The Business of Saving Lives: Inside RapidSOS

Summary

Run the Numbers Podcast

Episode: The Business of Saving Lives – Inside RapidSOS

Host: CJ Gustafson
Guest: Richard Haram, VP of Finance at RapidSOS
Date: August 6, 2026

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Episode Overview

In this episode, CJ Gustafson sits down with Richard Haram, VP of Finance at RapidSOS, to explore the intersection of mission-driven technology and finance in the high-stakes world of public safety. The conversation covers RapidSOS’s pivotal role in emergency response, using AI and data platforms to save lives, as well as frameworks for data culture, business intelligence (BI), and rapid organizational growth. Richard also reflects on his entrepreneurial journey, people management, pricing strategies, and personal lessons learned from years in the trenches of high-growth tech and service businesses.

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Key Discussion Points & Insights

1. RapidSOS’s Mission and Impact

• What RapidSOS Does ([03:25], [00:38]):
  • RapidSOS operates the world’s largest public safety AI-driven data platform.
  • Their system connects over 740 million devices to 6,500+ emergency call centers and streams real-time data to first responders.
  • Mission: "The more quickly we can get data and information from the source of where it's happening onto the screen of our 911 operators and then ultimately the first responders, the better outcomes we have in saving lives and making a difference in this world." – Richard Haram ([00:38])
• Real-world Impact Example ([03:54]):
  • Referenced the “13 Seconds” case: GPS and crash data enabled first responders to save an elderly gentleman 13 seconds before his car was engulfed in flames.

2. Modern Emergency Response – Challenges and Solutions

• Legacy vs. Modern Systems ([05:48]):
  • Bottlenecks include slow data transmission, limited operator availability, and operators forced to juggle critical and non-critical calls.
• AI as a Supplement, not a Replacement ([09:07]):
  • AI handles non-priority cases, reducing operator cognitive load and enabling focus on high-stakes emergencies.
  • Automated note-taking and triage support operators and first responders.

3. Business Model & Monetization Strategies

• “Layer Cake” Strategy ([07:49]):
  • Multiple paying nodes: Enterprises (e.g., Uber), device manufacturers (e.g., SiriusXM), and emergency call centers.
  • "If you ever ride an Uber and you press I have an emergency, it's going to come through our call center." – Richard Haram ([07:53])
  • ECCs pay for AI-driven efficiency features, such as automatic translation and transcription, which are critical for large events (e.g., the World Cup).

4. Data Culture & Cleanliness

• Dealing with Data Pollution ([08:57], [16:41]):
  • Volume and diversity of data streams have created new challenges in data accuracy and “pollution.”
  • "The more polluted your data, the harder it is to really understand how much you can trust it." – Richard Haram ([16:41])
• Signal vs. Noise ([15:53]):
  • The key struggle isn't data quantity, but clarity and relevance—identifying actionable insights amid the flood of information.

5. Building a Data-Driven Organization

• Evolution of the Tech & Data Stack ([18:31]):
  • Startup roots: Data was handled in spreadsheets with messy, manual processes.
  • Mature stack: Salesforce CPQ → Zone Billing → NetSuite → Avicom for visualization.
• When to Invest in BI ([19:31]):
  • Right time is when repeatable systems and product-market fit are clearer and data quality can be assured.
  • "Your BI tool is only as good as the data that you put into it."
• Framework for Analytics Maturation ([22:30]):
  • Questions → Processes → Data Collection → Data Quality → Trust → Insights → Decisions.
  • Focus on building a data culture rather than just dashboards.

6. Self-Serve Data & BI Team Evolution

• Decentralizing Data Access ([27:12]):
  • Modern BI tools (e.g. Omni, Hex) empower end-users to ask and answer their own questions via AI interfaces.
  • Goal: Finance/BI teams ensure clean data; end users derive self-serve insights.
  • “Really, our goal is to … empower people with the insights that can drive their decisions.” – Richard Haram ([27:12])

7. Rolling Out AI in the Organization

• AI as Culture and Fluency ([28:51]):
  • "A lot of people view AI as a tool. I think it's so much more. ... It's a fluency, it's a capability that your organization has to build."
  • Let people experiment, learn by doing (“You built a hotel when you just needed a house.” [29:25]).
• Progression Model for AI Adoption ([32:32]):
  • 1. Apply AI to existing workflows (summarization, drafting).
  • 2. Build tools or apps for specific tasks.
  • 3. Automate entire workflows and business processes.

8. Entrepreneurial and Leadership Lessons

• People Management and Development ([36:00]):
  • Emphasis on life-work balance; prioritizing individual passions and growth over rigid career ladders.
  • Use of Individual Development Plans (IDP) to honor unique ambitions ("Superstars" vs. "Rockstars").
• The Dangers of “Hero Culture” as You Scale ([38:29]):
  • Startups begin with “heroes” but must transition to systems and process to avoid bottlenecks and key-person dependencies.

9. Pricing Strategy and Value Recognition

• Common Startup Mistake: Underpricing ([41:33]):
  • "If you're winning every job that you quote, odds are you are probably the cheapest person." ([44:33])
  • Must update prices as customer value and organizational complexity grow.
• Real-World Pricing Adjustment Example ([42:57]):
  • Raised service rates significantly with no churn; clear communication about value provided does win loyalty.

10. Mistakes, Tools, and Final Career Advice

• Memorable Mistakes ([45:06]):
  • Automating Excel processes and presenting wrong data due to backend changes—“Never refresh your data right before the meeting.”
• Tech Stack Used Today ([47:48]):
  • Claude (for analytics and automation), Avacum (BI/dashboarding), Salesforce CPQ, NetSuite, Zone Billing.
• Advice to Younger Self ([47:19]):
  • “Take more risks. ... You're young, you can make things up. Just go out there and do it.”
• Expense Report Follies ([48:43]):
  • Someone openly charging personal gas to company card; led to an “easy firing decision.”

