The Exchanges

Q Can I ask, you mentioned obviously that your specific expertise with regards The enterprise software provider status that you have with airware. How does that differentiate when compared to consumer expectations that they have for the software provided? What's the fundamental differences between the two softwares?

A It's pretty different because as a consumer, people are typically interested in a couple of things. They actually like the drones are fun to fly. I can't tell you how many friends I have who've bought a drone, fly it around, crash it a few times, fix it up, fly it around some more. And that's a, that's a pretty entertaining and enjoyable experience. And the other thing that they want is just incredible, these incredible photographs that we're seeing all of the time on Twitter and Instagram captured by drone, as well as the videos that go along with that. And that's what most consumers are really making their purchasing decisions on and using a software app to enable them to do on the enterprise and commercial side of things. In many ways, it's almost the exact opposite. This needs to be as boring as it possibly can be. You want your team member to be able to show up with a drone, do as little as possible, have that drone just fly the pattern that's required to collect the aerial data, and then transform that data and deliver it in either the form of a report, uh, and, you know, like a PDF report in the insurance industry, or an integration with a claims management system, or in some cases, even an integration with your ERP system for asset tracking and management of financials. And so it kind of really needs to be as integrated as possible, as boring as possible, um, but to rea…

AI assessment note: “In many ways, it's almost the exact opposite. This needs to be as boring”

Q Can I ask, when you look at the landscape as a whole, you mentioned kind of the innovation that comes from the consumer to the commercial, How do you perceive the state of the commercial drone market at present being so integrated in it now with airware?

A Yeah, so a couple of years ago, it was a extremely early market, and so almost every company in the space was doing a little bit of everything. Some amount of kind of building hardware and building drones and software and data and analysis and a lot of manual work and probably flying drones on behalf of their customers, and that's certainly what we were doing at Airware. Now the market is really at a state where companies are focusing on a couple of business models that are kind of penciling out to make a lot of sense. You know, one is actually building the drones, and this is a space that's dominated by players like DJI, which are vertically integrated in China, really focused on being operationally excellent when it comes to manufacturing, and really focused on kind of rapidly iterating on the product line, churning out new products every year, The next model, I think, is companies like Airware, who are really focused on the enterprise software that connects with those drones, makes it really easy to fly them in an automated way so that you don't have to be a pilot or an engineer to be able to show up at a site, operate the drone, collect the data in a way that's needed to address those use cases and applications, and then really own that data processing and analytics system. Pipeline, as well as the user and data management to really kind of operationalize aerial data as par…

AI assessment note: “Now the market is really at a state where companies are focusing on a couple”

Q Can I ask, with regards to the enterprise software and the kind of pricing mechanism and business model around it, do you think going forward that will represent kind of very much a reflection of the existing enterprise models of kind of usage-based, uh, seat-based, or do you think we'll see kind of a new pricing model evolve around this revolutionary category of drones?

A My board members tell me all the time, That they're just shocked with what an incredibly new and differentiated technology drones are. Just how much the decisions we're making, the pricing models, the business model, the strategies we're pursuing at the end of the day are enterprise, uh, kind of a typical enterprise playbook, um, if you will. So we're seeing all of those business models that you've seen that, you know, seat based, user based, account based, per inspection fees, uh, Unlimited use all you want, a la carte pricing, all of those things. I think, uh, at Airware, we understand this is a really early market, and there's all kinds of challenges in using the technology, and so what we're really focused on is driving that business outcome for the customer and doing kind of whatever that takes, which is both providing the technology in a licensed format, but also the professional services, the consultative sales, um, the expertise and the enterprise support that goes along with it. So that we can really help the customer ensure that they're going to get the business outcome they're looking for.

AI assessment note: “strategies we're pursuing at the end of the day are enterprise, uh, kind of a typical enterprise playbook”

Q Can I ask, when you look at the landscape as a whole, you mentioned kind of the innovation that comes from the consumer to the commercial, How do you perceive the state of the commercial drone market at present being so integrated in it now with airware?

