← Think AI Podcast
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- [00:00]
A
Some people talk about technology as transforming the way that they work, and the technology is a tool, but it's not the transformation, it's the people that make the transformation process work.
- [00:12]
B
Welcome to the Think AI podcast. Each week we talk about the most exciting AI research tools, case studies, and more. I'm your host, Dev Goyer, and I've been working behind the scene in data and AI for over 3030 years. Whether you are an AI expert, skeptic, or something in between, this podcast is for you. Today I'm sitting down with someone who runs digital transformation at scale most of us only read about. Scarpeth is the global division CIO for measurement and analytics at ABB, a global industrial technology company. His word sponsor three business lines, more than 50 countries, 17 factories. He has spent over 40 years in technology, more than me, from his early days at HP through EDS and ICC, and he was a global finalist for the 2025 Ohio RB Awards. That's quite a lot. Scott. I appreciate you being here.
- [01:13]
A
Thank you for having me. I'm glad to be here. It's been a fun career and I'm still having a blast with everything that we're doing, so it's good to talk about it.
- [01:21]
B
That's phenomenal. I talk about my age. I'm 54 years old and 30 years in the industry. When I see your portfolio, I'm still a kid.
- [01:33]
A
I've seen a lot. There's still a lot to learn.
- [01:35]
B
That's a great attitude. So let's get started. I'm more intrigued about 60 countries scale. So you are running digital transformation across three business lines and, and more than 60 countries. Like you said, walk me through a normal day actually looks like when one of your decisions touches that many places at once. And how do you really optimize the time?
- [01:58]
A
Well, I plan for regular days, but I have very few. So I'm in the US and my manager as well as most of my team are located either in Europe or in Asia. So my days start really early in the summer. I start when it's light. Typically it's dark when I start and, you know, it's a cup of coffee, a Bible study, and then get ready to dive into the day. And, you know, first thing I do is look at the agenda for the day and what are we doing and what, what, what has come up since the last time we talked or in email? Anything that, anything that's, that's come up while we're, while I was away from work. And a typical day is, is full of meeting with business partners, talking about programs we're running for them and giving updates. I'm working with different technology teams. The way they're organized. I have three technology functions within the division. The. But then we also work with IAS teams across countries. And so there's always some something going on that we have to discuss. A lot of budget reviews, you know, a lot of discussion about investment in the future. My best days are when I'm able to sit with our business partners and talk with them about, you know, what they see happening with their business, where they're going and how I can help them from a technology. And it's getting rarer. The best time that I have is when I'm able to be with the teams and talk with them and understand what they're doing on a day to day basis. The thrill I have is, you know, when I can talk to the business and then I can also sit and talk with the team and find out how we're solving that problem and see real progress towards it. But the days are full with a lot of governance meetings and stakehold meetings, that sort of thing as well. So they're pretty full days.
- [03:36]
B
That's pretty amazing. I want to pick up on the team a little bit later. I love the people angle of what we do today. Even if we do through AI or not, it's the people who make things happen and you know, how you nurture. So I really want to go deep there. But I want to touch a quick point here. Is AI helping? And we will go deep on that point too. But is AI helping you in some way today? Managing this?
- [04:05]
A
You mean AI helping manage the part of our programs that we're running?
- [04:09]
B
That too. But more importantly, your own calendar, scheduling, time management.
- [04:14]
A
I do, I do use AI for that. You know, I'm becoming more and more fluent myself and I like that. But yeah, I think today with all the collision points that we have with schedules, with meetings, with updates, providing the right detail to people that they need, you know, for their discussions, that they're counting on me to provide something. Yes, I use AI for that on an ongoing basis and I think that it helps to just smooth things out a bit. And I think we. And we share between team leads and myself, the same platforms, the same process. And so it works very well for us to stay unified even though we're across multiple time zones.
- [04:52]
B
Yeah, that's very nice, Ben. Again, this global presence, is there an impact of AI more in certain places than the others? And why is that?
- [05:03]
A
If so, no, I don't think so. I really. I see, you know, there are some centers, like within abb, we have some, some centers of innovation centers. And so they're located across the globe. And so in those centers, we see them moving out way advance of us. We have them in Poland, we have them in Switzerland, we have them around the globe. And they're really far advanced because, you know, that's their job is to get out in front of us and test the new technologies, validate them and find out how we can better use them. And so we see that happening across the globe in those areas. But then teams are picking it up. You know, my team is picking it up, up. The teams we work with, they're picking up the, not only the, the lingo, but also the process flow, the data flows. How do we have to work a little bit differently based upon the technology and a lot of learning and training and a lot of trial and error as well, which is not the good.