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Memorable Quotes & Moments

• “The more quickly we can get data ... onto the screen of our 911 operators ... the better outcomes we have in saving lives.” – Richard Haram ([00:38])
• “Your BI tool is only as good as the data that you put into it.” – Richard Haram ([19:31])
• “I don't believe in the work life balance. I actually believe in the life work balance.” – Richard Haram ([36:00])
• “If you've gotten 80% of the information, you probably have enough information to make the right decision. If you have 90%, you probably waited too long.” – Richard Haram ([40:13])
• “If you're winning every job ... odds are you are probably the cheapest person.” – Richard Haram ([44:33])

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Timestamps for Key Segments

• RapidSOS Introduction & Mission: [03:25]–[05:01]
• Problems in Emergency Response: [05:48]–[06:52]
• Business Model Breakdown: [07:06]–[08:57]
• Impact of Understaffed Call Centers: [08:57]–[09:57]
• Data Overload, Pollution, and Trust: [13:35]–[17:30]
• Building the Data Stack & BI Implementation: [18:31]–[20:41]
• Analytics Maturity Framework: [22:30]
• Empowering the Org with Data & BI: [27:12]
• AI as Culture & Skill: [28:51]–[33:33]
• Entrepreneurship & People Management: [33:52]–[36:00], [38:29]
• Pricing Insights & Customer Value: [41:33]–[44:59]
• Rapid-fire/Life Advice Segment: [45:06]–[49:16]

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Conclusion

This episode offers a playbook for finance leaders and tech operators on turning overwhelming data and mission-driven purpose into organizational advantage. Richard shares practical, operator-level insights on building systems, empowering people, confronting pricing fears, and embracing AI as a cultural shift—creating an environment that’s as focused on saving lives as it is on saving time and scaling business.

Transcript

[A]

Maybe you could just speak to how you've been rolling out AI, Whether that's bottoms up or tops down.

[B]

For me, I think one of the first things is a lot of people view AI as a tool. I think it's so much more. I think it has to be two things. Number one, it has to be part of a culture, and number two, it's a fluency, it's a capability that your organization has to build.

[A]

What's a common failure mode here when there's so much data, the more polluted

[B]

your data, the harder it is to really understand how much you can trust it. And especially with the advent of AI putting a lot of information in the system, did it get it right? And so I think there's a lot more pollution that we're seeing.

[A]

Can you just give people kind of the appetizer here of what RapidSOS is and who it serves?

[B]

We're the leading public safety AI company out there and we're operating the world's largest public safety data platform. The more quickly we can get data and information from the source of where it's happening onto the screen of our 911 operators and then ultimately the first responders, the better outcomes we have in saving lives and making a difference in this world. And so literally our technology is saving lives every day.

[A]

Is this thing on?

[B]

Yesterday's price is not today's price.

[A]

Hey, are you liking this show right now? Do us a favor and we'll do you a favor. Share a link to the show on Apple or Spotify with your team at work. Drop it in a slack or God bless you. If you have to use teams, throw it in there too. But send a picture of you dropping the link in to benoslymetrics.com that is benoslymetrics.com our creative dictator will hook you up with some Run the Numbers swag. Welcome back to Run the Numbers, the show where I interview the world's top CFOs and finance leaders. Today I'm talking to my friend Richard Haram, the VP of Finance at Rapid sos. I met Richard back at abacom's annual conference this year and we hit it off. And let me tell you, me and Richard, big FPA guys, big amperstand guys. Richard works for one of the more interesting companies I've ever covered. They serve the emergency responding sector and they use AI to route calls to the right place at the right time, hopefully saving lives in the process. So I other than the fact that they're saving lives, for anyone building a business in vertical software. You're going to love how he describes their layer cake strategy to monetization. Richard was also an entrepreneur before becoming a finance leader and he takes us through some of the lessons he learned managing and motivating employees and how it impacts his leadership style.

[B]

Today.

[A]

We also go deep on the evolution of the FPA function and what the modern forecast engine looks like when you supercharge it with a strong BI function. And to really spice things up, let's add some M and A to the mix. We talk about FPA forecasting and consolidation, how you do it well, and what you do with your systems after you do a bunch of deals and bring new companies in house. Please subscribe to our show. It helps us with the algorithmic overlords. Let's get into the interview. Richard, thank you so much for joining the POD with me.

[B]

Yeah, pleasure being here. Looking forward to some vibrant conversation. I know you've created a great ecosystem here in San Francisco and so I enjoyed a lot of the events you put on in the conversation. So, looking forward to today.

[A]

I was hoping before we get into the meat and potatoes of today, like you work for one of the coolest businesses out there in terms of what it does, can you just give people kind of the appetizer here of what Rapid SOS is and who it serves?

[B]

Yeah, Rapid SOS is amazing company. Joined it about three years ago and a lot of it is really based on mission. So we're the leading public safety AI company out there and we're operating the world's largest public safety data platform. So what that means is really we sit in this world where we connect digital devices to first responders. And why is this so important? We know that in emergency responses, minutes matter. The more quickly we can get data and information from the source of where it's happening onto the screen of our 911 operators. And then ultimately the first responders, the better outcomes we have in saving lives and making a difference in this world. So I'll give you a simple example. Imagine driving in a car that runs off and it goes into some trees. You know, in the old days you would have to stumble out of your car, stun, go back to the road, find help, flag it down, and get first responders there. Well, now with technology that exists out there, if you have, you know, for example, SiriusXM crash detection in your car, they pump all of that information through our network. And so we know your car's been an accident, the speed you were going, the number of people in it, airbags deployed. Also, if you have a smart device, so a smartphone, a smartwatch, instantaneously, all that information now hits our system and we're able to let the dispatch and first responders know that there's been an accident, the severity of it, and so that they can immediately dispatch the help that they, they need for you, you know, and where you really see the difference of this, there's a great video on our website called 13 Seconds. This was a case where an elderly gentleman ran off the road. His car couldn't even be seen from the road. But because we had the GPS data, because we had the crash detection, police were able to show up and literally you see on the officer's body cam, him pulling this person out of the vehicle. And 13 seconds later, the vehicle is engulfed in flames. And so literally our technology is saving lives every day.

