cool product. It does ABC” without the messaging, “It solves all of these problems because we understand your industry. We understand your problems. We understand the value proposition that you need,” It’s a lot harder for people to understand what the product is. So, usually, the difference is whether the output is prioritization and communication or where it’s actually messaging and understanding.

Ryan: Yeah, I used to jokingly call product marketing, which I was one for a long time as well, the “go-to-marketer” – it is like a go-to-market type of role and connecting those dots, although often you’re in the market also doing it, I like the way you’re splitting those two things up. It makes a lot of sense.

Luisa: Yeah, the primary role of product marketing is figuring out how to take to market from the hands of development and product management.


Communications across teams

Ryan: And it’s really working with different teams. So, although you may have some crossover in different areas, the underlying language or syntax you have to use as a product manager and the types of people you’re talking to might be different than product marketing.

Luisa: Actually, that’s what attracted me originally to product marketing and then even more so to product management is that it does touch all areas of the organization, and it requires the ability to translate from these different areas as well. So you have to have really technical conversations with the engineering team.

I just had a conversation about our database infrastructure. And then, at the same time, I have to turn around and explain to our salespeople, “Hey, this is a cool thing that we make, and this is how you go in and you explain it to people.”

Ryan: Yeah, exactly. So when you were at Zoominfo, did you feel like you had to learn that on the job, or did you have good mentors there that guided you into it?


Learning from others

Luisa: No, that’s actually a really funny story of how that happened. When I left EnerNOC, I told myself that I really wanted to get a real product management role, but the hybrids that I’ve been doing between product marketing and product management just weren’t really working anymore.

So, I chose a role where I was hoping to work with other people and have this department around me where I could learn from people who have been doing it longer. When I accepted the job, there were four other people in my department. I joined in December 2016. By February 2017, every single other person in the department had left.

Ryan: Wow.

Luisa: That plan did not work. I thought I was doing “on-the-job” learning from other people. It turned out to be on-the-job learning, basically, trial by fire.

Ryan: Well, maybe you took pieces of it, and you could add it together into a single kind of approach. But yeah, things are ever-evolving in this space for sure.

Luisa: It’s very dynamic.

Ryan: Very dynamic. So, after Zoominfo, you made a shift, and you went to, I believe, Validity Inc., correct?

Luisa: Yes. So, basically, what happened at ZoomInfo after that is I became not only a product manager but a leader because I’ve rebuilt the team there essentially from scratch.

It was really trial by fire. But then I also got the opportunity to work in the data space, which is where I am now and probably will be forever more because I really love the data space. So then, when I left Zoominfo, I wanted to work with the concept of data. There’s a lot of data, and nobody knows what to do with it.

So, how can I help solve that problem? Going back to how can I optimize the processes around data? And so after Zoominfo, I joined Validity, which was a company that was doing data validity for CRM databases.

Ryan: Yeah, that’s even then a pretty important subject area, but now it’s almost imperative that you have experience in this, and I think the deeper you are in it, the more opportunity there is for leadership because you’re right. There’s just a lot of people who are trying to figure out what is the next step with the massive amounts of data that exist out there.

Luisa: Yeah. Again, I think it’s pretty clear from the way I explained my experience at ZoomInfo that it wasn’t very much an accident that I ended up in this data space. But it worked out really well because I really enjoyed it, but it was not intentional from the beginning.


Happy accidents and where they lead

Ryan: So, let’s talk about that happy accident for a second. On one level, you had to come into a data space, which you’re just learning about, getting to know -and you love it now, but then it was a new thing. On another level, you’re also being asked to build a team, which you weren’t expecting to have to do. Within all that, you’re really presented with the true challenge as a leader, which was to deal with these very unexpected, large things. And we’re going to test and see how you deal with them. What do you think in your perspective was what helped you and what’s important for a leader on that front?

Luisa: I think what really helped me was being able to really be adaptable and then prioritize. The team, which was not a small team, went from having four product people to one, and one was inexperienced, not only in product management but also in the data industry, but also in their product. I definitely relied on having grace from my coworkers and having as much support as I possibly could. And then it was very important to understand the priorities of the business and what we were tracking to deliver and be able to communicate that really well. I think it’s very humbling to be able to look at all of that and say, “I need help. I need priorities. I can’t do this all.”

