Conversations on Applied AI

Jean Machart - From Digital Transformation to AI-Driven Healthcare

• Justin Grammens • Season 5 • Episode 4

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The conversation this week is with Jean Machart. Jean is a transformation executive leading companies by applying digital and artificial intelligence to create future-forward products, services, and experiences. She enables growth at scale with industry-leading companies, Fortune 5 Healthcare, Fortune 200 Financial Services, and more, and is prepared to lead the next transformation. She's an artificial intelligence entrepreneur in residence at Digital DX Ventures,a  board member at Asbury Communities for Senior Living and HTEC, and recently was a chief operating officer at Children's Cancer Research Fund. Jean holds an MBA in information technology from the Carlson School of Management at the University of Minnesota and completed the “Competing in the Age of Artificial Intelligence” from Harvard Business School's Executive Education Program. 

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Resources and Topics Mentioned in this Episode

Enjoy!

Your host,
Justin Grammens

Jean Machart (00:00)

You know, you're seeing use cases of fall protection or prevention and protection so that you can see, okay, what does mom and dad need to do? They're about to fall. Let's change something in their environment. Or is it time now to have that conversation to move them into assisted living or move up to the next step? You you start pulling all these pieces together. You can see the trend that we're getting there. And at the R &D stage, I would say this is time now for the executive team to go deeper and build up more of that strategic point of view.

AI Announcer (00:33)

Welcome to the Conversations on Applied AI podcast where Justin Grammens and the team at Emerging Technologies North talk with experts in the fields of artificial intelligence and deep learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real-world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at appliedai.mn. Enjoy!

Justin Grammens (01:04)

Welcome everyone to the Conversations on Applied AI podcast. I'm your host Justin Grammens and our guest today is Jean Machart Jean is a transformation executive leading companies by applying digital and artificial intelligence to create future forward products, services and experiences. She enables growth at scale with industry leading companies, Fortune 5 Healthcare, Fortune 200 Financial Services and more, and is prepared to lead the next transformation. She's an artificial intelligence entrepreneur in residence at Digital DX Ventures.

board member at Asprey Communities for Senior Living and HTEC, and recently was a chief operating officer at Children's Cancer Research Fund. Jean holds an MBA in information technology from the Carlson School of Management at the University of Minnesota and completed the competing in the age of artificial intelligence from Harvard Business School's executive education program. So thank you, Jean, for being on the podcast today.

Jean Machart (01:53)

Love to be here, Justin. I love what you do for the tech community. You're sharing out really the AI, the latest in technology and application, then you're consulting, of course, with your organization. So, no, thank you.

Justin Grammens (02:07)

Yeah, awesome. Awesome. Well, I love having experts like you on the program and this we try and make this be very conversational, right? Kind of what are you seeing in your specific industry segment? At the beginning, though, I gave a brief bio. The other thing I'd like to really ask people to sort of kick it off is like, no, how did you get to where you are today? Right? Kind of what was your path? What was your trajectory of your career?

Jean Machart (02:26)

When I think of my career journey, I have three components. The first is financial services as a foundation. And there I started in finance and led wealth management product organizations and a billion dollar profit and loss. And, through that process, I learned how to run organizations and financials along with leading growth initiatives at that time, applying digital and technology platforms growth. So digitizing,

processes and moving to value. And then at the end, I reported to the CIO at Ameriprise, leading out client experience for the first time at the organization, along with a digital transformation. That really moved me then to the second phase of healthcare. So moved to UnitedHealth Group, looking at how to modernize operations and service applying digital, and then introducing personalized experiences.

so that we can use data to make recommendations for better decisions. So consumers have better data to be informed of what's the next path for them. And during that time then, I led the digital response for COVID-19 healthcare. And that meant new digital assets and support, not just for the members at United, but we shared out our data with Apple, with other health plans and with the White House. And what I wanted...from that experience was there is a leadership approach and model when the environment is fast paced. In that case, it was in a crisis and I led a financial product during the great financial crisis. And so that it's the same pattern of action along with shared frameworks. The pressure test, is this framework still working or do we need to move to the next framework? And I bring that up because I believe that's where we are right now with AI. It's not a financial crisis, it's not a pandemic, but it is a fast paced external environment. At the third phase then for me, it's been leveling up at healthcare. So going deeper and better. So to your point of the BC firm applying AI for early diagnosis, the CEO for Children's Cancer Research Fund going deep into oncology and looking at how AI is applied for speeding up this process that can take over a decade. How can we really get to those transformational insights and quickly go through or speeding it up for your clinical trials to breakthroughs.

