Introduction and Background
Ryan Davies: Welcome everyone to the Tech Business Roundtable podcast show. This is a podcast show dedicated to shining a spotlight on tech innovators, entrepreneurs, founders, and the compelling narratives behind the movements they’ve established. I’m your host, Ryan Davies, and I’m hosting today’s discussion on The Role of AI and Machine Learning in Business Intelligence with Chris Carter. Chris. Thank you so much for joining us here today.
Chris: Hey Ryan, thanks for having me on the Roundtable.
Ryan Davies: This is going to be a lot of fun. I think there’s going to be so much that our listeners are going to be able to take away from this. For our listeners, we already had a great getting-to-know-you session and the energy level is high on this one. I think we’re going to have a lot of fun on this episode. A little bit about Chris, you know, an extensive 25-year background in the it and tech industry. And really, he is a visionary, an impassioned leader at the helm of Aroyo. It’s a global entity that specializes in providing SAP, AI, and cloud solutions for enterprises. And Chris is really the driving force behind innovation and growth. You know, notable achievements, there are many, you know, recognized as a top business winner and ACQ award recipient, inc 505,000 honoree member of the Forbes Coaches Council. I think we can go on and on for a long time here. Right? You make me feel so.
Chris: Oh, and don’t forget a best-selling author now, too.
Ryan Davies: I was getting to that one next. There you go. And a best-selling author in the field of AI published four books around SAP and the ecosystem. I think should we just do a podcast on all of your accomplishments?
Chris: So, you know, I probably stole a lot of that stuff from good people out there.
Ryan Davies: But I think we’re the ones that are very blessed as well to have you here today to spend your time and share your knowledge with us. And you know, I’d love to kind of turn it over you if there’s anything else to color up that prestigious background to for our audience.
Chris: at the top of it. I’m very blessed to have an incredible wife who lets me do everything that I want to hang out in my office most days, hiding Christmas gifts behind me. And then I’ve got two fantastic daughters. One is a paramedic that runs into burning buildings in the scenes to help save people’s lives. Something that I, I don’t have the cojones to do. And then my other one is studying to be a nurse and dealing with emergency rooms and all that kind of stuff with her life, and I couldn’t be more blessed. I’m just a technologist. I get to play with code and computers and help people learn about Generative AI, what artificial intelligence does, and why, and then go around and speak around the country. That’s it. I don’t get to do anything else anymore. My life of being on a keyboard is so over. So now I just learn new things. I help people, and I guess I am doing some really cool things, but at least in my mind, I think they’re pretty cool.
Ryan Davies: I’m excited to dive in here. You know, they say being an entrepreneur is the second hardest job in the world. I think any front-line worker is probably at the top. So I mean, here are kudos to your daughters on that and, being on that side and then for all of our listeners, you’re taking on a lot of them taking on that challenge of, you know, being a founder, a business leader, an entrepreneur as well. And you know, a little bit about that, maybe your journey through being, you know, a business founder, entrepreneur side of things.
Chris: Yeah, I’m very blessed with the fact that,, you know, it started young. I literally started my first company when I was in high school, a little marketing company back then. All of a sudden, somebody show was showing me these plastic business cards and you could run them off. And I thought, oh, this is great. I’m going to get all my friends to do this. I’m going to talk to the teachers. I’m going to talk. I literally sold plastic business cards instead of a paper one to about 30 or 40 different people companies. And I had them running off through the school that I was in high school. So the boy and South High School go to Redman. And that was my first company, and then I went off to college, and I just didn’t have time anymore. So I was like, ok, we’re done with it. Well, then I got a tax bill, and I was like, holy cow, OK. I didn’t know about taxes. Nobody taught you that in high school, something that they really need to start teaching kids nowadays in school. But it started there. Then I started learning about computers, and I brought a Commodore Vic 20, an AppleTwoo E, to college at Georgia Tech. And my life changed because I loved it. I loved coding. I got an internship at Coca-Cola, and I started playing with punch cards in an old R two environment. And then all of a sudden, there’s SAP, and lo and behold, I started doing things with SAP, started having fun with SAP, and started going through activities with SAP. They took me. Then I started doing other things with technology, then the cloud came in, then virtualization, and then I was doing activities, and I just started growing my entrepreneurial spirit and my activities, and I just loved it, and so this is actually my, let’s see, this is my seventh different start-up, and I literally have three of them going right now. Four of them are going right now at the same time.
