Episode 4 | AI in Business: Driving Efficiency and Profitability

In Episode 4 of "Market News with Rodney Lake," Rodney Lake, the Director of the GW Investment Institute, discusses the pervasive impact of artificial intelligence (AI) on businesses. The episode explores how AI technologies, such as ChatGPT and other machine learning applications, are revolutionizing industries by enhancing efficiency and profitability. Professor Lake emphasizes the importance of evaluating companies based on their ability to integrate AI into business operations, from customer service to logistics, and how these integrations can drive cost savings and revenue growth. He highlights that while AI can reduce labor needs and optimize processes, the ultimate goal for investors is to identify firms that effectively leverage AI for significant profitability gains, thus affecting their market valuation positively. Professor Lake encourages listeners to thoroughly investigate AI's influence on their respective fields, as it will continue to shape business operations. The episode sets the stage for a deeper dive into Nvidia, a prominent AI player, in the next episode.

Transcript

Thank you for joining Market News with Rodney Lake. This is a regular show of the G.W. Investment Institute where we discuss timely market topics. I'm Rodney Lake. I serve as Vice Dean for Undergraduate Programs at George Washington University School of Business. Let's get started. Welcome back. This is the next episode of Market News with Rodney Lake. Number four, thank you for tuning in and great to see everybody back.

Today we're going to be talking about AI, which everybody is talking about. Of course, artificial intelligence. If you're not paying attention at home and its impact on businesses at the Investment Institute, we're always thinking about, okay, what's happening with business? How is it being impacted by different trends in AI? Seems to be a super trend now. What does it mean?

So we're going to talk about it in a couple of different things. And a tip for the next episode. For number five, we're going to talk specifically about the largest holding, Nvidia, in the GW Investment Institute portfolios. But for another episode, back to Nvidia. Now, when you think about AI, what are you thinking about? Well, right now everybody is thinking about ChatGPT and thinking about, well, you know, maybe I can write an email fast, or maybe I can use Grammarly, maybe I can use all these functions and it makes me a little bit more efficient.

But those things can add up to big efficiency. So let's really start thinking about, you know, businesses overall. We're going to use the framework which we talked about in episode three, which is the GDP Investment Institute framework, which is business management, price valuation, and balance sheet. So we're going to look in the context of that. And for this discussion, it's really going to be mostly centered around the business and the management. The pricing, the valuation is indirectly impacted by artificial intelligence, and so is the balance sheet perhaps. And we'll talk about a little bit about that. But let's first start talking about the business. So AI is going to impact different businesses in different ways, obviously. Or maybe obvious to most people. When you're an investor and you're thinking about, hey, how can we use this to our advantage? Or how can we start sorting out this company is going to be impacted in a positive way, and this company potentially is going to be impacted in a negative way, and making sure that we allocate our resources appropriately in our portfolio.

It's something that we have to really consider and think about and try to think through and think about what are those impacts, both directly and indirectly, and are the companies that we own and the companies that we're thinking about owning. But there's all sorts of things out there. So maybe some basics here is, you know, what is artificial intelligence? Well, you know, they call that artificial intelligence in PowerPoint. That's it's AI. But when it's actually being used, it's machine learning and statistics and all these things that happen behind the scenes. And there's of course real-world examples already that you interact with, which is if you're using ChatGPT, if you get recommendations, on your music or in Netflix, in these other categories, Netflix publicly traded company as well, and Spotify and Apple also.

So this thing permeates sort of everyday life in developed countries in any case, and it's just being more and more embedded. Now, who are the companies are going to take advantage of that? That's what we have to start thinking about. And what are the long-term impacts? It's happening everywhere, from things like customer service. So what's the customer service impact? Well, maybe now you're dealing with a chatbot. And so, if you have a banking application, there's been lots of advertising for different banks for their chatbot. But any time you log on to a different service and you're trying to get a customer representative, likely it starts now with, can I help you via this chatbot?

All that's really powered by machine learning algorithms, in the background. And likely that reduces headcount. They probably still need people, right? But the number of people that they might need for this labor-intensive activity starts to go down. So you start thinking about companies that are they going to benefit from this? Well, who are those companies? Well, companies that need lots of customer service representatives. And before we mention, I did mention Nvidia already. Any of the companies, just a broad reminder that this is not investment advice. Anything that the Investment Institute does, it's for its own portfolios. And remember, it's student managed. But we talk about companies. And so, just want to be clear about that.

Another kind of broad category would be marketing and sales. And so some of these things that you put online, you know, they can generate themselves, can create great marketing content. And so, again, back to the large language models that are out there, that's ChatGPT and Claude, and Gemini from Google. And lots of people are now getting in the act, including now X, what was formerly Twitter has their own feature called Grok, which is backed by, not open AI, that's the former with Elon Musk. But X I at the moment. And so you can use these large language models to create all this marketing content and sales content, and maybe that does start to replace some of the people that maybe are those, you know, first-year associates at a marketing firm and an ad agency.

