Goodyear CIO Raman Mehta on Making Speed the Strategy for Enterprise AI

0:00I think our journey is how we become from AI curious to AI serious.

0:05Peter High: Welcome to Technovation. I'm Peter High, president of Metastrategy. My guest today is Raman Mehta. Raman is the chief information officer of the Goodyear Tire and Rubber Company, a 128-year-old tire manufacturer headquartered in Akron, Ohio, that earned$18.3 billion in revenue in 2025. Raman took on the role in December of 2025 and leads the company's global technology strategy, spanning IT, digital strategy, cybersecurity, and infrastructure. But his responsibilities also include customer experience, dealer operations, internal productivity, and supply chain and logistics. Roman and his team are focused on the strategic adoption of new technology at an industrial scale, digital transformation, and enterprise platform modernization.

0:46Peter High: He's been a leading voice in the importance of enterprise data architecture and driving that transformation, developing an enterprise-grade intelligence framework designed to help large organizations build data systems capable of supporting advanced AI and analytics. Prior to joining Goodyear, Roman held CIO roles at Johnson Electric, Vistion, Fabronet, and EV across North America and Asia. He's a CIO Hall of Fame inductee as well. Roman, welcome to Technovation. It's great to speak with you today. Peter, always a pleasure chatting with you. Today's episode is brought to you by Tynes 3B. Organizations are pushing teams to move faster with AI.

1:21Peter High: That's creating a wave of AI-generated code and unsanctioned automations, leading to rising costs, security risks, and huge IT burdens. Meet Tynes 3B, a single, secure environment that enables employees to build with AI while giving IT and security complete control to run and govern that work. 3B enables effortless building. Describe what you need in plain English, and 3B builds a fully functioning workflow. It uses IT-authorized connectors, meaning credentials stay protected, and non-technical teams can innovate without taking on unseen risk. Also, with full visibility across every workflow, IT gets total governance, while features like Auto-Tune and Auto-Fix ensure workflows improve and self-heal continuously over time.

2:01Peter High: Employees get the freedom to build, while IT gets the control to govern. No shadow AI, no data leaks. TINES 3B moves IT and security from blockers to enablers, giving teams the speed to innovate without compromising control. Visit the TINES website at tines.com. That's T-I-N-E-S.com to learn more and try 3B for yourself. And now on to the interview. I share that sentiment. Thank you so much, Ramon. I've been looking forward to this conversation. Well, let's take a moment, if you would, and describe this 128-year-old business that you helped run. I would love your own words to describe this remarkable business, if you would.

2:39I think this is a remarkable opportunity for me to become associated with such a strong worldwide global brand name. So look at Goodyear, we operate in 19 countries and I actually have almost 48 manufacturing facilities. So very, very diverse manufacturing footprint. Look at the portfolio. We deal with consumers, commercial, aviation, farm sector, motorsports, recreational, the portfolio and the reach that we have through our B2B, through our B2C and B2B2C channels is remarkable. The product portfolio, the brand is known worldwide. And as a CIO, my challenge is to bring the startup mindset into a 128-year-old legacy organization that has gone through its ups and downs, and how do we ride the current technology wave and really serve our customers, our shareholders much more effectively and efficiently.

3:49Peter High: What a great place to begin, certainly, Ramon. I'd love to understand some of the ingredients to bring that startup mindset to a storied organization like yours. Definitely. So, Peter, one of my first messages to my teams when I joined in December was that the momentum builds clarity. You've got to start moving. You need to take the first step. You need to go beyond the pilots where you are actually shipping something meaningful that adds to the bottom line, that adds to a better customer service. So that's one of our main mantras to really make speed as our strategy. Now, to do that, we also need to look at our IT and digital landscape.

4:34Obviously, we have pretty much the best-in-class ERP systems, the manufacturing execution systems, PLM. How are these systems and our processes allowing us to take advantage of the AI-first business model is kind of my challenge right now. And we are reskilling our teams. We are having these tough conversations with our business partners. You know that we We just don't want to automate things for the sake of automation. But can we redesign our processes? Can we reimagine our processes? And a lot of challenges, as you mentioned in the beginning, the data quality, the silo functions. And this is what it takes a village to really uplift.

5:23I see a lot of energy. I've got the full support from our executive leadership team. And we are super excited about this journey.

