S12 Bonus: Why Your Enterprise AI Pilots Are Stalling and the Unsexy Data Pipeline Fix for 10x Engineering Velocity with Tyler Hochman, Founder & CEO of FORE Enterprise

[SPEAKER_00]: There's a couple really interesting decisions we made.

[SPEAKER_00]: The first one is how to know who was the right time to pivot, away from more place turn over to Pipeline, Implementation, and ultimately it just became a simple numbers game.

[SPEAKER_00]: We had a buy and farm majority of clients.

[SPEAKER_00]: who were already willing to pay for the employer workplace turnover solution, but were not able to set up the product without that implementation piece then they asked us to do the implementation piece as well and by the time we finished the implementation piece they had spent more than the cost of the workplace turnover product.

[SPEAKER_00]: My name is Tyler Hockman.

[SPEAKER_00]: I am the founder and CEO of Four Enterprise.

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[SPEAKER_01]: I'm your host, Noel Abhart, and today, how Tyler Hopeman created a company to fully deploy your AI as your premier AI Solutions Architect.

[SPEAKER_01]: Tyler Hopeman got started early in the world of entrepreneurship.

[SPEAKER_01]: In middle school, he got into gemology, fascinated by the formation of gyms, becoming a young GIA certifier.

[SPEAKER_01]: This taught him to get out of his comfort zone from which he started his first business as a junior at Stanford.

[SPEAKER_01]: Pass that, it's been a similar process of identifying a problem and looking at how to build the solution.

[SPEAKER_01]: Outside of technology, he is married with a one-year-old son and another child on the way.

[SPEAKER_01]: He loves spending time with the son and enjoying all the things parenthood throws at you.

[SPEAKER_01]: Tyler built a workforce turnover solution half a decade ago in the space of predicting employee turnover in a business.

[SPEAKER_01]: It got a lot of solid traction, but what he and his team noticed was that though people wanted to use the solution, they didn't have the infrastructure necessary

[SPEAKER_01]: This led he and his team to swim a bit downstream to build that data pipeline for customers.

[SPEAKER_01]: This is the creation story of four enterprise.

[SPEAKER_00]: So we started for Enterprise.

[SPEAKER_00]: It was interesting.

[SPEAKER_00]: We started as a workplace turnover solution.

[SPEAKER_00]: So almost now half a decade ago, we were looking at all this turnover that was occurring in the COVID era and the post COVID era with the great resignation and the different ways that that jobs were being affected by remote work.

[SPEAKER_00]: and we created a AI solution that could predict when employees were going to stay or leave at a business with a relatively high degree of accuracy called six months out, six months to a year.

[SPEAKER_00]: And then also tell employers the main reason we are predicting why they're going to stay or leave, and then from there the employer would be actually given targeted intervention

[SPEAKER_00]: And so that's how we started.

[SPEAKER_00]: It was a really cool AI product that got a lot of great traction.

[SPEAKER_00]: And then what's interesting about it beyond just the product itself was what we found is that a lot of people had a desire for that product, but they didn't have the necessary infrastructure to actually power it.

[SPEAKER_00]: Meaning they didn't have it, we consumed a lot of private company data.

[SPEAKER_00]: and we consumed it in a specific way in order to power the products for each business, and the companies didn't have the infrastructure necessary to give us that data.

[SPEAKER_00]: Everything from literally getting it themselves, just structuring it, formatting it, storing it, and then ultimately outputting it to the tool.

[SPEAKER_00]: So then we swam a little bit downstream and we're like, okay, that seems to be the biggest mode for a lot of our clients and we're using our product.

[SPEAKER_00]: So why don't we just go and actually build that pipeline for them and then they can go and power whatever AI tool they want and we help them do that and we also build additional AI tools as well but we really became very solid at that solution architecture piece.

[SPEAKER_01]: This will be interesting, so four, four enterprise.

[SPEAKER_01]: What would you consider, you know, the quote unquote MVP, right, of what you've built there.

[SPEAKER_01]: Maybe it's the workforce turn over solution or maybe it's like the first version of your team going and building those pipelines.

[SPEAKER_01]: I'm curious where you'll take that, but I'm curious about that and how long it took you to spin up and what sort of tools you were using to bring it to life.

[SPEAKER_00]: Yeah, I would say it's probably the latter, given how distinct pivot we made from the workplace turnover solution to the proprietary skill set and internal platform we developed to implement the pipeline for businesses.

[SPEAKER_00]: So I would say that was our MVP was after that pivot, and then the first time we did it.

[SPEAKER_00]: And it was definitely, it to your point, I think it was a problem.