But it also presented me with a really great opportunity as a leader. As I was hiring people, I would understand their area, hire them, train them in that area, and then move on and do that for a different area. So it just ended up that I understood all areas of the business really well to be able to guide the product managers that were hired under me and help them succeed, which is something that I still like to do to this day.

When I join organizations, I like to work in different areas before I assign them to people so that I know exactly what I’m asking other people to do. But really, the challenge was how do you learn to ask for help and how do you learn to prioritize so you don’t drive yourself crazy?


Finding the “sweet spot” as a leader

Ryan: Yeah, that’s a really good topic. Because asking for help can sometimes feel vulnerable and uncomfortable, but there’s also, like, this spot in what you’re talking about, which is the sweet spot of not taking over that job but training yourself to be able to give Someone insight on how to do the job at the most optimal level, where do you think that sweet spot is as a leader?

When do you have to get out?

Luisa: I think that sweet spot is – and what I tell my team is – that I need to know what they are doing to the level where if someone were to ask me in a meeting, “What is the status of this? How is this going? What are the goals?” that I could answer it, and I do not need anything more than that. I think that when it gets to the point where it’s transitioning off of my hands and onto the hands of someone else, I actually like to do it as quickly as possible. And then, have them ask for help when they need it and tell them that they need to be able to ask for help. They need to be able to realize when they don’t understand something and when they need that help. Because of that, it really helps with relationship building, and it also helps with this transition. I don’t even know sometimes what’s going to be challenging to transfer. I think there are a lot of lessons there about not being protective of things or of knowledge and sharing that openly and quickly with people so they can take over.

And then, if you hire the right people, they will tell you when the transfer is done.


Keeping the dialogue open

Ryan: Yeah, that’s amazing. Are there any specific ways in which you like to keep that dialogue open with someone? Do you like one-on-ones? Do you like to be in it, like actually solving problems? What’s your favorite way for that?

Luisa: My favorite way is I have one-on-ones with my team where I tell them it is their time. They can talk about whatever they want, and they can ask for help. And what I really do is if they’re facing a challenge, I like brainstorming with them about how to solve that challenge. And then, if we come up with something, it is theirs to go and present to the world. Because I know that’s one of the things that we’re going to talk about, the five tips. But we’re starting to encroach on that already because that’s one of the things – I like helping my team solve problems so that they look good, so that they get the credit for the problem, so that they are the ones who get to answer the questions about the problem about the product, even if we come up with the ideas.

So, my communication with my team is I try to make it as open and honest as possible. I want them to feel like they can come to me if they don’t understand something. If they’re having a problem, you don’t have to come up with a solution first. Let’s talk about it. If something’s going wrong, let’s talk about it as quickly as possible.

So it’s built on a lot of trust and communication that we’re all “rowing the boat in the same direction,” as I like to call it, and trying to do the right things. And we’re going at it with good faith, and we can communicate openly about that.


The challenge of being a woman in tech

Ryan: Yeah, those are all really important things.

And the industry’s, like, really evolved over the course of years. I think to have those kinds of conversations, especially on the engineering side, though it sometimes is, I think, dealt with a little bit differently. Do you think being a woman in the industry affects how you’re viewed as a leader or how you end up working with other teams or even the people who are reporting to you?

Luisa: I do think it affects all of those things. And in the organizations that I’m in, I usually try to get some unconscious bias training. And because I think a lot of it is unconscious – I know this is audio only, but not only am I a woman, but I actually look relatively young for the industry that I’m in, especially now that I’m working – again – in manufacturing.

So, the company that I worked for does artificial intelligence and machine learning data solutions for the industrial manufacturing space. So here I am, right back where I started. And that is a space that is not traditionally welcoming to women. And even for men, it’s pretty hard on men as they are younger because you are, it’s felt like you have to earn your place. It’s more hierarchical than what I’m used to. So, it is something that I usually have to overcome, whether it’s conscious or unconscious. It’s when I come into a new environment, to a new organization, to new people in a leadership team. There’s usually a period where I feel like I am just proving myself, where I have to bring up my previous experience, where I have to bring up that I’ve done all these things before that I wasn’t hired to for the first rodeo that I was hired because I have done this successfully in the past.

