#53 - Scaling AI from experimentation to real impact

Narrator: Talking Industry topical debate from the world of automation, engineering and manufacturing brought to you by DFA Media Group. This episode is sponsored by Drives and Controls, the voice of automation in manufacturing, bringing you the systems, components and ideas powering today's factories in print, online and on social media.

Aaron Blutchstein: Hello and welcome to another Talking Industry podcast. I'm Aaron Blutchstein, Managing Editor, uh, for Smart Machines and Factories, Portal Plant and Works Engineering and Hydraulics and Pneumatics magazines. Today I'm joined by Tim Dickson, Chief Digital and Information Officer at ah, Regal Rexnord. We'll be discussing digital transformation, the growing role of AI and how organizations are turning innovation into, into real business value. Tim, great to have you with us. To get started. Could you just tell us a bit about your role and what your day to day focus looks like and your journey to Regal Rexnord?

Tim Dickson: You bet. So first of all, thank you for having me on the show. Looking forward to uh, our conversation. Um, so I serve as Regal Rex Nord's Senior Vice President and Chief Digital and Information Officer, uh, the first uh, CDIO in the history of the company. Uh, my day to day focus is balanced between two things really. One, not only do I have IIT digital, but I also have the cybersecurity organization. So running a reliable, secure global technology function is first and foremost keeping the business going, uh, so to speak. And then the second aspect is really leveraging and accelerating digital and AI technologies to drive growth. You have 60, uh, year old, $6 billion industrial manufacturing company of electric motors and engines. Uh, most of my time is spent uh, partnering closely with the three business segment presidents and our corporate function as well, making sure that our initiatives like customer experience, digital commerce and obviously AI, um, are prioritized and funded in the right way, shape or form. And then we've got the right operating model to then go execute and deliver on those uh, programs and hopefully benefits and business outcome for the business. So not just technology for technology scape, but really leveraging digital and AI technologies to drive real growth for the company. Wow.

Aaron Blutchstein: Um, so that's a good background. So from your vantage point, uh, what's really stood out um, to you since stepping in, particularly in terms of what's been most interesting or what unexpected about how digital is evolving across the organization.

Tim Dickson: Yeah, so one, um, I would say, well two things actually. So one, just the ability for the team. Uh, when I started two and a half years ago I mainly inherited a on premise erp, uh and on premise infrastructure team. And over the course of the last two and a half Years, not only have we upskilled in the areas of cloud, uh, iot, uh, E commerce, um, AI, generative AI and now agentic AI, the team has really transformed in terms of embracing those new technology platforms, those new capabilities that are going to drive this new growth to the business that I mentioned earlier. So super proud of the team, super proud of the transformation that they have gone through upskilling to lead uh, these AI and digital initiatives. And a lot of the folks who actually have embraced uh, those new upskillings have actually been certified and have moved into leadership roles. So seeing some personal and professional growth of the team as well. The second aspect is obviously putting together a digital operating model that allows us to scale, um, maybe proof of concepts or prototypes that might uh, have found success in a team, in an organization, in a division and scaling those out to the enterprise. The name of my organization is the Enterprise IT and digital organization and it's rightly so. The goal is to provide value and drive growth across the entire enterprise. And when you have three separate operating business segments with three separate uh, P and ls, as well as a corporate function, you can imagine the amount of requests you might get for some of this newer technology improving success. We have this saying that says think big, start small, but scale fast. So we think big in terms of what the technology can ultimately do. We start small, we prove it out in one division, in one segment and then we look to take that capability across the enterprise to drive true scale and true value and true growth for the company.

Aaron Blutchstein: So there seems to be quite, quite a lot happening in there, particularly around AI. Um, so how are you actually approaching it within the organization, within your organization? And where are you starting to see real impact?