- [06:04]
B
And like you said, trial and error is the key. People who want AI to be picture perfect on day one gets fairly disappointed. I tell my clients the same thing. Take a small area, work on it, iterate, fail early, figure out what works, what doesn't work, what is more human side of it versus what is more AI side of it, and then move forward with the next one. What's your experience looks like there?
- [06:32]
A
Yeah, and they're the same. And so, you know, one of the couple of the other findings that we've. I've seen anyway with some of our work is that we are intending for one outcome, and what we end up with is a very different outcome than we started with. As we go through the journey, go through the iterations, you know, what we thought would be the problem statement, and the answer to that problem statement ends up changing, and we end up with maybe a slightly different problem statement and a very different way to address it. And I think that that's the exploration part of it, and it's that trial and error coming into play, but also just the freedom to say, okay, we headed down this path, we spent some time on it, we didn't spend too much time on it. But at this, right now is a time for us to switch, to go the right or to the left as we need to, to make sure that we are delivering a product and a feature or function that is going to deliver the value that we expect for it to. And that value proposition changes along the way too, is what we're seeing.
- [07:28]
B
So that's phenomenal. I want to switch gear A little bit go towards more foundational element. You said you're scaling global data management so analytics and AI can actually be effective. Walk me through one place where fragmented or bad data bite you or really harm what you do and how did you fix it?
- [07:53]
A
So one of the big areas we have is end to end order automation and that's being able to take a need from a customer, be able to configure it, price it, quote it, and then be able to transmit that then to the factory in an effective way for them to receive it, validate it, and they brought a confirmation back and then move forward with the manufacturing process. And you know, that includes product data, includes sales data, includes customer data. And you're also talking about at a scale because you know there are different denominations monitoring. When you're talking about pricing, you have to understand inventory, what the factory has on hand, what the factory process and the schedules look like as well. And so for us it was a cliff to climb and it took us a long time to do it, to be able to, to be able to prepare and develop this end to end automation, order automation we called it. And I'm so proud of the team. That's one of the things that I'm so happy about where they, they stepped forward. We worked on this for a long time and they, they came through with a solution. It took us several iterations to get it right. Working with the business, we got it right and now we're using that one process. The sales teams are using it, the operations teams are using it and the manufacturing using it. And that was a big success for us. But it took us, you know, not months, but years to get to that point. And we ended up rolling it out to somewhere like 45 countries that are using it as well. So it's, it's been, it was, it was a really good program for us and, and it's, it's been the backbone now that we're using moving forward.
- [09:30]
B
That's pretty amazing. And that gives me a lot of satisfaction, you know, value about the data. One question I am more curious to ask you is what good Data means across 60 countries with local definitions. Because you know, customer is a customer, product is a product, but still the definition changes per country.
- [09:53]
A
Good data. That's a good question. I think if you're thinking good data, it's reliable, it's correct, it's usable, it's available, and when people look at it from different perspectives, they can understand it from their perspective.
- [10:07]
B
So Scott, you said you're scaling data for Your organization and then analytics and AI can actually be effective together. Walk me through one place where fragmented or bad data bit you. And what do you mean by good data really in that context?
- [10:23]
A
We faced a challenge a while back when we wanted to roll out an end to end order automation, a connected automation process. And what happened there was we found that one, there wasn't sufficient data or it wasn't in the right form that we needed for it to be. And it took multiple data from multiple functions. And so that's what made this a difficult challenge, you know, because you're talking about a lot of our products as you're in the, in the sales process. You can, we, we will manufacture to order. And so you have to configure the product as, as you're taking the order. And so we run a very robust configure price quote process. And when you're doing that, you have to have the product data, you have to have the sales data, you have to have the details about the manufacturing, about what the levels of supplies that they have, what does the backlog look like? And we have to pull all that together to be able to create this quotation and then send it as an order to the factory for the factory to receive it and then start to process it, let alone the different country currency, the inflation rates in those areas, supplies in those areas. And so it was a massive process for us to get everything lined up and make sure that we understood what data was needed, when and how cleanse the data and make it available. And you asked about good data. Well, good data is data that's accurate, that's timely, that meets the needs, something that you can govern, it's something that you can manage, but it's also something that everybody will agree that it's the data that they need to solve the problem or provide the solution that they're looking for. And so we were able to do that. The team was able to. It took us a while, it was a long program, but we ended up creating that connective landscape again from sales teams. And you know, those teams are in over 60 countries and they're placing orders to 10 factories. And so the idea of having all that work in the right way took a long time and a team really came through. It was probably one of our biggest accomplishments to date. We're working on a second one right now, which I think will be another big win for us. We implemented it and now it's operations teams look at it, the manufacturing teams look at it, the sales teams look at it. And it's the data. And everybody understands that that's the data and that's the process we use. And it's now centralized across our division, which is a really good step for us moving forward. Especially when you're thinking about digital and AI and being able to get everything in the right place at the right time. It was a big step and it was a good one.