5:32Peter High: Phenomenal. And how much of your first year, to date at least, has been spent modernizing legacy practices and technologies to be able to set a foundation to realize the vision you've described? Absolutely. So my big message is, first, we have to do the things that we are hired to do right. And it includes ensuring that our infrastructure is resilient. We have the full cybersecurity in place. And on top of that, what we are trying is really looking at what does AI mean to us? Obviously, everybody's looked into personal productivity. Every company has co-pilot. My goal is to take us to the industrial AI scale, where we are actually solving some of the big, meaty, cross-functional problems.

6:21And when I talked to our CEO, he's very big on messaging. So we kind of agreed that we need to create this momentum by actually building some big wins. And we are calling them lighthouse projects. So my first six months have been to work with my senior leadership counterparts in supply chain, in manufacturing, finance, and find those problems that they can put their weights behind, where we can make a difference on our P &L statements and take those challenges, work with our partners, and ride on the success of those projects. What that does, Peter, it takes away the inertia of, hey, we don't have the clean data, we don't have the right process in place right now.

7:09But this muscle is helping us develop AI-first mindset and bring more and more of our partners who've been waiting to help Goodyear to bring their offerings and apply the Goodyear context to the problems.

7:27Peter High: I want to get to that data point, which I find so interesting. You've mentioned it to me in the past in other conversations. Legacy master data quality is one of the biggest obstacles to scaling AI enterprise-wide, as you have said. Talk a bit about that insight, but also the pathway to reconciling that issue to be able to, again, and take advantage of it in the ways that you've described? Yeah. So what I would say, most of any enterprise system in any large company, a Fortune 500, they were designed for a different era. They were designed rule-based where something ends up on a dashboard, a human takes notice of that, and the workflow kind of continues.

8:07Now, we are talking about the AI systems that can reason. so they can only reason well if the underlying data is of reliable quality. You know, when you look at the generative AI, it's mostly probabilistic. But in an enterprise, you need very deterministic outcomes. You just cannot put a customer on a credit hold based on some, you know, probability. It has to be very, very accurate. So what we are doing, we are going after some of the main master data entities like customer, suppliers, product bill of material. There is a company wide initiative as a program, as a product to fix those things and really define what good looks like.

8:56That's one aspect we are going all in. But the change I've made is with the Lighthouse projects, we are using AI to clean our data. We are creating these data agents on our data lakes that actually suggest what good looks like. And we are teaching these large language models that have the best knowledge on the worldwide web, but know nothing about Goodyear. So this is the ontology. This is the knowledge graph journey. This is the business context of Goodyear bringing into AI on a very, very clean data foundation. It's going to be a journey. But the moment people see that the data can be cleaned, sanitized, create the relationship among different entities, I think the light bulbs start to go.

9:47What people need is a few success stories. That these things do not take years and decades. these things are actually getting done in weeks and days. That's the speed I'm talking about.

10:01Peter High: Yeah, back to the mantra you talked about at the outset. I can understand the importance of that. You also are a big believer in data democratization, Rahman. And I wonder, talk a bit about the philosophy behind that and how you bring that about in a responsible way, ensuring that while you're providing broader access, you also have appropriate governance and controls in place to make sure that the organization doesn't regret those moves? Absolutely. So, you know, last year I coined a new term called enterprise-grade intelligence, EGI. The whole world is talking about AGI, but I think what we need is really the enterprise-grade intelligence, right?

10:43So what that does is really works on the clean enterprise data, The problem is that most of the insights, when they leave a system of record boundary, you have a set of spreadsheets. And those spreadsheets are actually capturing a lot of tribal knowledge. And the problem is that your information flows are getting disjointed because there's a spreadsheet in between. What I mean by democratizing is really taking that tribal knowledge, using some of the AI tools to really bring them back into our enterprise systems and bring that context, bring that knowledge, bring those dependency into our large language model.

11:32Once you do that, everybody is operating on the same set of information. When you are presenting your sales numbers, when you are doing your forecast, when you're looking at days on hand of your inventory, people are not scrambling for four-day-old data that was different for different geographies. And you need a meeting after meeting to interpret that data. By democratizing it, it's all available to everybody at the same time. So I am very big on creating marketplaces for data products. When I see a data product, we put a lot of hard work and say, here is the commitment on the data quality. Here is the commitment on how recent this data is.

12:18Where is the governance? Where is the security? I want to take all that burden away from our subject matter experts. They should just have clean, reliable data, and their job should be to create agentic flows based on that data. Just democratize. You do it one time, and you have the governance process that stays clean. It's a pretty big problem to solve, and it's not an IT problem. It is not a technology problem. It is a problem where the entire business leadership have to put their weight behind. And I've been very successful. We can see the benefits, the decision making, the precision of our decisions have been becoming much more, you know, reliable now with this approach.