A Yeah, so a couple of years ago, it was a extremely early market, and so almost every company in the space was doing a little bit of everything. Some amount of kind of building hardware and building drones and software and data and analysis and a lot of manual work and probably flying drones on behalf of their customers, and that's certainly what we were doing at Airware. Now the market is really at a state where companies are focusing on a couple of business models that are kind of penciling out to make a lot of sense. You know, one is actually building the drones, and this is a space that's dominated by players like DJI, which are vertically integrated in China, really focused on being operationally excellent when it comes to manufacturing, and really focused on kind of rapidly iterating on the product line, churning out new products every year, The next model, I think, is companies like Airware, who are really focused on the enterprise software that connects with those drones, makes it really easy to fly them in an automated way so that you don't have to be a pilot or an engineer to be able to show up at a site, operate the drone, collect the data in a way that's needed to address those use cases and applications, and then really own that data processing and analytics system. Pipeline, as well as the user and data management to really kind of operationalize aerial data as par…

AI assessment note: “Now the market is really at a state where companies are focusing on a couple”

Q Can I ask, with regards to the enterprise software and the kind of pricing mechanism and business model around it, do you think going forward that will represent kind of very much a reflection of the existing enterprise models of kind of usage-based, uh, seat-based, or do you think we'll see kind of a new pricing model evolve around this revolutionary category of drones?

A My board members tell me all the time, That they're just shocked with what an incredibly new and differentiated technology drones are. Just how much the decisions we're making, the pricing models, the business model, the strategies we're pursuing at the end of the day are enterprise, uh, kind of a typical enterprise playbook, um, if you will. So we're seeing all of those business models that you've seen that, you know, seat based, user based, account based, per inspection fees, uh, Unlimited use all you want, a la carte pricing, all of those things. I think, uh, at Airware, we understand this is a really early market, and there's all kinds of challenges in using the technology, and so what we're really focused on is driving that business outcome for the customer and doing kind of whatever that takes, which is both providing the technology in a licensed format, but also the professional services, the consultative sales, um, the expertise and the enterprise support that goes along with it. So that we can really help the customer ensure that they're going to get the business outcome they're looking for.

AI assessment note: “the pricing models, the business model... are kind of a typical enterprise playbook”

Q Can I ask, you mentioned obviously that your specific expertise with regards The enterprise software provider status that you have with airware. How does that differentiate when compared to consumer expectations that they have for the software provided? What's the fundamental differences between the two softwares?

A It's pretty different because as a consumer, people are typically interested in a couple of things. They actually like the drones are fun to fly. I can't tell you how many friends I have who've bought a drone, fly it around, crash it a few times, fix it up, fly it around some more. And that's a, that's a pretty entertaining and enjoyable experience. And the other thing that they want is just incredible, these incredible photographs that we're seeing all of the time on Twitter and Instagram captured by drone, as well as the videos that go along with that. And that's what most consumers are really making their purchasing decisions on and using a software app to enable them to do on the enterprise and commercial side of things. In many ways, it's almost the exact opposite. This needs to be as boring as it possibly can be. You want your team member to be able to show up with a drone, do as little as possible, have that drone just fly the pattern that's required to collect the aerial data, and then transform that data and deliver it in either the form of a report, uh, and, you know, like a PDF report in the insurance industry, or an integration with a claims management system, or in some cases, even an integration with your ERP system for asset tracking and management of financials. And so it kind of really needs to be as integrated as possible, as boring as possible, um, but to rea…

AI assessment note: “In many ways, it's almost the exact opposite. This needs to be as boring”

Q I spend a lot of my time in the enterprise space, traditional enterprise space that is, and a lot of the excitement is always around kind of the potential for upsell and new applications and features. With regards to this specifically, what are the key transformative technologies that you think will enable new applications of the technology, and then a potential market expansion and almost upsell opportunities?

A Yeah. So one of them that we're, we're already utilizing heavily, um, is machine learning, and we're finding that it's able to take a existing data set from customers. So like, uh, if you've overflown a building as an example, maybe the first thing that you're looking for is rusting and corrosion and things that are pretty easy to identify with machine learning. Um, but as we continue to expand the types of things that we're able to detect with it, We're finding that you can also identify water intrusion. You also, over time, can take historical data and get an assessment of how quickly is the, uh, the rooftop, uh, wearing, and is that on track, or is it aging more quickly than expected? And so, machine learning broadly, um, and as we've applied it in the insurance industry, we're finding that a lot of the algorithms and the technology we've developed is almost immediately applicable for another vertical market, the mining and coring industry, in which we're also engaged. So, we're seeing a lot of benefits From addressing kind of multiple industries and applications with that technology. In terms of broader use of the drones, better and better obstacle avoidance companies like DJI are working on so that you can just fly closer to structures, you can really get in there, you can do new types of inspections. And then I think the last one is technology enabling beyond visual line …

AI assessment note: “one of them that we're, we're already utilizing heavily, um, is machine learning”

Q And that came together in the form of airwear?