And I feel like more than men, I have to bring that up over and over again. “I have done this before. I have done this before.” So that’s the difference that I feel.

Ryan: So the unconscious piece is almost like having to justify your resume over and over again in any given situation? Am I understanding that?

Luisa: Yeah, the sense is that when a man, especially an older man, walks into a role, people look at him, and they just assume that they know that he knows what he’s doing because he was hired and he’s here – whereas with me, I feel like sometimes people look at me and go, why is she here?

And I have to say it’s not, it’s in the back of their heads, but I can, I have to bring up ” ‘ve done this before. I have years of experience doing this. I have done this successfully in the past. Here are examples of times when I have dealt with this before. This is why you should listen to what I’m telling you now.”


Ways to tackle the unconscious bias

Ryan: Yeah, that’s really interesting. What are some techniques that you have to consciously deal with this unconscious aspect of the way the industry is working?

Luisa: Sometimes, the best way to deal with unconscious bias is to bring it to consciousness. I don’t advocate doing this a lot because it is uncomfortable – and, usually, in a work setting or a business setting, you’re not aiming to make people uncomfortable. You’re aiming to do the exact opposite, whether it’s a customer prospect or a coworker. But the way I deal with it is every time I start working with someone new. I gently nudge on the topic and say not directly, but “Here, I have a lot of experience doing this.” And sometimes humor helps. Like I know I look like I’m 15, but I am not. I have a lot of experience. I have done this before, and we can talk about ways that I’ve solved this problem in the past, but basically, we need to set the expectation that I will have the answers to the questions- and that’s why I was hired. And so I’m getting better at it. It’s hard.


Reasons behind age and gender bias

Ryan: What do you think is the reason for this combination of age bias and gender bias that might be playing into people’s thoughts around whether somebody knows something or not?

Luisa: I think there’s still an image of what a leader looks like, especially in some industries, that was built over decades of looking at leadership. And this is an AI thing, too, which is like there’s a now famous experiment where Amazon uses a machine learning algorithm to figure out which resumes to look at and which people to hire. And they fed resumes of successful employees at Amazon, which just so happened to all mostly be men, white men who went to good schools, which was just self-enforcing, right? Which is if those are the only people that we hire, they’re the only ones who get a chance to be the best employees. And then, therefore, they’re the only people that we’re going to continue hiring. And so I think it’s something similar to that, where we have this vision of leadership, which is mostly men who have done this for many years, and therefore they are older, at least, and I don’t fit that mold and in any way shape or form and so I think it’s just this And again, I feel like most of it. It’s unconscious, and I actually have to do some work Myself to be aware of my own unconscious biases. But I think there is this expectation of how much does she know? Like, how could she understand this industry, and how much can she know being a woman and being so young?


Can AI be biased?

Ryan: Yeah, that makes sense. I think it’s interesting too that you’re a specialist within the world of AI, and one of the really core topics around AI at the moment, and it has been for the last couple of years, is the prejudicial aspects of it in terms of the training data, in terms of the way that it is a mirror for a lot of the flaws that we have how do you actually develop a product that lowers the prejudice?

Luisa: That’s actually a really good question. One of the main things that I talk about with AI is that my fear of AI is not that of the Terminator scenario. I don’t think the robots are going to take over the world. I don’t think that the people who are quitting the AI field now, who are specialists, who are saying they don’t, no longer want to work here because they’re scared – they don’t mean that the robots are going to kill us.

They mean that these algorithms are being created, trained, and used in a way that has very little oversight. It is very much a black box. Our legislators do not understand technology, let alone AI or machine learning. And yet, we are giving these algorithms the freedom to make a lot of decisions that we really should have a better handle on.

And especially as it comes to the example that I gave about hiring, there are other examples about making decisions about lending, making decisions about accepting to schools. First, there needs to be an acknowledgment of that risk. There needs to be a drive to reduce That bias and that risk.