- [12:54]
B
No, that's quite a value you are creating there. And one thing I also wanted to touch upon is good data is being produced by doing unglamorous cleanup that nobody really talks about. The data quality. The team and the people who have the knowledge of data are really important. How do you see that playing while creating this good data? The data quality issues?
- [13:18]
A
Yeah, you're right. I think everybody's focus and everybody has a responsibility with data clarity and quality. As you said, I have an application team, I have an IES team and then we have a data management team. And so we all work together. And I think that creates a lot of unity as well, because we're all looking at it from different standpoints and holding each other accountable. You're right. I think that the effort that we put into that program before, we learned a lot from it and now we've applied that. And I think that we're a better team now as well too. Working together pretty good.
- [13:51]
B
One of the thing that also prompts me to ask you this. So consolidating sales system across 60 countries and then upgrading ERP many factories is not glamorous. It's not really glamorous AI work where you are using fancy LLM model. It's really plumbing. That's how I define it. And why does the plumbing have to come first? And what breaks when companies skip it?
- [14:16]
A
The plumbing has to come first because it's hard to retrofit the plumbing once you've built the house or built the system and now you try to build the plumbing later. And so for us, we had to get, you know, that end to end order automation. We had to get everything in place and get it down and get it correct. And in doing so, we were then able to build a stable platform that works. And we know that it works and everybody has confidence in it. And we can rely on the data. And so getting to the plumbing is the part that nobody really wants to do. It's all the cleansing, it's all the validation, it's all the reviews. But it's essential. And I think that we learn a lot from it. And hopefully what we start to do is not only improve the data and change the data, but we also now start to understand and start to work at the source level as well. So that now in the production of data, you're producing it the right way and the right, and the right framework that you need and with the right variables so that it can be used downstream without a lot of rework. So yeah, the plumbing has to go in first. Otherwise I think that you'll start to look at what this, this product is going to be at the end and forget about the, the lifeblood of it, which is the data.
- [15:30]
B
True, true. And continuing on that analogy, you know, people would want AI demo or a system working on day one. And the honest answer is building demo is easy. I mean, you can pick up something, you can build a proof of concept. It's the overall thinking, what you just mentioned, figuring it out, how your house needs to look like, what's the foundation, what's the room where you need the water, how many bathrooms, so on and so forth, and hence the plumbing. And once you design all that then speeds up pretty quickly because now you have the right foundation, right plumbing, right structure, right design in picture and it just works out. What do you think about that?
- [16:14]
A
No, and I think you're right. And that's the toughest time that we have in explaining what we're doing from a technology perspective to our business partners because, you know, they don't see a lot of progress because they don't understand the detail that we have to go through and the effort we have to go through to get the plumbing in place over time now they're seeing this more and more. They're coming on board, they're, they're sympathetic and they're supportive, but you expect data to be right. At the end of the day, a lot of people expect data to be correct and for it to be able to be leveraged in many different ways. And unfortunately that's just not the way that, that the data that we have, and I think everybody can attest to this, is set up today because you weren't sure of what use cases you're going to have. You weren't sure of what you're trying to produce, what type of decision making you need from that data. And as you start to think about that, you now have to take a step back and retrofit or cleanse the data and prepare the data to be used in that way. And that's the hardest thing that our business partners have to understand. Now they're understanding more and more as we, as we Tell, you know, talk to them. And they, and they see what we're doing from a detailed perspective with the data at each step and they see what we have to go through. And because their teams are partnering side by side with us as well, they're facing the challenges. And so now there's a lot of understanding. Hopefully we can get quicker at this over time with tools and with some knowledge, a little bit of expertise under our belt as we're gaining more momentum. But it's been a challenge and I think everybody will say that that's a challenge for them as well.