Justin Grammens (05:13)

Wow, a lot of stuff here as you're talking here. I've been kind of writing down a bunch of different notes because yeah, your background is really broad, but there's this sort of common thread that you were sort of talking about. I really was thinking about transformation, certain companies or organizations or industries kind of reach this threshold, like you said, like you got to move forward now. And you talk about frameworks.

I guess how do you know the framework that you're working on is actually still being successful in like the broad sense? Well, we'll get into AI specifically, but you kind of tipped something off in my head. was like, how do you know that you're kind of on the wrong path?

Jean Machart (05:49)

You know, it really becomes for me two components. One is you're co-creating internally with across all of the departments. So it's one team as opposed to silos. Also up and down your organization. So as an example with COVID, it was developing, here's the framework to support consumers, providers overall, and pressure testing internally.

of this, this look right, everyone bringing their expertise of what about this or here's, you know, it sounds like a nuance, but it becomes a better framework of what we're trying to achieve as our goal. And what are those then key lanes that we're going to put our development effort, our strategic effort, and obviously our people against it. The second thing are signals externally, always pressure testing is something moving. Is there a new problem that we're not solving for? Or is there a new opportunity that we could be leaning into?

Justin Grammens (06:52)

Yes, yes, for sure. And I guess both of those, if you think about AI, they can be applied in both of those cases, right? And so we can focus on cancer research. I know we'll talk a fair amount here about healthcare just in general, because you were at UHG, UHC, the United Health Healthcare Group. Is that what it was? Yes. Yeah. And so, you you were seeing it on that broad base level, but then you became the chief operating officer at Children's Cancer Research Fund. how are you seeing things different on the broad base versus kind of what you're doing as a COO?

Jean Machart (07:22)

You know, at United, so much of the healthcare elements are owned within one umbrella. So it was a great experience to your point of learning and end healthcare from a payer health plan to care delivery, to home health, to pharmacy, all connected along with behavioral health. When children's cancer is the opposite, it's really a deep dive into one particular condition or disease type, but informed by and impacted by the full set as we think about mental health and the actual care delivery, health plans of how that integrates then with providers. The new element for me was going deep into the medical research perspective and stepping back and looking at how do we think about funding medical research and how can we use that same kind of end in thinking.

to fund basic science and as it continues to find insights and I'll call them wins, to continue to fund them all the way through then to clinical trials to get to children. Because at the end of the day, our goal was to have new treatments, better outcomes for children with cancer. And I found that to be the most rewarding mission to have.

Justin Grammens (08:45)

Yes, for sure. I think a lot of us, maybe as we get up in age, myself included, is like you're kind of looking for the project that has a little bit more meaning than just maybe making a company a little bit more money, right? So I healthcare is one of those places that at least I feel personally drawn to it in some ways. My dad was an oncologist for his entire career, oncologist hematologist. So a lot of leukemia, but all sorts of different types of cancer. And it's funny, late in my career now, he's basically sending me New England Journal of Medicine articles that I'm actually opening and listening and reading too, you know? And again, I think I maybe have to have a little bit more of this maturity, but when you take a look at what you were doing there, were you coming up with a lot of the solutions or did you have, just wondering how children's cancer research worked because were you having doctors on staff or were you getting grants to try and help, you know, do the work? Like, what was your relationship with kind of moving the ball forward?

Jean Machart (09:39)

It's really three components. One of course is funding. So gathering the funds that we'd be able then to grant out to organizations and having highly expert professionals. It would be like your father on a board who informed the science and the medicine along with then experts in AI. we're looking at it across disciplines. But I found

those experts to be some of the best humans I've met. They have the expertise, but also then they're directly working with the children. It's a gift and also a heavy heart at times to work through that. And then thirdly, we influenced across then at a national level with other experts and looking at, okay, what is our bigger picture strategy and how can we support your work and breadth of work?

Justin Grammens (10:34)

Yeah. So what were some applications of AI? You know, we kind of call this conversations on applied AI. is it, you know, a lot of it is drug research, I'm guessing some of these things, or you guys looking at devices or I don't know, let you tell me what some of the stuff that you have sort of seen it as you've been seeing, you know, as you're working through this.

Jean Machart (10:50)

And you're right, some of it is really drug application. So it could be an existing drug that's already approved for an adult that you're testing. Can you apply it for a child? And the other thing, because the numbers are lower with children with cancer, we also applied AI for the data side. Since it's a rare disease, you're looking at how can you not only aggregate the data, but use AI to find those signals and biomarkers.