[A]

And that's crazy. So me and my family, we were actually in a car accident last week. For the first time ever, everybody was okay. So we were totally fine. We were trying to take a left in this busy intersection and there was another car in the other lane to turn that was blocking the car that was incoming. And so we ended up getting hit by this box truck. And so it was a low speed accident, but the thing completely just demolished the front of our car. And I never been in an accident before. I didn't even like, know what to do. And we had the kids in the car and it's like this scary moment where I felt like I wasn't using my whole brain of like, what do I do next, who do I call, et cetera. Why is the traditional system broken? Because luckily, like the cops showed up. They were in, they were in a parking lot next door. But what's broken about the traditional system in terms of like working with, I guess, the phone operators and stuff?

[B]

Yeah, there's a few main problems out there. So number one, if you just look at the plethora of data that we're getting into the system. No. Now we have all these connected devices and getting real time information is something that really helps escalate and speed up response. So for example, in your case, somebody probably needed to call 911 before that police officer knew to connect and come over and find you. The first big problem that kind of exists is the speed at which they get the data. Number two is just the availability of 911 operators. It's an extremely hard job. The things they go through on a daily basis. Some of the calls they have to put up with be people who are at the worst time of their life, a critical emergency just happened. These people are put in just an environment that is extremely critical, where they really need to provide a connection to human beings. And at the same time they're also getting a lot of calls. Maybe somebody's calling because a cat is loose in the neighborhood and they need animal control to come out and get it. With the limited capacity and availability we have of these 911 operators, finding ways to really focus their attention on what matters most, where they can make a difference in the outcomes. That's one of the huge bottlenecks that exist in the system that we're able to solve through our platform and our technologies.

[A]

And it sounds like you're sitting at the center of a pretty complex network here. There are emergency call centers. I also know that you work with enterprises or like actual companies. Then you have the first responders. Richard, when you think about this network, who is your actual customer?

[B]

The safety data platform. So we have over 740 million connected devices that feed information to our platform. There's roughly 6,500 ECC or 911 call taking centers. And then you have hundreds of thousands of first responders. Really this platform is used so that any of the nodes can connect to each other. So if we go back and use the example of that car crash, SiriusXM is one of our customers and they pay for access to put their data on that network. Why? Because it produces quicker outcomes for them. And then the first responder, when they're responding to that crash, now all of a sudden they know the make of that car, they know how many occupants are, they're able to do a lot of that triage before they get on site. And so, so that really gives them better outcomes for their communities. So really any node that connects into that safety platform is truly who we are out there trying to serve.

[A]

I'm a business model nerd. How does the company monetize across that ecosystem?

[B]

Number one, you know, just the platform in itself is part of what we have to get out there and provide to the community. And so how do we get all these devices connected? And then each different node of this has a different what it's looking for. If you ever ride an Uber and you press I have an emergency, it's going to come through our call center. And so what we're able to do is we're able to provide better ride service and outcomes to Uber customers. So they're willing to pay us on a per rider basis. And then if you transfer that to the 911 operations centers. A lot of what we're able to do is figure out how they can reduce the load on their 911 operators. World cup is here right now. It's pretty popular. There's a lot of people here that don't even speak English. These communities that are hosting World cup sites, they can't pay to have translators on site 24 hours a day during the World Cup. So instead we have software and technology that can translate, transcribe all the calls so you can have communications regardless of what language it is. So for an ECC perspective, we're often figuring out what are the modules that really help them maximize the efficiency of their operators so that the operators can really focus on the human connections and eliminate a lot of the manual work that comes with data entry or things like the translation transcription.

[A]

I want to drill into that because you had mentioned one time that we hung out that the ECCs, the emergency call centers, they're staffed at like 60%, which blew my mind. How does that shape how your product needs to behave?

[B]

So, you know, really we think of the product in two ways. And one of that is how can we supplement what a traditional operator would have to do? So if we go back and use the example of animal control, that's usually a non emergency type of call. And a lot of these municipalities, the emergency operator is also taking a lot of the non emergency calls. And so if we can find ways to use AI to supplement that, here's a case in which if the AI gets it slightly wrong, it's okay. No AI is perfect. Those are first places that we can get there. And then we can truly save the emergency calls for the operator where the time is most critical. And the second thing we're doing is how do we reduce the cognitive load on the operator in terms of the work they need to do. So they're having to take a lot of notes, they're having to do dispatch at the same time they're trying to really assess what's going on the scene. So the more of this kind of copilot we can give them where now maybe all the notes automatically populate onto the screen. It can work on how do we work with the dispatchers to go ahead and do some automatic dispatch or give the first responders access to this data while they're in the field. And then the first responder doesn't have to spend time with the 911 operator in terms of trying to triage the. And the operator can really focus more on where their time is most valuable.

[A]

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[B]

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[A]

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[B]

It can't be. I think this is where everybody in the emergency response system who's really working on this problem of how do we automate information to get in the hands of these dispatchers and first responders really have to think about this. So you know, if you look back into the car example, we know in connected cars we can tell whether your glove compartment's open or not. However, we know if we're dispatching an emergency, that's not something that we really need to know. You know, the whole ecosystem is focused on this problem like what are the things that we really need to pay attention to. And honestly it changes by the emergency. You know, in your case, luckily nobody in your family was hurt. But if it's a low speed fender bender, the dispatcher is going to need different information than if this is high speed crash. How do we supplement AI with all of this data to really surface the insights that are needed? And this is where kind of the co pilot really comes in. It can listen into the call, it can understand the context of what's happening. So let's say there is a car that's run into a late and the person is going to have to break the window. The AI can recognize this. It can go ahead and look up in the instruction manual from the manufacturer exactly what to do. Surface that information so that the 911 operator can focus just on that. Having that AI in the product is really what allows us to consolidate all that data to the most important information.

[A]

Bear with me as as I attempt to stick the landing here, but I do feel that what you're doing for 911 operators is in many ways FP and a team, something we're both passionate about, should be doing for executives in terms of surfacing the right information at the right time.