Tim Dickson: Yeah, really exciting time to be part of this uh, AI journey. It certainly has come um, uh, quite a bit since uh, two and a half years ago when I first started. So the first thing that we did was put together an AI policy and an AI governance structure and AI strategy. And the very first aspect of our AI strategy is that we start with use cases that we think can create measurable value. We don't waste time on um, proof of concepts and use cases that we don't think are going to drive huge enterprise scale. But we start with those that we believe can drive some sort of measurable value, whether that be internal value like user productivity, cost savings or cost out, or external values such as revenue orders, shipments, uh, accurate forecasts as an example. And so um, a lot of the things that we have done started off with proof of Concepts that proved out how the value was going to achieve and then we scaled those across the enterprise. We have a number of agents that we've deployed uh, over the last couple years. Uh, a lot of those agents uh, are based on Microsoft's Copilot Studio technology. They recently did a case study study uh, with us that demonstrated uh, the value achieved of some of those agents that we've deployed. As an example, our internal GPT, we call it rrxgpt. It saves time for associates giving answers to commonly asked questions about the company. About 2,000 associates, 2,000 inquiries per month are used by associates to query that GPT and get quick valuable answers as a result. Coming back, we deployed a chatbot on our regalrexnerd.com website as used by over 1000 users a week to find documentation, specs, you know, videos, images about our products with an 80% satisfaction rate. We deployed a knowledge base for our customer care customer service team where we've seen an estimated 12% productivity improvement for customer care reps to get, uh, get customers answers to commonly asked questions. So we not only started with business case that we think or thought could provide measurable value, we actually delivered them and then we tracked and we measured them and shared that with the business to ensure that we were getting the value from the investment time that we put into it.

Aaron Blutchstein: So where would you say you're seeing the most meaningful value from AI so far? You mentioned obviously the chatbot, but um, there are obviously other areas which um, you're finding some value.

Tim Dickson: Yeah, the most meaningful value has shown up really in two places. Uh, one is internal user productivity, reducing time spent searching, summarizing, drafting and navigating various different processes, uh whether those are your individual processes as a knowledge worker or your team or organization's processes. Uh, we've deployed thousands of licenses of Microsoft Copilot, uh M365 Copilot, where on average each user who leverages the M365 Copilot license saves at least two to three hours per month uh, in terms of productivity, time save and uh, two thirds of those folks have told us that they use that time to give back to Regal Rexnord in some way shape or form performing another task, reaching out to a customer, value added tasks in the company. So the first value absolutely has shown up as internal user productivity. People are more productive with AI than they were before. The second by far is customer experience, making it easier for customers to find product information on our website in our chatbot, as I mentioned, uh, get support quicker Faster, better than they had before. So when you combine the impact of a better customer experience, finding more information quicker, better, faster than previously, we ultimately think that results in some sort of making it easier to do business with Regal rexnorb and somewhere down the line that drives additional value with the customer through additional revenue or long term relationships. So we've proven agents can work internally and externally with the organization. We've proven that users can become more productive as a result of these tools. And, and we've proven that customers like to interact or like to interact with us in this particular way. And that drives some longer, drives some longer term value.

Aaron Blutchstein: Okay, um, so unlocking value is one aspect, but uh, scaling it is another. From your perspective, what challenges do organizations face when trying to move from experimenting with AI to actually scaling it?

Tim Dickson: Yeah, going back to that. Think big, start small, scale fast. That is a mantra that has a number of different meanings and where I think a lot of organizations um, have success is that third is that first part thinking big and improving out some sort of proof of concept or prototype within, you know, smaller part of the organization. The hard part is scaling it because the hard part of scaling it across the enterprise requires data readiness, requires uh, solid uh, and clear and aligned. Governance requires change management. Pilots are pretty easy when you think about it. You m got a lot of technology vendor partners who are willing to work with you in terms of proving out various different AI pilots. But when it comes to scale, you got to do the hard work. And the hard work ultimately allows the scale and the benefits of the scale to happen long term. Without data readiness, without uh, a common data store and clean and quality data, without governance in terms of prioritizing, deciding what this AI model can do and is it ethical and responsible for the organization and without change management or what I like to call personalizing change management. Basically describing why users should buy into this particular um, uh, project or deployment, what's in it for them? How does their role change? How does that benefit the organization? Really kind of personalizing the change uh for those involved is a key aspect of ensuring that this provides scale for the organization. So um, fairly straightforward now with uh, lots of easy models and partners to quickly prototype and pilot things, but much more difficult to scale across the enterprise given the data aspects, governance and change management.

