A Third Path to Talent Development: Delta’s Michelle McCrackin
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Today we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast. How can organizations take advantage of existing deep domain knowledge?

Find out how one airline is upscaling its frontline workforce on today's episode. I'm Michelle McCracken from Delta Airlines, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and business strategy guest editor at MIT Sloan Management Review.

And I'm Sherwin Korubande, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Today, Shervin and I are excited to be joined by Michelle McCracken, who's the Senior Manager of Analytics Learning and Development for Delta Airlines. Michelle, thanks for joining us. Welcome. Thank you for having me. All right, let's get started. We got interested in what Michelle was doing because we learned about this Analytics Academy. So why don't we start with that? Tell us about the Analytics Academy and what that means.

Analytics Academy is a program that we started about a year ago with Georgia State University, and this program was designed to create a career pathway for our frontline employees, our operational experts, those individuals that you are interacting with day-to-day in the airport. So it might be a flight attendant, a gate agent. They are what we believe is truly the experts on what happens and what it takes to run the airline.

And this program is a nine-month program that allows them to stay in their nine-to-five job

and they get to learn analytics on the side, and then ultimately at the end of the program, transition into a full-time analytics role. And we do this by the first semester, bringing them up on data foundations with Georgia State University. So they're doing this three to five hours a week on their own time, all asynchronous. So they're not in the classroom day-to-day, but they're able to work through it on their own time on the computer.

And then the second semester, they spend with my team, the analytics learning and development team, working on how we take data and apply it at Delta and what levers we pull, what do we look at, how do we move forward. And this really allows them to take that information they learned in the first semester and really apply it to real-life examples they're familiar with. So for example, we look at whether an airline is going to arrive on time.

That is something we report out to the DOT, but it's also a metric that we look at internally and measure against every day. We teach them how they might use analytics to drive that calculation, and this allows them to deepen their knowledge that they learn.

learned in the frontline to start with. And then the third semester is where they really get to apply that information. And that is when they get to join a full-time analytics organization for an internship. They leave their full-time job for the first time in the program, and their nine to five is now an analytics internship. And they're still paid normally, but they get to, instead of focusing on their frontline role, they get to focus on applying those new analytics skills right here in the organization.

That's really interesting. Can you explain what's included in the first semester? What skills are students learning there?

With Data Foundations, our goal really is to challenge the student to think about data as a whole. Think differently and really challenge them to look at how to ask the question, how to get there. But then furthermore, they learn a bunch of tools to keep in their toolbox. Advanced Excel, Python, SQL, they learn the start to Tableau and a little bit of Power BI. How long have you been doing this? We launched our first class in May of 2022.

And our second class launched in August. And so our first class actually graduated in December. And we are excited to report that 100% of participants got placed in a full-time position. Oh, that's great. So how many people were involved in this? We had 750 applications the first time around, which we brought down to 24 students split over two cohorts. So 12 students in each cohort.

Wow, 750 people are interested in this program and only 24. That's great. So that's great from your side, but it actually speaks a lot to the demand for these sort of skills. How did you get people interested in this? How did they publicize it?

Definitely. I would say the biggest thing with interest for the program, one of the things we found with our frontline is there's absolutely a desire to do more and learn more. With Delta, you know, I've worked at other companies prior to coming to the organization. One of the things I found is most of our employees intend to stay at Delta for life.

And they love the organization. They're very bought into the culture. And so for them, it's not what's my next move outside of Delta? It's what's my next move here? And so there is a really great desire to move up in the organization. And with the ever-growing demand in the analytics space,

there is an increased demand in how do I get involved? And honestly, that's how this program got developed is because we kept having frontline agents reach out saying, hey, I'm really interested in getting involved in analytics. Where do I even start? And quite honestly, we didn't necessarily have a path other than saying,

"Hey, go back and get another degree in analytics or go join a bootcamp." And really how this started was because we were at first referring to different bootcamps in the area, but there wasn't necessarily the perfect bootcamp that checked all the boxes for every single thing we needed and still gave them the Delta background.

And that's how we decided, you know what, let's create something of our own that we can customize and really map out what we want them to learn, how we want them to learn it, and then teach them to apply it throughout the program. Makes sense. I mean, in picking from such a huge pool of people down to the 12 and 24 size cohorts, how did you make those tough decisions? What were some of the criteria that you looked at in determining who was in the program and who was not?