[B]

If you look at just what's happened across Rapid sos, the number of data points that we have that are coming into the availability of data into the finance team, before it was maybe a lot of the salesforce data and then you get a little bit of data from maybe the PDE team on their JIRA tickets and things like that. Now we have connected devices and we can pull all the analytics from that that help us understand decision making where we need to make a better product. You start looking at the CS teams, everything they're out there working with the customer on in the field. They have AI on all their calls now. So all these calls are recorded. The amount of analytics we're getting from that to really understand what the true problems our customers are having that we're trying to solve. You start looking at Zendesk and all of the now AI tickets that are being handled there. Where before you had a human relating some of this stuff back to you, now you don't. It's all in tickets with tons of data. You know, we're just getting a plethora of data that's coming into the finance team and that is one of the challenges we have. What data is it that really matters that is producing the signal and what is the noise?

[A]

Five years ago, I felt like I was just dealing with financial data and sales data. I was in the CRM, I was in the H R I S, I had the financial data. There wasn't as much product data, but everything that you're rattling off now, the majority of that is actually stuff that's upstream from the P and L. We

[B]

get all that and for us, really what is the signal through the noise? It's not becoming about how do you create a dashboard that just shows numbers. It's about how do you really sort through all of the different data coming in to really understand, especially at the time of me, that the division is currently at what is most important to them in that situation. As companies evolve and grow, the needs of data are different at each inflection point. And so then being able to come back and say, okay, now I have this, this, this abundant data. What do I really focus on for the current needs of the company? That's really hard to suss out in some of these cases.

[A]

What's a common failure mode here when there's so much data?

[B]

You know, one of the big things we've been solving is the cleanliness of data. Pollution is something that the more polluted your data, the harder it is to really understand how much you can trust it. And especially with the advent of AI putting a lot of information in the system, did it get it right? And so I think there's a lot more pollution that we're seeing as we get more of this data. And a lot of that's we're thinking, how do we really pay attention to where that source of truth is and how do we solve that? Pollution at the source of truth, but then also how do we think of data in levels? So what is data that is really trustworthy and trusted? And then how do we think down to, okay, what is just new data that we've now put in? We really have to work through it, understand what it means, what it tells us, how trustworthy it is. Let's not just jump in and see a new data stream and assume it's all right. Let's really get in and analyze it, understand it. Because there probably are things that we have to work through to refine it, to make it really gives the intelligence that our model needs.

[A]

It sounds like your company's been on quite the data journey here.

[B]

We're a data product and that's really what the power of our network is. How do we pull all this data into one place to better inform these decisions? It's the same thing with our company as we scale, really. We have our three different platforms. We focus on our large enterprise customers, our 911 operations centers and our first responders. Each one of them produce a unique set of data. And whether it's the go to market motion, whether it's the product we're building down to how we're supporting and engaging our users, we have to really understand also how those are all interconnected. So, you know, if you look at a typical enterprise model, a lot of what our enterprise customers like is that we're connected in basically 99% of the ECCs across the United States. So if we make a decision on our ECC platform to maybe try to monetize a feature, but it decreases the number of people on the platform now our enterprise customers don't have as much value. And so the interconnectedness of all that data is also something that really we have to focus on as we try to stitch all this together.

[A]

Richard, what did your data stack look like early on versus today?

[B]

Early on, we looked a lot like most of the early startup companies with. When I came in three years ago, it was mainly spreadsheets we were using. I remember we started mapping some of the Platform, some of the processes from getting data from Salesforce to ultimately our ARRQ, there were about 10 different steps that it went through in different spreadsheets to ultimately get it to that end result. And I think this is what you would normally expect at that point, product market fit wasn't fully where we needed it to be and so we needed to have much more flexibility in the system. It wasn't like we could go in and plumb data through the entire system because we didn't know what it would look like at the end. And through that process, as we've gotten that product market fit, as we've seen repeatable sales processes, repeatable contracts, we've been able to now plummet. Where Salesforce CPQ is the main place most of our CRM and contractual data lives. That all then goes through to that suite where we use zone billing to do all of our invoicing and revenue recognition. And then ultimately we pump all that information through to Avicom, which is our visualization layer for that.

[A]

We're big fans of Avakum. Do you wish that you built this BI layer sooner?

[B]

That's a good question. I think there are two sides to this. So the first thing is right now we're at a point at the company where everybody is demanding better intelligence. And I think that's how we know we definitely are a little too late. But from the other side of this, we probably couldn't have been earlier in this. And there's two reasons that waiting has probably been beneficial to us. Number one, in any high growth company, you have limited capital. Most of our capital went to how do we go to market motion and how do we build a better product. And so for us, investing that capital into a BI function in the earlier days probably wasn't there. But number two, we didn't really have the right system set up. And so your BI tool is only as good as the data that you put into it. And so if you look at it, really focus on how do we get Salesforce set up, how do we make sure all of our systems and processes in there are collecting this clean data. That for us was really the focus over the last few years. And so across the entire enterprise tech stack, that's really been our focus is ensuring that we have the systems implemented right and producing the right amount of data. And so then from that we can now then pull it into a larger BI ecosystem. Whereas if we had started there, we probably would have had a lot of other problems that we weren't able to then. Properly implement it.

[A]

We often talk about on the podcast the different steps of. Of like going through this analytics escalator to get to a point where you're making decisions. Did you start off by creating some dashboards and feeling pretty good about that, and then. And then decided, hey, there's another level we can take this through? Like, what was the maturity curve?

[B]

Some of the problems I have had in the past is actually starting at the end, which is the dashboards. Really, dashboards are the last step in the process. Where I start is understanding why we need the dashboards. You know, really what are the decisions that you have to start with? And so for me, it's taken it through kind of a little bit of a framework. So what are the questions we need to ask first? So why are we even setting up this dashboard? And odds are, if you go ask five different groups within your company, they all have a different view of it, a different definition. You know, for example, ARR, everybody can define that a different way. And so then once we really get to the questions, it's what are the processes that are going to produce repeatable data? So a dashboard is only as good as the data underlying. And so how do we really focus on the integrity of what that data is? And so understanding our processes and how data moves through the system is really critical to do that. Then you move through the data collection, which I think is sometimes the hardest part, producing that clean data on a regular basis. You know, for example, if you look at sales pipeline, we had an instant the other day where a salesperson just accidentally put in three extra zeros, and all of a sudden our pipeline overnight, you know, went from several million to a couple hundred million on just this one product. I'm like, oh, wow, you know, we're really knocked this out of the park. When you're producing that plumbing on that data, how are you putting the right checks and balances in on that? Every day somebody's having to monitor the dashboard for finance and really make sure it's right. And then I think what all that does is builds the trust. So really, how do you get the team to have the trust behind the dashboard? So I think if you do all of that stuff in the right order, then you're able to then produce the right dashboards. And then in the modern dashboard, is it actually producing the right decisions in the groups that you want it to produce?