13:08Peter High: You have an expansive purview, Ramon. I mentioned, of course, some typical CIO responsibilities, IT, digital strategy, cybersecurity infrastructure, but also customer experience, dealer operations, internal productivity, supply chain logistics, really broad. Talk a bit about how your team reflects that breadth and how you work with the leaders of the various areas that you influence through the team that you lead, of course, as well. Yeah. And Peter, I'm a very simple person in terms of the communication. The messaging has to be very accurate. One of the things I tell my teams, what's your brand?

13:49Who knows you outside IT? Are you perceived as a subject matter expert? And I have found through all my CIO experiences that the digital and IT teams know a whole lot more about the business process. We are the people who know order to cache, record to report, inside out a 360 degree view. We need to take advantage of that knowledge now with some of the modern tools. So what I do, the mindset that we are changing at Goodyear now from a traditional IT, we are becoming product IT. Everybody's kind of working on the concept of a product. So you're getting beyond a project. When you talk about product, it is now kind of anchored into business domain.

14:36You have a business counterpart. You have the right KPIs. You know exactly what outcome you are going to influence with this partnership. And I think this is the best time to be a technologist. You've got all of these agentic AI tools. AI, six months back, could think, could summarize. Today, AI can act. and it changes the game completely. You need to put a lot of guardrails. You need to have a really solid decisioning framework. Otherwise, you can actually create more nuisance than the value.

15:12Peter High: Great insights all. You mentioned earlier also the necessity to re-skill teams. And I'd love to have you talk a bit more about the methods you're using to do so and some of the substance of that re-skilling as well, Rahman. Absolutely. I think that is the most fundamental aspect of our transformation. If we don't have our people committed to it, reskilled, think of it. In last six months, the measure of number of lines of code have become totally meaningless. We are at a point where you don't even have to write requirements. You can do spec-based development. You can do test design development.

15:53right? So it requires a totally different skill on the agentic AI coding. All of your old DevOps, tool chains, the paradigms are getting shattered. And what we need is to really put a career path for our team members that this is the journey, how are you going to get reskill? And I'm very big in terms of learning by evidence. We don't want to throw a bunch of courses, hey, go to cloud, go to Anthropic. No, we want to see evidence. So everybody is getting assigned on a live project. And we are creating mentorship where people who are native AI, they are teaching people who are domain experts how to match the two worlds together.

16:36And when you throw in the business value, the whole organization that's hungry for transformation, I think good things are happening.

16:47Peter High: fascinating again you've you also have spoken about the connected factory something you've you've noted as sort of the the backbone for the organization i wonder if you could talk a little bit about what that means what it entails and the progress that's being made on that front as well and peter you know my passion around manufacturing and i was very fortunate to spend five years in greater China. And I've seen the China speed firsthand, you know, the level of automation, the tooling, the fearlessness. And I want to embody all of those things in our massive footprint that we have at Goodyear.

17:24And it all starts with collecting the plant data very, very effectively. You know, this operational technology is a different beast. You have to recognize that some of these plants are legacy. You know, they are like 30, 40 year old plants. Some of them are modern. They have different kind of machines, PLCs. One of the things we are doing is really creating a backbone to effectively collect this time series data from our concept to how things move between different stations, different work processes until they finish good. Once you do that, you collect the data, And the new world of NES, the manufacturing execution systems are now becoming agentic.

18:13You build on top of a data layer and agent looks for the quality issues. It knows about what does a good tire look like from a visual picture, from a chemical composition and all that knowledge is brought to this agentic NES. And it knows all the tools like a model context protocol connecting to our different systems. And then it is acting like a digital worker on the plant floor. You collect the data, you bring some action and create a closed loop process in which if you see a process deviating from the design boundaries, designed process controls, it can actually figure out what's going on because it's got the full traceability of the raw materials, the person who's operating the machine, the machine tolerances, what is the spec required?

19:11And actually, our vision is that it should be able to feed back and take the corrective action. We are not quite there yet, but once you build the secure governed data layer and define the right plant ontology where you can add more assets dynamically, I think this is the future that we want to be stepping into very, very soon.

