The following was transcribed from a recent interview on The Agile Brand with Greg Kihlström podcast.
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This article was based on a recent podcast interview with Scott Love of Lovelytics. We talk about artificial intelligence and machine learning in the enterprise, where it is today and where it’s currently headed, as well as the role of place in building talented data and technology teams.
[Greg Kihlstrom] Certainly AI and machine learning have been getting a lot of attention lately. What do you think happened to cause so much attention so quickly, when really AI has been around for decades?
[Scott Love, Lovelytics] I think you already said it, right? I think ChatGPT happened, to most of these orgs, and to people, right? And they’re exactly like you said. I mean, data science, AI, ML, you know, whatever buzzword we want to use has definitely been around for a long time. But I think so few people were exposed to the technology outside of high-tech companies or individuals that were in those roles. And so, as that technology has progressed, and especially with ChatGPT, people were able to see the value of generative AI every single day and in their personal life. Obviously, in work people are using it to write blog posts or other items and they’re seeing how much time they can save and the value that it can provide. But, on the other end, people are googling “What’s the best way for the Browns to win the Super Bowl,” for example, you know, different things, individually, that they’re going in and just asking questions and amazed by the outcomes that are there. Maybe that level of technology hasn’t been available forever. But, you know, a certain level of it has been at large enterprise, which is now very cool.
I think what’s most exciting about it is it just democratized this technology to the world, right? And these tools are available to everybody, whether they want to use them for commercial uses or they want to use them for individual uses. It’s just an incredible technology that got put in the hands of everybody.
As you mentioned, the generative AI getting a lot of attention there, but what areas of AI are you most excited about? And how does this relate to the work that you do for your customers?
What’s interesting is really, with the release of ChatGPT, as we talk to our customers, generative AI, or just AI in general, skyrocketed to the top of everyone’s discussion list, everyone’s priority list. How could we use it? Obviously it’s helping to drive valuation of a lot of companies that are using it effectively. And so it definitely is of interest to a lot of organizations. Like me, obviously the LLMs, or essentially what ChatGPT is, is really what excites me the most probably, like the rest of the world, right? I think the reason that it excites me is much more around the amount of time savings we can have for people, and not only the time savings that goes into it but the higher-quality outputs that also come out of it. So we’re not just taking a task that used to take a day and it now takes a minute; it’s also producing what would be a higher quality output than what we would have seen in the past. So to me that’s really what’s exciting, and then applying that to a multitude of customer use cases that we have.
For example, let’s say we do some work in the retail and consumer goods space. So let’s say you’re a product company. You have a ton of user reviews. You have a ton of client feedback that you’ve received about your individual product. Historically, these type of projects may be centered around doing some sort of sentiment analysis. Maybe they centered around looking for individual keywords that you were looking to do. You’d pull those out. You tried to do an analysis on thousands or tens of thousands or hundreds of thousands of reviews. Now, with LLMs, we essentially can take that data; we can summarize it; and we can put it into a usable data format. So now these companies are able to really, genuinely analyze the feedback that they’re getting and summarize it. And it takes a fraction of the time that it would have taken in the past. So, to me, those types of use cases are amazing just because of the time savings and the quality that we can drive at the same time.
So certainly there’s a lot of hype – well- deserved and sometimes hype – with AI, and you mentioned some great use cases and things like that. But what are businesses not paying enough attention to, or maybe what are some opportunities that you think that businesses are missing out on?
Yeah, I think that’s a really good question. So, in data and analytics, in the AI space that we’ve been playing in for a while, I always talk to everybody about the really fun use cases that we do, right? We’re predicting revenue from music artists or we’re driving in-game player decisions for professional sports teams. Those are the things everyone loves to talk about. But whether it’s AI or ML or just business intelligence dashboarding, the thing a lot of these companies are missing is that they don’t have that foundational data infrastructure that allows them to utilize these technologies. And when I say “foundational data infrastructure,” I’m talking about taking however many data sources a company has – let’s say they have a CRM system, a finance system, an EHR system for a hospital, whatever it may be. Putting all of that into some sort of centralized location that we know is governed so that the data in there is accurate, that it’s updated automatically and that it’s made available to users. And so many companies today are still reliant, heavily reliant on Excel. So many companies are heavily reliant around old technologies, you know, on-premise databases, things along these lines.
