Kelly Kohlleffel (00:00)

Hi folks. Welcome to the Fivetran Data podcast. I'm Kelly Kohleffel, your host. On the show, we bring you insights from top experts across the data community, covering AI, machine learning, enterprise data, analytics, and more. Today, I'm pleased to be joined by Sadie St. Lawrence. Sadie is an advocate for education and inclusion in the data space. She's taught award-winning SQL for data science classes at UC Davis. She also founded the largest global community for women in data and AI. She's also the CEO of the Human Machine Collaboration Institute, where she's pioneering new ways to foster collaboration between humans and AI. Sadie, it is great to have you on the show today. Welcome in.

Sadie (00:45)

Thanks, Kelly, I'm so excited for this conversation today.

Kelly Kohlleffel (00:50)

I am as well, and we're going to talk a little bit about closing some of the critical gaps that are out there in the data community today. We're going to talk about the evolving relationship between humans and AI. This is happening so fast right now. And I think you're going to probably bring quite a few practical strategies to bear in how we meet these challenges head-on. So before we get going, I'd love to hear a little bit more about your work at the Human Machine Collaboration Institute, your role, what you're doing, and the mission of the Institute.

Sadie (01:26)

So the Institute is almost a year old, so we're fairly new. But it really came out of some time that I took last year to sit back and say, “Okay, what's the new thing that I want to work on?” And I had lots of ideas. And really what came out to me was a question that I wouldn't stop working on. It was really expanding human consciousness. And you may start and say, “Wait, I thought you’re human machine collaboration, what does consciousness have to do with all of this?” But I try and build things that are not for today, but are preparing us for tomorrow. 

So our whole focus at the Human Machine Collaboration Institute is what we call the three epochs of how humans will evolve with work. And the first one is AI for knowledge workers. The second one is working alongside robotics. And then the third one is elevating consciousness.

And our whole goal is to build those on-ramps for humans to effectively work and use technology in a meaningful way.

Kelly Kohlleffel (02:25)

The AI for knowledge workers is so interesting to me because I feel like up until say two years ago, AI was not approachable for most of us. I mean, you had to be, I think you're a data scientist, you had to be a data scientist, you had to be an ML engineer to even start working with this technology. And then all of a sudden, boom, two years ago, ChatGPT comes on the market and we're all, you know, some sort of fledgling prompt engineers using AI in our daily work. I think most of us today, I mean, that's happened so quick. Talk to me, break that one down, the AI for knowledge workers a little bit. 

Sadie (03:05)

So we tend to see really more of a bell curve with this in terms of your AI power super users who they are using it on a daily basis and finding new ways to use it and others who are still scared and terrified of the technology. And so what we want to do is really bridge that gap between the two because both have valid points, right?

It's great to have some of those naysayers in the conversation because they may bring up security concerns or ethical concerns. But you also need those power users to be able to show the capabilities of the technology. And really the goal is to bridge the gap between those two to find that effective use case and make profitable use out of it.

Kelly Kohlleffel (03:47)

Yeah, you mentioned scared and terrified. I think that can definitely apply to individuals, and I think it can apply to organizations as well. Like you have some organizations that are embracing the technology. I can think of a large airline right now that is using GenAI in a really innovative way to provide better customer service. Yet I can think of other organizations and other industries like, no, not for us, at least just yet.

Sadie (04:13)

Yeah. So we're in a really different time with organizations, where prior to the AI and ChatGPT revolution, it was very much a top-down approach with new technology, right? Leadership would come in and say, “Okay, we need to modernize our systems. And there would be a push in a strategy top down. What I've seen that's very different with this time, it's almost been a bottom-up approach for a lot of organizations. 

So what has happened is individual users have gone out and found these tools and started using these tools. And they've got ahead of a lot of organizations strategy policy and what they're doing with it. And so even for the organizations who are a bit on the scared side of things, they know that their employees are using it. And so they're almost being forced their hand to do something about it.

And that's the thing that I think is really different than past technologies. In the past technologies, again, it would be a top-down approach. And this one, it's where employees have the power and are almost forcing organizations to move faster than they typically do.

Kelly Kohlleffel (05:22)

So you talk a lot, Sadie, about the balance between humans and machines. Where are some of the gaps, where are the biggest gaps today in human and AI collaboration, and what can we do to start bridging those gaps effectively?

