2833: Accelerating SMB Marketing Success with AI

[00:00:00] In the rapidly evolving world of AI and products innovation, how do companies balance the

[00:00:08] cutting edge of technology with the practical needs of their customers?

[00:00:12] Well today we're going to be diving into this question with Ryan Johnson, Chief Product

[00:00:17] Officer at a company called Call Rail. Not error-complete the forefront of leveraging AI

[00:00:22] to revolutionize how smaller medium-sized businesses track, attribute and optimize their marketing

[00:00:28] efforts. And today's guest has got a rich background that spans from spearheading AI initiatives

[00:00:35] at pre-revenue start-ups to playing key roles in significant corporate acquisitions. But

[00:00:41] not only that, this is where things get incredibly cool and interesting. Ryan also brings a unique

[00:00:46] perspective to the table because his passion for AI, combined with a keen interest in car racing

[00:00:53] provides an intriguing backdrop to our conversation on driving product innovation at speed while

[00:01:00] also navigating the complex terrain of customer needs and technological capabilities.

[00:01:05] I've got you intrigued, right? So today I'm going to invite you to join us as we explore

[00:01:10] the intersection of AI product development, customer collaboration and so much more with

[00:01:15] Ryan Johnson who's also going to tell us how Call Rail is steering businesses towards unprecedented

[00:01:22] growth and efficiency. Yeah, I had to get a call racing pun in there and no, I'm not going

[00:01:26] to apologize. But before we get today's guest on it's time for a quick shout out to the sponsors

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[00:02:27] And now let's get today's guest on. So buckle up and hold on tight as I beam your ears.

[00:02:33] All the way to Atlanta, Georgia where Ryan Johnson is waiting to speak with us.

[00:02:39] So a massive warm welcome to the show. Kite's only listening a little about who you are and what

[00:02:45] you do. Sure so my name is Ryan Johnson I'm the Chief Product Officer at Hallreel. A little bit

[00:02:52] about me, I reside in Atlanta, Georgia with my wife and two beautiful girls that keep me busy.

[00:03:02] I'm a huge auto racing motor sport enthusiast. I take my car out to the track that's kind of my

[00:03:11] extracurricular activities. And you know here at Hallreel on the professional level,

[00:03:18] you know leading our product organization through your through our strategies of how do we

[00:03:26] and I know we're going to get into this. But like how do we really capitalize on all this

[00:03:30] innovation that's happening so quick and bring that to our customer base?

[00:03:35] Incredibly cool. There's the two worlds must conflict a little bit there. One's the nice

[00:03:40] safe corporate world and the other is getting out there on the racetrack. Is it dangerous as

[00:03:45] sounds or not too bad are you sensible? So for what I do, they call track days or a high performance

[00:03:55] driving education. It's all around safety. Like there's no trophies. You know you're out there with

[00:04:04] a lot of people that you make great friends with, not shockingly. There's a lot of technical

[00:04:09] people out there that have met other product leaders and technical leaders. And so you know it's

[00:04:17] always about safety and kind of learning where your limit is. And in most cases, you know your limit

[00:04:25] is a lot lower than the cars. But you see things happening every time there's an event. Someone

[00:04:32] crashes and their car has gone. And luckily with safety innovation, very rare to have injuries. But

[00:04:40] can certainly injure your pocketbook with damaging your street car or race car out there as well too.

[00:04:49] Just for a few minutes do you channel your inner cold trickle from Daiso Sunday? Do you

[00:04:54] fail that for a few moments? I don't know. I started doing it more seriously about two years ago

[00:05:01] and I think my first, you know, you think that I go to like Mario and Dready as like a big

[00:05:07] indie car for me. I went to him and thinking like, oh, I'm just going to get out there and be

[00:05:12] amazing at this. And then you realize how terrible you are. And you're like, oh my gosh, this is like

[00:05:17] learning how to drive all over again. And so I think it's a little bit of shock when you start to be

[00:05:24] like, oh this is really fast. This is not like going 70 miles per hour out of a nicely paved

[00:05:31] highway. This is going 90 miles per hour through a curve with that's three feet from a wall in

[00:05:41] all those types of things. So I think it's a balance over time. Certainly my adrenaline has come down

[00:05:48] since the early days, but you know I track my physical activity. It's kind of amazing

[00:05:54] comparing it. It's like going for a run every time you go out there for 20 minutes.