And why that’s important when we talk about AI, in the same way that we’ve talked about other breakthrough technologies in the past, is these companies aren’t going to be able to effectively utilize it if their data’s not in a good spot, if it’s not clean, if it’s not organized. And so these companies that historically have spent a lot of time; they’ve implemented modern data technologies like a Databricks, for example; they’ve spent the time to create processes internally to clean this data, and they made it available. They’re going to be able to use AI; they’re going to be able to use ML, everything, off the bat. The companies that have not done that, they’re really starting behind the eight ball here, right, where, really, they need to be able to come in, they now have to do that foundational work and likely have to greatly accelerate it, and then they can start using these technologies. And by then, some of them are going to get left behind. So that really is something that I think is not discussed enough with these technologies, is how many companies are not at the level that’s really required to use it effectively.
Would you say then that’s really the foundational step, is just getting the data house in order, so to speak?
Oh, absolutely, for sure. And as it relates to our business, we’re definitely doing a lot of ML and AI, but we’re doing way more foundational data work that’s there simply because there’s a larger number of companies that just don’t have that ready. What’s the most exciting piece is, once you get that ready, the work that we’re doing around talking with companies about how to use LLMs, how to use AI, like really future vision-casting, a lot of that information, that’s what excites me today. Because I think there’s so many use cases and so many opportunities that companies aren’t thinking of that our team can pull from our experience or work that we’ve done elsewhere.
In addition to all of those great possibilities with data and with AI, there’s also many potential challenges beyond even just getting those foundational steps in place. So, you know, this could be ethics; it could be security, or other things. How do you approach some of these challenges with your customers?
Yeah, this is definitely front and center to a lot of the discussions we’re having today. Starting with the ethics side, I mean, everyone is talking about this right now, right? And I don’t think there’s a right or a wrong answer. I think it’s something we definitely need to be cognizant of, and us as an organization in particular, making sure we’re working with businesses that are doing the right thing and making sure that we don’t put ourselves into a compromising situation, is obviously critical and something we’re well aware of. But beyond the ethics piece, which is, again, very, very important in these discussions, the biggest challenge I would say we face on a day to day basis is much more around security I know there was a really popular article or news story that came out a little while back about some Samsung employees that entered some proprietary code into ChatGPT and that made it theoretically available to the public.
You know, those types of discussions of things, our customers are very concerned about, right? Yes, there’s incredible power with these technologies. But there’s also incredibly sensitive information that’s there. And I think that security risk is real in certain situations. I think there’s other times where maybe it’s just bigger companies have to take some time to feel good about the new technology. We saw that with Cloud Migrations, for example, when companies were so hesitant to move to the Cloud, where you’re going to see healthcare and finance Industries probably move a little bit slower because of the sensitivity of a lot of their data, where let’s say media and other gaming companies, for example, that are probably going to be leveraging these much faster, just partly because of how they operate as a business. So the security piece definitely is something we see a lot and we see as something that needs to be addressed on every engagement, for sure.
However, there are a lot of interesting solutions coming out to address this. For example, one of our technology partners and investors, Databricks, they have a tool called Dolly, and Dolly is essentially a specific LLM product for an organization. So this will allow companies to essentially train their own internal models to be specific to their business. This significantly eases security concerns because it’s inherently internal, but more importantly, it also curates it and customizes it to a business’s needs. So things like that, I think, we’ll see much more of moving forward.
Speaking of Databricks, and you had mentioned that you’re now private equity-backed as well, I wanted to talk about some recent announcements that Lovelytics has had. So Interlock Equity, a private equity firm, recently invested, as well as Databricks Ventures. Can you talk a little bit about this, and what’s the impact that this is going to have on the business?
We’re really, really excited about this news. We’ve talked a lot about AI already in this discussion, but this drive and the significant increase in needs of our customers for AI, for ML, it just inherently is great for business on our side, but really drives a lot of demand. And so we were lucky enough to find two great partners, that you mentioned, in Interlock and Databricks Ventures, who believed in the vision we have for the company and really want to be part of our team. And so what this does for our business is a myriad of things. One of those is accelerating and expanding our investment in AI, in data science, in ML, both on the services front but then also in products and solutions that we feel that we can develop as an organization.