[A]

This is beautiful. And I'm just going to play it back for the benefit of listeners. So the framework is questions to processes, to data collection, to Data quality, to trust, to insights, and then finally decisions and listening to you go through this. I know you're using this framework, but I think what you're really doing is building a data culture.

[B]

Yeah, that's probably the most important thing I think that a company can build inside of their team is how does everybody really focus around the integrity of the data and the culture of it and understanding the why of it. I don't think anybody says we want to put data in inside the system that pollutes it. There's a lot of requirements. You know, collecting data has a cost. It's not free. Every point that we collect from our sales team has a cost to it. They have to put that information in a CRM. Every time we ask the product team to get in there and fill out a card in jira, that's time that they're spending on that that they're not spending on something else. The other day we showed internally a view of our pipeline of what the different products were and where we saw the pipeline coming to our product team. And one of the first comments I heard from somebody was, wow. And that was then really helping them connect to what products they were building to where we were actually having success in the field selling and vice versa. It's good for the sales team to see, okay, here's what the products team pipeline looks like. So that when you're out talking with the customers, you can really set the expectations of when we think these new features will be released. Collecting all the data has a cost to it, but there's a significant amount of value. And helping people connect those together and understanding how they play a role in that I think is really critical to setting up a good ecosystem. That really is about what you said, the data culture.

[A]

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[B]

Another benefit for us being, you know, probably a Year later on the BI journey than I would have originally thought is the speed at which the tools in the BI world have changed is pretty incredible. Yeah. Two of the ones we're laying out there, Omni and Hex, it's just like you're chatting with Claude or ChatGPT and it has access to your universe of data, it can help you sort through what is the actual question you're looking to answer. And then it can build the dashboards, the analytics, the insights for you, real time. And so really what that's doing is it's helping the decentralization of the knowledge from being a team of BI experts or finance team people and really taking it to the hands of the end user. So if our COO wants to wake up tomorrow morning and maybe he says, I want to understand how our pipeline for our channel partners is impacting our close rates, he can just sit there, talk real time to a AI and get those answers for himself, build a BI dashboard for that and send it out to his team so they have real time visibility. And that's something that didn't exist before. And I think that's, that's one of the great things about IT Finance, really our goal is to how do we empower people with the insights that can drive their decisions? And so it's often been a friction. We've been the bottleneck into really driving some of these things forward. And so now our role is really how do we ensure clean data inside of the system that really lets them then find the information that they need quickly.

[A]

I love that. And maybe this is a nice dovetail into speaking about AI and how your company's been adopting it, because I know that you're passionate about creating an org that feels motivated to learn on their own. They enjoy taking something and building something, having a culture of builders. Maybe you could just speak to how you've been rolling out AI, whether that's bottoms up or tops down.

[B]

For me, I think one of the first things is a lot of people view AI as a tool. I think it's so much more. I think it has to be two things. Number one, it has to be part of a culture, and number two, it's a fluency, it's a capability that your organization has to build. I got access to Claude, Claude for Excel, which I think is the probably biggest innovation of the world's seen in the last hundred years from a finance perspective. Over the weekend I got used to it and I just started building with Claude code some programs that I wanted to do. I really overbuilt for my first round of this. And I was trying to put in some API connections to live bank feeds that I just had no business.

[A]

You built a hotel when you just needed a house.

[B]

I just needed a house. And so part of what that was, though, was a learning process for me. You had a podcast guest on, and y' all were talking about running, and I think y'. All, you're a big runner yourself, right?

[A]

Yeah.

[B]

Me who never runs. Do you think I could come out and run a marathon with you right now?

[A]

That would be difficult.

[B]

But if I sit back and train and say, hey, let me go do my first mile and then work at that, learn some technique, get endurance in there over time, I can go run the race with you.

[A]

You.

[B]

And so really, the mistake I made when I was first starting my AI journey, it was I was trying to run that marathon with somebody before I even knew how to run my first mile. Really, what I think the journey that everybody needs to go with AI is start where they're at. This is really a fluency and skill people are afraid to build. You know, we've seen this throughout Rapid sos, and I've seen this when I talk to other finance people. People are given claw and they have no clue what to do. And so I think everybody has to be given a path to say, here's how you produce your skills. You know, another comparison I make to it is the MBA. 20 years ago, everybody, nights and weekends, you went and got your mba. That is what helped you get to the next level of your career. Career. And really, AI is in the same place right now. Everybody has to understand this is no different than an mba. It's a skill that you're going to have to build on. The second part of that is how businesses embrace this. You see two sets of companies. One set of company goes out and says, here's AI. We want you to use it for this specific use case. And we're going to track exactly what you're doing, how many tokens you're going to spend, and we're going to want immediate results. Then I think you have another set of companies which are just, we don't care what you use it for. Go out there and explore. And I think AI is in such an early phase of adoption that the companies that are telling their teams, go figure out what you can use it for and just innovate and adopt. Think of yourself as an entrepreneur. And really, the cost of this in the first six months needs to just be the same thing. As investing R and D into product, that people have to figure out how they use it. And it's different for every team. Finance team uses AI different than a sales team does. And so I really think if you can build a culture where everybody thinks about this as an entrepreneur, they have experimentation and they're also told, hey, you got to go build your skill set in this. I think you're going to have some pretty significant outcomes that'll drive the company forward.

[A]

Well, something that you said that really stuck with me is you got to think about this as inspiration versus training.