[00:06:00] Absolutely. And of course your day job you've talked about call rail there. But people that are

[00:06:05] hearing about call rail for the very first time, you know businesses track and attribute each lead

[00:06:10] in their marketing journey capture and manage every call, text chat and form. And also you'd real time

[00:06:16] insights to optimize their marketing. Is that right? Anything else you'd like to add to that?

[00:06:20] Just put people hearing about you guys for the first time.

[00:06:23] No, I think that's right in our goal is to help our customers market with confidence

[00:06:30] and really empower them to turn more of those leads into better customers. But yeah, that's right.

[00:06:38] And as this is a tech podcast, one of the reasons I really wanted to get you on here is you've

[00:06:42] got extensive experience in artificial intelligence and machine learning product development. So I've

[00:06:47] got a lot to begin with. How do you approach integrating AI into call rail's products? What was the

[00:06:53] key principles that guided you in ensuring these technologies create real value for users?

[00:06:58] Because it's a lot of tagging on a thing going on at the moment and a lot of people trying to get

[00:07:02] their AI narrative, their AI story. But a lot of other people sat on the sidelines thinking how do we

[00:07:09] get AI in here? So I'm curious the approach that you took from the from the absent.

[00:07:13] So before call rail, I worked at a pre-revenue AI machine learning tech company out west.

[00:07:23] We actually raised quite a bit of money. We raised a hundred million dollars from SoftBake.

[00:07:27] We were 39 people. So quite the right. But we were developing a lot of the AI technology

[00:07:36] computer vision and LP in house. And it is so expensive. And it's so hard

[00:07:44] to keep up with the Googles and the Amazon's back then. This is before open AI and

[00:07:49] in the Anthropic and some of these others out there. So I learned how exciting it can be. I learned

[00:07:55] how very expensive it can be. And that was our goal life at that time was to build best in class AI

[00:08:05] technology to basically detect things events in the world as they happen. And so coming to call rail,

[00:08:13] I think the biggest thing that I took from there is our core when we get up in the morning,

[00:08:21] what do we think about? We're thinking about how do we help our 200,000 plus SMBs market better

[00:08:28] and get better return on investment and close more leads. It's not this AI shot.

[00:08:35] And I think from the beginning, I've been at Cal Real six years when I came in. I've always said,

[00:08:40] we have to utilize this technology but we also have to partner with leaders and kind of

[00:08:46] verse trying to build everything internally because I'll show you a budget that will scare you

[00:08:51] very fast. And so from the onset, it's been even on basic things like speech to text,

[00:09:01] so taking a call recording and converting it to a transcript. We wanted a partner with one of

[00:09:08] the best companies out there. Our partner is Assembly AI. Their focus is on voice to text

[00:09:16] and all the intelligence behind it. And we started working with them four years ago

[00:09:23] and it's made it really easy, I think in the last year obviously things have accelerated at a

[00:09:30] rate that probably none of us thought it would. But partnering with someone that has your own vision,

[00:09:37] us certainly voices such an important aspect of it because of phone calls.

[00:09:42] But also other messaging mediums whether it's SMS or chat or social. But I think finding a really

[00:09:48] great partner that their job is to build AI that's amazing to help support your customers.

[00:09:59] I actually get a question quite a bit of what's the hardest problem when you're trying to solve

[00:10:04] an AI? To be honest, a lot of the problems are solved and the results are amazing.

[00:10:11] Hard is to actually productize this stuff. How do people consume it? Even from a UX perspective,

[00:10:21] how do they interact with this data or how do they interact with auto-generated things in

[00:10:27] this way? And so I think from our perspective that's the challenge. Our key principles are

[00:10:36] we want to make it purposeful, so valuable. It has to provide value. We want to make it accessible.

[00:10:43] Cost-effective. You don't have to be this big enterprise to be able to get this wonderful

[00:10:50] intelligence. Of course being reliable, so making sure that in a production environment that

[00:10:56] it works and similar to what your expectations from your customers for the platform.

[00:11:04] And then finally, scale was they grow being able to skill on this intelligence at the same time.