Aaron Blutchstein: So as you said, underpinning all of this is the role of data. How important is data enabling, um, in enabling digital and AI initiatives and how do you approach building a strong data foundation?

Tim Dickson: Hugely important. I say that uh, the AI, uh is only as Good as the dale, as the data that fuels the AI. Uh, in addition to those aspects of the organization that I mentioned earlier, it digital and cybersecurity, I also have the master data management function of which has hugely progressed over the last two and a half years. Once again, AI is only as good as the quality, consistency or accessibility of that data. So at Regal Rexnord we really are serious about data. Our approach is to treat data like a product, uh, establish governance, establish common definitions, clear ownership, who owns what so we can build these integrated views, especially around um, the customer and the customer360. So two huge aspects of our data evolution since I started. We fully have deployed an enterprise customer data model that's leveraged by each of our different uh, business segments. And we have fully deployed a product, a uh, data product or a data product hierarchy that is also used by our three segments across the organization. And those two things are fundamental for any transformation in any business segment of any nature. So a focus on data and data is the fuel that drives your AI insights.

Aaron Blutchstein: It's always helpful to uh, bring this to life. As an example, do you or can you share an example of a digital transformation initiative that really stands out for you and what you've learned from it?

Tim Dickson: Absolutely. My first one in fact, uh, so I started October of uh, 2023 and uh, January of 2024 I hosted the company's first ever hackathon. Uh, you can see some of the signs here on the top of my cabinet here from that first ever event never happened before in the history of the company. And so we pulled together 100 different IT and digital team members as well as some business partners. Um, and still two and a half, two years ago, uh, chatbots and generative AI were still fairly new. And so we partnered uh, with a technology firm for my team to build our first ever, uh, chatbot generative AI chatbot called Rexy that sits out on the Regal, uh, Rexnord uh, website. And initially in terms of uh, understanding generative AI technology, uh, what data is required to power, uh, generative AI technology. In the early days of that particular LLM, one of the smartest decisions that we ever made was in essence enabling that chatbot on the website to just ingest data that was already on the website, uh, in our SharePoint, uh, knowledge base, but just wasn't searchable or findable in some shape or way, some way, shape or form from our customers. So make it easy, make it risk free, enable the advantageous, uh, aspect of allowing customers a different way of finding that information that they were having challenges before and they can now have a conversation with a chatbot and find a new different way of interacting with the company. Since then we have um, significantly enhanced and scaled out the chatbot to truly, truly support uh, images and videos and interactions and connecting them with a service, um, agent if they have further questions connecting uh, with the chat, uh, as a part of an agent if they want to go through a different chat experience than the one through the chatbot. So that was really the first digital transformation enabling a generative AI chatbot on the website once again of an industrial manufacturing company. At 60 years old and had never really operated in that way, shape or form. Most of our business and most of our interactions up into that point had been through a customer service or customer care, uh, phone interaction. So putting something out there that we thought could drive some value, seeing all the interactions and the way in which customers interacted with it then and are now interacting with it now, it demonstrated a new model of iteration and agile, uh, aspects of digital transformation with this new generative AI technology. So proud of the team for the early incubation of that and proud uh, of our customer set for hanging with us and maturing uh, the chatbot to really be an industry best in class experience for our customers.

Aaron Blutchstein: Well can I just shift slightly to the external perspective? So how do you actually think digital platforms and online presence influence how customers perceive a company today?