There needs to be legislation to reduce that. But all of that is a lot. And I’ve read a lot of books that have many suggestions for how we can do that. It is a multifaceted, nine-headed problem. But from the vantage point that I have, as you mentioned, of being a specialist in the field, all I can do is try to get the awareness out there, try to teach people about it, and then make sure that the products that I work on have that awareness.


Can robots be free of prejudice?

Ryan: That makes sense. Yeah, these things are very real. I think some of the examples you brought up are really great. I’ve been thinking about it, though. What do you think about the possibility that AI becomes the actual arbiter or solution for prejudice as a concept in general? Because prejudice is human, I feel like it’s a pretty distinctly human trait. I don’t think robots carry that level of prejudice to start, maybe much like children don’t, but how do you think the robot itself could begin to see a path toward improving it?

Luisa: So you are right. But you’re also wrong. The robot’s prejudices, except for the fact that they do because the people who make the robots have prejudices, and whether they know it or not, they are adding those prejudices to the robots.

Ryan: So, the unconscious predators, right?

Luisa: Every decision that you make is a decision, right? Every choice that you make when building something is a choice. And those choices are made by the people who are building it, and they are based on those people’s biases, right? You cannot escape that.

But could you teach a robot to assess the concept of prejudice and see if it could learn how to get better at becoming less prejudiced? It’s like, maybe, something that a human would have a hard time doing.

Yes, it is possible. It takes intention, obviously, but I think more importantly than that, I like to compare the moment that we’re in with AI to the moment that we were in with the early Internet and, like, the mid to late 90s when the Internet was the Wild West, and it had all of this potential to transform the world, which it did.

But we didn’t quite know how exactly it was going to transform the world. The Internet could have been the great equalizer, the tool that lets everyone have access to the same information and the same opportunities and bring this peace and equality to a level that we hadn’t been able to do before because there were issues of accessibility to information that no longer exists, right?

It could have been a boon to make society a more just and equal place, but it wasn’t. I think we’re at that stage with AI as well, which is where we could do all of these wonderful, beautiful things with artificial intelligence. Absolutely, we could. That takes intention, that takes legislation, that takes the people who have control over these robots, over these models, over this intelligence, to want to use it for good.

Whereas the experience that we have with the Internet is when that opportunity presented itself in the past, they chose not to do that, and instead to figure out how they can use the Internet to make money, to expand capitalism, to generate opportunities for a few, and not for the many. I think the issue that we have is that all of that is possible.

It’s possible to use it. For a lot of good. But is there interest?


Solutions in the works

Ryan: Yeah, that’s a very interesting question, and it’s interesting. I absolutely agree on the regulation side, and I am glad that the level of conversation is happening around it that it needs to be happening right now. Maybe, really, that needed to start five or six years ago, but people probably couldn’t understand it then. But I also look at the level of conversation going on amongst the creators, too. So, I look at environments like Hugging Face, which is a marketplace of models, basically. Are the solutions to these problems actually presenting themselves within those environments?

And I haven’t directly seen it, but I guess maybe I haven’t looked closely. Do you think the creators are also consciously thinking about this?

Luisa: I think some are. I think there is a drive there. The solutions are there. There are smart people who are thinking about it that way. It’s just a matter of the people who have the power to make that change. What will they decide to do?

Which, again, is like the early Internet, like there were people who were developing things for good. But honestly, I’m more hopeful than I sound right now. I think there’s a lot of hope for things to be done the right way. But one of the questions that I get a lot is about these people who quit the AI field, especially the ones who are working for corporations. Why are they doing that? Are they scared of the robots? No, I don’t think they’re scared of the robots. I think they’re scared of exactly this: the decisions that I made at a corporate level with the amount of information that they have, the models that they have, the algorithms that they have, and the decisions that they’re making with it.

That’s what they disagree with. It’s not the robots that they’re scared of. It’s the corporations and the way that they’re using that massive amounts of data.


Nuance is for people, not robots

Ryan: That makes sense. It’s interesting because the whole time we’ve been talking about this, I’m also echoing back to your conversation around gender unconsciousness as well.

And I was thinking right back to the way that you were like, “I deal with. this gender unconscious bias in kind of this nuanced way that plays with kind of repetition and humor and like different pieces like that.” But we don’t really have that same kind of freedom and, I guess, that same kind of latitude for generational learning here.