[00:11:13] And something else that really stood out for me was that at Call Rail Labs, you also represent

[00:11:18] a significant initiative to involve customers and bring them directly into the development

[00:11:23] processor. Can you share any insights on how that collaboration has helped shape your use of

[00:11:28] voice AI and the development of new product capabilities? It seems so obvious, but I've not

[00:11:34] heard too many people do this before. Yeah, it's funny. It kind of spawned from a conversation

[00:11:41] I was having with our CEO. And he had mentioned, like, gosh, I'm just seeing all this

[00:11:49] thing. It's released in the market in this. How can we do that at Call Rail? Actually,

[00:11:55] we can. We have access to this technology. If we're not worried about getting it fully

[00:12:05] GA or go to market, we could do alphas and put it in the hands of our customers to learn whether

[00:12:10] it's valuable or it's not. And so it started with a simple idea of like, hey, could we release one

[00:12:18] alpha on a monthly basis? And then it was like, we should have a program along it. So that's

[00:12:23] kind of the genesis of labs and said, okay, the purpose of labs is to get this in people's hands,

[00:12:30] get their feedback. Make sure that you know, they're getting value that we thought we saw. We're

[00:12:36] learning a lot of like different ways that they utilize the data or different ways that we can

[00:12:41] improve it. And so yeah, I think for us, it's been really, really great to get that input so

[00:12:50] early because they're hearing this information out there in the market. Now they're getting

[00:12:56] their hands on it and we don't have to be at least in the very short term. So concerned about like,

[00:13:03] oh gosh, okay, where is this feature going? And what product and pricing and packaging? Like that

[00:13:08] stuff will come. But really more focused on is this valuable to you all? And if not, tell us why and

[00:13:16] help us improve in the feedback has been really great. I think our customers enjoyed as well

[00:13:24] if you think of SMBs and agency, they may not get this opportunity to get deep into

[00:13:31] some of the intelligence stuff with voice. So I think it's really exciting for them too.

[00:13:37] I've been reading a lot recently about how conversation intelligence technology has got

[00:13:42] this potential to almost revolutionize our businesses, understand and engage with their customers

[00:13:47] and someone right in the heart of the space. Have you seen the technology involved? And what are

[00:13:52] some of the most significant impacts you've observed on marketing performance and lead conversion

[00:13:58] and all these things? Because we hear a lot of hype around this technology but it'd be great

[00:14:02] to hear more about the business value that it can help deliver. The business value, absolutely.

[00:14:09] So I think for us, particularly to call rail, we always have to start on the speech to text. And so

[00:14:17] you want to have the best automatic speech recognition models because that transcript is going to be

[00:14:26] it's kind of like good in, good out, bad in, bad out. And one of the in the last year and a half

[00:14:36] two years is that, that has just gotten way more to the human accuracy level of transcription

[00:14:42] versus what it was just a few years ago. And so starting with a really amazing foundation where

[00:14:49] this is highly accurate and then doing the intelligence on top of it is really

[00:14:57] a big piece of this. And so those ASR models helped. We've seen, I would say of recently

[00:15:06] what's the impact to our customer base? Summarization is novel, it's simple as it seems.

[00:15:13] It's really valuable to customers. Their ability to see a 30 minute phone call

[00:15:22] summarized to three sentences of exactly what was discussed and what the outcome was.

[00:15:28] Like we're hearing anecdotes of just the massive amount of time savings like 50% less time

[00:15:36] reviewing and analyzing these interactions, these conversations. You know, 60% less time qualifying

[00:15:43] leads. So now they can allow conversation intelligence to automatically qualify these leads based on

[00:15:50] the conversation that happened during the phone call. Again, it's very heavy lifting for those

[00:15:55] customers. And then of course, you know, flowing all this information back to how do they improve

[00:16:02] their marketing OI, ROI, whether they're advertising on Google or social media or those types of things.

[00:16:11] And just seeing increases in lead volume coming in from the intelligence they're able to glean.

[00:16:18] So I love hearing these stories every day. I think it's just a massive time saver for a lot of

[00:16:24] businesses that don't have a lot of extra time. And then being able to like focus more on their

[00:16:31] core business and allow this technology to really, you know, improve their business performance.

[00:16:39] And I would imagine enhancing customer experience and enhancing agent performance is quite a tricky

[00:16:45] balance. And almost considered a silver bullet in some circles. Is anything you can share around how

[00:16:52] your AI is leveraging insights from phone conversations to improve those customer experiences and

[00:16:57] agent performances and maybe even bring it to life with a couple of examples of how it has

[00:17:03] transformed those customer service strategies for your customers. Sure, this is a great question.