Ryan Davies: Incredible.
Chris: That is, three of them are wrapped around it.
Discussion on AI and Machine Learning in Business Intelligence
Ryan Davies: I think this is amazing. I think we’re ready again. We’re going to be able to attract so much here. We want to really talk again. You’ve got incredible expertise around AI, as you mentioned already, and beyond, as well as just with the SAP tying it all together, and really, you know, how are you seeing what we’re talking about leveraging AI and machine learning for business intelligence and strategies? How are you able to successfully do that with a pro and with you, and seeing that successfully done out in the world today?
Chris: Yeah, the great thing with AI and with the activities wrapped around it happening right now is that companies understand that this is going to help their company. I go to these conferences and speak, and I wrote a book on it for SAP specifically, and I tell them your people are not going to go away. You are going to make better staff members. You’re going to make better people. You’re going to make better employees. You’re going to make better companies by leveraging and utilizing the activities wrapped around Generative AI and the AI practices that are there. Start to leverage and use it, and you will see the growth that will happen within your organization and within yourself. It’s crazy. All of a sudden, the light bulbs come on, and they’re like, oh, I thought this was going to take everything away. I thought this was going to change the world. I thought Arnold Schwarzenegger was going to come through and say he’ll be back. No, but what you’re going to get is you’re going to get these employees that are going to be like who I will be back. This is pretty cool, and they start to grow and grow, and you see these employees when I’m out and meeting with clients, and I’m at customer sites, and you see individuals all of a sudden who maybe you are pooing it a little bit and all of a sudden it’s holy cow. This is pretty cool. I can do this every day. Wow, I can do more of this, and I can do more of that, and I can bring this in, and they will start to see the data capabilities. That’s when the light bulbs turn on, and the smiles come in, and their eyes start to glow, and I get excited talking about it because I get to see these individuals who maybe were just, oh, they’re going to get rid of me. What am I going to do next? I’m at this age now, where I’m in my fifties. I don’t want to relearn things, but you don’t have to relearn a lot, and so it’s pretty cool.
Ryan Davies: I think that’s a big thing, right? Of course, Skynet fears that everybody keeps that sort of narrative there, right? Of like, oh, they’re coming, it’s you said it hasn’t happened. I don’t think we’re going to see that, so I’m pretty stunned.
Chris: But not unless Elon builds it, that’s for sure.
Ryan Davies: I think instead of cyborgs, like you said, what we’re seeing here is more of being able to use AI and machine learning to enhance data analysis capabilities, right? Do you have some examples of maybe where you’ve seen a significant impact or even anecdotal impacts of, like, really, where these valuable insights are being extracted and how they’re being used?
Chris: Oh, absolutely. We’re very fortunate we’ve got several customers that are using our tools specifically that we built wrapped around AI and the Generative AI activities and machine learning, especially around the customer service side of it for cloud infrastructure SAP activities. We built a tool called Overwatch. Now, that doesn’t get a little bit more Skynet than I don’t know what it is, but we called it Overwatch. That tool will manage, run, and support from an AMS structure, your technology landscape. So it will monitor the things that are going on inside it. But also, what’s trying to attack it from security vulnerabilities and being able to monitor it proactively? This is where the AI fund comes in proactively. The capabilities of what’s going on with your disks. What’s going on with your network? What’s going on with your databases? Are they getting too compressed, being able to tell you what is happening to your system not only in real-time but also saying in seven days, this disk file will be maxed out? You need to do something about it. Now, holy cow. Within the next seven days, I’ve got an archive. I’ve got to take data off there. My backups are getting twofold. Maybe a backup didn’t run, or maybe a backup did run, but it didn’t delete the old backup. Now, all of a sudden, my discs are now all of a sudden. You have this tool that is telling you these things. So, as a customer support person for my network and my team, I’m now able to say, oh shoot, hey, I can make these changes because my tool told me if I don’t do this in the next couple of days, my system is going to crush, and I want to be a hero. So I’m now the hero. I mean, making heroes out of your organizational team members.