It feels like it’s got to be faster and more aggressive. Am I wrong on that?

Luisa: No, you are not wrong. There’s no room for nuance there. Robots are not good with nuance. People are, so it’s a different approach there.

Ryan: But do you feel like you have to get the people that are actually generating the models and thinking about them or even the corporations, which is a much more complex situation, obviously? Do you have to get them conscious?

My question to you is: if you had a thoughtful approach to creating a consciousness that actually cares about the way humans interact via robots, how do you think we could get together and do that?

Luisa: I think there are people out there who are willing and able and trying to do that. That’s why I said I’m more hopeful than I sound. I think that, ultimately, people don’t want to do things incorrectly for the sake of doing them incorrectly. If there’s a way to do it better, I’m sure that people will want to do it better.

I think, much like the conversation about the unconscious bias around gender, the first step is acknowledging that it’s there. And so with AI, the first step is acknowledging that the dangers are there and what they are specifically. And then how to deal with them is you get a bunch of smart people who understand what that means and how to counter that and come up with real solutions on things that can be implemented.

And then I think, honestly, if there’s a drive to do that, which I think there is, the implementation of that would make it so that all of these algorithms that are making decisions would make it much more equitable than we did when we had humans without unconscious biases making those decisions.


Hiring a good product manager

Ryan: For you as a Head of Product – and talking to other Heads of Product out there are people who may become heads of product in the future- what in your hiring practices can you think about that would find those people that you’re talking about?

Luisa: it’s tricky to hire a product manager.

And that goes back to what we were talking about. That’s not something that’s taught in school. So product managers are, by their nature, people who want to understand problems. And figure out how to solve them the best way that they can. And so I cannot think of better people to deal with this than people who are trying to understand problems and figure out how to solve them the best way possible.

The way I like to hire product managers is people who like solving problems. Even I love puzzles of all sorts. I do jigsaw puzzles. I do crossword puzzles. I play chess. Everything I look at is a puzzle, and I need to figure out how to solve a problem. And so I look for people who are trying to find the most important problems and how they can help solve them. That’s what I try to find in people.

Ryan: That’s amazing. And in your current position at Arch, as you’re working with these industrial changeovers and working with kind of new technology within the space, Do you think this is an example of changes that are going to be happening across multiple industries or lots of different industries that are out there?


Processing data like never before

Luisa: I’m actually really hopeful about what Arch is doing in the field of industrial manufacturing. There is a wealth of data in this field that has historically gone unused because there was no real way to use it because there is so much of it. And what Arch is doing is really trying to get all the arms around that data to try to understand it and have analytics and insights that can make manufacturing processes better.

And so there’s definitely what I like about that is that it’s trying to make things more efficient, and more efficient things are better for the environment. They’re better for people, they’re better for the bottom line, they’re better for everybody, which is a great application of being able to use that data.

Ryan: Do you think that focusing on efficiency, though, perhaps also comes with bias and might impact people on a job front or on their value in the industry front?

Luisa: That’s actually a good question. The industry that we focus on is the electronics industry, which is historically people-light and machinery-heavy.

But it’s definitely a risk. Right now, the only change that making the process more efficient would do is just having fewer machines. And having them operate more efficiently would mean just having people working more during their shifts than not. But there is definitely a risk of making things, making different processes more efficient, which would affect people. And then I want to believe that while it’s doing that, it’s also creating opportunities on the other end for people to work on the data side and be able to help out on making things more efficient instead.


AI-powered evolution of the workforce

Ryan: Yeah, that makes sense. I think this is the conversation you hear a lot going on right now, which is the people who were really positive about the impact of AI saying, “Yeah, it’s going to evolve into new opportunities,” which is, technically, what we’ve seen over the course of history with technology.

But there’s also the possibility that maybe this time it doesn’t work. Where do you stand on that? And what do you think about the evolution of the workforce as AI becomes more and more ubiquitous?

Luisa: I’m going to go back to the early Internet, and it was theorized that having access to all of that information would make some knowledge-gathering positions obsolete.