I think it's to be determined on how that impact happens and where that happens and how companies can filter that. And so if you're a company, publicly traded company like Nike, as an example, you know, you're likely outsourcing a lot of your sales and marketing to third-party agencies. But are they at the agency using it? Are you now going to be more embedded with those agencies and thinking about how you're using AI and what recommendations come out of that, and what efficiencies can you get out of that? And really then it becomes about tailoring your spend. And so all that already stuff starts happening when you're using analytics for different platforms. You can think of the, an old one called Facebook, and something that people are using now or the kids are using, TikTok, for example.

And what are the algorithms that are steering people to those products and services and what AI is being used, for that? Maybe that starts to replace a lot of the traditional positions within companies. And maybe these companies become more profitable as a result. So you have to start thinking about, well, if I'm eliminating labor, if it's a minuscule portion of the cost structure in any case, well, maybe that's not going to actually have a big impact on profitability. So maybe that company is positively impacted, but it's not going to make a big enough difference in the gross margins, in the net margins, you know, to have a significant difference in the outcome of, okay, the net profitability does change dramatically. So that's another thing to think about. Some of the other categories just to consider are on the supply chain and operational management.

So you think of logistics companies. And one of the you know, a few of the best-known logistics companies would be Amazon, for example. I'm sure everyone, or almost everyone has received packages or maybe picking up a package off your porch as we speak, or getting it delivered to your building or wherever you happen to be, in FedEx and UPS.

And so moving all these packages around is a complicated process. Takes a lot of thought. And, you know, how do you optimize this system? Well, if you apply machine learning algorithms to say, okay, let's optimize this network, you can probably drive efficiencies that were not as obvious before to a human being. Even somebody that's really experienced. Well, that might change the operational setup.

And it might, for example, make Amazon more profitable on the logistics part of the business. It might make FedEx more profitable on the logistics part of the business. And same for UPS. And so what's the impact for them and can they use it? Now just because they can use it, will they use it and can they be effective with it?

Those are different questions that need to be answered. So when you start looking at these companies, you got to start thinking about, okay, what type of AI applications are they currently using? How are they implementing those things, and are they really driving efficiencies that then turn into profitability for the company? So the gross margins come out and expand and the net margins ultimately expand.

And so then where you're connected to the valuation piece, when we talk about the business management, price valuation, a balance sheet is on increased profitability. Companies that have higher net margins, higher gross margins tend to get higher multiples on their PE as an example. So that's the indirect impact. When I talked about you got to think about these indirect impacts on price valuation and balance sheet.

As some examples for all of these companies, that's what you're really looking for. What is the impact and really is it going to drive profitability? So as a shareholder of a company or a potential shareholder of a company, you're going to be thinking about what applications are they specifically using to drive efficiencies that then drive profitability? If they drive efficiencies that don't really turn into anything?

Well, that could be nice. That could be kind of a parlor trick for some of the things. But if it really doesn't make a difference in the bottom line, well, you know, kind of a nice to have. And it's cute. And maybe it makes sense to deploy some of that. But if you're a shareholder, what you're really looking for is material and meaningful impact on cost savings or revenue generation or ideally, both.

And so when you're talking about the sales and marketing side, you're saying, well, how can we more efficiently drive revenue from sales and marketing? And how can we more efficiently cut costs and right, you're cutting that. You're driving the top line and you're cutting things off from the bottom line, this leads to higher gross margins, higher net margins, and ultimately a higher valuation for your company.

As a CEO, you need to consider capital allocation, your top priority. Dive into the management aspect: how do you drive capital allocation? Management must evaluate applications, capital requirements, and time commitments for projects. Analysts need to scrutinize management's spending through press releases, annual reports, and earnings calls. For example, Tesla's full self-driving project could significantly increase its value by transforming cars from depreciating assets to cash-producing machines, assuming the technology and regulations align.

Tesla's extensive investment in AI, like their Optimus robot, aims to unlock substantial value, but success isn't guaranteed. Analysts should consider potential risks, like projects not panning out despite heavy investment. Effective capital allocation involves investing in growth, improving products and services, paying dividends, or reducing debt.

Analysts must assess management's actual implementation of technologies versus marketing hype. Overreliance on fragile AI systems can backfire, affecting customer service and overall resilience. Successful companies build durable competitive advantages, as seen with Microsoft's integration of AI like Copilot, enhancing their product offerings and market position.

Understanding AI's real impact on business profitability is crucial. Increased profitability usually leads to higher market valuations and improved balance sheets through better free cash flow management. Companies like Nvidia and Microsoft exemplify these benefits.

In conclusion, as an analyst, focus on separating talk from action, assessing the real economic benefits of AI implementations, and understanding the broader structural changes AI brings. This insight is vital for evaluating companies and making informed investment decisions.