Confident, strategic AI leadership

0:03Welcome to the Practical AI podcast, where we break down the real-world applications of artificial

0:23We'll be right back.

0:48Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm joined as always by my co-host, Chris Benson, who is a principal AI research engineer at Lockheed Martin. How are you doing, Chris? I'm doing very well today, Daniel. How's it going? Yeah, no complaints. It's been an interesting week of AI things with, I guess, OpenAI being open again. I'm sure we'll talk about that on this show at some point. But maybe there's a connection there to responsible practices. But I'm really excited today because I've seen a lot of things popping up on our friends channel, Demetrios over at ML Ops Community from Allegra Guinan, who is co-founder and CTO at Lumiera.

1:46Welcome, Allegra. Great to have you here. Thank you so much. Great to be here. Yeah, yeah. Well, I'm kind of alluded to responsible AI things, which is certainly something I'm sure we'll get in. But it may be useful just to hear a little bit of your background and kind of how you got into what you're doing now, which I understand is, you know, advising business leaders around AI principles and responsible AI practices and strategies and that sort of thing. correct me if I'm wrong but yeah I would love to hear a little bit of that background and kind of how you arrived to to doing what you're doing right now.

2:26Yeah for sure I have sort of an unconventional background and path towards CTO and co-founder it's definitely not something I ever thought I would land at but happy I did. I got into the the tech scene over 10 years ago now I was living in San Francisco I'm actually based in Lisbon now similar cities in some ways but I I was in the heart of the tech scene sort of by default. I had actually studied in the arts and it was a different path than I expected. And I started working at a startup that was doing 3D visualization that was quite ahead of its time. And I was working on the data platform team there.

3:06So I worked a lot with backend engineers and figuring out what our data architecture would look like essentially for this 3D visualization e-commerce that was working in the interior design space. So that was obviously very new to me as my first tech job. And I had started in more of an ops role and then moved quickly into product management. So I was building out internal tools, which is why I was working so closely with those teams and focusing then a lot on search optimization, figuring out which words would yield which results, you know, couch, sofa, you get the same sort of thing in the end.

3:40Seems very basic now, especially, but back then it was a lot of work. And I got really excited working with data teams and it kicked off this sort of interest and passion in a way for technical projects. And then I moved over into the fintech space. I was working at Chime, which maybe you've seen them in the news recently. I was also working on the data team there and really saw it scale. So I was a part of the technical program management team. So I was managing technical portfolios with a lot of different programs, usually longer scale and multiple year data programs and figuring out how to get real time data to those who needed it within the organization.

4:20And that often meant ML engineers. So that was sort of my first touch point in the ML space, working a lot on fraud and security within the finance space, which was also extremely interesting. And I got further into this data engineering world. And I built up this dev advocacy within me. And I really loved working with those teams. And so then I shifted again to Cloudflare. So I was working as a technical program manager there. It was a multinational or is a multinational organization, of course. So a bit bigger scale, again, than I was working on previously. And I got to then get deeper into a lot of AI initiatives across the enterprise.

5:01So that was sort of my path. And I met my co-founder at Lumiera, Emma, who's the CEO around four years ago on a beach in Lisbon. And we didn't realize we had this similar interest in the AI space and in technology. And then many years after we already became friends, we realized that. And she had already had an organization around consultancy and business strategy. And we came together and formed what is now Lumiera about a year and a half ago. And really, it has grown over time, especially as the space is shifting so much with technology and AI. But as you mentioned, we are really trying to guide leaders in making more responsible decisions around this new era of technology, which is how we got the name Lumi for Light, Era for Future, so Brighter Future.

5:52And it's something that I'm really excited to be a part of and to be leading in. And I'm glad that my voice is sort of starting to come across the ecosystem in various places. That's great. When you were talking, I was thinking back to a conversation Chris and I had, I don't know if it was a couple episodes ago, but we were talking about the services industry kind of more broadly. And it does seem like there's such a need for good, responsible strategy and insight from the services standpoint around AI. But also there's kind of like on the other end of that AI eating up some of the maybe services industry around, certainly around whether it's marketing agencies or maybe like prototyping new projects and that sort of thing.

6:45So what is it like, I guess, being part of that services industry in this age of AI? And how do you see kind of the role of advisors, service providers, either shifting or I don't know, what are your thoughts around that in terms of the best value that even kind of services companies can provide during this time? where maybe certain areas of what they provided before is getting kind of gobbled up by some of the things that the AI is providing? Yeah, there's a lot in there. So I'll start with sort of the challenge of being in this space, and that is that we're not offering a silver bullet. We're not offering a tech product that will solve all of your problems.