[B]

Yeah, 100%. For me, a lot of what I encourage people to actually do when they get AI is actually don't even build on work products. One of the problems we have in our accounts receivable department is how do we bring everything to a collection portal. So a lot of people were trying to build on that. I actually said, go build on something you enjoy. So one person actually used it for their fantasy football league. And so they're all, I'm going to create some type of software that I can get in here and do a better job of managing my rosters. There was another person who used it for their wedding. I think what happens when you do that is, number one, you get this kind of energy about it and then all of a sudden it's not. It's not work, it's play. The other thing I tell everybody is you haven't really built anything until you share it with your friends. And so, you know, this person who built this fantasy football league, going and showing that to his friends, all of a sudden it had real world consequences. What I'm building, people are like actually going to judge beyond. And I think it's in a healthy way when people start to really look at it as like, how do I become passionate about this? And they become good friends with it. And then it's not just this tool over here on the side. We were having a conversation this morning with how do you personify your AI? Is it a he, she, they, them? It's pretty interesting that when people start to get that type of relationship, I think they have so much more power with it.

[A]

Is there a proficiency progression that you've observed going on?

[B]

Going back to the example we were talking about running, you're at a much higher progression of this than I am. So I've laid out kind of three type of phases that I think everybody goes through in their journey with AI. And so the first one is really, how do you apply AI? And this is where I Think most people traditionally use it. I'm going to take a spreadsheet and put this into Claude or Gemini or ChatGPT. I'm going to put an email in here. I'm going to have it search my drive. And really what it's doing for that is it's taking existing workflows you have and it's drafting documents or summarizing things. Then from there it's okay. Now how do I take this and actually build something with this? So it could be taking a skill and saying, I'm going to build a skill that will actually automate some of this workflow for me and actually do some of the things. And I'm doing this without having to have any engineering involvement in. Maybe I'm creating some type of app that I can get on. Then you really get to the third stage, which is how do you really identify stuff, this or automate. And so how are we actually having it perform the workflow for us? And so I think each one of these builds on each other. And I think you got to start at the beginning and master each level before you go to the next one. But I think that really then will take somebody through the progression of how they can master AI.

[A]

I love that. One of the things that I got a ton of energy from in hearing about your background was your long journey as, as an entrepreneur before moving over to the finance side, maybe to bring people up to speed. Could you describe the business that you had started and worked at for? I think, I think it was almost a decade.

[B]

You had this business close to eight years. So my first business was actually as a kid, 8 years old, mowing lawns. And I remember my entrepreneurial journey started when I actually hired one of my friends to help me. And my mom said, why are you hiring help? Why don't you just keep all the money? And I'm like, mom, I can mow more yards. In return, I don't have to pay him. What I'm getting with Zaya, really what that came about with started that with one of my good friends. And there was this recognition that where technology was going in terms of building automation was moving away from this just go install to prepackaged conference room instead. How do you really do a design build process? So you have all these commercial users that were starting to integrate things like Zoom video conferencing. Technology was really starting to move forward. The biggest thing that helped building automation was actually the iPad, because now you could actually have a touchscreen that actually had some type of user interface. On it. So really we recognized where the overlap between technology and business was really starting to come in terms of how people use technology in the worlds in which they work. And so from that it came together, we basically bootstrapped. The company had 50% growth every year for over eight years. And finally, about eight years into that, three major cities, large customers, everything from Disney, Universal, Fortune 500, Harvard, Boston College. And there was kind of a deviation between where the other founder wanted to go and I wanted go to. And I am all about, how do you scale companies, how do you continue to grow and excel? And a lot of that is empowering people to really ride the business. And he liked a little bit more of the lifestyle type company. So we came together and basically said, you know what, we've had a really good run at this and found a way that I sold my part of it. And it was a little bit of bittersweet. It was a great ride. I really enjoyed it. But I think it taught me a lot of lessons that really helped me elevate to the next adventure I went on in life.

[A]

Something that stuck with me from your story when we've caught up, is how you gave different people in the company room to grow and how you supported them. Just as a manager, I feel like this is a great lesson, not just for finance people, but anybody rising in an org and how to put people in the right spot on the court to succeed. Because I know you dealt with tons of different personalities throughout the company.

[B]

Human capital is the most important capital that any business has. I always tell people I don't believe in the work life balance. I actually believe in the life work balance. And that is your life should always come before your work. And if we're going to find the best human capital, we have to really treat them with the respect that they deserve, and we have to treat them as our most valuable asset. And so one of the things I've always been a huge proponent of is an individual development Plan. So an idp. And with this, what we do is we come together every six months and we ask them a couple questions. You know, when you're sitting there on your deathbed, what is it you hope you remember in life? And anybody who says, oh, I remember that Saturday I came into the office like, okay, that's not really what you value in life. You know, it's seeing your child score their first goal at the soccer game. Cool. That's where you want to be. That's what you prioritize in life. Now, how do we work back and say, what do you want to be five years from now? Where do you want to be in the next year? And from that, we then really help them get a plan of how they develop themselves professionally and personally. One of the things that, to me, is a critical part of that is recognizing that not everybody wants to be the next CEO, the next entrepreneur, the next boss. There are some people who actually just want to be the absolute best at the skill and craft that they have. There's a great book where they talked about the difference between superstars and rock stars and helping people understand do they want to be a superstar who's somebody that rises through the chain, or do they want to be somebody who's a rock star who says, I want to master this expertise, and I want to be the best at this in the world? And then how do you take what they really want to do and help them develop their career cycle? So, you know, one of the good examples I use, his name was Sean. He came in a technician. When you talked to him, you knew he was absolutely one of the best technicians out there, that he existed. Like, there was no doubt about it. And when he was talking about his career progression, he always, I want to go into management. I want to go into leadership, because that's what he thought he would do. And we always knew, like, this is not what you are best at. You're not going to enjoy this. But we gave him the chance at it. And so I remember the first time he came in, okay, we got some goals for him. We gave him some ability to come in and sit behind a desk and actually do some of the work at a computer. And he came back six months later, like, I don't like this. Okay, cool. And so we put him to different kind of jobs every time he went to idp, and after about a year and a half, he's like, you know what? I love being out there in the field. I love building these projects, and I love absolutely every day saying, I'm the best expert that does this. It was just really cool to see his progression. But ultimately, you know, he's gone on to be very successful in what he does. But everything we do is about, how do you bring the best out of the person that it's working on your team?