- [17:46]
B
Very well said. And you know, that's one thing people do not understand. Data system is not like a typical application systems where you are designing certain screens and you know the look and feel and some validations. The business comes first. There's a term business intelligence for that right reason that it's talking about the business. Business is complex in nature. It's not picture perfect. But how do you really use that data? Get the insights, get the actionable items out of it. That's the journey that what you are targeting and you are doing it phenomenally well, managing so many variations, velocity, volume in different countries. And it's a lot of hard work. So that takes me to another question, by the way, which is also close to my heart. You talk about retirement wave, a lot of knowledge and wisdom. I have spent my time in data projects quite a lot and teach, preach and implemented data systems, data confirmations like Ralph Kimble, but that's more technical. What comes is the business acumen and the business knowledge. So for this decades of knowledge that you have for field services and you're using different technologies to capture all this and you have this veteran head. So if you walk out of the building, how do you take it further? This legacy knowledge and wisdom that will live on for your organizations and the people that you really love to work with.
- [19:19]
A
That's a really good question because I think some organizations don't plan for that, but we do. We have some folks that have this travel knowledge, right. And they've been in a role for a long time. I'm thinking about a couple of data folks, a couple application folks on our teams, and they know the answer. And now you're bringing in new people and with new insights sites, which helps a lot. They help to shake things up a little bit, help you see things differently, but they also have to have some of that knowledge. And so we do lunch and learns. We also partner people together on different programs and then we switch that partnership as they move from project to project so they can pour into each other and mentor each other. And what I think you find is that some of the folks that have been here for a long time and they understand the data, they understand the setup, they understand the specifics, they can share that with others while they also are learning how to, how to change a bit themselves in this new environment of the way that we're designing, developing and delivering from an iteration standpoint, moving forward. So I think it's a mutual learning prospect, mutual learning case that they have together and I think that pays dividends long term. So I think everybody's got to pay it forward and you know, it's my responsibility as well as everybody else's. And I think that we plan for that and we try to build that into our program so you don't have to have these sessions, you know, this, this transition sessions later when, when something's changed, when people are changing or something's changing, we're hopefully building for that right in the process flow today.
- [20:49]
B
That's an amazing process. More people oriented approach, I would say. I built something similar. I'm calling it my second brain. You can call it an AI employees team where me and some of my key members in my leadership team have a lot of knowledge and wisdom and we are capturing it for the next layer. I want to retire differently. I'm a disabled entrepreneur, so I want to give it back to the people who struggle building businesses. I built nine, five of them are miserable failure. So I'm also putting all this institutional knowledge from me and from others so that we can move it forward and pay it forward for that matter.
- [21:29]
A
That's right.
- [21:29]
B
So this is great story. Thank you so much for sharing.
- [21:32]
A
And you know, it's. We do it now and it's not really a structure. It's not, you know, you do, you know, you take this step and this step and this step, it just becomes, it's just part of our nature of who we are now, which is good. You share and you learn and you go and you take somebody with you when you do it.
- [21:47]
B
That's really good. And one of the open thread we left before is you talked about the team. I think team are the rock stars. When we asked you what makes your work unique, you did not talk about yourself. You said it. It's your technology team, local and global team working in unison and you call them the rock stars. Tell me one moment where the team pulled off something you did not think that was possible.
- [22:14]
A
Yeah, it's a loaded question. There's so much. The first I think was the creation of that end to end order automation we talked about and then implementing that, which was fantastic. The other, what I would say is building a data management team that also does data analytics in a high fluency way. They understand the process, they understand the flows and they deliver, they deliver insights that we have not seen before in a very effective way.