[SPEAKER_00]: What's interesting about it, it's not a very sexy problem.

[SPEAKER_00]: Being able to say, oh, I can predict your employees leaving before they even know themselves or leaving.

[SPEAKER_00]: It's something that an employer can immediately jump on and be like, wow, that sounds amazing.

[SPEAKER_00]: I would love to have that.

[SPEAKER_00]: So we had a lot of traction there.

[SPEAKER_00]: I think we have more real traction now in the sense that the problem that we're solving is very boring, very unsexy, but completely necessary to really do any AI tool that you want to or any AI tool to a high degree of quality that you want to power.

[SPEAKER_01]: I'm curious about maybe a decision or trade off you had to make in that pivot and moving to that, building that internal, the internal tooling to help you build these pipelines for your customers, a decision or trade off you had to make and how you coped with it.

[SPEAKER_00]: So there's a couple of really interesting decisions we made.

[SPEAKER_00]: The first one is how to know who is the right time to pivot.

[SPEAKER_00]: A way for more place to turn over to Pipeline, implementation, and ultimately it just became a simple numbers game.

[SPEAKER_00]: We had a buy and farm majority of clients.

[SPEAKER_00]: who are already willing to pay for the employer or place turnover solution, but we're not able to set up the product without that implementation piece, then they ask us to do the implementation piece as well, and by the time we finished the implementation piece, they had spent more than the cost of the workplace turnover product.

[SPEAKER_00]: And so we're like, okay, the dollars here are in this

[SPEAKER_00]: Let's go and do that, and then we can continue to do the AI truly and so on and so forth, but it was very much not being afraid to say that we have something that is very cool, but just because it is so cool, doesn't necessarily make it the right product at the right time.

[SPEAKER_00]: And I think a lot of the eyed businesses right now are facing that exact problem, like the possibilities of AI are incredible.

[SPEAKER_00]: But our businesses set up in a way to leverage those possibilities.

[SPEAKER_00]: We say, no, they need an architect such as ourselves or there's others out there that can come in and treat the necessary contractor, create the underlying foundation and then they can go and run with it.

[SPEAKER_00]: But I was one pivot, a second pivot which in terms of internal tooling that we're using.

[SPEAKER_00]: We ourselves apply how can we're a bunch of software engineers, right?

[SPEAKER_00]: And so I actually think that the number one problem that AI solved

[SPEAKER_00]: Ironically, because it's created by software engineers, is a software engineering problem.

[SPEAKER_00]: Like, your ability to output code as a software engineer is, it's 30X.

[SPEAKER_00]: I mean, it's an insane amount of output that you can create now.

[SPEAKER_00]: And so, how do we, how do we continue to grow our team responsibly?

[SPEAKER_00]: While maintaining a very fluid and dynamic understanding of what AI tools exist to help our coding process?

[SPEAKER_00]: and not overexpand when really maximize each person's potential, I think has been very cool.

[SPEAKER_00]: We've actually decided to slow our hiring down because we're able to get so much more output out of the existing team.

[SPEAKER_01]: I'm curious about maturation, right, of for enterprise.

[SPEAKER_01]: How have you matured what you offer and, you know, that could be from building things internally or process or things like that.

[SPEAKER_01]: But I'm really curious about roadmap.

[SPEAKER_01]: How did you go about building your roadmap for for enterprise and, you know, what sort of criteria or, you know, things that you follow to decide that, okay, this is the next most important thing to address.

[SPEAKER_00]: I think if you looked at our roadmap from a series of paths and decisions, it would look like the craziest tree you've ever seen.

[SPEAKER_00]: It would be, there are at every point numerous options we can take and being open to those options I think is what has allowed us to continue to have success.

[SPEAKER_00]: In terms of the criteria that we evaluate, but each of those paths, the number one thing that I think of is, at any point, given how successful AI has been, how revolutionary it's been on businesses, how business models and business functions are changing rapidly, we are open to pretty strong pivots.

[SPEAKER_00]: Now, that being caveat with the fact that once as we've matured,

[SPEAKER_00]: The degree at which we pivot has lessened, but for at the beginning willing to do a 180 degree pivot now we're at a 90 or a 75 or a 65.

[SPEAKER_00]: But we are constantly going into building our AI pipeline infrastructure in industries that we never touched before.

[SPEAKER_00]: One of the industries that we've really gotten a lot of interest in recently isn't real estate.

[SPEAKER_00]: and we were not in real estate before, but figuring out how to use AI to automate a lot of the admin functions that take place in real estate.