Ryan Davies: I was going to say, you know, I think I can already tell that that is a big part of who you are, and your culture and your leadership style is again, you know, putting people in the spotlight, making them shine and all of that kind of a thing and as you mentioned, you know, you’re not here to replace people. You don’t want to do it whether it’s at your organization or others you want to empower. You want to bring more to them and be able to enhance what they’re doing instead of replacing what they’re doing. You know, you talked a little bit about it already, but the idea of quicker learning curves, user-friendly interfaces, accessibility being so important on this obviously, for you, I’m sure there’s a focus on these advanced technologies making them more user friendly than they have been for like the non-technological people that are, you said, maybe in different stages of their career. Can you speak to that a little bit? And some of the tools and how this is evolving in this landscape and ecosystem.
Chris: The evolution of where we’re at right now in anything AI, machine learning, generative AI, the activities of the we are. So, at the very, very top of what is going to happen, there is an iceberg of information and changes just alone if you see where we go, we came from in the last. Now, mind you, AI has been around for many years, and people have been trying to replicate it and trying to get it to the point where it can get massive adoption, and that was a long time ago, but being able to use it on a day in and day out, logistical basis for any company or any individual. Look at just where ChatGPT is taken. I now have two flavors of ChatGPT on my phone. I have three, actually four, different variations of AI-based tools on my phone. You have things such as Grammarly and others that are on there. Those are all AI-based tools. You did not have those up until 24 months ago. Nobody could figure out how they could put that and how they could clean up. All those changes are just the first reiteration of these activities. So now you start going, let’s see what happens in another 24 months. It’s going to be built into your Gmail pretty soon. Google has already talked about they’re going to build their AI system even though they’re getting slapped today about that demo that they did that got a slapping on the hand because of what they put out there in that edited video. Let’s call it. But that being said, they’re going to build that into their Google Mail. They have more users in Gmail than all of Microsoft has right now because individuals have these Gmail accounts, and then you got businesses using Gmail. But think of the benefits now it’s built into it. So you can reference it and ask a question based on your mail. How huge is that? I don’t have to search the toolbar anymore on Microsoft Outlook for a document that I’m looking for. I can just say, hey, find this document inside my Gmail account. Done. Yeah, that’s pretty cool through my Gmail.
Ryan Davies: Through my drive, through my whatever, right and pull, pull everything together. Like you said, workspace, everything kind of coming together, and it’s just there, so these processes and the efficiencies are incredible. You talked a little bit about it as well there in automation, and, you know, with business intelligence processes using AI and machine learning, maybe touching on some of the key benefits and efficiencies that really are gained in the context of business intelligence and what we’re seeing and how they’re being and practically used or how you’re seeing best practical usage.
Chris: Yeah. If you look at those activities from practicality, use organizations are starting small, start small, and you take an HR activity or take something in finance, take something very small and begin to then sprout. You do not want to boil the ocean. That’s the first thing. Don’t boil the ocean. Start small. Be very practical, be very mindful. Second of all, use only your data, only the activity that needs to happen for your data and that data only in those activities do not go outside of your organization. The only people who should be utilizing outside data are Elon with his project through Project X, where you’re getting all the tweets and all that stuff. If you do that, you are going to have data overload, and you’re also going to have activities that are going to bring in dirty data, just like in security. You don’t want dirty data. You want clean fresh data that’s going to be giving you the right interaction for you to make those decisions. Then, your Generative AI tools can leverage clean data to make a clean decision for you. Once you ask the question, give it the prompts for those activities. Those are the two things that I would say, and I would be very conscious of HR or finance. Perfect example, and we do this from a customer service standpoint internally. We leverage AI as a chatbot for people who want to take time off. You want to take your time instead of having to call HR or go through a portal. Just literally goes into the chatbot. The chatbot is automated into our HR systems and gives them the flexibility to be able to log their times off and also tells them you have X number of days or hours left depending upon who you are.