- [22:44]
B
That's phenomenal. And any team member in particular or the team that you are really proud
- [22:51]
A
of, there's so many there. There are folks on the team that have been part of ABB for a lot longer than I have. There are some that started as an intern with me a long time ago and are now in a functional leadership. Everybody has their own journey, they're all making contributions, they all want to succeed and the product to work and they want the division and ABB to succeed and meeting customer needs. And so overall I'd have a hard time answering that question if you asked me to narrow down the, to individuals because I think all of them bring their right, bring that aspect and the knowledge and the expertise that we need. And I get to say that too because when we started this team six years ago now, I had the unique opportunity of actually hand selecting the folks. And so we did that over time and every one of them continued to grow into what we need and that we face a lot of challenges but you know, we huddled together, we figured out how we're going to deal with it. We put a plan in place, we put plan to work and work to plan and if it doesn't succeed, then we drop back and we figure out, recalibrate and figure out what our next steps would be. And until we solve it and we, it may take us time or two to get to the end or get to that solution that we're looking for. But once we do, it's well received and if it provides value to the business, then we're golden.
- [24:14]
B
Well, that's so good to hear and definitely melts my heart because it shows up that you care for the people who work for you. Every transformation I've been proud of came down to a few people who refused to let it fail. And the tech got all the credit. Well, actually the people did the work, not the technology. And one more thing I also believe on is there's not wrong people. It's you know, right people, wrong seat. So it's as a leader, it's our responsibility to put them in the right seat or give them a little bit advisory or coaching what they need to do. A lot of people could not figure out what is right for them and they just go with the flow and they get frustrated and this is where you see the performance struggle or the issues of how they need to get things done. Any experience in that angle?
- [25:07]
A
Yes, I think you're right. And I think that you also have to allow people to explore a bit and understand what their groove is and there's a spot for them and they figure out for themselves how they're all going to work together. We don't dictate you're going to do this role and you're going to do this role, you're going to do this role. So everybody comes in, you know, your function is testing, your function, business analysis, your function is development. But the way that that all comes together within the program itself, the team, they figure that out. We give them the assignments and then how they work together and the interactions that they have and the way they support each other, they handle all that. And if there are any issues that come up or if there's some rough edges that need to taken care of or sanded down a little bit, then that's what we do as leaders. But the idea is to stand in front of them when it's needed, to protect them and stand behind them when they're either being celebrated or when they're making the progress they need to make. And so that's been the philosophy for me for a long time is that it's all about the people. Some people talk about technology as transforming the way that they work and the technology is a tool, but it's not the transformation, it's the people to make the transformation process work.
- [26:18]
B
So well said. And that does prompt me to another question. We are talking about figuring things out. So you have spent over 40 years in technology from HP to ABB. What is one thing you believed early in your career about technology that you completely reversed today? So you thought about something that this is true, but it's not anymore. Or your belief system has changed from then to now.
- [26:44]
A
When I started, I was at eds working at General Motors and I saw the IT leaders and what they were doing. They were maintaining the mainframes. Their job was to keep the system up and keep it alive. You could have got caught in that phase and think that that was where they were going to stay. But the way that technology has now grown since then and the way that the technology leadership has changed as well. So first it was keep the lights on. Then came ER and CRM systems. And so the technology Leaders then started working side by side with the business leaders and they started figuring out, okay, here's what we need, here's how we work. So they started become more of a partner together. Then came, you know, the mobile and cloud computing and Y2K and all these things. And as a result, the business then started to lean more and more on technology and work side by side. So now they had a seat at the table. And now if you look at where we're going with AI and the transformations that we're talking about with through people and the way that technology is going, the CIO has a seat in the boardroom now. And so for me, that transformation from keeping lights on and feeding the system to now being a true business partner, sitting side by side with everybody that sitting around the president or CEO as an equal and deciding, okay, here's how I can help you move forward, here's how my organization can support. And it's just that transformation that I've seen over my career has been fantastic. But I think that from an IT perspective is perspective, I think technology will change to be what we need to be in the future to drive the results that we continue to drive.
- [28:27]
B
Yeah, very well said. And that's the great insight. And your point about the technology, specifically AI. You know, we have these transformations throughout our life cycle. We are old enough to know that right from electricity to Internet to self driving cars to now, AI is kind of taking over. The jobs are also transforming. So it's not that jobs are going away, your skill needs to be upgraded and AI is still a tool. It's just that it's too intelligent at 2, but still a tool. You know, if you use a calculator, a smart calculator, it's going to do calculation, but it's you who decide what to do and how to do it. And AI is merely that. I'm pretty pro on AI and I think it's going to change the mankind, especially researchers. I have a patent in AI and it's helping us in the ways that we cannot do it before or the code we share. What used to take months and months takes days now. But if you are a bad developer, bad architect, you're just going to produce that bad output fairly quickly. That's the only difference AI would make. But you still have to have that architectural thinking, the business knowledge, the goal, what you need to deliver and how people will work together. So I think AI has that great shape and foundation in place. It's going to hurt some people because of that transformation. So we need to educate our kids to learn how to use AI, not to build AI and what they can make change in the world using that. I'm a pretty good believer on that.