[A]

What did you learn about depending on heroes or hero selling or hero technicians and, like, the superstar people and dependencies? Because I'd imagine there were some people in the business that, like, I've had this in my own team. Like, I'm screwed if that person Leaves

[B]

probably a lot of the same journey you went through. I'm not sure you had somebody who logged into your podcast ahead of time to make sure all the audio video was set up. Your first few events you did. I'm sure you were handling that all yourself, all the rsvp, printing out, the name badges and things like that. I think every company starts with actually heroes, and that's really what builds it. You think of an entrepreneur. An entrepreneur in most cases is hero. There's somebody who can do everything. They, they can find all the solutions. But the issue comes with as you scale. And as you scale, you start to see these, these issues where now this entrepreneur or this founder is really good at making all the decisions. But in the beginning, maybe there was five decisions to be made a day. Now there's 50 decisions to be made a day. And if everybody has to wait for that entrepreneur to make that decision, you now have a bottleneck. One big part of this is as companies scale, they just have to recognize we're going to move from more generalists who do more things to more experts, people who are really good at their skill and do fewer things, but they do it incredibly well. And so I think that that's one place we see heroes. I think the other place we see heroes really comes with people who build this absolutely incredible Excel spreadsheet. You know, I'm sure you built some in your career. I built some. You know, they have all these macros, they do all these really amazing things. As soon as you go on vacation, it breaks. You're done. And so now as companies start to scale, we have to start to think about how do we take this process away from being a human oriented to systems and process. That's another place that we see the hero system break down. That's a lot of what we've done at Rapid sos and the FPA department said, how do we institute these systems so Salesforce, CPQ to Abacom so that you don't have to have the human in the loop to make it work.

[A]

You've worked through most of your career, whether you were the founder or you're working for a founder in these ecosystems where there is a personality that, that's leading the place and it's a rapid hypergrowth company. What have been your reflections or kind of the parallels of, of, of being in those environments?

[B]

Well, first of all, you make a lot of mistakes. One of the biggest things I've learned, if you're not making mistakes, you're either not moving quick enough or you're being too conservative. Too many people are paralyzed or afraid to not make decisions because of the outcomes that'll happen. You know, one of my good mentors, Gaby, you know, because it was something I struggled with early on in my days of, you know, how do you know you have enough information to make a decision? And he says, if you've gotten 80% of the information, you probably have enough information to make the right decision. If you have 90% of the information, you probably waited too long to make the decision.

[A]

We drill into this. This is so amazing, because I think it's true.

[B]

Yeah, no, it is. I mean, you think about it. Getting the first 60, 70% of the data is probably the easiest part. But then you have to start digging into the nuances and you start questioning, okay, if I make this, what's the second order ramification of this? And eventually you get to the point where, like, I'm pretty comfortable with this. For us finance people, we're really analytical. We start to play out the chess game. What are all of the different things that could happen from this? And so then we want to say, okay, man, cool. I'm sitting here looking at raising pricing on this product. Well, what happens if I start to have churn? You know, how can I go back and win the customer? What places are going to churn? Let me go start doing a B testing. Meanwhile, you've wasted another six months to a year. And had you just gone and raised pricing, you probably would have seen nothing happened. You would have been able to react quickly enough if it did to really, you know, change the outcomes of it.

[A]

Sometimes I'm like a dog with a bone. So I heard the pricing example there. Do you think that startups often undervalue the product that they're selling or trying

[B]

to bring into the world A hundred percent? You know, I'll use an example that we had in our company with Disney when we first went in there. You know, Disney is notorious for beating up on its suppliers. On the reverse, though, they don't have a problem charging you a premium for ticket when you go there.

[A]

So I paid top dollar to bring my whole family there.

[B]

As an entrepreneur with a small company like Disney makes a difference in our trajectory. And so you go in there and you want to make sure you win at all costs. And so you will go in, you'll underprice your product. And you can honestly do that because you don't have a big infrastructure built out around you like a lot of the bigger companies. Companies do. But then as you start to win those types of projects, now, all of a sudden, you have to have this infrastructure. You have to hire the project manager. You have to hire more people in your accounting team to handle all that. What happens is you're not adjusting your price relative to the value that you're providing to these companies. And so I think the natural tendency is to think that, oh, man, if I go in here and raise my price, the company's all of a sudden going to dump me. I think what most companies realize is they will pay for the value they get. And so as you're able to provide them more value, as you're able to get in there, really understand the customer better, there is actually a lot of appetite for them to pay you more, because if you're providing that value, they don't want you to go out of business. They don't want to go find new people. And so I do think a lot of entrepreneurs, especially when they get that first big anchor customer, they never go back and revisit, okay, now how can I update my pricing to really reflect

[A]

the true value I'm bringing to make that more real? Was there a time where you raised prices by a good chunk and then you had to see if they churned or stuck around?

[B]

Yeah, I remember in the first parts of Zio, the AV company, we had, we raised our rates from about $85 to $120. And so this was the early 2010s. That was a pretty significant increase. We expected a little bit of churn, but we actually had zero churn. We had one customer call in, and we just simply explained that, you know, we think we provide good value of service. We're actually one of the only companies in our industry that gives health benefits to our team members. This is what it costs to service you. And hopefully you find that we provide the value.

[A]

For somebody listening out there, maybe we have a lot of entrepreneurs that listen, founders and a ton of finance people. What are some signs that you may be underpricing your product when your margins start to compress?

[B]

One of the things I think that a lot of entrepreneurs don't understand is what actual their margins are. So let's use the example of the AV company I was in. How much does it actually cost us to deliver service? When you talk to some of these founders, they'll say, oh, I pay a technician 20 bucks an hour. Then maybe, you know, $10 an hour for things like vans and stuff like that. When you actually look at it, a technician, on average, works about 60% of the time you're paying them. And so right there you now have, you know, roughly a third of the time that, that you have to allocate up for. Then you have all the overhead and all the other resources they can sue. And so what about the time they're stuck in traffic? What about all the benefits you have to provide them? And so really once you get in and you really allocate all the costs to that and you really understand your cogs, you're going to start to see your margin slipping. And as soon as you start to see that margin slipping, you're either not being effective, inefficient, or you're not truly pricing for the services that you're providing. So the second place I've seen it is when you're winning everything. If you're winning every, every job that you quote, odds are you are probably the cheapest person.