7:33We are not just handing this over to you as an answer. We're really developing leadership. So our core product is a executive education program that spans eight weeks. And we are covering all of the challenges that we're seeing right now in AI that are not necessarily technical. It's a very human centered approach to technology. And of course, it's easier and people want something that's fast and they just want the answers. And what should I build? What should I buy? Like, how do I do this? And then can I pay you to do it for me? And we're really offering a counter narrative to that. And instead asking you as a leader, you have already built yourself up, you're leading this organization, however big it may be.

8:17What can you do now to make the right choices instead of offloading that onto somebody else? And it's really testing folks and it's putting them up against a wall sometimes to be a better leader. that's a personal choice not everybody wants to invest time in being the best version of themselves or maybe they're tapped out and they don't want to do this anymore that could also be the case but we're working with the ones that are trying to position themselves as really leaders in this in this age that we're in and to continue to lead and to bring their entire organization around because I'm sure you're seeing there are a lot of failed projects a lot of missed returns concerns expectations that were not met with ai in the past couple of years and most of that is a human issue or a leadership issue it's not a technical issue the tech is there we are missing this communication and translation layer and that falls on leadership in our minds which is what we're trying to address it's really refreshing to hear that you're that you're trying to change the narrative because i mean i i would imagine you know i know that daniel and i are constantly bombarded and, you know, with different companies that are out there telling us that, you know, they have the solution.

9:31It's AI and it will solve everything having to do with that. And that's so common. And so kind of hearing it being grounded in leadership, I guess, kind of going toward a question here, I would imagine it is very hard for leaders or aspiring leaders to kind of process the never-ending rapid change that's occurring with this space? Because over time, having seen decades of business and stuff, this is much faster cadence than it has ever been. We've seen change over those times, but now literally every week there's a collection of new things to consider that are hitting you up in whatever you're at.

10:16So how do you deal with that? I would imagine that your leaders are coming in with some level of anxiety and some level of uncertainty and maybe even some fear of making decisions that are going to bite them on the rear end by not very far down the road by making choices now? How do you get through things like that? How do you get through that kind of fear and anxiety? Yeah. I mean, you called out some of the main challenges that we're seeing and through many conversations with many leaders across the board, we're hearing the same thing. So one is this like noise exhaustion and information overload of trying to keep up.

10:54Another is fear of missing out and getting left behind. So you either end up in this position where you're sort of paralyzed and you're not sure what move to take. And so you're getting left behind in a sense, or you fear that you are, or you're moving really quickly. You're making a lot of decisions, but they're not necessarily the right ones. They're not grounded in anything. It's just based off of this reaction to what you're seeing around you. And that could be a very narrow echo chamber of information that you're being exposed to. Maybe you only check Twitter for your updates, or you only check one sort of newsletter to get your information, and you're not creating this landscape of multiple sources of information to decide which move to make.

11:34So yes, there is this stress, anxiety, and exhaustion that we're seeing, which is also what we're trying to address. And we do that by not focusing on every latest model, what the latest type is, we're focusing on what your challenges are as a leader in your organization. And that is not going to change every single week, you know, in theory, maybe it is for some folks. But if you're a mature organization, and you have strategic goals, probably you know what they are, or you should know, and then you can start to address what the technology is that would help you achieve what your goals are related to those goals, related to those challenges, sorry.

12:10And so it doesn't matter if there are 10 new models that came out last week, you don't need to know what all of them are right now as a senior leader or as an executive. You need to understand what numbers you're trying to shift, what kind of transformation you're trying to move forward within your organization, within your workforce. And then you can find, you know, iteratively the best solution technically for that. And so we're trying to help people build that mindset and that scaffolding to understand the ecosystem. So we do have a section in our program. We have it split up into three foundations.

12:45The first is on confidence, which I can go back to in a second. But the second is around action. And that's understanding risk. And it's understanding the industry and industry radar. And it's about setting your vision for AI, not for your organization, but as yourself, what is your personal stance as a leader? What do you care about? Is it security, privacy, transparency? What are those principles that really resonate with you that you can then use to make your decisions? Once you have an understanding of how to evaluate risk, Once you understand what's sort of out there in a general sense of capabilities, not every single minute detail, but a general understanding, then you can start to think about what opportunities you have in front of you as far as use cases.