[00:17:09] And I do think it is something that's talked about in the market quite a bit. You know, you can start

[00:17:16] with a simple kind of broad thought of call coaching. So, you know, what how can you utilize AI to

[00:17:23] provide really good feedback to that agent or that person that's answering the phone call?

[00:17:28] What went well, what were opportunities to get better on? Things like being able to see if they

[00:17:36] follow to script or being able to follow a playbook that maybe they have. It was interesting about

[00:17:43] it is that, you know, I think people who get this feedback are more open to it as well because

[00:17:50] it's technology giving you feedback. It's not a human. So, you don't have that human bias of,

[00:17:56] you know, I joke, you know, my CEO could come in and be like, Hey Ryan, you talk too much during,

[00:18:02] you know, this clienty meeting whatever I could throw back into space and say the same thing,

[00:18:06] hey, you talk too much over here. When AI tells you this, it's very different. There's not a,

[00:18:13] there's not the personality behind the human that could affect how they are coaching you.

[00:18:20] So, it's much more agnostic. Of course, you have to be careful with bias and those types of things in

[00:18:25] AI but from a, you know, helping your agents get better and then being able to utilize technology

[00:18:33] that say like always, you know, mention things that were good. Always mention things that they need

[00:18:37] improvement on and then how do you allow? So, that's at the individual level but then the next level

[00:18:45] of this stuff that working on right now is to aggregate this and be able to say, Hey Ryan,

[00:18:52] you know last month these are like the three things he just did wonderful on. Here are the three

[00:18:58] opportunities that he could improve and then how do we improve this throughout the organization or

[00:19:04] the location or the office. And so, really trying to boost performance in a very, very scalable way

[00:19:11] which today it's, you know, it's random. It's like, Okay, I'm going to QA one call and listen to it

[00:19:17] and see and there's just a lot of luck on what call you hear and how you even as a person are analyzing

[00:19:26] it. So, there's a lot of benefit there. It gets into things like next best steps for the agent to

[00:19:36] so, hey post phone call, you know, the person mentioned send a follow up email like how do we say

[00:19:44] like send a follow up email? Here's the email address and even here is a draft of that email based

[00:19:50] on the conversation that you had. And so, really helping the agent as well post call.

[00:19:58] And we're just, we're learning about so many, like the antidotes I get are interesting because

[00:20:06] you'll hear stories of where hey, we have this playbook and we wanted people to follow it and

[00:20:11] we followed it and now your technology is telling us like if we adjusted it this way would be better

[00:20:17] and so it was like they were in their head of like this is the greatest thing and then

[00:20:21] their team is actually proving them wrong with better performance allowing them to update. Hey,

[00:20:27] this is what our process should be from a customer experience perspective. And ultimately, you know,

[00:20:35] hopefully, you know, in my head I want our customers customers to have a better experience. So as

[00:20:42] much as we're able to help with coaching and the call in next best steps, but is the person on

[00:20:51] the other end or they getting a better experience because of this technology? Because to me that's

[00:20:56] magical. I love being on the other end of the conversation or phone call and it's like this person

[00:21:03] knows what I last called about and they know, you know, these things about me. I don't have to

[00:21:09] explain to someone all over again. You know, the reason I call previously so I think there's

[00:21:16] you know, my hope is that that experience just as is so much better downstream for their customers

[00:21:22] as well too. Absolutely love that and of course, AI is rolling optimizing marketing efforts. He's

[00:21:29] also becoming increasingly critical but you can't improve what you don't measure. So isn't he

[00:21:34] can share around how call rails technology can help businesses do things like track and analyze

[00:21:40] key performance indicators and ultimately achieve that high return on investment.

[00:21:45] Yes, so at the foundation it you know, we can analyze how individual keywords are performing

[00:21:53] or channels and kind of everything in between. And so, you know, really utilizing this technology to

[00:22:01] help provide more data to usually third party systems or integrations in our world.