[SPEAKER_00]: And even some of the analysts and associate functions, we've really cut our teeth in now and been spending the last, call it, four, five months doing that.

[SPEAKER_00]: But that is a pivot in terms of we were never in that industry before.

[SPEAKER_00]: But we were open to where where the possibilities of AI could take us and always and never thinking that we had the answer that the place we were in is the place we had to set.

[SPEAKER_01]: I'm curious about team, right?

[SPEAKER_01]: How do you go about building your team?

[SPEAKER_01]: What do you look for in those people to indicate that they are the winning horses to join you?

[SPEAKER_00]: The thing right now is all about being open to education, and I think it can take place at within any demographic of type of person, which is what's so cool in my mind, like AI is really democratizing the whole space, but as long as you are open to continue learning, and again there are differences obviously between junior mid-level senior people, but AI is shrinking that gap, at least especially on the software engineering side.

[SPEAKER_00]: And like, at four, we do almost our president of engineering.

[SPEAKER_00]: He does almost a weekly kind of class that is literally updating the company on the new techniques that have come out different ways, different prompts, different coding styles that are being particularly effective with certain tools.

[SPEAKER_00]: And that takes place almost every week.

[SPEAKER_00]: And he's learning.

[SPEAKER_00]: I'm learning the company's learning and really staying open to learning.

[SPEAKER_00]: I think is foundational in someone that we would look for.

[SPEAKER_01]: Okay, I'm curious about scalability, right?

[SPEAKER_01]: How you approached that in the beginning, and maybe there's been interesting areas where you've had to fight scale as you've grown.

[SPEAKER_01]: And that could be, you know, execution people, technology, all the things, tell me about scale.

[SPEAKER_00]: I think our process to scale one day we could only hope to be as successful and whether or not how you take the company, but they've been very successful, which is like the Palantir approach to scaling.

[SPEAKER_00]: Now, they have these four deployed engineers and everyone said they're a linear scale business because...

[SPEAKER_00]: You only get in so much output evident in engineer and each engineer is on a unique project in kin day.

[SPEAKER_00]: How do you get the huge gains that come from a true software business?

[SPEAKER_00]: There's exponential style gains if you're just deploying using for deployed engineers.

[SPEAKER_00]: And ultimately they prove that well you develop a skill set and then you build a platform.

[SPEAKER_00]: And I think that's what we did, and so in the beginning, a hundred percent, and we were on a linear growth trajectory where every person is responsible for X amount of output, and that pretty much, and then you just have to hire another person, because there's no way to scale that beyond linear scale.

[SPEAKER_00]: But I think we took that same model.

[SPEAKER_00]: We developed and refined our skill set.

[SPEAKER_00]: We built internal tools and internal platforming and now we're able to get more exponential gains where we can keep the team relatively as lean as it's always been but take on new clients and still deliver that same or a greater level of quality.

[SPEAKER_01]: So as you step out on the balcony, you look across all that you've built with four enterprise.

[SPEAKER_01]: What do you most proud of?

[SPEAKER_00]: What I would be most proud of is that our clients are very educated on the true cost of AI solutions and AI implementation in a way that I think most of the of any industry is not.

[SPEAKER_00]: What I mean by that is that we take a lot of pride in being very transparent with our clients and very open to heavy, relatively heavy upfront expenditure in something that is very and we try to make it as tangible as possible, but it definitely has a amount of intangibility.

[SPEAKER_00]: In terms of, can you really tangibly see, oh my god, my data ingestion engine just got X times faster, and now, you know, things are becoming so much better, you know, structured and being stored in different storage, you can see the cost savings that and there are some, but it's definitely a little bit intangible.

[SPEAKER_00]: That being said, it is the most important thing towards being able to access these tools and then deliver ROI.

[SPEAKER_00]: And so I think our clients are really understanding of that and stuck with us through the more boring process of setting up the infrastructure and then finally are seeing the they're getting to reap those rewards and we're very transparent about the timeline we like to try to make the ROI for working with us within six months.

[SPEAKER_00]: But it takes an illegal faith as the wrong word.

[SPEAKER_00]: It just takes an element of not being able to see something as tangible as, okay, I'm now using this chatbot and it's reduced my customer service times by 50%.

[SPEAKER_00]: That we will get there, but in order to make that happen, you gotta do the unsexy work first.

[SPEAKER_00]: And that's what I would say is the thing I'm most proud of.

[SPEAKER_01]: Let's flip the script a little bit.

[SPEAKER_01]: Tell me about a mistake you made and how you and your team responded to it.