13:26So we really do focus on that rather than trying to put a lot more information in terms of like technicalities in front of you. And then just going back to the confidence portion. So we have that as our first foundation because we want people to develop this mindset as leaders of, okay, I already have a lot of strengths to move forward with. I've already built myself up and my organization up. Understanding and knowing every new model drop is not going to be the differentiator here. It's how I communicate with my workforce, how I keep people engaged, how I can manage everything that's transforming in front of us and keep people excited to be here and to be a part of what we're building, whatever that is.

14:07And so you need to have that resilience as a leader and that confidence in yourself and to be informed before you can start taking action, before it even makes sense to start reading all of the latest news, because it won't mean anything to you unless you have that personal understanding.

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15:57Yeah, Allegra, it's really encouraging to hear your perspective. I can second Chris there. Of course, when we're working with customers, when we're talking to people on the podcast, when we interact in our companies all the time hearing like, oh, this new model came out and now OpenAI has open models and I need to, should I switch? And yeah, I think having this sort of internal piece that even if no one ever released another model, you have more than enough to be very transformative in your organization. Don't worry about it. There's a long way to go there. Yeah. So I think that that's really interesting.

16:41I love also the perspective on leadership because one of the other things that I think we're seeing a little bit, and I would love to get your perspective on this, is kind of the executives in a company kind of dictating, like, we are now going to transform with AI, right? And everyone in the company really not understanding what that means practically. Or leaders like, oh, I'm a manager in an engineering team and I want all of my developers to be more efficient. So I dictate to them, all of you need to be using these AI tools, right? And really, no one ends up using them. Everybody kind of has the workflows that they're used to.

17:32And so there's really not that kind of trickle down transformation that happens. I'm wondering about your perspective on that. And maybe even for me as a leader of a team in my company, I really want to understand that element better because I want to both lead by example, but also understand to your point how to lead well my team forward in a way that is embracing the right AI technology and being transformed. But I know that I also can't just walk in one day and be like, everybody use more AI and then I go sit at my desk. And even whether I'm using more AI or not, it really doesn't matter. Yeah.

18:17I mean, this is one of the critical mistakes that we're seeing now. And there have been some recent news stories coming out of leaders sort of having to roll back their AI first organizational approach because they weren't expecting the backlash that they got. They sort of assumed everybody was thinking about AI the same way as they were, which is not the case. Everybody's coming to this from a different level of literacy, from a different perspective. Everybody has a past relationship with how they view this technology as it relates to complexity, if it's actually more useful or not. So you can give people, you know, any tool you want.

18:53You can give them a stipend, like free money, go try whatever you want. But unless you help them understand why or what it would help them solve and really bring up that level of AI literacy across your organization, it won't make a difference because people won't understand what you're trying to do here. And unless you also communicate that clearly as a leader, you're going to come across as very prescriptive. I think, you know, especially in the engineering space, we all know that that's not ideal. We don't like when people are super prescriptive and just tell us what to do. We like to explore and to do research and to get there on our own.

19:26And what's interesting about AI is that this is really coming from the bottoms up in a lot of ways. Three times more employees within organizations are using AI than their leaders think. That was from a recent report this year. So it's not that people are not ready or can be engaged, but it's meeting them where they are and having a conversation that's very honest. So what are you using AI for? It shouldn't be stigmatized. If you want to encourage usage, then help people understand that it's okay to share where they're using AI and why they chose to do it that way. Have, you know, open sessions where you're sharing with one another.

20:04Establish this like sort of AI champion culture and a fail forward culture as well. You have to invest time in experimentation and research and know that it's not all going to be perfect and to make people feel like that's OK and that they can try these things and share openly. Because if you don't do that, then it doesn't work. Like people won't use it if they're not part of it and if they're not involved. And they already are using it. That's the thing. Most people are using something to the side of their work, whether or not you put it in front of them, you know, purposefully or not. They're using ChatGPT or Cloud, or they're coding with something on the side.

20:40They have an AI-driven IDE. Something is happening in the organization, whether or not you built a program or initiative around it. So it's better to do that in a very open way, in an honest way, where everybody is involved. One of my favorite things that I worked on at Cloudflare was piloting different coding assistants, AI coding assistants. And it was a large group of engineers from various teams. and a lot of it was qualitative. And this was sort of my approach coming in. And I don't know if they liked it. I think they did because the results were good in the end. But it's a lot of just understanding what people like and having a lot of channels of communication for like, did you try this thing out?