- [30:12]
A
Yeah, I firmly agree with you. I think that AI is here and it's not going to go away. And so we have to figure out how to deal with it. And I think we will. I think it'll take some time. And I see it today. I think you can see the progress as well as you talk to other leaders, the progress that we're making now that we weren't making a year or two ago, and the progress we'll be making in another year moving forward. It's going to continue to escalate and we'll continue to see more and more value. And as a result, the roles that people play will change over time, and they have to. But just like we have in the past, the technology leaders, from the basement to the boardroom, for example, what a transformation that's been. And so I think that technology leaders will continue to transform even through this wave of what we're dealing with.
- [30:55]
B
No, that's really good. I have a few things before I let you go. One of the personal thing you had, which is 2025 global finalist for Ohio RB Awards. What does that recognition looks like and what it actually means to you at this stage of your career?
- [31:13]
A
That's a good question. It was really nice to be recognized. And not only recognized because what, you know me, but recognized because of the difference that my team and I are making in the company that we're making with AB today. So for me, that, that was. That was fantastic to see that. I love the idea that we celebrate technology and we recognize teams that are making a difference. And I think that's what the orbi was for me. It was. Was being able to tell the story about the people I get to work with every day. And that was really a privilege to do so. And as soon as I get that chance again, I'll do it again. It was fun and it was a good process and I learned a lot from it. And it also got me connected with other CIOs and being able to share more with them, talk with them. And so now I'm within the CIO councils themselves. A lot of good sharing goes on, and I really enjoy that now, too. We can't figure it all out ourselves, but we can share a lot of good ideas and thoughts with each other. And I like that. So I think it's about continuing to grow yourself, not be Stagnant and also celebrate and give opportunities to the people you work with for them to move forward and step into new roles and to really deliver results that will make the bottom line better on a company's work. But the idea is that it allows people to contribute to something bigger than themselves that solves a business problem and hopefully contributes something that makes life better for somebody down the line.
- [32:40]
B
So what you said last prompted me to something. You spend your career in four major elements. As I see data and AI, transformation and adoption, leadership and people. What advice? One advice you will give to new age CIO working in these four areas and how they can change their life and the people surrounding themselves.
- [33:04]
A
That it's not technology first, that it's again, it's about people, it's about process. It's also about understanding the challenges when, you know, a lot of times we get requirements and they say something, but they don't really mean what they say. You know what I'm talking about, where they have written requirements but doesn't really solve the problem that they have. And so I think it's about seeing through what the requests are, understanding what's needed and understanding how you can then step into a solution. And technology is a part of it. But I think there's so many components have to come together. The process itself, the people have to come together. You have to understand what outcome you're driving to. You rally around that outcome, you drive that outcome together and you do it as a team. And so that's the other thing about new if you come in as an IT leader and you think it's all about you, or if you are looking for your next role and you're coming in to just drive something, looking at you for advancement, I think I was sort of the wrong for me, that's not the way that I work. And so I would encourage you to do the best that you can at what you're doing with the people around you. Make a difference and then you can take those next steps together. I don't know if that makes sense.
- [34:17]
B
It does, it does. And it came out very well. Anything that you want to add to what you just said? We had a great conversation.
- [34:28]
A
I appreciate the discussion. You have good questions. I like your, your style. I think that you've allowed me to talk about my career, you allow me to talk about what I care about the most and you know, that's driving results with people. It's been 40 years. I don't know how much more there is in a tank, but I know it's been fantastic so far and I just apprec the opportunity to talk with you and hopefully this adds some value out there on the market someplace as people hear this.
- [34:56]
B
Scott thank you. That was a grounded, honest conversation and I think a lot of leaders needed to hear that. The team and the data comes before AI or any other buzzwords. To everyone listening if you got value from this, subscribe to Think AI podcast so you do not miss what is next. We will be back soon with another conversation about AI doing the real things. I'm Dave Goyal. Thanks for listening. Thank you Scott thank you Dave.
- [35:26]
A
I appreciate it.
- [35:28]
B
You have been listening to thinkai podcast with Dave. Take one idea from this episode and turn it into action.