One fun example is paralegals, and I can tell you paralegals are not extinct. And so it’s just, it removes, I think what AI is going to do is going to remove some of the repetitiveness and the predictability of a lot of roles where it’s, you can have a computer figure things out. People are already doing this with chat GPT. What it’s doing is creating a role for people to understand the technology and then teach it back to other people, which is what happened with the Internet. This is easy for me to say because I’ve been in this industry for a while now: the best thing is to really understand what the changes are that are happening and figure out how to fit into this new paradigm. And I think there’s room for more people in this paradigm as long as not everyone was as crazy as I was to think that this was going to be a thing like all that all those years ago, but I think there’s still an opportunity to jump in and figure out how to go into the data field, the AI field and do something different.


On the future of product leadership

Ryan: Yeah, that’s great. Okay. So, last question for you in this segment. Given this kind of shifting paradigm, what do you think is important for product leaders to keep in mind on a go-forward basis over the next 3 to 5 years?

Luisa: I think it’s important for product leaders to understand: now, as I mentioned with chat, GPT, It’s become a lot more mainstream to talk about AI and how that’s working. So I think for product leaders, understanding tools like that and how they can help with product teams and just generating content, understanding things, explaining things. – That’s what I use chat GPT for the most. To try to figure out ways to explain different things to different people. I think it’s really important for the product function to understand how those technologies work and how to make the best use of them.

Ryan: Great. Thank you so much for the rundown, the time, and the perspective on the path you’ve been on. It’s really amazing and obviously very relevant in today’s world. So I’m really excited that you could share that with us.

Luisa: Yeah. This has been really great. Thank you so much for having me. This is fun.

Ryan: Yeah, absolutely.


Part 2. Top leadership tips

Ryan: All right. Welcome back, everybody. Again, we’re here with Luisa Herrmann, and we’re excited to go through her top five tips. So, Luisa, let’s jump into leadership tip number one.


Tip 1: Be open to being wrong, receiving feedback, and new ideas

Luisa: So these are in no particular order. So, number one is to be open to being wrong, be open to receiving feedback, be open to new ideas, and be open to hearing from people.

And when I say “be open,” really be open, open your ears, open your mind, open your heart, and really listen to what people are saying. It’s the moment you start thinking that you know better and that you don’t need to listen to people or receive feedback or be wrong as the beginning of the end for anyone in the product.

Ryan: Yeah, that makes sense. Do you feel like being open has limitations to it that you have to keep in mind? Or is it just, in general, taking as much as you can and then making a decision?

Luisa: There are limits. I would say that the limits are not in how much you listen. It’s in how much you take into consideration.

I think it’s always important to listen, but it’s okay to listen to someone and then, in the back of your mind, say,” I’m going to file that into the ice box, and we don’t have to think about it.”


Tip 2: Give people credit for their work

Ryan: I love that. Yeah. Okay. How about tip number two?

Luisa: Tip number two – and I touched on that a little bit when we were talking about having a team give credit – give people credit for their work, give people credit for their ideas.

I like giving people credit as much as possible. I say to my team that their wins are their own, and the losses are mine. I like when I can give credit to someone else for coming up with a really good idea, a really good solution. And I like to do it publicly and often because it’s really nice for everyone to know who’s doing the work.

It’s really nice for people to feel good about the work that they’re doing. And that’s not limited to product teams. That’s Everyone that you work with when you’re a leader, give credit.

Ryan: Yeah, I think that’s a great tip, and I’d love to hear more leaders share that as well. Do you think there are times when too much credit is given to someone?

Luisa: I don’t. I think if someone puts in effort, they deserve the credit. I don’t vouch for giving people credit that is unearned. I actually have a great example of this. We were trying to solve a problem at an organization, and I legitimately could not come up with a solution.

And then someone on my team messaged me on the side and said, “Hey. I had this idea. I don’t know if it’s a good idea, but would this be a solution?” And it was a great idea, and it was absolutely a solution. And I had already said that I didn’t have a solution. And so I went back to the team and said this person on my team -named them- so they came up with this really great idea that I didn’t even consider. And so, let’s move forward with this.


Tip 3: Find ways to communicate that make people want to listen to you

Ryan: Yeah, that’s great. That makes a lot of sense. Okay. How about tip number three? What do you get for us?