Justin Grammens (11:19)

Sure. Like, how do you see healthcare changing in the next couple years, I guess, with AI? What would be some dream come trues for you, I guess?

Jean Machart (11:27)

Well, when you look at it, I'll cover three trends that you're seeing. One is healthcare is moving into a consumer-driven, consumer-centric industry. And I feel like it's the last industry to move to more decisions made by the consumer in their daily lives. And so, as an example with wearables around health tech, AI pulling in and monitoring that data

but then also starting to aggregate the data for the full determinants of health. We know that genetics and care is a component of drivers for your health. Now we expanding across and with AI, you're able to see pattern matching then and moving to more recommendations directly made to the consumer rather than just through, let's say your doctor. And you know, for that, one of the startups that I'm entering CEO for

He has an AI first, a multiple AI agent company, and he's using it for market research where you'd have different personas for market research. But I think also you could expand it to, let's say, behavioral change. And that goes into the second trend of healthcare moving to prevention as opposed to just sick care. You know, if you're able to look at behavior change, how can we look at for who we are in precision medicine and, the best food?

You can think about vitamins, can think about exercise, of course. But it's moving then as an individual, what are those best informed decisions and how can I change my daily life?

Justin Grammens (13:06)

Absolutely, yeah, know the earlier you can catch it upfront and how can you change? I spoke on AI at this health and wellness conference a couple months ago or so, and that's really what it was all about. It's all about in the workplace, like how can you put these incentives, how can you incentivize people to kind of have better healthy habits, whether it's just doing 10,000 steps a day type thing, eating more fruits and vegetables, all that stuff, you know, it's kind of like recycling, it feels like. People will think, well, what can I do, right? This is just one little piece thing that I'm doing, right? I'm going to recycle this bottle. What does that mean? Well, it adds up and it's the same type thing. think these behaviors, AI is such a broad term. It covers, you know, I tell people kind of covers machine learning and deep learning. And now we have this gen AI thing. Maybe you could talk a little bit about maybe how you're seeing, well, obviously machine learning and deep learning and some of this drug discovery stuff. But are you seeing it applied to generative AI as well?

Jean Machart (13:54)

Yes, so from generative AI, in addition to the persona company we were just talking about, we also have been applying it in the sales and marketing arena along with then strategic development. It almost says that brainstorming partner. Sometimes first draft, sometimes it's an edit or what's missing. And so I find with generative AI, we've certainly fully moved to this new, what I call intelligent transformation.

Given that we're now in this exponential change because the models continue to improve, we have new tools, we have new adoption, higher adoption. And then what I think about is tech stacking on top of and we talk about wearables a little bit, but you think about IoT overall, you know, the amount of data now that we're collecting that's available that we can use them from a generative side. You know, did you see other insights from your health conference that had an aha moments.

Justin Grammens (14:56)

Yeah, I guess what I'm seeing, I'm not going to pick on that industry, but I'm just seeing all industries in general don't seem like they understand the power of what AI can do for them. Right? Most people have used chat GPT at a level where, okay, I'm just going to type in some things that'll maybe help me rewrite my email, for example. Right. But then kind of taking it to the next level of basically saying, well, why don't we have it rewrite your health and wellness policy within your company. Right. And then obviously having it take some best practices and put those into place.

You might have a health and wellness policy today, but wouldn't it be great if you could basically have it review that? Like you said, it's just this external thing that you could now, using good prompting and good conversational techniques, you could actually have it review my policy and am I meeting these specific guidelines or these specific requirements? I think as I talked to various companies that were there, a lot of them are doing these wearables. There's all sorts of apps out there. We've been doing this for a long time. having kind of the gamification and having people sort of go against each other. But what I don't think companies are leveraging is that aggregate data information, then also kind of getting it back. they're having a tough time. And again, I'm just sort of talking off the cuff here, but I think a lot of companies are even having a tough time proving the return on investment, right? That, okay, now we're having personalized nutrition plans or personalized emails that we're trying to get people enrolled in various programs. And it's… difficult for them to sort of show because of AI or because of some of these personalization techniques we're actually seeing this, but we all know that it's moving the needle in some capacity.