Tim Dickson: Well I would say that the early days, two and a half years ago, being early days of AI and generative AI now gentic AI, there were a lot of sort of fly by night, um, vendor platforms and we took a shot at some of those and gave it their fair due. But once again, if scale was ultimately our goal, to prove the business value and the metrics that we're hoping to receive, we needed mature platforms that could handle an enterprise data set, an enterprise um, internal or external business or customer experience. And ultimately we landed on a fairly significant set of enterprise grade technology platforms that now serve a six to seven billion dollars um, global enterprise. Uh, all of our data is housed in Snowflake, uh, from a data warehouse and data platform perspective. All of our machine learning uh, AI um is through uh, databricks, uh, which is an enterprise grade machine learning AI platform. Uh, all of our business uh process Automation is through UiPath, which is an enterprise grade uh RPA now uh Intelligent automation platform. And most all of our 500 agents that we have running around the environment have been enabled through our partnership with Microsoft and Copilot Studio. And so you had to show that you matured throughout the course of this AI journey. And these platforms have matured as your employees and your teams and your knowledge of them have matured. And we just ended up going all in with those enterprise grade platforms because we saw those ventures, strategic vendor partners invested in me, invested in our team and we had the avenue for our team members and our associates then to upscale and be experts and get certified in those platforms at the same time.

Aaron Blutchstein: And as those digital capabilities grow, how do you see customer expectations evolving as digital?

Tim Dickson: So customers, yeah, customers are smart. They increasingly expect consumer grade or B2C experiences. In this world of B2B, 90 plus percent of our uh, revenue is driven through B2B. So we better have a darn good B2B experience. I do believe, uh, we do. In fact we have answers to questions very quickly. Uh, we use sitecore and sitecore AI for personalization, uh, profiling customers who visit the site and personalizing uh, products and interactions and campaigns for them. We like to think we have mobile, friendly, uh, interactions with our customers and ultimately we want to provide them frictionless support once again. All in the area of making it easier to do business with us. And we do believe that if we make it easier for customers to do business with us in a digital world through a digital channel, leveraging tools like AI to personalize and make it more personal for them, we do believe that that long term business and long term revenue will come across the table.

Aaron Blutchstein: So bringing it back to the business itself, uh, have there been any recent developments or opportunities that really stood out in terms of the scale or impact on the business?

Tim Dickson: Yeah, Microsoft, uh, Copilot Studio is one example in our development of agents. Um, just so you have sort of a background in terms of how we weave some of these newer technologies into the team and into the organization. Uh, I have a monthly lunch and learn and have had one every month since I've been here. Um, uh, that basically allows a strategic vendor partner to come in and educate and present to my team over the course of lunch, uh, with um, some new technology, typically AI technology that they're developing, that they're launching and to get my team, uh, interested and excited as well as the business interested, excited in that new technology and what it could do that sort of gets the juices flowing in terms of what this newer technology is and then it gets the business and my team working together on potential solutions, uh, for that technology. So agents was no, uh, secret to that. A year ago we started having a number of agentic vendor platform companies come in and present the concept of agents. But Microsoft in particular, given our ecosystem investment in Microsoft, uh, allowed some partnership, uh, with their product teams, uh, allowed some partnership with their technology and consulting partners to come in and really show my team how to leverage agents in a way and really go nuts with them. And so over the course of the last year, year and a half, uh, we've got about uh, 500 plus agents continuing to develop every day uh, in the environment. Some of those agents uh, were, most of those agents were developed by my team in conjunction with the business. But a lot of agents were business partner uh, and business led and business developed as a result of the learnings that they gained from those lunch and learnings. And so of the 500 agents I would say 10 to 12 of them are really used um, ah, significantly within the environment today. But the fact that there's both business led and developed and IT and digital led and developed agents in the environment, it means that the learning is happening and those solutions can potentially scale. So I wouldn't necessarily call it a citizen development approach because we're not trying to just allow the business to um, uh, develop on their own. We want to do it in conjunction with them, we want to monitor that, we want to make it secure, we want them to embrace these low code, no code tools in such a way where they're developing it with some sort of value in mind for their team, uh, with their organization. But we want to be able to observe that, we want to be able to monitor that because we might want to take that solution across the enterprise for further scale. And so as we move in, sort of the next evolution of where this space is going, observability and monitoring is really going to be key. Once we've enabled these tools and allowed our business partners to start developing agents, the key is going to be how we can make sure that they're being used and governed in a responsible and ethical way. And then how we can make sure that we get the investment, return on investment and scale that out to the enterprise.

