How I Invest with David Weisburd
00:03:03 · David Weisburd, Joseph Coyne
[00:03:03] S0: That number you said from Deloitte, ninety five percent of pilots fail. I haven't heard that number, but I experienced it firsthand. And you're see... You're seeing these hundreds of enterprises implementing AI over and over every day. What are the best practices in terms of the five percent that do succeed? [00:03:19] S1: If they come from a deeply technical background that's already been doing deep data analysis and has great data infrastructure plus security, then they have the foundation of success. Right? So that's like already tipping the scale in the favor of tech companies that are pretty well poised to do that. Some of the legacy companies, legacy industries that are lower tech, we've seen implementations where once we get under the covers, the data is pretty unruly. And so getting the data to a point where from an infrastructure standpoint, it's ready to adopt AI is half the battle. Now, I think once we cross that chasm, a lot of these AI best practices can start to proliferate and accelerate. [00:03:57] S0: What does it mean to have good data versus bad data when it comes to implementing AI on the enterprise level?
00:03:57 · David Weisburd, Joseph Coyne
[00:03:57] S0: What does it mean to have good data versus bad data when it comes to implementing AI on the enterprise level? [00:04:02] S1: You see in large enterprises, right, they're... The larger the enterprise, the more complex, generally speaking, and they have lots of data silos. That means data tagging fidelity across different fields and tags can be different. And so when you try to run enterprise grade AI on top of it, you have all sorts of sometimes conflicting tags and data structures, which have to be resolved before you can really do it in a meaningful way. And so those are some of the data transformation projects that are foundational to then running great AI and and analytics on top of enterprise. [00:04:32] S0: Something I hear over and over about AI implementation is that it's mostly a cultural issue. Yeah. Sure. There's data. There's processes, but starts at the culture level. How much does culture play a role in terms of enterprises being able to implement AI within the organization?
00:04:32 · David Weisburd, Joseph Coyne
[00:04:32] S0: Something I hear over and over about AI implementation is that it's mostly a cultural issue. Yeah. Sure. There's data. There's processes, but starts at the culture level. How much does culture play a role in terms of enterprises being able to implement AI within the organization? [00:04:49] S1: That's right. And it starts from the top within organizations. Right? If if senior leadership is embracing it, it has to be, in a lot of cases, top down to reach sort of the most mission critical parts of the enterprise. And so, you know, some leaders, especially within tech companies, are thinking about this on a daily basis. And I can tell you for a fact that some companies leading huge banks, huge consumer companies, huge airlines are not native to this. And so there's a lot of education and foundational work that needs to happen for them to really reap the benefits of AI's, you know, power and and and possibility. [00:05:21] S0: This episode is presented by Juniper Square, the operations partner for private markets. So if I'm the CEO of Delta, let's say I just put my foot in my mouth with a Starlink and saying that I'm... Don't want Starlink and and don't like SpaceX, all these things. But let's say I want to rehash my image and I want to show that I'm leaning into technology and leaning into AI. Is that the problem set that you would go into, which is elastic pricing for seats? What would you advise the Delta CEO to do? What's the next steps?
00:05:21 · David Weisburd, Joseph Coyne
[00:05:21] S0: This episode is presented by Juniper Square, the operations partner for private markets. So if I'm the CEO of Delta, let's say I just put my foot in my mouth with a Starlink and saying that I'm... Don't want Starlink and and don't like SpaceX, all these things. But let's say I want to rehash my image and I want to show that I'm leaning into technology and leaning into AI. Is that the problem set that you would go into, which is elastic pricing for seats? What would you advise the Delta CEO to do? What's the next steps? [00:05:53] S1: It's looking at a lot of historical data to understand how they've approached it in the past, but then also having a horizontal view of pricing within the industry. One of the ways you can get a data moat, which is very important these days, is to have horizontal insights about an industry. Meaning, you see hundreds of airlines. The airline you're talking to probably sees mostly its own data most of the time. And so when you bring horizontal insights around best practices and price elasticity and price per mile, what type of seat across different organizations, you can then bring that to bear within large high willingness to pay customers. And so I think it's, a, looking at their own historical data, but, b, gleaning a lot of model insights across thousands of other potential opportunities. [00:06:39] S0: This connects to what we were talking to a couple weeks ago, which is you said that the vast majority of data is not accessible to LLMs. Why is that?
00:06:39 · David Weisburd, Joseph Coyne
[00:06:39] S0: This connects to what we were talking to a couple weeks ago, which is you said that the vast majority of data is not accessible to LLMs. Why is that? [00:06:47] S1: One of our favorite stats, and it actually it actually came through an enterprise that we work very closely with, which is that about 80% or more of the world's data behind firewalls. LLMs have done an absolutely heroic job. Right? Going from infantile phase a few years ago to in the pockets of almost every American and around the world. So would never speak ill of what they've accomplished, but they've accomplished it with the publicly available data, similar to how Google crawls the public web. To get a lens into private data, you have to earn customer trust, you have to bring unique insights, and oftentimes, you need a subject matter expert to be able to have the trust and expertise to enter a market like airlines or banking or insurance. When we see companies that are starting to unlock those types of value, they get access in a highly secure way to proprietary behind the firewall data, the 80%. When they do that, they can start to produce new insights for the company and for the industry they operate in. And that's where really high value opportunities exist. Years ago, we were just pure play enterprise software investors. Now, we always see those companies come with an AI strategy. The best of those companies come with an enterprise software workflow that systematically helps companies to unlock their data and then run AI best practices on it in a highly secure way.
00:08:05 · David Weisburd, Joseph Coyne
[00:08:05] S0: You made a very important but subtle point, which is the reason 80% of the data is behind the enterprise firewalls is because it comes from the customer. In order to get that customer data, you must provide a good product. So everything is still downstream of having great product. The difference in AI world today is that your data moat starts to be one of the most sustainable forms of competitive advantage. So, again, 95% of enterprises according to Deloitte fail in terms of their pilots. The ones that are succeeding, what are some early wins that you're seeing in enterprise that are maybe not not as obvious to the outside? [00:08:39] S1: Enterprise software that is creating workflows that unlock new data feels like the oil rig company pulling new oil out of the ground for enterprise. And what we're seeing [00:08:51] S0: Give me an example.