What’s the BUZZ? — AI in Business

Top 3 Learnings For New AI Leads (Guest: Sara Hanks)

July 26, 2022 Andreas Welsch Season 1 Episode 8
What’s the BUZZ? — AI in Business
Top 3 Learnings For New AI Leads (Guest: Sara Hanks)
What’s the BUZZ? — AI in Business
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Show Notes Transcript

In this episode, Sara Hanks (Senior Director Program Management) and Andreas Welsch discuss the top 3 learnings for new AI leaders. Sara shares learnings on becoming an AI program lead and provides valuable advice for listeners looking to move into a similar role in their business. 

Key topics: 
- Learn what to expect as an AI lead
- Find out the top challenges in that role
- Hear how to apply it to a new AI project 

Listen to the full episode to hear how you can:
- Identify early adopters and keep them engaged
- Think like a marketer and identify message for your stakeholders
- Learn to prioritize which AI use cases to pursue

Watch this episode on YouTube: https://youtu.be/Z6J69rXGmmQ

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.


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Andreas Welsch:

Today we'll talk about the top three learnings to help new AI leads have a successful. And who to better to talk to about it than someone who's actually been in that role. Sara Hanks. Hey Sara, thanks for joining.

Sara Hanks:

It's great to be here, Andreas.

Andreas Welsch:

That's awesome. I'm so glad we get to do this. I know we've talked for a little while and so I'm really excited. But hey, why don't you tell us a little bit about yourself?

Sara Hanks:

Yeah, sure. So my name is Sara Hanks. I currently work as a senior director of project management. And if I were to break my career up, I would do it into two sections. The first half of my career was really focused on more of like mechanical manufacturing, engineering. But then for the last decade, I've really been a translator between the business and technology. And a little over five years of that, I was a program manager in our data lake organization. And then I was also the senior director of data analytics for a number of years.

Andreas Welsch:

That's awesome. So it sounds like you've really seen quite a few things when it comes to data and in AI. So that's awesome. To our folks in the audience, if you're just joining the stream, drop a comment in the chat. What do you wish you knew when you got started in AI and automation? And if you're just starting, that applies as well, what you would like to know. But Sarah should we play a little game to kick things off?

Sara Hanks:

That sounds great.

Andreas Welsch:

Perfect. So you see, I don't have my usual setup here. But maybe let's let's still play a game that I like to call fill in the blank. Okay. The way I usually run this is that I'll start a sentence. I'd like you to complete it with the first thing that comes to mind and why. Okay. Fill in the blank. And so to make a little more interesting, I'll only give you 60 seconds for your answer. And again, for those of you watching us live, drop your answer in the chat as well and why. Sara, are you ready for What's the BUZZ? Yes, I am. That's awesome. Great. Let's see. Data is just dot, dot, dot. fill in the blank. 60 seconds. Go.

Sara Hanks:

Data is just one of the most fundamental things that you can do to solve any problem. Quick sentence.

Andreas Welsch:

I have a feeling I need to tweak my questions. Huh? They're way under 60 seconds.

Sara Hanks:

To the point, no, I think, anybody that knows me understands that I have a significant passion for data. Even back in my early days when I was in manufacturing as a quality engineer, I was highly dependent. Data and getting it out of the systems to be able to solve problems. And I think for me that's really where the benefit of data comes from is it cuts through the opinions. It can show you things that people might have perceived to be true to be not so correct. And it helps justify opinions where you do believe or you have that intuition or that hunch, right? It can back those things up as well.

Andreas Welsch:

So that's awesome. Yeah. I'm just looking at the chat here. So from data is problem solving stuff. What Aamir was saying to it can be a pain. So I think we've all been there in different variations, right? Working with data in all the ups and downs and challenges. Yeah. But maybe let's pivot a little bit. So I know you've been in this AI lead, program lead, even CoE lead role before, and I'm sure many in the audience are eager to learn from you and hear what's the number of number one advice you would give to anyone that wants to move into that kind of a role. I'm really curious, what do you wish you knew when you started in that?

Sara Hanks:

So my number one advice for anybody moving into a program management or an AI CoE leadership role is really to identify those early adopters, but then also looking to keep them engaged throughout. An example for me in 2015, it was the first time I was a data lake program manager. I had a small team of data scientists and product owners, and one of our first use cases, around the commercial test of a big engine, and the engine would get a whole bunch of sensors, and then each sensor was evaluated. And if it was outside of its spec limit, the engine was failed. It was put off to the side for engineering to review the data later. And what we wanted to do is replace that, not replace it completely, but to speed up the process by using machine learning. to create a recommendation for the engineer and to deliver that recommendation real time so they didn't have to take the engine out and it would be able to save up the ability to ship the engine on time. You would actually be able to get through that process faster. And we had a ton of enthusiasm and early adopters out of the gate, but one of the things that we failed to do along the way was to keep the team engaged and. Decisions along the way, we encountered a number of technical hurdles and throughout that process we should have been pulsing our stakeholders and understanding like, is this hurdle significant to the point where maybe it's offsetting the overall benefit of the project? And unfortunately, by the time we got through everything, the team had changed. They were less enthusiastic. Unfortunately the project didn't get adopted like we had originally hoped from the beginning. And then the second part of your question is, what did I wish I knew going in? Is I think being a leader, you need to think like a marketer. And what I mean by that is really understanding who are your stakeholders and how would you define their persona? How do they need to consume information? And then how do you craft your messaging on what the project. What AI is doing. And then also how does it need to be delivered so that it's done in a way that they relate to it, but then also focus on things that are relevant for them.