[00:22:08] You know, being able to use technology to auto qualify this was a good call and then sending that

[00:22:14] data back to like Google for example so that Google can take that information put it into their AI

[00:22:20] algorithms to help your performance. And so, piece of it is getting data to the right places. So

[00:22:26] you know, we send this AI data like summaries and sentiment and some of the other things we talked

[00:22:32] about to advertising networks. We send it to CRMs like Salesforce and HubSpot and we make it

[00:22:40] harder that workflow. So I think, you know, it's critical that you have those connections because

[00:22:47] if you don't people say well, this information is great but it's not really helping me do this

[00:22:52] because I can do that scale. I can't get this information into these other systems that we use

[00:22:57] to make our you know, our buying decisions in budget. So that's a big piece of it and I think

[00:23:05] you know more importantly what we're seeing now with was kind of the the cookie list future

[00:23:11] that's on the horizon. You know, utilizing this technology is even going to be more important.

[00:23:18] So as businesses and advertisers kind of lose some of that information about

[00:23:24] about an individual, this just makes conversation intelligence and what happens during conversations

[00:23:31] even more important to you know, hopefully you know not only fill avoid but maybe even be better

[00:23:39] because the results are just are kind of magical.

[00:23:45] And is there any other examples you could share of how customers are using AI to gain a competitive

[00:23:50] edge but in multiple industries because it's so important. I think it goes right across home

[00:23:55] services, legal healthcare sectors and so many others but are there any standout use cases where AI

[00:24:01] is really making a tangible difference or is it right across the board? I think it's right across

[00:24:07] the board, you know if the antidote from a home services company, you know they were advertising

[00:24:16] through a lot of different mediums and you know they say 10 to 20 hours per week, automating

[00:24:23] the lead and the tracking that would otherwise be manual through spreadsheets or those types of

[00:24:28] things. You know in the legal industry you know you mentioned being able to improve what ads are

[00:24:36] converting the best. You know antidote is you know one of our customers, you know we help them

[00:24:43] identify that for 75% of their clients that some of these channels were just not performing well

[00:24:51] that they thought otherwise and eliminated a considerable spend in the thousands of dollars every

[00:24:57] month for their customers. And then healthcare too, you know healthcare cares a lot about

[00:25:03] quality and the experience in the phone call there's of course PII and those things that we take

[00:25:09] very seriously but they get so many phone calls especially you know if you think of your daily life

[00:25:17] most of us have to pick up the phone to call healthcare professionals. So this is just improving

[00:25:21] their quality of life to be able to understand what's happening during these conversations at scale

[00:25:28] and to you know never miss those opportunities. I just go back to your own career here,

[00:25:35] are you transition from a focus on accounting and finance to leading AI initiatives? It feels

[00:25:41] like there's a bit of a backstory there so would you agree to the tech industry and AI specifically

[00:25:46] how was that analytical mindset influence your career trajectory again there's got to be a story

[00:25:51] that right? There there definitely is a story so I went to a very small liberal arts college in

[00:26:00] Michigan called Albion College. It's 1200 students when I was there so almost the size of a high school

[00:26:08] and part of that liberal arts education is they're supposed to expose you to a bunch of different

[00:26:12] things and so even though my you know studies were focused on finance accounting economics

[00:26:20] I decided to take a CS class as one of my electives and even my at the time I laughed my advisor

[00:26:30] was like what are you doing? Like it takes something easier like you have a full load of the stuff

[00:26:34] that you're studying for and then you're going to take this really hard technical and mathematical

[00:26:39] course like what do you think in best decision ever? It was a very young professor in his career

[00:26:46] he kind of knew that you know I wasn't going hardcore in a computer science and I learned so much

[00:26:52] about technology from that one course and then fast forward you know a few years down the road after

[00:26:59] graduation I spent some time in finance. In a friend of mine happened to create a digital marketing

[00:27:07] company doing SEO and SEM in the early 2000s for law firms and the business was growing really

[00:27:14] fast he needed some help I knew HTML and he's like I can teach you SEO and SEM and some other things

[00:27:22] and that's really where it started was there and I went back and forth between finance and

[00:27:29] in technology certainly I had an intersection at one point in student financial services

[00:27:38] financial aid where I was able to work on projects on the technical product side but also have

[00:27:46] this domain knowledge of a highly regulated industry especially here in the states as far as

[00:27:53] financial aid goes. So I think with everybody in product we all have our stories like sure there's

[00:28:00] product and it's all the other day you can even get a degree in AI now at some universities and so

[00:28:10] to me it was a fun journey and it made the transition a lot easier and honestly it pays off a lot too

[00:28:19] as in this day and age I talk about you know product managers as being trusted consultants

[00:28:27] and they're being pulled into more conversations on the business side and so you know understanding

[00:28:32] all those things I think has been really helped accelerate my career like I know ARR I know you know

[00:28:38] LTV to CAC like I understand accounting and finance and all those those levers and so I think it's

[00:28:48] helped me be a better product leader and help grow our team as well to say hey there's business impact

[00:28:55] here and we need to have a really good understanding of the business and in the impacts that we make

[00:29:02] and the decisions we make and so you know I certainly wouldn't have thought this is where I landed

[00:29:09] would land 20 years ago but I couldn't be more happier that this is the way it all paid out.