Jean Machart (16:29)

You know, it's right. think, you know, we've seen some of the data. There's a spectrum of companies and some really have taken to it. They have a strong pipeline. They're driving gen AI and have been doing machine learning for a long time. It looks like a huge chunk of companies are to your point in this testing R and D phase and unsure about is this going to work out? And how I think about it is it's a huge sea change. So this is a large wave that we're

at the beginning stages of that you need to get fully into the game. And to your point, you need to step back and say, okay, when the models continue to improve, what's possible or what problems are going to be about to be solved and how will that change our strategy, but the overall market. And to your point with the data that's being captured on wearables as an example, electronic health records, EMRs continue to get better.

And we know another trend for healthcare is healthcare is moving into your home. So that becomes, you know, the smart home, all of your appliances, your daily habits can be built into your home for health. But also age tech as the baby boomers now continue to age, you know, you're seeing use cases of fall prevention and protection so that you can see, okay, what

What does mom and dad need to do? They're about to fall. Let's change something in their environment. Or is it time now to have that conversation to move them into assisted living or move up to the next step? You you start pulling all these pieces together. You can see the trend that we're getting there. And so I, at the R &D stage, I would say this is time now for the executive team to go deeper and build up more of that strategic point of view.

Justin Grammens (18:06)

Yeah. Sure. And that's kind what you do, right? Your job is to come in and take a look at what these companies are doing in this space and have them make sure that they are going to take advantage of as much new tech that they can, right?

Jean Machart (18:35)

It is, and what I'm seeing also from my two boards is that we have now hit a tipping point, not only from an executive strategic plan, but also now boards are shifting into technology leadership and AI is a core part of what they're doing.

Justin Grammens (18:53)

Yeah, it's going to become table stakes, I guess, in some ways, right? It's just the expectation is you're going to be leveraging AI in some capacity because there's just so much data. Tell me a little about the conversations around hallucination because especially in these industries, like healthcare in particular, there has to be a fear of that with these companies that you're counseling and you're on the board of or as you do work with them. How are some of those conversations?

Jean Machart (19:16)

You know, to your point, obviously, accuracy matters, but there are a couple different ways. Some are in terms of deploying additional monitoring to ensure the accuracy. Obviously, human the loop becomes a component of many of these use cases when it really matters from a health side. But healthcare still has, let's say, sales and marketing, other components where you're to get benefits of, let's say, lead generation, et cetera, where you're not at that same quality. standards for initial drafts or additional research. So I don't see it as an all or nothing. And when you think about it from a board perspective, we've been biting and leading organizations through technology continuously. And even with humans, of course, quality standards and metrics are a component of what we do. And so this is just pulling in AI into that same risk management model, the audit committee now than it had been even a year ago.

Justin Grammens (20:16)

Are you guys having any conversations around the future of work? I was out at a medical device company recently and we did a big workshop with them and you could sense the undercurrent was, when's AI going to take my job? Because it's becoming so capable in a lot of ways around the frontline engineers, for example. It's actually writing pretty good code. How are some of the conversations going with you and your colleagues and people at the board level?

Jean Machart (20:41)

You know, I think that it's at the early stages when I went deeper into, let's say, multi-AI agents and can see how specialized roles you can create. You can immediately go to, let's say, you know, a team of teams approach with specialists where they're collaborating, but also they're deep into knowledge. And...

you can design it so that they're solving and automating end-to-end processes where they're creating drafts, et cetera, or even how close to a finished product could they get to you. I do find it brings questions up of not only how are you building that first set of multiple AI agents, what is the end game in an organization of how many would you have, how are you designing it, and the architecture.

But then how do you think about your organization and your employees, their development, and continually reinventing their role?

Justin Grammens (21:42)

For sure. That leads me into probably the next line of questions I'm going to be asking you. And it really is about how do people get into this field, right? I think about people that are coming out of college and the job field is always changing, right? I answered the job market in the mid nineties. The internet was just taking off, right? It's just sort of getting going. And I, you know, kind of chose the internet as really thank goodness for the internet. It really has sort of helped me. It was a great place for me to find a job. And I've had a lot of, you know, obviously some great.

companies I work for and built some fun technology along the way because of the internet. But I think about people that are leaving school now and just that idea that now these machines that we've built over time might actually be in competition with them. And I am like you. I sort of take this idea that I think it's gonna make us to kind of like level up. We're gonna have to continue to be creative and curious and make sure that we look at what's next. So imagine me, I'm coming out of college. Like what are some suggestions that you would have for somebody wanting to get into this field?