Aaron Blutchstein: That kind of leads on to my next question really because obviously transformation, uh, isn't just about technology, it's about people. Um, transformation often sounds great in theory, um, but what does it actually take to bring people along on that journey?

Tim Dickson: So we have a saying here. Uh, we're going to bring everybody along, we're not going to leave everybody behind. Now as a result of us attempting to bring everybody along. If there are those that fall out as a result of that and don't wish to participate, that's fine. Everyone in the company knows that the world has changed and we're expected to leverage tools like AI to make our jobs easier and become more productive and do more for the company. So at a higher level that is the culture that we have enabled. Here is one is how can we leverage AI to make our jobs better, more productive and do more for the organization. The second aspect is that it really takes clarity of what you're up to, um, consistency and message, enrolling a team in the vision and then ultimately the leadership trait that I love the most of any new AI leader is good old fashioned empathy and listening to your team members and listening your business partners um, into ultimately what they would like this thing to do. And so you need to have some sort of narrative that rolls all those in, rolls the messaging of all that, um, very consistent and listening at the same point to get their buy in, to enroll them in the strategy and to bring them along and make them feel that they are actually driving the transformation. Not a central organization like it or my digital team that we're partnering together and they're a big part of enabling that transformation and they're a big part of forming the operating model in terms of how the teams are going to execute and work together to get the win wins at the end that demonstrate and showc the team is embracing these tools and delivering, providing value for the organization. So feel that they're enabled, feel that they're part of the vision, part of the story, part of the value generation and that they're not threatened by this

Aaron Blutchstein: new technology and obviously building the right skills. So you've been investing um, new skills across your teams. Um, from what um, we've had discussions we've had before.

Tim Dickson: Absolutely.

Aaron Blutchstein: What approaches have actually worked best in helping people adopt to new technologies like AI?

Tim Dickson: Yeah, we've tried. I feel you have to have a little bit of variety. Um, you have to have some fun. You have to make it fun because you want people to participate and if it's more fun they're uh, gonna participate. And then you have to provide food and we like uh, we like cake. Um, and in all serious though, on all seriousness though, you have to provide that um, that variety because different people require sort of different aspects to take on an upskill. Uh, as an example we had um, I mention uh, hackathon early on that demonstrated the art of the possible. In fact we had two different hackathons. One here in the US in our Chicago digital office and then one in India with my India team, uh, to demonstrate the art of the possible. What could potentially be possible when you get people interested in learning and doing something and delivering a prototype together with a strategic partner who's willing to invest in us. That was kind of the first form of learning what could come out of hackathons and could that really provide value in terms of tangible benefits uh, to the organization. The second aspect is that we held a generative AI uh workshop, uh, sort of a show, uh, and tell from each of our strategic technology partners that demonstrated that they were interested in investing in me and my business partners. And we would have little booth like sessions where my business partners had a chance to interact directly with the strategic vendor partner themselves and ask them any questions, uh, and come up with any sort of idea in terms of how their technology could be used by the person who's closest to the business and by the person that's closest to the customer. So an interactive generative uh, AI workshop was the second aspect. And then just recently this year, uh, while I was in India with my team in January, uh, here in the U.S. uh, in March, we had over 250 people from my organization in our various different business segments participate in what I call a certification day. And so I do feel that in a lot of this uh, area of newer digital and AI tech that you do need to go deep and become a subject matter expert and get certified so that you demonstrate you have credibility in the space and trust um, and some sort of uh, executable uh capability. So we held a uh, certification day uh, amongst my India and US teams and we had over 250 people participate and actually get certified in any one of six technology vendor platforms of which those technology vendor platform partners came to the session, provided hands on Q and A and knowledge if there was a challenge with those courses that the folks were taking. But basically take a half day out of your work day or work week dedicated towards certification, dedicated towards improving yourself, improving your personal growth, having fun, uh, sharing a cake, uh, and celebrating with the team once they have their certificate and were able to be certified in that area of new tech. And that was an awesome uh day to see not only folks get um, certified and be very proud, uh, but also post their certification on LinkedIn and really show that a person in one area of uh, the organization could get potentially certified in a technology completely outside of their own area of expertise. So sort of stretching and branching out. But between hackathons and generative uh, AI, uh workshops, those lunch and learns that I mentioned and now most recently Certification day. You have to give variety to allow people to interact uh, with the vendor, technology partners, become experts, ideate on their own, ideate with business partners and actually get something generated and created through a prototype in some way, shape or form. The next uh, event that we'll be hosting here this summer is an event that we're calling Scale AI. So we've done all of these things, we've launched all these agents, we're going to bring our business partners together and we're going to show them what we've done across all um, the company and we're going to leverage their expertise to determine if something that we did in one particular division in one segment could scale out to another part of the organization and eventually out to the enterprise. So with that scaling benefit and scaling value generation in mind, this will help us accelerate that and get people excited about what's to come. So Scale AI, uh, I would say