A Well, that was kind of all, um, leading up to airware, and then I was briefly a commercial airline pilot, and I had been looking at the space, attending trade shows, and I had kind of thought that surely somebody would apply this technology to commercial use cases while, you know, I'm doing my first job out of college and learning to fly more, and when five years went by and I saw the potential for this technology and all the different commercial applications that it could address, and almost every company in the space was still thinking about the drones themselves and the drone hardware. I saw a big opportunity to develop a software stack that could take the data collected by drones and actually make it actionable, um, you know, business intelligence for large enterprises, and was hearing a lot of companies in the utility space, in the insurance space, in oil and gas, in agriculture, really wanting to use that technology, but there were just a lot of challenges for them to be able to do it at that time, both from a regulatory standpoint But also just from an ease of use and actually making the data useful for them.

AI assessment note: “Well, that was kind of all, um, leading up to airware”

Q You mentioned some of the challenges there. I'd love to discuss the alternate side of the table. We've obviously discussed the excitement around the industry. So what are the current barriers potentially to the adoption from the corporate element of the market?

A Well, a couple of years ago, I would have had a kind of almost a long list of, uh, kind of existential risks on the business, including, you know, the regulatory landscape and whether the FAA was going to move And whether large enterprises were going to adopt this technology in a similar way to other enterprise IT technologies they're using. But today, we're seeing that the regulations have been in place now since last summer in the US. They're really enabling commercial drone use at a broad scale. Large enterprises are making kind of enterprise-wide decisions, you know, in the CIO's office about adopting, evaluating, and implementing this technology just as they would Uh, a lot of other enterprise IT. And so at this point, it's the normal challenges of adopting a new enterprise type of technology, and that is training your employees on how to use it, making sure the data is integrated in the right place so you don't just end up with another data silo as part of your business, being able to make sure that it's going to drive the ROI that you think it is once it's implemented and it's being used properly. To ensure that the data is accurate enough for your needs, and to be able to drive that business decision. Those are some of the big challenges.

AI assessment note: “training your employees on how to use it, making sure the data is integrated”

Q I spend a lot of my time in the enterprise space, traditional enterprise space that is, and a lot of the excitement is always around kind of the potential for upsell and new applications and features. With regards to this specifically, what are the key transformative technologies that you think will enable new applications of the technology, and then a potential market expansion and almost upsell opportunities?

A Yeah. So one of them that we're, we're already utilizing heavily, um, is machine learning, and we're finding that it's able to take a existing data set from customers. So like, uh, if you've overflown a building as an example, maybe the first thing that you're looking for is rusting and corrosion and things that are pretty easy to identify with machine learning. Um, but as we continue to expand the types of things that we're able to detect with it, We're finding that you can also identify water intrusion. You also, over time, can take historical data and get an assessment of how quickly is the, uh, the rooftop, uh, wearing, and is that on track, or is it aging more quickly than expected? And so, machine learning broadly, um, and as we've applied it in the insurance industry, we're finding that a lot of the algorithms and the technology we've developed is almost immediately applicable for another vertical market, the mining and coring industry, in which we're also engaged. So, we're seeing a lot of benefits From addressing kind of multiple industries and applications with that technology. In terms of broader use of the drones, better and better obstacle avoidance companies like DJI are working on so that you can just fly closer to structures, you can really get in there, you can do new types of inspections. And then I think the last one is technology enabling beyond visual line …

AI assessment note: “one of them that we're, we're already utilizing heavily, um, is machine learning”

Q And that came together in the form of airwear?

A Well, that was kind of all, um, leading up to airware, and then I was briefly a commercial airline pilot, and I had been looking at the space, attending trade shows, and I had kind of thought that surely somebody would apply this technology to commercial use cases while, you know, I'm doing my first job out of college and learning to fly more, and when five years went by and I saw the potential for this technology and all the different commercial applications that it could address, and almost every company in the space was still thinking about the drones themselves and the drone hardware. I saw a big opportunity to develop a software stack that could take the data collected by drones and actually make it actionable, um, you know, business intelligence for large enterprises, and was hearing a lot of companies in the utility space, in the insurance space, in oil and gas, in agriculture, really wanting to use that technology, but there were just a lot of challenges for them to be able to do it at that time, both from a regulatory standpoint But also just from an ease of use and actually making the data useful for them.