19:37Peter High: I'm fascinated, Rahman, how your team and you think about the strategic adoption of emerging technologies, but also think, of course, about what can apply to an industrial scale, truly moving the needle at the scale of an organization, the size and complexity of a good year. Talk a bit about the balance of those, making sure that your team is well aware of emerging trends and assessing potential applicability back to an organization like yours, but ensuring that these are not simply a bunch of proofs of concept that aren't, in fact, helping the organization scale for the long term. Exactly. I think our journey is how we become from AI curious to AI serious.

20:20And you have to have the right balance. So what I do when we motivate our people, at least 10 % of their time, I want them to spend on what is happening outside. And if you will see how fast things are changing. I mean, Anthropic just released a model yesterday. If you look at Kimi K3 in China, those are like, you know, open source, open weight models. How are we keeping track but develop the right muscle for the taste? You want to be having things that are applicable in our context. And how do you do fast experimentation when all of this data, the governance framework is available and people are actually dropping a business problem without waiting six weeks to get their infrastructure?

21:12That's the key. and so you allocate some time they find something and everything that i do here there has to be a business sponsor on the other side so this is how we create this healthy dynamic and you know in the agentic world it is very important to have a kill switch if these agents go wrong i am also you know proposing that we should be smart enough to kill the demand if a pilot did not work out the company culture should be such that yeah hey we made a bet didn't work out let's have the courage to kill that project move to next one next one and once you have you start to have people not fearful of failures i actually call it return on learning it's not roi it's return on learning And from every initiative, you're bringing this knowledge back into.

22:08And we are pretty good with wordplay. At Goodyear, now we call it Good AI. That's our brand name. Everybody knows what we are doing, what that platform is going to provide. And it's going to be a joint partnership with our business.

22:25Peter High: Talk a bit about other emerging trends that excite you, Rahman. We've talked about a number of them already. And I wonder what else is making its way onto your roadmap, your team's roadmap that has you particularly excited. I think one of the things, Peter, that's super exciting to me and which can have an order of magnitude impact is around the physical AI. You know, with the humanoids and advancement in the vision language models, you know, where a robot can actually see a process and without pre-programming, it can actually follow a human. That is very, very exciting to us. And really taking our digital assets, capabilities, and bringing them onto our shop floors with physical AI, I think is going to be very, very exciting.

23:15I mean, I can already think about safety, security on the plant floor with a kind of a robot dog moving around, looking at everything that should not happen. I think those things are becoming very, very real. So I think that's really exciting. Other thing would be, obviously, the agentic AI becoming much more context aware of our enterprise systems. I think the agent should know if a purchase order is getting delayed, what is the prioritization to expedite that PO depending on the impact on a customer commitment, depending on the impact on a P &L. I want all of that knowledge to be imbibed in AI with Goodyear context.

24:06And it's going to take some time. It's what I call AI is compounding the knowledge every day. And we can't just wait to take a start. If you're not starting today, you're already a year behind your competition. So we are committed to get on the racetrack literally and keep moving.

24:30Peter High: Fantastic. I wonder also, Rahman, as somebody who clearly needs to remain cognizant of the shifting state of the art, how do you do so? What do you read or listen to or who is of influence to you, as the case may be, to help you stay current? I actually find, and this is one thing I tell my team, on a given day, I probably read an hour, you know, look at what's happening, some of the people that I follow. One of the authors that I'm quite impressed with, a guy named Samir Pahl Chaudhary, he's written an excellent book called Reshuffle. I highly recommend that. Truly, it talks about AI as an orchestration engine, not just a task engine.

25:16So I think this is how I kind of keep myself up to date. I talk to almost all of our partners, whether it's Microsoft, Azure, you know, we've got AWS. All of these people are innovating. And you really find out what is the contextual use case that we can start to pilot. So kind of, you know, attend those executive briefings, read a lot. And I think my last source of my information is the young people at Goodyear who are fearless, they are reading, they are experimenting. You find a channel onto them, break those hierarchies and they feel empowered. And I think the good things happen. So it's a triangulation.

26:00Peter High: Great suggestions all around, Rahman. And thank you so much for a wonderful conversation. conversation. Phenomenal to hear that still within your first year, the remarkable changes that are afoot, the guiding principles that you put in place, and the progress that you and your team are already making from going from AI curious to AI serious and developing that return on learning you described before as well. Thank you for a terrific conversation. Always a pleasure, Peter, talking to you. We are just getting started here. Thank you. Thanks for listening to Technovation. If you enjoyed this conversation, subscribe and leave a rating that helps more leaders find our podcast.

26:38Peter High: Thank you. And I look forward to seeing you next time.