Andreas Welsch:

So focus on early adopters, but also make sure that you keep them in en engaged and that it's relevant. Yeah, I remember seeing some of these things that you mentioned in, in my previous roles as, as well. It's one thing to, to get the project started, it's another one to keep that flame burning or even growing. And because cycles tend to be relatively long because it's not as straightforward project. Yeah.

Sara Hanks:

And people want early results and it's not always reality.

Andreas Welsch:

So that's where I think these examples, like you mentioned around the engine where it's tangible, right? It's adds business value. It is something from real life. It's not just something that, that somebody's dreamed up. But there is a lot of opportunity there if you go after these examples. So I'm taking a look at the chat real quick. Aamir asked do you think ML AI awareness is much higher compared to, say, 10 years ago? What do you think in the enterprise or in business from your perspective?

Sara Hanks:

So I think if I were to go back 10 years ago there wasn't a whole lot of awareness of AI and ML at that point in time. I would say between 2015 and 2017, There was a lot of buzz around AI and ml. I think a lot of false hope too and maybe some disappointment, which I think then took us to a period of like almost scar tissue, right? Like people were afraid of AI and ml and I think it's starting to make a good comeback and I've seen. Leaders that were not necessarily early adopters start to incorporate that into their thinking and, start to want to explore that as far as their business strategies go. That's just based on my observation.

Andreas Welsch:

It sounds boring, but I must agree with you. I'm seeing the same thing and, much like you, having been through that hype cycle it's quite encouraging. I feel now to see that it is picking up that we are talking about realistic and real examples. And then we haven't moved beyond the the hopes and dreams of the. So maybe then moving on to the next question. I think you've touched on, on, on challenges. If there's anything else you wanna add, maybe that might be a good area, otherwise we can move on to what we had talked about as our third question. Anything else or challenges that comes to.

Sara Hanks:

I think, I guess one other common challenge that I'd like to hit on is sometimes it's difficult when you're moving into a new role to really prioritize those use cases and also to identify when it, when you need to either pivot or stop. And I think, from in the beginning when it comes to prioritization, I think really understanding what's the problem that you're trying to solve and then what does the data look. Because I think you can probably eliminate some of those early requests if it, if the data's not sufficient or it's not clean, or you're gonna have to invest a significant amount of time into making the data into a useful state. I think knowing what that looks like upfront and prioritizing it accordingly is the needing to prioritize is a challenge, and the way to overcome it would be, to really evaluate the.

Andreas Welsch:

Did any suggestions for how somebody coming new into the role could spot that? Again I remember it's not that, it's not that black and white, right? You slide into it and then you have a use case and as you un untangle it and start working on it you are likely to come across these issues are challenges, but any red flags that, that come to.

Sara Hanks:

So I think the first thing is depend on your team. Chances are if you're coming new into a role, there'll be somebody in the team that's got data science background or experience an industry that you can certainly lean on. But then like red flags I think one of the things that I've seen is a lot of requests for like very specific hypothesis testing. Is this specific thing the reason why this is trending? And that is something that you can really look at with basic statistics as opposed to asking somebody to look at it from a machine learning perspective because you might be over overthinking and over putting energy into the analysis itself.

Andreas Welsch:

That, that makes a lot of sense. Going with some simpler ways, but definitely send an expert to make that assessment and give you that feedback. Awesome. So I know you've mentioned a bunch of things to, to watch out for in some of the challenges. But I'm also curious to, to hear how have you seen all of this come together, maybe in a great example of a use case that the team and yourself have worked?

Sara Hanks:

If I was to really like highlight where I've seen all three of these in a successful project right out of the gate, I have to give a shout out to my colleague Milan. He had a team that was similar to mine, but they were more focused on research and development within engineering and trying to show or prove to the business that AI could be. And I think he got this formula right on his first major AI project. It was a quality inspection using image recognition, and the decision that the AI needed to make was in a way that it didn't require tons and tons of labeled images to be able to build the algorithm. He was fantastic at picking a problem statement that was relevant. This was an issue that was 10 plus years. And then continuing to market the value to the different stakeholders. And then in terms of like early adoption and keeping people engaged, he had the right rhythm set up right out of the gate. And, I just, I wanna give credit to that cuz I think he really did a nice job of pulling all three of those together within a single use case.

Andreas Welsch:

Awesome. Thank you. So thank you so much for sharing. I know we're coming up on, on time what would be the key three things you would want our audience to, to take away from today's sessions? What are the key three learnings? Just in summary.

Sara Hanks:

The key three learnings for me for someone moving into an AI leadership would be, first is identify who those early adopters are and keep them engaged. Second, think like a marketer and identify messages that are relevant to your individual stakeholders and meet them on their on their playing field. And the third is just learning how to prioritize, which use cases to go after and when to pivot or stop them if they're not working.

Andreas Welsch:

That's awesome. Thank you so much for summarizing. I know we're getting close to, to the end of the show, so I'm really excited that we had the opportunity to do this together that we were able to come on the show. Quite frankly I haven't found too many people in similar roles, so that's why it's made it even more exciting for me personally, and I hope also even more valuable for those of you in the audience. To hear from you what you have to share and key learnings, things to look out for and how to how to set yourself up for success.

Sara Hanks:

Yeah, I appreciate the conversation today and thank you for inviting me to be on.

Andreas Welsch:

Awesome.