We also will expand our team, like a lot of private equity investments. This helps with accelerating the pace at which we bring new team members on. We bring in folks that can really help us continue to scale this business. As it relates to us in particular, we’re looking at much more industry-specific expertise, so being able to apply the technical solutions we have to really specific business use cases and real money gaming in healthcare and financial services, you know, other industries that we know, or places we have deep expertise in and want to double down on that. We want to continue to expand our sales team to work with more organizations that are out there. We want to keep adding more technical experts on our team to continue to deploy top-quality work to the customers we work with. And, really, lastly and most importantly is, as a consulting firm, we’re a people business, right? So people are essentially buying our team members’ expertise and thought leadership as our product. And so this investment also allows us to invest back into our team.
So that revolves around training; that can be more benefits; that can be company events, you know, other things that we just want to make sure our company continues to be an attractive place to work and one where the culture is really front and center and trying to do things a little bit differently than traditional consulting has in the past. So we’re, again, really, really excited about the investment. We’re lucky to have two great partners, and it should really lead to a lot of fun growth and opportunities on our side.
One last thing I wanted to talk about is the role of location in your business. So, as you mentioned, you’re headquartered in Arlington, Virginia. You also, like many other companies, have remote employees all over. The world has obviously changed a lot over the last several years in relation to the workplace with hybrid, remote, all of the above becoming more prevalent. How do you look at location? And how much does location still factor into how you build your company?
So you’re spot-on. The world has definitely changed a little bit in the past couple years in terms of work. I mean, we went from 90 percent of our team being in office, for the most part, every day, in Arlington here, to having let’s call it 50 percent of our team in the Arlington area and then the rest spread out across the U.S. and Canada. So for me as a small-business owner and someone who really has to have a huge emphasis on culture, that was really first and foremost in a lot of what we tried to focus on, that is someone who’s living in Texas, for example, and working with us, who may not get to see people from the company on a monthly or even quarterly basis, how do we make them feel part of the team? We put a huge emphasis on really trying to make sure our virtual team members have opportunities to come to Arlington to visit the team or to other offices, to bring people together in different cities, do virtual events and things like that.
But as it relates to Arlington, where we’re headquartered, we really have a lot of advantages being here. I can’t sing enough praises about how great Arlington has been to me as a business owner and just someone who has started and invested in the area, in terms of adding jobs and investing in the ecosystem around here. They’ve been incredible partners. So one thing I think of is just hiring local talent and building a hub of people. Really, Arlington is incredible at the amount of talent nearby, and a lot of that is a factor of just the other great companies that have moved into Arlington, right? And that also plays a role from a business perspective, like with Arlington bringing in Amazon and Gerber and Nestle and all these different organizations that are coming here that are very attractive to people, it increases the pool of talent. It also increases the pool of opportunities for us to work with local, large enterprise organizations where we’re just down the street from their headquarters. You know, it really is an exciting time.
So, really, the two things, having a great talent pool that allows us to continue to hire locally, that obviously makes culture and everything a lot easier, if you have a lot of people in a centralized hub. And then from a business perspective, it just really allows us to have some opportunities with large enterprises that are local to us here.
About the Guest
Scott Love founded Lovelytics in 2017 after leading sales for other services partners in the data analytics space. Scott is originally from Indianapolis, IN and spent 7 years living abroad in The Netherlands. Currently, Scott Love resides in Arlington, VA.
About the Host, Greg Kihlström
Greg Kihlstrom is a best selling author, speaker, and entrepreneur and host of The Agile Brand podcast. He has worked with some of the world’s leading organizations on customer experience, employee experience, and digital transformation initiatives, both before and after selling his award-winning digital experience agency, Carousel30, in 2017. Currently, he is Principal and Chief Strategist at GK5A. He has worked with some of the world’s top brands, including AOL, Choice Hotels, Coca-Cola, Dell, FedEx, GEICO, Marriott, MTV, Starbucks, Toyota and VMware. He currently serves on the University of Richmond’s Customer Experience Advisory Board, was the founding Chair of the American Advertising Federation’s National Innovation Committee, and served on the Virginia Tech Pamplin College of Business Marketing Mentorship Advisory Board. Greg is Lean Six Sigma Black Belt certified, and holds a certification in Business Agility from ICP-BAF.
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