[00:29:16] Absolutely lovely and we've now come full circle bringing back to your call rising now I was

[00:29:22] also reading you've drawn a few comparisons between AI product development and call racing so I've

[00:29:28] got to go there what are some of the commonalities between these two passions of yours and how

[00:29:33] was the world of call racing maybe even influence your approach to AI and product innovation again

[00:29:38] is another cracking story there right sure yes um in general I think auto racing has been on

[00:29:47] the forefront of adopting new technologies and you see that stuff kind of come down to the cars

[00:29:53] that that we drive every day so if you think of you know hybrid that started in auto racing hybrid

[00:30:01] engines you think of break regeneration for electric cars that came from auto racing even ABS

[00:30:09] and in a lot of these things in the past all came from auto racing and so they utilize AI

[00:30:16] at another level happen for some time you can think of these sophisticated models that they're running

[00:30:21] you know that you know 10 years ago maybe they needed these massive wind tunnels and all this

[00:30:26] expense and now they can do that through a computer and they can simulate all of those things

[00:30:31] to make the best performing car so I think you know auto racing is going to continue to be

[00:30:39] at the forefront and push that in many different areas and I hope you know the technology

[00:30:44] advancements that they uncover you know come come down to our road cars as well too um

[00:30:51] you know on the track what what's what I find and this is very difficult in our roles is there's

[00:30:58] a lot of distractions and when you're out there on the track you can't think about anything else like

[00:31:04] you definitely don't want to think about anything else um you want to be in the moment you're you're

[00:31:08] looking far out in the distance you're looking at your rear rear rear your adjusting um

[00:31:14] and it comes at you very very fast at a velocity that I think most folks aren't used to

[00:31:23] and I think in how you react to those I think is really kind of shaped how I think about

[00:31:31] product when something happens and so you know I two stories being out of the racetrack one is

[00:31:36] I was doing a a check ride they call it so I can move up a group with an instructor and another car

[00:31:44] passed me their brake line was was leaking and it sprayed brake fluid all over my windshield

[00:31:52] not too bad still could see well my automatic windshield wipers that I didn't turn off thought

[00:31:59] it was rain and kicked on and it smeared brake fluid across my entire windshield so I could not

[00:32:05] see out the front of my car and I had to quickly think okay you know slow down safely

[00:32:14] all off to this side eventually was able to to wash it off but you know with with the windshield washer

[00:32:22] go back to the pits you know then everybody's going uh brake you know fluid can't be on paint

[00:32:28] for that long so you gotta like wash it off so here I'm scrambling and so I think you know for me

[00:32:33] the biggest thing is um is is how you react to those things and it's funny in that instance my

[00:32:41] car's automatic reaction was to turn on the wiper blades because it thought it was rain um so it's

[00:32:48] kind of interesting as you build products you may say oh yeah I want this automation but are you

[00:32:53] thinking about other things that could happen the non-happy path um and I was definitely in the

[00:32:59] the very non-happy path um and so I tried to to learn from that decision making obviously it's

[00:33:09] it's a lot less um velocity wise a lot less certainly you don't have the safety element in there

[00:33:16] something happens while you're in the car compared to um the product but um I I think there's just

[00:33:25] the the the focus out there has really helped me too so that when I come and it's like hey I need to

[00:33:30] think about X um or I need to really dig into AI like I need to have that like turn slack off

[00:33:37] move the cell phone away like just focus on that and it may only be for 20 minutes and that's usually

[00:33:43] along about at the track at a time it's like 20 minute sprints um and so I think that helps to

[00:33:49] be like okay why wouldn't you try to take that focus that you use on the racetrack when you're

[00:33:55] when you're thinking through these new um opportunities and challenges with technology