The biggest thing for us is process improvement. So the desire to take that step and, you know, make those process improvements in their organization. We have them go through a multi-phase interview process. And so in one of the phases, we ask them questions about in your current role, can you give us an example of an idea that you've brought up that would be a process improvement for your space?

Other things would be just overall, I would say, excitement around analytics, but the interest to know more. And so a big thing for us that we talk about in our analytics space is around data curiosity. And we can teach all of the coding and the background and all of that. But if there's no curiosity around data and the desire to learn more or ask why, we can't teach that. And that is really fundamentally what we look for when we look for the right candidate.

I'm curious. We know there's a war for talent and particularly tech talent. So let me play devil's advocate here for a second. You could also hire data science specialists from outside. Can you comment a bit on if you also take that approach?

One of the things with Delta is we are very much grow from within from a talent standpoint. And so we really believe that the heart and soul of our organization is our frontline employees. And while we can absolutely give those analysts that are coming out of school, and we do give them the opportunity to rotate into the operation, this is just another channel and another way for us to utilize that talent pool that's out there. One of the things as we looked at

How we really got here was we're ultimately trying to broaden our talent pool. One of the things we found is more and more every single year, we're the organization that everybody within Delta comes to to look for analytics talent. And we continued to get tapped for resources and tapped for resources. And at one point had the conversation of,

We have to change how we're looking for talent because we're getting to the place where we're just, we're not able to compete in the market when everybody wants the same type of talent and how can we do it differently? And so we still see value and we've still find hiring directly from a university or hiring someone that has, you know, 10, 15 years of experience just as valuable and giving them those rotations into the operation. Yeah.

But this is a third path that really was underutilized before that allows us to take those frontline operators that are excellent with our customers and do an amazing job and really teach them the analytics side on the back end versus kind of having to do the other way around. That makes sense because people are coming from different places. And I think what I'm hearing you say is that you're

looking to build this out from lots of different directions and it was, why not try everything versus putting all your eggs in one basket? You can try several different approaches. Definitely. One of the things we found is, we say all the time, data drives Delta.

And every, like I said, every year we continue to see a greater need for not just data analytics, but for the more in-depth machine learning data scientists as a whole. And we're just not able to meet that demand with our traditional recruiting strategies. So it's, this is a way for us to broaden that and get more talent in the pipeline.

Makes sense. I was thinking about this third semester. I think you described it as more of an internship within your organization. I guess I have a clearer picture of what the first semesters look like because they're more familiar to my academic background. But what's that third semester look like?

The third semester is going to look very similar to the traditional co-op experience or summer internship experience where they're coming in, they're learning. A lot of them may not be interning in an area they have experience. For example, on my team, I have one side that's strategy. I could have had an intern and that intern could be working on building dashboards specifically for people metrics. Now, if they came from airport customer service and

they were one of those people we would have been reporting out on, but they would not have been looking at that type of data day in and day out. And so the first part of it is really integrating them in to understand the business they're starting to support.

And then they give them a project that they work on throughout that semester. So they're able to have a tangible project that at the end of the semester, they're able to show to those interviewers that may be hiring them full time. Here's something I've worked on. Here's a dashboard I created. Here's why it's important. And they're able to do that on their own. Example, I guess, one is this dashboard. What are some other examples of the kinds of projects that the first 12 have worked on?

They look at, for example, turn time. Turn time is the amount of time it takes us from the time a plane touches down on the ground to the time that the plane pulls back from the gate and they are off to their next destination. We had one of our interns do a complete analysis on how do we reduce the turn time in a particular airport.

One of them works in the technical operations space, and he was able to help from an inventory standpoint, create an app that would help them on a day-to-day basis. And so it's interesting because we've seen them take some of these things and apply them into their normal day-to-day jobs, not even just in their internships. That makes sense. I think probably people listening might be curious about how you ended up here, but I'm also curious, are there things that you would have done differently? You've gone through two cohorts. What's changing in your thinking? What can people learn from you?

maybe what didn't quite go perfectly? I think the first thing we probably would have done differently or the thing that we've actually pivoted to do a little bit differently with our second cohort is bringing more subject matter experts in to talk to the students in semester two. So in semester one, they're getting those foundations. They're just starting to understand

And then semester two, really sitting down and having them have exposure to, for example, a leader from revenue management, a leader from reservations and customer care and how they use data. From our standpoint, we obviously support data analytics from across the organization, but it's better when you can get someone that's dealing with it day in and day out and they're able to make that connection earlier on.