AI assessment note: “Well, that was kind of all, um, leading up to airware”

Q You mentioned some of the challenges there. I'd love to discuss the alternate side of the table. We've obviously discussed the excitement around the industry. So what are the current barriers potentially to the adoption from the corporate element of the market?

A Well, a couple of years ago, I would have had a kind of almost a long list of, uh, kind of existential risks on the business, including, you know, the regulatory landscape and whether the FAA was going to move And whether large enterprises were going to adopt this technology in a similar way to other enterprise IT technologies they're using. But today, we're seeing that the regulations have been in place now since last summer in the US. They're really enabling commercial drone use at a broad scale. Large enterprises are making kind of enterprise-wide decisions, you know, in the CIO's office about adopting, evaluating, and implementing this technology just as they would Uh, a lot of other enterprise IT. And so at this point, it's the normal challenges of adopting a new enterprise type of technology, and that is training your employees on how to use it, making sure the data is integrated in the right place so you don't just end up with another data silo as part of your business, being able to make sure that it's going to drive the ROI that you think it is once it's implemented and it's being used properly. To ensure that the data is accurate enough for your needs, and to be able to drive that business decision. Those are some of the big challenges.

AI assessment note: “it's the normal challenges of adopting a new enterprise type of technology, and that is training”

Q in terms of kind of oil and gas, insurance, agriculture. I'd love to hear then what you think in terms of very traditionally incumbent heavy industries. So with that in mind, do you think this will be a highly acquisitive consolidatory market, or Or will this be a market of aggressive adoption whereby the new, the innovators, the airwares of the world supersede the incumbent heavy market that exists today?

A Well, I think we're seeing a little bit of both. I think long term, there's a lot of value in having, um, some companies that serve the variety of different vertical markets with a common kind of platform approach to it in terms of like the machine learning benefits that I talked Where you might develop it for one vertical application and find that it almost immediately benefits you in another vertical application. Um, with the large incumbent companies, you know, I think this is important for companies to determine whether this is core to their business or whether it's context. And we've seen some companies make both decisions. I would say most companies, while they may begin thinking this is core to their business in terms of developing some of the drone technology, Really realize this is context for them, and it's best for them to partner with a company who really brings that aerial data expertise, both in the forms of being able to leverage the drone, but also being able to leverage the data, and so we're seeing more and more of the large companies really pick partnering over bringing this in-house.

AI assessment note: “Well, I think we're seeing a little bit of both.”

Q And then you're mentored by John Chambers, ex Cisco CEO. What have been the biggest takeaways from that close mentored relationship?

A John is probably the most incredible person I've ever met at working with people, and in every way. He's just an incredible people person, and, and so many people I've met in my career, they either know people really well, and, and that's maybe a way to get what they, they want sometimes, or they're really great with people, but they're naturally that way, and so they, they may not even know what they're doing well that attracts people to them and helps them have great relationships with people. John's a incredibly unique person. In what a natural, charismatic, just genuinely nice person he is, but he's also an astute kind of study of people, and so there are so many times with John where he'll kind of demonstrate something and then turn to me and say, hey, Jonathan, did you see what I did? Is that something you can do as well? What are your thoughts on it? And so he's really a great teacher and a great mentor, and probably one of the areas that he's been mentoring me the most is just How to build great long-term win-win partnerships with customers and make it a lot more about the long-term relationship than about any given transaction and how to make sure you're always doing right by the customer.

AI assessment note: “How to build great long-term win-win partnerships with customers and make it a lot more”

Q Redbird Acquisition. What makes a company attractive, and how do you evaluate that prospect?

A Well, with Redbird, you know, we were looking to enter into a new vertical market, and so I would say that that's one of the reasons that you start potentially looking at M&A opportunities, or at least it is for us, and maybe in the future. Um, but in terms of picking the right company, you know, it really came down, people were probably the most important aspect. We had talked with Redbird previously about doing some kind of commercial partnership, and ultimately decided, let's do the closest Partnership that we, that we possibly can be, because we really saw each other working as part of the same team day in and day out. Their team coming over to San Francisco, our team going over to Paris, and that in combination with the very close relationship that they had with their customers, and how well they understood their customer needs, and the technology they had developed, really made it an, a compelling opportunity.