Sadie (05:38)

So the problem then that we're faced with today in terms of human and machine collaboration is the pace of technology is starting to outpace our human biological ability to adapt.

And the biggest barrier that we see with that today is the capabilities that we have with AI have caused us to shift away in our mindset and our thinking. Before we had tools where we would go in and do things, and we had to still orchestrate what we were doing, but we were still very much in the driver's seat. Today we're shifting from doing to asking.

It's an individual contributor now who has that ability to be that conductor, that orchestrator. So essentially, if we take an orchestra, we were all the individual musicians in the orchestra, right? Maybe you were the chair violinist, and I'm the chair cellist. And now with AI, it can do those roles. And so we get to be that conductor. And so the biggest challenge we see today is really that shift in mindset of going from doing to asking and updating our processes and the way that we work.

Kelly Kohlleffel (06:52)

Yeah, absolutely. And when you start looking at that, there was a fairly recent report that PwC had put out about the percentage, fairly high percentage, 25 % of jobs exposed to AI will require new skills. And I'm just curious as we're talking about this going from doing to asking as you talked about, what strategy should organizations think about and adopt to close the skills gap, stay competitive, and really think about doing things in a new way?

Sadie (07:26)

So I'll start with what not to do and then I'll give you the solution for what to do. So what I see a lot of organizations doing is the first step is they create a policy or a guideline, legal gets involved and they go, whoa, whoa, whoa, everybody's using this. We need to put some guide rails on it. Then typically what they do is say, okay, now we're going to give you a tool, and we're gonna set up either co-pilot for the enterprise or ChatGPT and they enable it.

They kind of just stop there and see what happens. That doesn't work very well for a couple reasons. One reason is because when you're setting up just the policy and the guidelines for it, the tools are continually changing, and if you don't have a good diagnosis for how you're going to be using it or what you're going to be using it for, it's hard to create a really strong policy and guideline. Additionally, just giving people the tool, this is an update in their mindset. So, yes, anybody can go in and ask a question, and you're going to get a response back. But that's only going to get you so far. 

Knowing how to use it effectively in your workflow is really where you start to see the bottom line differences in your organization. And so what we do is we find all the tasks where AI can support what type of AI and what's the best tool suited for that and have essentially this map of all the work that your organization does and where essentially you can think of as like a little AI buddy can come in and support. From there, that allows you to one, proactively and prescriptively select your tool. Two, you can create a policy that you know exactly how people are gonna use it or how you may not want them to use it. And three, it allows you to have really personalized training. And this to me is really key. 

Kelly Kohlleffel (09:18)

Very, very interesting. You talked about you can't completely remove and there's a huge element of this human involvement. What do you say to those folks that are worried about losing their jobs to AI?

Sadie (09:30)

I would say it is a very real fear, and it is a rational fear. So where a lot of the fear really comes from is not knowing what's on the other side and knowing where that is going to lead. So I do not tell people, “Hey, I can 100% guarantee that like your job will never be replaced by AI. But the one thing that I can guarantee is that if you have a mindset of curiosity and a mindset of growth, there are going to be so many new jobs and new opportunities that it will be a much more exciting world to work in.”

And so it's important to make sure that we are staying present with that continual learning, continual testing, and continual evolving as the technology evolves.

Kelly Kohlleffel (10:18)

And I think that's great advice. I mean, this industry, it just demands it. And I think now that that gen AI is starting to proliferate in AI across industries, not just tech, but everywhere you'd need that innate curiosity and continue to learn.

Sadie, you've been a really an advocate for diversity in this data world. You've said that without diverse perspectives and AI development, we risk creating a biased future. Can you share a little bit with us about what inspired you to focus on this issue?

Sadie (10:53)

So before I got into data science, I worked in neuroscience and worked in a lab studying emotional learning and memory. And I was quite young in my career and worked on a very diverse team. I fell in love with data science, switched careers. And then about a year in, I looked up around me, I was like, okay, something seems a little off, what's going on here? I'm like, yeah, there's no other women I'm working with. What is this about, what's happening? 

And it was odd to me because I just picked the career because I was very interested in it and passionate in it. And at that point it was 2014, and I really started to get concerned because the big data and machine learning revolution was just taking off and I said to myself, “Okay, if we're just starting at this point, what does the future look like? This trend line isn't going to go up anytime soon.” And I knew that if I was going to survive even in that career, I needed to have a community of people around me. 