Jean Machart (22:36)

think about it, the three threads for my whole career as I've moved further into AI across industries has been, you know, learning. So you have obviously formal education that you can do, but also, and because AI is so fast moving, I think about podcasts, additional reading. Of Sarah has amazing classes. There are other online classes. And, you know, Justin, what you offer through Applied AI, also puts that real education and conversation. The second thing I think is actually using the tech. so, you know, going deep on it, thinking about different use cases in your personal life. But then also it could be volunteering for a nonprofit on a side project. It could be volunteering for a project within work if you're not in an AI portion of the organization. And the third thing I think is really relationships and networking.

So in your area nationally, who are people you can be mentored by? Who can you pair up in learning cohorts? How can you expand your network?

Justin Grammens (23:43)

That is fabulous. No, I love that. I hadn't actually thought about I learn, I use it, and then relationships and networking. So yeah, and I will say for people that are maybe the coders out there, I always encourage people just to hop on an open source project, right? There's just so much. I mean, that kind of combines number two and number three. You start networking with people that are oftentimes, you know, building some really interesting stuff. You can learn a lot from them. And then you also get a chance to network with them. It might be your next job, but it also is a great resume builder, I will say.

As I'm looking for new talent, I'm always asking people, yeah, what are you working on outside of your day-to-day job? Like, do you have a pet project or something you're exploring and experimenting with? So that's fabulous. That's fabulous. So yeah, we've covered a lot here. One of the things I do like to ask is, yeah, what else have I missed? Was there something you came in that you wanted to make sure that we sort of talked about,

Jean Machart (24:31)

Well, for me, it's as we step back, the key goal is for us to lead the technology and not have the technology lead us. And you can see the momentum behind LLUMS as an example, but broadly AI. And that really does mean from an executive side, from an employee side, we need to look at the strategy of the company and again, business led or partnership, but we need to… not just look at the tool, but what's the big picture? I think of the people as you brought up. I think of the operating model for delivery and for monitoring. And then of course it's the data tech, AI, infrastructure and architecture. That's really moving from this one use case to an organizational view. And it means instead of just bolting on AI, you need to step back and having it embedded throughout again the full framework.

Justin Grammens (25:31)

Yeah, for sure. One of questions I was going to ask you is I don't know if you read any books recently if you wanted to share that you thought were interesting in this thing. But I did. I walk around and I tell people a lot about this book. And maybe you and I talked about this at one point, this Co-Intelligence by Ethan Malik.

Jean Machart (25:47)

Yes, I was at a conference with him.

Justin Grammens (25:49)

Yeah, that's right. That's right. Yeah. So I would be fascinated to see him speak. I've watched a lot of his YouTube videos and I've listened to his book a couple of times and he has these four laws of AI that he talks about. one of them is having, you know, making sure that AI has a seat at the table. The other one is making sure that the human remains in the loop, right? We work collaboratively with this AI. The third one is, you know, treat AI as a person, but make sure you tell it what kind of person you want it to be. All that is around prompting, right? So make sure act as this or respond to me in this type of way because that actually has a number of that will impact the way that the large language models will converse with you. And then the fourth one, it has a couple of different nuances to it, but he says, assume this is the worst AI that you will ever use. And there's a couple of different components to that. Number one is, of course, it's just always going to get better. And so there's these sort of areas where AI doesn't work very good, but try tomorrow and it might work better. But the other sort of sub message behind that is that you need to make sure you build your systems in the way that you use these things.

in such a way that it's not just a one-off project. It's, you know, just understanding that you're going to be able to plug in new AI models and new systems along the way. And that as an organization, you really need to not view this as a, I'm going to do this whole AI project and then we'll be done with it, right? It's going to be continually embedded in what you do. I think that maybe speaks to some of the things that you were talking

Jean Machart (27:09)

It is, and we are in such a fast curve right now. And again, when you look at where we're leading, I don't see the same kind of movement. So there's going be, you know, some businesses I think are going have a catching up period. The other thing he says though, that we can reflect on for a while or talk about is for AI, there's no master plan and there's no user guide. And so

You know, when I think about that, I think about, okay, that means we are in a race building technology, let's say overall, and it's to your point, what you make of it. And we need to think about strategically what's the first, second and third order effects of it. And so, you know, to your point of the internet and mobile and social media, there've been first, second, third order impacts that

you and I went to thought about in mid 1990s for sure. So for AI, what are those now? And how do we think about, again, leading the technology rather than being led by it or caught by surprise when it's, we feel like it's so far down the road, you can't move it back to what we want it to be. So I find this again with those components as to CEO, you know, I reflect on, you know, do you have the right executive leadership team?