Luisa: Tip number three: find ways to communicate that make people want to listen to you. And that one is, that one took me a while, but you need to communicate things. It’s part of the role, whether you’re a leader in a product or another field. If you’re just trying to communicate because you have to, it’s likely that your message is going to get lost.

And so. Try to find a way to communicate with people that will make them want to listen. Whether that is making short presentations – whether that is highlighting things or humor – we’ve talked about that – make sure that people want to listen to what you’re saying.

Ryan: How do you get insight on what that might be before you get into that actual communication or presentation?

Luisa: The easiest thing possible is to picture yourself as someone who is receiving that information, someone who is present at the meeting, someone who is being talked to or presented to. And is that a presentation that you would want to be in? Is that something that, is that information that you would want to receive? Pretty basic.

Ryan: It is a simple concept, but it’s funny how many people don’t take a moment to actually imagine that. I think it’s, I think it’s a product manager skill set in general, which is like imagining the end usage or the solution that’s being done. But there are many people who maybe don’t think that way.

Luisa: Yeah, it’s just, I didn’t think that way originally; it was a change.


Tip 4: Be generous with your knowledge

Ryan: Awesome. How about tip number four? What do you get?

Luisa: Tip number four: be generous with your knowledge. A lot of a product is understood differently. We talked about translating different things between different departments and between different people.

Sometimes, there is a drive to hoard that knowledge because that knowledge is valuable. And the product has all of it. So why would we want to give it away? Don’t do that. And that’s because every experience that I’ve had where I have shared knowledge has been a net positive because then people feel like they can share and should share knowledge with you as well.

And it turns out that when you share knowledge, you don’t lose it. Someone else gains it. So it’s a net positive. So when you’re thinking you should maybe not share something, share. Be generous.

Ryan: Unless, of course, you’re under an NDA, which is a good time not to do that, but yes.

Luisa: It’s within your organization, maybe not with your customers.

Ryan: Yeah. It’s interesting. Cause I’ve worked at really forward startup organizations that are central to the entire existence of it. And then I worked for some pretty big corporations that don’t like you to do it at all.

And it’s, I think, the sharing aspect of things creates innovation too. So, especially for innovators, it’s a really important piece.

Luisa: Yeah. And you mentioned larger companies. There is like this drive to keep things neat and tidy and have lines and boxes.

And I get that. But there’s also the unofficial knowledge transfer, which is really the one that gets creativity and innovation going. It doesn’t have to be an official thing.


Tip 5: Understand the value that you bring

Ryan: That makes sense. All right. Last tip number five. What do you get?

Luisa: Understand the value that you bring. And that goes back again to product management is not understood. It is not clearly defined most of the time. So, the way you define the value that you bring as a product leader is defined by you alone. And you need to understand that first and foremost. The reason why you need to understand that is that understanding the value that you bring helps you prioritize what you do because then you understand the activities that will bring most of that value.

Also, it helps you advocate for yourself and for your team. So if you know the value that you’re bringing to the organization and you’re prioritizing the right things to bring that value, Then you can advocate about how your team is doing a great job and even you yourself are doing a great job. And that’s super important.

Ryan: I love this one. This is really good. It’s interesting because since starting this podcast a while back, I probably wouldn’t have thought about that concept, but in recording so many technical leaders and hearing their thoughts, I’ve seen each recording draw out a person’s need to actually assess, “Okay, what is my value as a leader?”

And like, “How do I do this?” And it’s been, I think it’s been really a very great experience for everybody that’s done it. I really concur on this one. I’ve seen it really provide a lot of insight into leadership to the people who are actually holding those keys.

Luisa: Yeah, I said at the beginning, “in no particular order,” but I did say the best for last,

Ryan: It was really good. Yeah. And it’s hard because sometimes we’re focused on customers. We’re focused on employees. We’re focused on, but sometimes it is, “Okay, what am I bringing to the table here? What matters? “And cause that is going to be a guiding light for a lot of people. Great tip.

All right. Luisa, thank you. It was amazing talking to you. So many good aspects of the conversation. I’m glad we got a chance to chat, and I look forward to sharing this with everybody.

Luisa: Great. It was really fun. Thank you so much for having me. Ryan.