We've also tied those specific areas, tied to certain concepts that they're learning. So they're actually getting a reinforcement of those concepts in semester two. So for example, with Python, when they go into Python training, we tie that with crew so that we have a leader from our crew space where they look at how do we make sure we have the flight attendants where they need to be and our pilots where they need to be. That leader is using examples and showing them in Python how they support that data.

That's great. Can we jump back to Sam's first question, Michelle? How did you end up in this role at Delta? My story is actually really unique. I was the HR business partner supporting analytics for two and a half years prior to and then through the pandemic. I've always had an analytical background and a passion around using data to support the story. And I would define myself in the scope of analytics as a storyteller.

But that being said, I never had imagined or dreamed that I would ever move into the analytics space. And we got into this conversation where we continued to find ourselves looking for talent and how are we going to get it and what does that look like? And my passion is really around strategy. And so I had built out a strategy that was, how do we look at this from multiple angles? And pitched the idea of, let's launch this program. Let's launch an academy. Let's train from within.

And the opportunity presented itself for me to come over and lead this team and spend

It's been an amazing journey because along the way, I somehow became an analytics leader and am now leading an analytics team. And I wouldn't trade it for the world. I think the more that I get in the space of analytics, it pulls me in more and it makes me want to understand more. And I think it's a lesson to everybody to be open to the opportunities that get put in front of you because your next opportunity may be something that's totally outside of the scope of what you think it may be.

You've mentioned that you've just graduated and all 12 of your first cohort have gotten placed in, I think, would you describe it as analytics roles of some sort? Yep. How'd that feel? Fantastic. It's one of those surreal moments that, you know, on paper, it feels really good. So you're like, okay, great. All of them are placed. And then you take a step back and you, you know, we're at graduation and you feel like

you change 12 lives. These are 12 lives where they went from being in a frontline operator role where they could have been working on midnight shift. It wasn't until the graduation day that I think it all really hit me that it all kind of came full circle and really felt like it really is changing people's lives. And I was able to be a little part of that. So what's next? I mean, this seems to have worked. Is it 12 to 24 to 36 to 48? Or is it something different?

We are going to have three cohorts, about 60 students will go through. This program was the start and was able to help us launch what's called Delta Data University, which is a university that focuses on three main pillars. The first is Pivot, and that's where Delta Analytics Academy lives.

The second is around Accelerate, and that is the building blocks or e-learnings that students can go through to kind of get additional information or additional learning on, for example, data engineering or data science, right?

And then the third is Enrich. And so that program is really meant for those that want to stay in their current job and not pivot to a different area or not change or accelerate to a different type of analytics world, but just understand, for example, there's a Python course that's in there that would allow the student to understand and learn Python, but the goal would be for them to use it in their day-to-day job that they're in. We'll be focusing on launching an analytics course for station managers, which is going

which will help us raise that waterline of understanding and analytics across the board. And our hope is that we just are able to increase data fluency across the organization and really allow us to solve more complex problems across Delta.

So we talked about the analytics. How does this fit into the overall broad talent strategy? You've got these three pillars, but is this you're thinking about AI and analytics strategy? I mean, there must be something about pulling people in from outside. I think you mentioned before earlier, how does this fit into the even bigger picture?

Yes. So I would say twofold. First and foremost, the develop within our bigger picture of how we recruit talent from the outside. We absolutely are still recruiting from the outside. But the bigger question is once they come here, how do we retain them?

And so that is really what Accelerate hits on, is we want to be able to offer ways for our employees that are here in the analytics field that want to continue to grow. That is, when we look at our employee survey results, when we have conversations and roundtables, we continually hear,

I don't have enough education. I want to be able to go back to school. I want to get more exposure to this. I want to do this. This allows us to open up and really give them the courses that they're looking for. And so this will be ever evolving as we see a need for different items, machine learning, different things that come up for us to be able to create those customized courses and offer them to our employees. That's really the goal.

And so that does not deviate from us changing and wanting to recruit from outside or inside. The goal is we just want to make sure that we're retaining that talent once they're here. That makes a lot of sense because it's changing so quickly, too. I mean, that's the other thing you're up against is that even if you had everybody all perfectly trained today, then the decay on these topics is pretty rapid.