[A]

Isn't that counterintuitive? You're winning too much?

[B]

Yeah, you are. But this is where a lot of companies get in trouble is they go out there and they win too much. Now all of a sudden I have too many projects, I have to hire a whole bunch of other people. As that increases the burden on the system, my profitability goes down. And so now I'm probably actually going to be losing money because I won too much business. I can't get out of the contract, so I still have to service it. And I think that's where you see a lot of times these companies go out of business just because they've gotten out ahead of their skis.

[A]

Richard, I'm going to take you into what we call our long ass lightning round. So you're a Renaissance man, but what's one thing you've messed up on the job before?

[B]

As an entrepreneur, I probably made million dollars of mistakes. But the one that always comes to mind for me, I don't know if you'll be able to relate to this, but I was an intern and worked for a semiconductor firm. So this company did about a half a billion dollars in revenue. And this was back in 2003. If you know anything about semiconductor world, they produce a ton of chips that probably cost a few cents apiece. And so it's actually a very small cost of goods sold for each chip.

[A]

I actually didn't know that. I assumed it was expensive because Nvidia stocks through the roof.

[B]

Yeah, you look at Nvidia, but below that are a whole bunch of semiconductor wafers and individual chips that go to produce a component in a package. And so each one of those little wafers. You know, it may cost $10,000 for a wafer, but they're fitting 20, 30,000 chips on that wafer. And so when you still really start to go look at the cods of these things, you have a significant amount of data you have to put in. So at this internship, I basically automated an Excel model using macros and everything. You loaded the data in, you press the button, an hour later you came back and it gave you the results. The first of the month, I was scheduled for the first time to present basically in front of the whole company. Had the CTO in there really showing them the cost profile. Made the mistake of loading fresh data the morning of before I did it, press the button, came back right before the meeting presented it. Come to find out, the data team in the back end had restructured the way that they had done all the data. And so I did not notice. But five minutes into the presentation, the CTO calls out, I think something's wrong with your data. Needless to say, he stopped the meeting right there. So I very quickly learned, never refresh your data right before the meeting.

[A]

I've been there before too, and I'm like, I've, I've uploaded this and refresh it a thousand times. Let me just run it through the model and I'll go live. But that one time, like, oh, shit, something's off here.

[B]

You can appreciate how salespeople are with their demos with customers. You know, if the demo goes, great, but as soon as you get for that customer, it's going to break.

[A]

I have this pain in my heart whenever I watch someone do a live product demo during a conference like, oh, I really hope this goes okay.

[B]

As the FPA team moves to more of this kind of product related infrastructure that we're putting in, I feel the same way. Before it was a spreadsheet, I knew how it was going to operate. You know, just the other day I was presenting somebody on Applecom and we had some really good dashboards and the back end, somebody was refreshing data from the quarter end and in the middle of the presentation, my data just changed. Luckily, this was internally and everybody could laugh at it and we just kind of moved on. But yeah, it's always that fear.

[A]

If you could give your younger self advice, knowing what you know today, what would you tell them?

[B]

Take more risks. I think I was, you know, really conservative as a finance person. You always weighed the risk versus return and sometimes you just got to throw it out the window. You know, just, you're young, you can make things up. Just go out there and do it.

[A]

In many ways, risks are wasted on

[B]

the youth, you know, and sometimes I even ask myself, hey, you know, even though we're older, is there any reason we shouldn't take just as much risk? So maybe I should be out there doing that now as well.

[A]

Next one I got for you, more of a technical one. What tools does your team use today to get the job done?

[B]

I'll give you the first answer, which is probably everybody's saying, claude, not only from a data analysis perspective, but especially as we've had so many acquisitions, there's just been gaps we've had in a lot of our systems and processes. We've been able to build to develop our own software. You know, so that day we did it with collections portal. We've actually designed an entire collections portal. So now we've unified collections across all the different companies that we have, all in one system where we can see it. Another answer, though, is also abacum. You know, I've had that now for almost three years. And that's something from my perspective, where from a finance world, I can log in and see all my data in one place. There's no opening 10, 20 different spreadsheets. But it's also easy enough where I don't need some type of BI analyst to get in there and actually build my dashboards. And so I'm living in that all day long. My team's in there all day long. Really. We don't go to spreadsheets anymore. That's where we do everything. And so that's been a huge unlock for us and to being able to connect a lot of the different pieces of the company into. How do we see that in one quick, efficient dashboard?

[A]

What's the craziest thing you've ever had someone try to expense?

[B]

This is a place where I've been very fortunate. We really try to educate people on how they can be good stewards of the capital of the company. Back in the day, we had somebody who worked in the office, and so they had a credit card to go out and use for a lot of the vendors. And one day we started seeing gas charges. Charges pop up on this. We go confront him, hey, what's going on here? Why are you having gas charges on this? He openly admitted that he was charging his personal gas. That's company credit card. He knew it was against company policy, and he had zero problems with doing it. It was probably also the easiest decision of how to fire someone that I've ever made as well so wow, what a move. Maybe that's his modus operandi with different companies.

[A]

Richard, it's always a pleasure to hang out with you. I appreciate your storytelling ability and also just telling us all about what you're doing with data.

[B]

Thank you for everything you do in the community. Huge assets and I know we all get value out of everything you produce, so thank you.

[A]

Thanks man. Run the Numbers is a mostly media production yelling an intro by Fat Joe Artwork by Meg d' Alessandro show is executive produced by Ben Hillman. Nothing said on this podcast is intended to be business or investment advice. It's the sole opinion of me. A guy who feeds his dog way too much ice cream and has a history of net operating losses. Lol. If you like this podcast hit subscribe and give us five stars. It will take like two seconds and our algorithm overlords love it. Drink water, call your mom and have a great day.

[B]

Peace.