Leveraging AI for Predictive Analytics and Future Outlook
Ryan Davies: I just love that again. It talks really about whether you’re a technical or non-technical stakeholder, gaining accessibility to and, again, really increasing that stickiness with your internal employees, right? Because if you make their life easy, they’re going to have more reason to stay and to do things and increase their productivity, right? Like if I got to go in and I got to book time off, and it’s this whole three email, two approval process that I get it. Do I have to follow up on the that’s time being used, right? That’s that of some soft turns into hard cost, right?
Chris: So, exactly, you’ve got HR activities, you’ve got 401k activities, you’ve got our sick days, you’ve got activities that you can do in regards to time and expense accounts, your timesheets, your expense reports, make these things an automated AI BASED solution that helps you get through this process, clean it, make it more conducive for you and for your employees.
Ryan Davies: I mean, again, I think there’s so much here that Chris is sharing for our listeners just to go. Wow, it’s a great idea. I never thought of that. Never saw it that way. I kind of want to drill down this path a little bit more because I think we, again, you mentioned it in sort of going through that increase in AI increase in machine learning. You talked about, you know, using clean data, your data, this is handling sensitive data, right? So, there’s always the question of how you address concerns related to data, security, and privacy in this space. And really, you know, what measures are key to be in place to ensure that responsible, ethical use of AI when it comes to business intelligence or anything to do in this space, you bet.
Chris: The first thing that you have to do is focus on the data inside your company and your company only because all of your employees and all of your staff members, all of your company activities need to be wrapped around your company’s clean and concise data. You start going outside of your company’s data, then you start getting HIPAA violations, and you get Fed reds, and you get all these different variations because now you’re bringing outside, and you don’t know if that data is clean. You may not even know if your internal data is going to be very honest with you. A lot of companies have that problem, but if you lock down the doors and put up four walls around your company’s data and only use that, even if you only use a subset of that data, lock it down, do not allow your company to take dirty data from outside because you don’t know if it’s clean or dirty. That’s the number one most important thing. Then make sure you have individuals who may not know everything about your company, and then they may not know everything about AI and how to utilize Generative AI and these activities for it. But get individuals who at least have got training wrapped around it, who can understand the right question to get an answer for because prompting the right question gets you better answers. If it’s a very generalized question into the environment, and that happens too, it can start the process, but you might slow down a couple of steps, get individuals, and then also bring in individuals from your company and the business units to be able to leverage their knowledge of the business, to be able to answer questions for you as well.
Ryan Davies: I think that’s a great checklist to go through there, right? To make sure you’re following all the right things, and I know, there’s such a temptation for the, but there’s a bigger data, like out there I could draw from it if I just pull in all of this other information, but like, I don’t do that, you don’t even know, like, what other stuff, what’s their languages? What’s their, everything, right? Like the variation, just the problems that can create are monumental, and I think that leads into not just that but one of the biggest, in my opinion, especially in business intelligence and anything to do with, you know, business growth and in this space is using AI and machine learning for like predictive analytics and for forecasting, right to really do that with a scientific methodology behind it, right, going away from the I got a hunch or well, here are the numbers we should be hitting because I want to hit this growth next year and you say show your work and they go, I don’t have to because I just, that’s the number I want to hit, right? This gives you a legitimate view of predictive analysis and forecasting, and maybe you can shed some light on that for us as well. Maybe instances where these predictive models have led to more accurate predictions or just sort of the thought process about how to take advantage of that as well.