[SPEAKER_00]: I would say was committing to, and speaking to that earlier, the workplace, our workplace turnover solution, we had a lot of virality and a lot of interest in, and I think we allowed that to continue to run that business for potentially longer than we should have.

[SPEAKER_00]: We kept allowing new business to be created, and then didn't see as quickly as we should of.

[SPEAKER_00]: how difficult it was going to be to actually implement the solutions for those clients.

[SPEAKER_00]: And so I think that was a mistake we made in one we've learned from, when terms of if a client wants that solution, they want to see ROI quickly.

[SPEAKER_00]: And a big piece of that is now what we build, but before then we weren't able to accurately describe why it was taking so long.

[SPEAKER_01]: So this will be interesting to hear.

[SPEAKER_01]: Tell me what the future looks like for enterprise, for what you offer for your team, where that position for the industry, all the things.

[SPEAKER_00]: We hope to keep the team lean.

[SPEAKER_00]: I think AI software engineering tooling is becoming, it's continued to become better and better.

[SPEAKER_00]: So we hope to stay open to a learning, keeping the team lean by just making everyone better.

[SPEAKER_00]: And then in terms of the business directive, continue to refine our skill set, to continue to refine our platform, and then through that, be able to,

[SPEAKER_00]: touch industries you've never touched before, continue to help the industries and the clients were helping now.

[SPEAKER_00]: And even we've started this really cool project where now we're achieving some of the products we build with our actual clients, which allows us as an example, like the real estate industry, now we're actually not only are we building the solution for a client, but we are a part of a part owner of that solution, and wanna go and take it to the broader market in that industry.

[SPEAKER_00]: So I think that's a future,

[SPEAKER_00]: but at the same time, open a new opportunity.

[SPEAKER_01]: Tyler, let's switch to you.

[SPEAKER_01]: Who influences the way that you work?

[SPEAKER_01]: Name a person, or many persons, or something, you look up to and why.

[SPEAKER_00]: I most influenced by my family, I came from a family that prioritized education, a heavy emphasis on education, and so my father, he always pushed for us to be open to learning, and obviously I keep saying that, and that's the biggest thing that I take away in my path is just the openness to new opportunities, the openness to say that

[SPEAKER_00]: I, what I don't know and be willing to admit that readily that learned and then get better.

[SPEAKER_00]: Um, and so I take a lot of that from my family.

[SPEAKER_00]: I got a, I had a lot of great mentors at Stanford, interestingly enough, I had a professor who taught convex optimization, which was this very cool class, and he was almost one of the fathers of it.

[SPEAKER_00]: It's a unique type of math, and...

[SPEAKER_00]: Longward with me saying there was very intense study in finals that you had to do and because of that, got to experience just like the long grind, right?

[SPEAKER_00]: Like 72 hours take minimal bathroom breaks and just grind it out.

[SPEAKER_00]: And so I think that kind of taught me that a little bit.

[SPEAKER_00]: But yeah, I continue to find new people to draw from in the AI space.

[SPEAKER_00]: I think you've got a lot of heroes in, in, obviously, a lot of the big tech guys agree or disagree with them, but they are all pushing the frontier, and I think that's super cool.

[SPEAKER_01]: Last question, Tyler.

[SPEAKER_01]: So you're getting on a plane and you're sitting next to a young entrepreneur who's built the next big thing.

[SPEAKER_01]: They're jazzed about it.

[SPEAKER_01]: They can't wait straight off to the world and can we show it up to you right there on the plane?

[SPEAKER_01]: What advice do you give that person having on down this road a bit since middle school?

[SPEAKER_00]: I would say that the journey of entrepreneurship is one where, especially if you are the founder, 95% of the time, especially as businesses mature as well, you get to hear the things that aren't going well.

[SPEAKER_00]: And to me that's defined at least my journey of entrepreneurship is no one's ever going to pat you on the back for doing a good job, and it's literally the implication of what you

[SPEAKER_00]: other people on the back for doing a good job and very clearly understand that no one will pat you on the back and nor they should.

[SPEAKER_00]: You always should be the most incentivized to make your business work.

[SPEAKER_00]: But just with an understanding that most of your day is going to be putting dealing with problems and putting out fires but there is beauty in that and there is something that is it's unique to the entrepreneurial journey and I think it's not something to be afraid of but if you can make it something that you're excited about then you will have a lot of success there.

[SPEAKER_01]: I think that's fantastic advice.

[SPEAKER_01]: We'll tell you, thank you for being on the show today, and thank you for telling the creation story of four enterprise.

[SPEAKER_01]: Thanks for having me now.

[SPEAKER_01]: And this concludes another chapter of Code Story.

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