Yes. And for us, it's not even necessarily about a gap in skill set or a gap in somebody not being able to do something. It's more about giving them the opportunity to continue to advance if they want to. We hire a lot of go-getters. They want to keep going. They want to keep learning. And we want to be able to support that as long as we can. So one of the things also fun about this is

Let's go back in time 10 years ago. I think if you said, oh, yeah, I'm going to put people through a 12-week program to learn data science, you'd laugh at people because how could you accomplish anything of that magnitude in this amount of time? I think one thing that we're seeing as we're talking to lots of people is that

how these tools are getting more accessible for people. And I think it seems like what you're doing is trying to capitalize on how these are getting easier and then, well, recognizing that you can do this sort of thing in 12 weeks versus having someone stop for two years and go out to a separate master's program. Definitely. And I think the other thing to point out too is, you

You know, with any candidate that comes in, whether they're coming in fresh out of university from another company or coming from our frontline, having come through one of these programs, there's always going to be some kind of skill gap, whether that is a analytic skill gap, whether that is a skill gap with not knowing or understanding the aviation industry. We just kind of have to decide what the right skill gap to take that risk on is and how we fill that gap.

And so that's really what this is, is it's another way for us to kind of look and evaluate to fill that gap. I think the other thing too is within the Atlanta market, which is primarily the market we hire from, we have a lot of tech companies that have moved here. And so we're in a hyper-competitive market within the analytics space. And so for us, it's how do we remain competitive and remain relevant? And so being a company that continues to invest in

our employees and continues to give them those opportunities to learn and capitalize on different programs and some of the newest technologies that are out there. It's just another way to open that door. Michelle, we have a segment here where we ask our guests a series of rapid fire questions. Just say the first thing that comes to your mind. Sam, do you want to do it? What have you been proudest of regarding artificial intelligence? I think the biggest thing is being able to launch this program and be able to place 12 students in analytics roles.

I thought you might say that because if I was looking at your story, that seems like the story that I would be pretty proud of there. We've heard a lot about bias and ethical issues regarding the use of data. Maybe beyond that, what else worries you about applying artificial intelligence?

I definitely agree with you on the bias and ethical issues that exist. I think when we start to apply artificial intelligence, especially in the operations segment of the business, we a lot of times plan for what we consider a blue sky day. And we obviously calculate in what if a catastrophe happens or what if a gray sky day happens.

But we don't plan for things like COVID-19. And we don't plan for things that are absolutely catastrophic that could completely disrupt that space. And so I think part of it is how do we find a happy medium and being able to rely on that data but not necessarily take that all the way. What's your favorite activity that does not involve technology? Traveling. Traveling.

Which is probably cheating because I work for an airline. Exactly. What was the first career you wanted? Like, what did you want to be when you grew up? HR analytics? I wanted to be an accountant because my dad was an accountant. And so I just, I knew I wanted to be in business. I was fascinated by business since I was a little kid.

And I went to Michigan State University, went to my first accounting class, failed my first accounting class, and decided perhaps that was not the place for me. And found myself very quickly back into HR and haven't stopped since. Absolutely love the field and honestly love being in business. But I think for me, being able to blend the world of impacting people backed by data really is where my sweet spot is. That's great. We've had people want to be...

All kinds of different things, but accountant is definitely a first. Yeah, I wanted to be an accountant. I thought that was my dream job. And guess what? My accounting 101 professor did not think that was my dream job. What's your greatest wish for artificial intelligence in the future? What are you hoping we can gain from this? I will say I really hope that we are able to, as you mentioned before,

One of the biggest concerns is bias. I want us to be able to find a way to work around that bias. And how can we use artificial intelligence to remove that bias? Because whether it's data masking or whatever that may look like, in order for us to get to that final answer, I think

human nature, whether, you know, we can go through all of the trainings, we can do everything we possibly want to ensure that there is no bias and ensure that, you know, we're doing everything the right way. But I think until we allow data to kind of back that for us, we're not going to be 100% there.

Michelle, Shervin, I've really enjoyed talking with you today. I think there's a lot that other organizations can learn about your approaches to reskilling talent. And this may be a model program for lots of organizations. Thank you for taking the time to tell us about it and to share your experiences. Thank you for having me. Appreciate it. Thanks for joining us. To hear how another organization is learning to use AI, join us next time when we talk with Anders Butzbach-Christensen from The Lego Group.

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