Chris: Well, that’s what I made reference to our Overwatch tool. We base that on a predictive analytics toolset for our customer support, and we provide this tool to every single one of our managed services customers. So, every one of them gets their own Overwatch tool. So, Overwatch is all their landscape of the infrastructure that all comes into one repository for each of them. Nobody shares that in that data in any way, shape, or form, but what it does do is it also gives us the flexibility to be able to talk about ticketing and fixes that have happened that then go into another central repository for all of the customer service agents to be able to gather information. Well, if you see this happening in table TEO six, this could possibly be an opportunity for you to fix it in this regard. Be proactive. If you’re seeing if you’re seeing a runoff or a data log that’s going longer and longer predictively, like I made reference earlier, this table will fill up in four days, or this disk will kill or die in a number of days. It gives you the capability to be predictive weeks in advance, especially in an SAP environment. We can be more predictive. Now, with those with our tool and with that activity, we don’t want them to shut down. We don’t want the data to shut down. We don’t want the drives to shut down. Even from a cloud perspective, we tell the cloud, you will need to up the size of the cloud or the amount of space for that cloud that we have in Azure, AWS, and GCP within X number of days because of the rapid growth that we’re seeing and we’re taking from predictive analytics. If we are seeing an X point X percent on a daily basis, we know that it’s going to do X and give you that capability. Take some drums out of it, and I need that we all do, right?
Ryan Davies: How for, you know, again, we’ve got a lot of tech leaders here that are part of our audience that are likely considering, you know, similar integrations, considering, you know, where, how do I get started on this path? Or alternatively, I’ve got this system. I don’t know if it’s hit maturity, I don’t know if I’m getting everything out of it, or I’m just struggling to use it, right? Any lessons learned, anything you can share with them to sort of consider, you know, here’s why you need to change, here’s what you need to be doing, and you’re really stressing the importance behind it.
Chris: Yeah, from a technology leadership perspective, think about this. First and foremost, do your research on the tools that are out there. There are a number of enterprise-grade tools that you can utilize for organizations running enterprise-grade applications. Then, once you decide on the tool, make sure you have the right people to help you with it. Don’t go for the cheapest, but also contemplate who they are, what they do, and what they’ve done. You know, I’m very fortunate I’ve been on a lot of these projects. I’ve created books, and as the best-selling author, blah, blah, blah But it’s, but it really comes down to when I talk to these individuals, I bring them knowledge from doing it, and I’m not gonna bullshit them. Pardon my language to anybody that’s offended. You can’t bullshit this stuff. You’ve got to be accurate. You’ve got to give them the knowledge that they desire. Even if it’s not your company that they’re going to go to and do the work, give them the ability to make the right decision with the right tools for their company’s future. Don’t just say, oh, of course, our company uses every one of these tools, and it’s the same. No, it’s not the same thing. It’s not the same tools. It’s not the same setup. It’s not the same data. It’s not the same as locking them down in the box. There’s a lot of, I’m not going to say there’s a lot. That’s a bad word. I apologize that it’s a terrible statement. There are organizations that will try to sell you anything and everything and say that it’s Generative AI or it’s machine learning. Do your research first to make sure it’s the right solution for your organization.
Ryan Davies: I think that’s key, too. When you get in, you know, a new service, a new toy, a new technology that people are unaware of but have hype behind it. It’s easier to get sold on something. It’s easier to be told something and go, well, you’re the expert, I guess, and if someone speaks confidently enough about it, then that’s the truth for you. Where, as you said, I think there’s more research to be done, and you’ve got to make sure, and I think people, again, listeners here, you’re doing your job with that, right? You’re understanding what questions to ask and and how to recognize them and things like that. I want to have one more thing before we kind of transition out here, Chris, and really, you know, looking at the future ahead, how do you see the intersection of AI and business intelligence and how tech businesses can kind of best prepare for this rapidly evolving landscape?