21:21How did it go? We're going to give you a fully supported space and time to invest in trying this. And then we're going to do it with something else. And we're going to compare them very honestly because we're not just going to choose a solution that seems the best on the market right now. We're going to choose the best solution for you, for this specific group of engineers that are part of this organization. And so I think being quite humble as a leader in that sense too, that you don't know the best thing for everybody. You're at the top. You don't have your hands in every single initiative. You shouldn't anyways.

21:51That's my point of view. So you have to trust that the people that you hired have an opinion that's worth hearing and then give them space to share that. You mentioned something just now that I was really wanting to dive into. You mentioned the word trust coming in there. And that's complicated in that there's trust in multiple directions. There's not only the trust that the leader or leaders must have in the teams that they are overseeing, but there's also the trust of those being overseen that are doing the work, the engineers, and trusting in the motives of their leaders. And that raises some interesting things, several of which you've mentioned.

22:32As you pointed out, there's this reality that employees are using AI in areas that maybe they have even been told explicitly not to, or at least they're finding a place to kind of bring it in, whether it's noticed or not. And you also have these top-down, thou shalt go forward and use AI, with employees worried about, you know, what does this mean for my job, you know, job security, is this AI eventually going to replace me? There's so much involved in this, and probably more so than I've observed in the past when we've had, you know, cloud computing was a pre, you know, before the AI wave, we had the cloud computing wave, and before that we've had other waves.

23:18and there wasn't, there was a trust in the technology and privacy and stuff, but there's now an implicit trust within your own organization that exists, you know, those factors. How do you address that with, you know, with, with leaders as you're getting into and, and get them if they don't recognize that upfront, how do you get them to recognize and take action on, on that, that kind of new dynamic that's now in the workplace? Yeah. And trust is so critical here at every level, at the technical level, at the human level across the board. I think the way people notice this, unfortunately, is when things don't pan out or there's some sort of, you know, internal rebellion, as we're seeing with this backlash that I mentioned, or they're not seeing, again, the returns that they expected because people didn't adopt in the way that they anticipated.

24:05It's because there wasn't that relationship building and that trust because the people that they're trying to involve, you know, the workforce were not a part of those decisions. And if you have a vision as a leader and you want to be AI first across everything, but you're not communicating that, you didn't set any standards, you didn't publish any policies that help people understand what's okay and what's not okay, then that doesn't elicit trust in the environment. Again, it just comes back to something that feels very top down without involvement, which won't lead to any results. And then there's trust in what you're actually building.

24:43And this is something that I also try to advocate a lot for because right now engineers are the ones really pushing this forward in a lot of organizations. There are groups that are just trying things out, you know, again, whether or not it was dictated that it should be done, that's just sort of what's happening. And so what's being built might not necessarily have trust built in by default, because that wasn't something that was thought of at first. Maybe you're just trying to build something cool. You got access to something, a new model came out and you just want to throw something together.

25:12And that can sometimes escalate to being now used by the company or, you know, some leader wants to see that in production, even though it wasn't really tested thoroughly. And how can you expect somebody internally, if you're building for, let's say, another internal user to trust what you've built, if you don't communicate why it was done or how, and you can't really explain where the outputs are coming from. And there's no, you know, documentation around it, we're sort of sort of abandoned, like the product approach and thinking and anything around documentation or thorough testing when it comes to AI.

25:46It's sort of just like throwing things out there and some things go into production and are used and sometimes they work, but a lot of times they don't. And so it's hard to build trust when you're moving that way without a lot of intention and without a lot of clarity that you can express to other people. Something that we see failing a lot, you know, even when something works really well technically and it's sort of perfectly executed. if you can't explain that to maybe somebody in risk or compliance, it's not going to get very far and they won't be able to roll that out. And it won't be, you know, it won't be trusted, even if you as an individual that built it feels like it's good.

26:21So again, it's about this transparency and open communication as you're going and why you can't really abandon documentation and you can't abandon, you know, the reasons that you built things or having observability or logging, it's not enough to just make something that seems really cool. You have to actually back it up and to be able to explain it to the others around you. Yeah, some of what you said there is definitely applicable internally and externally, but certainly a lot internally in terms of the documentation, how reliable something is, the testing, all of that sort of thing. Part of what I was thinking in my mind while you were talking is internally here, we like to talk about certain ways in which we would like to build things.