But more importantly, the opportunity within this space was so large, I felt that knowing that this was even a career option for individuals, the word was not getting out, and we weren't going to make a change. And so that's why I started Women in Data in 2015, was really with the personal need for community, but a vision of equality in the future. And I truly believe that, you know, the more people that we bring into the conversation with diverse perspectives, the more exciting solutions that we find because we're able to identify problems properly because we're looking at it from multiple lenses. And secondly, it's just an amazing career in my opinion, to be able to work in technology and to be able to have those skills where you're not afraid of what the future looks like, but more importantly, you feel like you're an active creator in that future.

And so the more individuals that we can bring in and along the journey, the better for all of us as a society as a whole.

Kelly Kohlleffel (13:01)

Well, 2014, that was early days in data science. I think back to that time, I, what you're describing and kind of the state of where we were from a diversity standpoint in 2014, related to data science, I think that was the state pretty much across technology. It doesn't matter if it was technology sales, technology sales engineering, product development. I mean, that, what you described just 10 years ago, that was it.

When you look at where we are today, what are some of the challenges that women are facing in these fields today? Just say the technology field in general, and how is the community that you founded helping to get past those?

Sadie (13:44)

One of the challenges that I see often is individuals get into a career because they know somebody was in that career. And so we don't have enough role models, but more importantly, we don't have enough individuals who are sharing the opportunity of what this career looks like. And also debunking some of the myths behind it, right? Like I think there's a lot of maybe from TV, which is like if you work with even computers or do some technical degree, you're some hacker in a back dark room, right? And that's anything further from the truth. I got asked once before, like, do you ever have meetings with people and talk to people? It's like, yes, if you work in technology, you still have to talk to people. And having communication skills will help you a lot in your technical career. 

So one is just like spreading that awareness of the different opportunities because it's also continuing to expand. I mean, the data science family of jobs, I swear, has a new job title every year coming out. And so just what that opportunity looks like, we can do more in terms of the awareness. 

The second side of things is really then not only having that awareness, but having that support of individuals to help guide you on that journey. So often I see men who have friends or can go to hackathons and network with one another. They help them get into jobs, they're able to work on projects together.

And so that's really a core part of what Women in Data does is building that community through our chapters, providing learning pathways where individuals can learn with one another, participating in datathons along with other women, and giving individuals that opportunity to test their skills. 

Kelly Kohlleffel (15:27)

Let me ask you, so for you personally as a female leader, Sadie, what's a leadership lesson? Just give me one, I'm sure you have a lot of them. What's one leadership lesson that you've learned along the way that has shaped your personal journey?

Sadie (15:40)

Yes, there are many to pick from, but I think the one that stands out today, you may get another one if you ask me tomorrow, is to put your own mask on first as a leader. I am a big fan of servant leadership and creating space for individuals, but one of the things that I personally have struggled with and I also see a lot of other female leaders in particular struggle with is putting everything first for their team so there's nothing left for them. And at the end of the day I have to make sure that I'm okay, and I'm in a place that is stable so that I can create that space for others as well. 

This comes from a variety of aspects, and that's why I use the analogy of what they tell me every time I get on a plane, which is, know, in case of emergency, put your own mask on before you help someone else. And I think this is not something that gets talked about enough in leadership, but making sure that you have your own personal board of advisors, your own personal network and your own support system. Because when you have that support system and are in a stable place, you're able to show up so much better for your team and for those people around you.

So any new leaders, any leaders who are burnt out, because I've been through that before, put your own mask on first. Take care of yourself because it will provide then tenfold for what you're providing for your team.

Kelly Kohlleffel (17:13)

That's great. That is great. 

Well, Sadie, thanks so much. This has been fantastic. Really appreciate you joining the show today. And thank you so much.

Sadie (17:24)

My pleasure, this has been a fun conversation.

Kelly Kohlleffel (17:25)

Fantastic. And I'm definitely going to keep up with everything that you're doing at HMCI. Hopefully we can have you back on in the future as well. 

Well, a huge thank you to everyone who listened in today. We really appreciate each one of you. We would encourage you to subscribe to the podcast on any of the major platforms. You got Spotify, Apple, Google. You can find us on YouTube as well. You can always visit us at fivetran.com/podcast, and then send us any feedback at podcast@fivetran.com. We'd love to hear from you. 

We'll see you soon. Take care.