Chris: Oh, how can they best take your time, take your time, and, like I made reference in the last question, do your research? Take your time. Find individuals who are willing to tell you the truth and to understand the activities wrapped around it. Don’t jump at the first piece. Be very cognizant. There are a lot of options and opportunities out there. There are also a lot of wolves in sheep’s clothing out there. Be careful, be cognizant, and take your time. But once you’re ready to make those decisions and you find the right people and you find the right application with the right organizations, go all in, jump in with both feet, and make sure that you are 100% committed because you’ve got team members that are committed and they’re ready to go and then you’re going to be able to go forward pretty nicely and pretty quickly. I love it and, you know, for our listeners here, I’m going to put you on the spot a little bit here, Chris, but I’d love to, I just want you to take a minute and tell us, you know, how people can get in contact with you and learn more about Approyo. If you’ve got a podcast, promote it. If you don’t criminal because you a dream for, I think, and being able to share your information, but a little bit about your books and stuff as well because I know our listeners are going, I didn’t get enough of them in 25 minutes here, and I’m going to need more. So, I’d love to have you extend that out to our listener base.
Chris: Well, I appreciate that. Thank you very much. Yeah, so my company is Approyo. Very fortunate. I’m the founder, Chairman CEO. We’ve got a great team wrapped around everything that we’re doing. We’ve focused on the SAP, the AI, the data intelligence, and the cloud space with organizations around the globe. Very blessed to have a really great set of individuals that are out there globally. We’ve got team members in the UK, Spain, India, United States all over. So it’s great, and then there we go. Those are my books back there. We got four books on SAP called Mastering Sap. It’s a four-book series. I’m very blessed. As of last week, I became a best selling author. Didn’t know that until somebody told me, hey, you know, your book is on the best seller list here on Amazon. I’m like, now I can hold that over my wife’s head. Do I have to take the garbage out tonight? Yes, you do. So I’m very blessed with that. But my goal with those books is actually to help teach folks. We’re looking for a million new consultants within the SAP ecosystem because we’re in the number one upgrade cycle. We’re all going to move to the cloud; we’re all moving to AI-based tools within the SAP ecosystem, so I wrote the books, and actually, I launched the AI-based book for Mastering SAP two weeks before they announced it at Sapphire. So I was on the right track, and I just love to go talk to the universities and the American SAP user groups, and I bring books, sign them off for people through all the different conferences that I speak at folks. So it’s fantastic to be able to really do that.
Ryan Davies: Incredible, and Chris, you know, after all of that, you still manage to find, you know, time for, for me and for our audience today, and I am truly appreciative of that for our listeners. Approyo.com. Give it a check out again. Check out the books. Chris Carter will have all of that in show notes here for you and whatnot because I know everybody’s going to be looking for more. I can’t thank you enough, genuine heartfelt. Thank you for spending your time with us and sharing some information with us today, and I’m hoping we’re going to have you back again. I really hope that we can have multiple conversations because as, as we talked before we came on, I think we had a dozen topics we could have done today. That leaves us at least 11 more to cover right off the top of our heads. So I tell you.
Chris: There’s so much more that we could have some fun with, and I can’t wait.
Ryan Davies: There we go. I think so. That sounds good. You can come in your Brewers Gear. I have my friend here sitting right beside me on the desk. We’ll be frenemies through this, Chris. We’ll be frenemies. I love it. So, thank you.
Chris: Still my favorite manager. There you go.
Ryan Davies: There we go. You’ll still be cheering for him under the radar. Right? You hate it. It’s dirty, but I get it.
Chris: For him. Not for the team there.
Conclusion and Appreciation
Ryan Davies: Exactly. So with that, thank you again to Chris Carter for this absolutely amazing podcast, the role of AI and machine learning and business intelligence. I also want to thank our listeners; as always, we can’t do what we do without you. So, until we meet again with another amazing TBR episode, I’m your host, Ryan Davies. Thanks, and take care, everybody.