Aaron Blutchstein: this is a whole new podcast I think so much to talk about there. Um, I mean you mentioned obviously the globally distributed teams and um, working with uh, globally distributed teams. How have those teams actually contributed to the transformation?

Tim Dickson: Yeah, so I have a 600 person uh, organization, about 300 of those are in um, a wonderful town called Hyderabad, India. And um, probably the best thing that's ever happened to me in this role is having that India team uh, set up what we refer to as an AI CoE in India and they're responsible for the execution and so that is where all the upscaling happened first around these technology platforms. Once again being able to take an on PREM infrastructure team and on Prem from ERP team and transform them into an AI and Automation CoE in Hyderabad, India. To lead the development of these initiatives was hugely transformative, hugely um, personal uh, for me uh, and them and so proud in terms of how they've been able to upskill and then handle all this work that has come their way. So they're the execution arm, they're the ones that develop the solutions and get them out the door. But what my US team does, in fact it's very functional based. So whether you're in order management or E commerce or manufacturing, finance, sales and marketing, you are expected to be an AI expert with that technology platform or platforms that are serving that space. So as you have become an expert and potentially certified in that technology, the business partners are going to come to you and trust you and build relationships with you since you know what you're doing and you know where this technology can do and where it's going. They're going to trust that you will be able to take their idea to the next step and ultimately communicate and interact with this India coe that then executes on the technology from that perspective. So a very distributed model. Um, and although some people do have AI COEs more here in the US and they're more where all the work gets done. I distribute the ideation, um, and I distribute the business knowledge out to every part of my organization and function. I allow that partnership and those relationships and that ideation to occur and then we channel the execution of all that to a central team in India. So that I'm sort of trusting that that is a uh, of data security and cybersecurity nature. So I can fully tested um, and quality tested. So I ensure the product coming out from that operating model is of high quality and delivered value for the business.

Aaron Blutchstein: I can see how um, uh, um, the global teams mean quite a lot to you.

Tim Dickson: Absolutely, yeah.

Aaron Blutchstein: So looking back on the journey so far, uh, what would you say you're most proud of when you look at at what your teams have achieved over the last couple of years?