Because this does mean you're looking at, it's more abstract, so you need conceptual thinkers, and it requires deep level of thinking where you're connecting the capability thinking along with the business thinking. And then you're looking at these dots, not just this year or the next 18 months, but again, many of these components moving simultaneously or within a period of time. And so when I talk to my peers, you'll hear them talk about this is humbling right now because it's moving so quickly. You know, we're all learning as quickly as we can, but still humbling because you know, not one person can know everything.

Justin Grammens (29:15)

For sure, yeah, so everyone's going to contribute. No, I love that thought, there is no master plan or user guide. I also view it as, almost feel like we're at the early days of like Newtonian physics or figuring out how planets orbit around because that's literally what people are doing right now. They're doing experiments, right? We're all experimenting in this space, not really knowing exactly where it's all going to play out.

Jean Machart (29:38)

And we do have additional scaffolding or to your point dropped pins from the history that we can look back and say, okay, what did we learn and where were we surprised and how can we apply it now?

Justin Grammens (29:52)

Yes, for sure. Yeah. Taking a historical view. The other book that I've been recommending a lot is a book called Nexus by Yuval Noah Harari, which is all about information networks. And he's a historian by trade. That's what he does. So he looks back at history. And if you haven't had a chance to read that one, that's really good. But this is where I personally fall down a lot too. It's actually taking the time to think back and say, we have hit these frontiers like this before in the past. There are some very unique things that I believe make AI different than just the invention of the calculator, people say, but the idea that like it's going to cross over in all these different areas that we haven't even thought of is absolutely true. I forgot where I read somewhere was like somebody imagined the car and the internal combustion engine, right? But they didn't imagine the drive-through, right? Like, you know, it was so far out of people's, you know, and so who would have thought a hundred years ago or whatever that we would actually take cars and the entire fast food industry would be born literally because of drive-throughs, you know? And it was a technology that was never even dreamt of, but that's where we're at today. You would have never predicted that, right?

Jean Machart (30:56)

It is. And so to me, I think about, OK, how can we apply AI? Again, we talked about health care. So how do we improve health? How do we solve, let's say, climate change? So what are new options there? How do we rethink energy? How can we create, I'll call it an abundant society so that we can solve some of these larger problems that we can't think about solutions for as we sit here today?

Justin Grammens (31:22)

And coming back to the beginning, how do you bring these frameworks into place? Right? So we talked about the very beginning.

Jean Machart (31:29)

Which, you know, I don't know if you've read The Competing of the Age of AI. I really like that book. Okay. And it was written by two Harvard professors. The frameworks there hold up of how to think about AI. And they have a simple one I really like about creating value and capturing value. So I like the line of thinking that can then ground an executive leadership team so that we're looking at what's the end game.

versus let's say just starting from the tech and kind of chasing that.

Justin Grammens (32:02)

No, yeah. One of the things that I talk to organizations a lot about, yeah, you can get kind of see the new and shiny and you need to start throwing technology at the problem first. And a lot of takes organizations a while to sort of backtrack and say, okay, what is the problem we're trying to solve here? How do people get a hold of Eugene? I want to make sure that you get a chance to tell people. We'll put all this information in liner notes. I'll mention competing in the age of AI. We'll put a link to that book. But yeah, how do people find you Jean?

Jean Machart (32:28)

The easiest way is through LinkedIn.

Justin Grammens (32:32)

We'll put a link out to that for sure. And hopefully there will be some people on our program that want to engage with you and services. I see this just growing. There's a lot of, like you said, organizations that are sort of taking the leap and they're jumping forward, but there's a lot of them that need help. And that's what you provide to these companies is that leadership and that thoughtful planning and these frameworks to make sure that they have a chance to succeed.

Jean Machart (32:55)

Fantastic, Justin.

Justin Grammens (32:57)

Excellent, excellent. Well, no, this was great. This was great conversation. I appreciate your time and you and I have been on a couple panels together and it's sort of like crossed paths. And so this was awesome to have you on the program. I look forward to having you a part of the applied AI community in future too.

Jean Machart (33:12)

you

AI Announcer (33:13)

You've listened to another episode of the Conversations on Applied AI podcast. We hope you are eager to learn more about applying artificial intelligence and deep learning within your organization. You can visit us at appliedai.mn to keep up to date on our events and connect with our amazing community. Please don't hesitate to reach out to Justin at appliedai.mn if you are interested in participating in a future episode. Thank you for listening.


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Justin Grammens

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