AI assessment note: “people were probably the most important aspect”

Q And then you're mentored by John Chambers, ex Cisco CEO. What have been the biggest takeaways from that close mentored relationship?

A John is probably the most incredible person I've ever met at working with people, and in every way. He's just an incredible people person, and, and so many people I've met in my career, they either know people really well, and, and that's maybe a way to get what they, they want sometimes, or they're really great with people, but they're naturally that way, and so they, they may not even know what they're doing well that attracts people to them and helps them have great relationships with people. John's a incredibly unique person. In what a natural, charismatic, just genuinely nice person he is, but he's also an astute kind of study of people, and so there are so many times with John where he'll kind of demonstrate something and then turn to me and say, hey, Jonathan, did you see what I did? Is that something you can do as well? What are your thoughts on it? And so he's really a great teacher and a great mentor, and probably one of the areas that he's been mentoring me the most is just How to build great long-term win-win partnerships with customers and make it a lot more about the long-term relationship than about any given transaction and how to make sure you're always doing right by the customer.

AI assessment note: “How to build great long-term win-win partnerships with customers”

Q But you've said before, The superpower you'd most like to have is to best interview someone in 30 minutes. What's your favorite interview question now then?

A Great question. I, you know, interestingly, I think the thing I've learned most about interviewing people is that you can't show up with a standard set of questions. You really need to tailor questions to that person's background and take maybe what you learned in the first interview and then really come up with tailored questions for the second interview so you can kind of just keep going deeper and deeper and deeper and And the biggest mistakes I've made in interviewing people are when you interview somebody who is really senior and polished. And so everything sounds really great. And if you don't dig deep enough, um, you're going to think it's a great interview. And when you do dig deep enough, maybe you find that the person didn't do some of the things themselves, or maybe not in the way that's relevant for your business. And so that's my approach in interviewing in terms of one question. I love to just start off by asking, you know, if I'm interviewing for a head of marketing, as an example, I say, Look, there's lots of different types of, you know, marketing leaders, some more focused on the communication side, some who care a lot about demand gen, some who really are, uh, are thinking all the time about brand. What kind of marketing leader are you and how did you get to that place in your career? So really kind of a, a, a big open-ended question to have the person really…

AI assessment note: “What kind of marketing leader are you and how did you get to that place”

Q in terms of kind of oil and gas, insurance, agriculture. I'd love to hear then what you think in terms of very traditionally incumbent heavy industries. So with that in mind, do you think this will be a highly acquisitive consolidatory market, or Or will this be a market of aggressive adoption whereby the new, the innovators, the airwares of the world supersede the incumbent heavy market that exists today?

A Well, I think we're seeing a little bit of both. I think long term, there's a lot of value in having, um, some companies that serve the variety of different vertical markets with a common kind of platform approach to it in terms of like the machine learning benefits that I talked Where you might develop it for one vertical application and find that it almost immediately benefits you in another vertical application. Um, with the large incumbent companies, you know, I think this is important for companies to determine whether this is core to their business or whether it's context. And we've seen some companies make both decisions. I would say most companies, while they may begin thinking this is core to their business in terms of developing some of the drone technology, Really realize this is context for them, and it's best for them to partner with a company who really brings that aerial data expertise, both in the forms of being able to leverage the drone, but also being able to leverage the data, and so we're seeing more and more of the large companies really pick partnering over bringing this in-house.

AI assessment note: “Well, I think we're seeing a little bit of both.”

Q And then final question, what's the next Five years for you and for airware. What's the roadmap ahead?

A I think in five years, companies are going to look back on the way things are being done today as part of their business and think about how archaic it is. People climbing up on towers, climbing up on rooftops, waiting days and weeks sometimes for accurate information, and it's going to be difficult to imagine how we had done some of these jobs without commercial drone technology and without the aerial perspective. And, you know, at Airware, we are really the tip of the spear in terms of making this technology that kind of boring, everyday part of people's jobs, but where they really rely on it to get the job done, and to be able to do that for really large enterprises, not just as a point solution and a replacement for a piece of technology that they're using today, but in a way where it's really integrated into their business processes, it's driving business transformation, Uh, and it's leading to significant operational cost savings.

AI assessment note: “in five years, companies are going to look back on the way things are being done”