Tim Dickson: Yeah. So once again first CDIO in the history of the company. I was brought in to prove that digital and AI technologies could provide value for the business in some way shape or form. And I'm most proud of the fact that we have been able to do that. We've been able to show that both digital technologies and AI um, have gone from proof of concepts to actual real practical outcomes and deployments that are showing value in a number of different business KPIs and metrics that are uh, proven productivity gains are being adopted by our customers, are having some real sort of customer facing impact. Um, so that is, you know, check the box. That's what I was brought here to do and I believe that we have been able to accomplish that in some way shape or form in what I refer to as the first evolution of uh, this world of AI. Uh, there are more evolutions to come and we have to alter our approach going forward for sure. But the second thing I'm proud of is obviously the team's growth. Uh, I am only allowed to do wonderful podcasts like this as a result of my team and how they've grown and upskilled and how my team has been able to build this digital and AI fluency delivering both foundational capabilities like data and data platforms and then really innovating at the edge with these agents in various different aspects of our business. So most proud of uh, the transformation from turning digital AI to Actual practical outcomes, and then most proud of the teams growth, both the team in general, the operating model that we built, both some of the personal stories of the individuals of the team that really, um, without this upscaling probably wouldn't have moved on in their careers, uh, as fast, uh, as they have once they've been developed and invested in, through these lunch and learns and through these technology upskilling and certification initiatives. So certainly proud of the team and the team members that have grown themselves and have developed over the course of the last two and a half years. I'm very proud to see them move into new roles, lead new initiatives, lead AI initiatives that they never thought were possible but now absolutely are as a part of this uh, organization change.

Aaron Blutchstein: Uh, and finally, I mean I suppose you've always touched on this already, but looking ahead, what do you think will actually define successful organizations navigating digital and AI transformation over the next three years? I mean it's quite difficult to answer that. But um, I think you have a clear idea.

Tim Dickson: I think we're at an inflection point. I really do and I think everything I've described up until this point has been great, super, um, proud of the team and what we've been able to accomplish. But the pressure is on now going forward with all the wonderful AI capabilities that have progressed over the last couple years. Um, success in my opinion will be defined by companies who can take these isolated tools and really put them together and embed those tools into a capability or what I refer to as an end to end capability for the business. Can AI truly operate an end to end business process for a company? Can you integrate it into your daily workflows that are connected to core systems that can be governed responsibly? Things like forecasting, things like demand planning, things like psyop, things like mrp. And so until you can prove that AI really can take over these end to end business processes, a lot of people might think this is just going to play at this point and you might not see the value um, from these initial AI generative and injected tools. But I do see the possibility of, of especially agentic AI and multi agent orchestration really running some of the hardcore daily workflows of the company. And that is going to be a game changer for companies like us who've been around for a while. Um, but it's also going to be a game changer for our business partners and our customers to have automation where they didn't think was possible before, whether that be in insights, whether that be in reporting, whether that be in some sort of customer interaction, uh, or connection. Because that's, I think the next evolution of this is truly leveraging AI for core business processes across your company.

Aaron Blutchstein: Tim, it's been really great speaking with you. Thanks for sharing your insights and experiences and I think there are probably a few more podcasts I think we could develop.

Tim Dickson: Awesome.

Aaron Blutchstein: Um, and um, thanks to our listeners for also for joining us on this episode of Talking Industry. If you've enjoyed the discussion, please subscribe and stay tuned for more conversations exploring the trends shaping industry today. Um, Tim M. Before, before we wrap up, uh, if you had to leave our listeners with just one piece of advice on navigating digital and AI transformation, what would it be?

Tim Dickson: So don't treat AI like a shiny object or tool. Treat AI like a transformation. And when you look at what is a transformation, transformation is not incremental step function improvement. Transformation is game changing process or improvement across the organization. That's not just technology alone. Think big, start small, scale fast and think of AI as a huge transformation enabler for your business.

Aaron Blutchstein: Great. Thanks a lot Tim.

Tim Dickson: Um, thank you so much Aaron. Really enjoyed it. Can't wait to listen.

Aaron Blutchstein: Hope to see you again soon.

Tim Dickson: Take care of.

Narrator: Thank you for listening to Talking Industry. Stay tuned across all podcast apps. Follow us on social media, subscribe to our newsletters and keep up to date. Uh, @talkingindustry.org this episode is sponsored by Drives and Controls, the voice of automation in manufacturing, bringing you the systems, components and ideas patterns in today's factories, in print, online and on social media.