[00:34:02] one amazing story and you have been on an incredible journey in your career and I love how it

[00:34:07] has evolved for you and how you've been able to combine these two passions of yours incredibly

[00:34:12] cool but before I let you go I'm going to ask you to leave everyone listening with one final gift

[00:34:17] and that is we've got a Spotify playlist on here for our guests to leave their favorite song

[00:34:22] or an Amazon wish list to leave a book or I'm going to ask you what would you like to leave

[00:34:26] everyone listening with us one final gift before you leave us today so I'm going to leave you

[00:34:31] with a book of course it's going to be racing and automotive related the book is called The Driving

[00:34:37] Force Extra Ordinary Results with Ordinary People and it's by Peter W. Schutz who's the former CEO

[00:34:46] of Orchette AG who is the first American came from a very interesting background and just

[00:34:55] the tidbits of leadership and how he thinks through things being thrown into you know operating

[00:35:03] an international company language barriers you know different types of board structures and in

[00:35:11] all this craziness it's fun it's about Porsche but there's so much about leadership in there and

[00:35:18] I think as leaders we get thrown into these scenarios or these businesses even and we may not

[00:35:25] understand at all and so you know learning about his journey was just like oh wow like it's

[00:35:31] it's amazing to see you know what he was able to accomplish and you know the impact he had on

[00:35:38] the company in its employees. Louis well I'll get that added straight to the Amazon wish list

[00:35:44] hey I might even have David Coverdale's last note of freedom from days of thunder soundtrack just

[00:35:48] because it won't remind me of that they're very stupid I won't say no to that one

[00:35:55] we'll get that on there and I'll reminisce about the story of the bright fluid on the windscreen

[00:35:59] now absolutely brilliant story and for anyone listening just want to find out more information

[00:36:04] about call rail though and everything we've talked about well do you like to point everyone

[00:36:09] yeah the easiest way to find this is on you know call rail that come but I post regularly on

[00:36:15] LinkedIn and so does call rail so you know shoot me a note love to talk shop love to talk about new ideas

[00:36:24] but yeah the our website in LinkedIn are probably the two best ways

[00:36:28] well so much about this conversation I'll remember for long after we finish recording that

[00:36:34] I love from the tech side of things how you're inviting customers to influence the companies use

[00:36:39] a voice AI a via early access to new product capabilities and opportunities so many cool stuff

[00:36:45] there and also your technology called conversation intelligence and how call rails AI minds phone calls

[00:36:51] conversations enables your customers provide insights valuable information but all that was

[00:36:57] and you're amazing back story how you've been able to somehow combine AI and motor racing

[00:37:02] incredibly cool and I'll remember this for a long time but thanks for joining me today sharing

[00:37:06] your story yeah thanks for having me on it was super fun so as we cross the finish line yes

[00:37:12] under the racing pod card help myself sorry of our engaging conversation with Ryan I think

[00:37:18] is clear that the journey towards integrating AI into product development is as nuanced as it

[00:37:24] is complex and exciting a true Ryan's leadership call rail he's not just racing your head with AI

[00:37:31] it's doing he's doing so with a keen eye on the rear view mirror I'm pushing my look here

[00:37:38] but seriously in doing so he's ensuring that every innovation directly enhances customer success

[00:37:43] so for me today's discussion just shed light on the importance of collaboration customer feedback

[00:37:49] and that strategic approach to AI that prioritises real world application over the allot of

[00:37:56] technology for technology sake but I'd love to hear your thoughts on this email me tech blog writer

[00:38:01] outlook.com twitter linked in instagram just at Neil C Hughes in fact because I've enjoyed myself so

[00:38:07] much with the days of thunder theme throughout this episode I'm going to see if I can put a clip

[00:38:11] together of that story that he shared there with an instrumental song from the days of thunder sound

[00:38:16] track so watch this space on your socials keep an eye out for it it'll be I will post it on

[00:38:21] LinkedIn and twitter again just at Neil C Hughes but remember he's not just about the speed but

[00:38:28] the direction that counts so what steps will you take to ensure your business is not only keeping pace

[00:38:35] but leading the pack in this AI revolution but as I desperately run out of car racing puns I'm

[00:38:42] going to walk off into the sunset now so show your thoughts with us let's keep this dialogue

[00:38:46] moving forward and I'll speak to you all again bright and early tomorrow