In an exclusive interview Arthur Hu, Chief Information Officer of Lenovo and SSG Chief Delivery & Technology Officer of Lenovo, reveals the strategy and culture behind the tech giant’s radical transformation. Arthur has been with Lenovo for over 16 years and has played a pivotal role in the company’s transformation and expansion into new areas. From his experience in business consulting to his current positions at Lenovo, Arthur has witnessed and led significant changes within the technology industry. We’ll delve into his journey, Lenovo’s shift towards offering solutions as a service, the implementation of AI in Lenovo’s solutions, and the company’s embrace of metaverse and blockchain technologies.

Credit: Arthur Hu(CIO Lenovo) with Anna Tutova (Founder AI Crypto Minds)

Lenovo’s record $20.5 billion quarterly revenue, with 30% coming from AI-related products and services, proves its strategic bets are paying off. This success is fueled by its ongoing $1 billion investment, which powers initiatives like the expanded AI Innovators program – now featuring over 165 solutions from 50+ partners.

The company’s vision is evolving from smart hardware to intelligent partners, developing agentic AI that can automate complex tasks and potentially double workforce productivity by 2027. Looking ahead, Lenovo plans to launch a “Personal AI Twin” in early 2026, an AI agent designed to orchestrate a user’s devices and act on their behalf.

Anna Tutova: So, can you tell us how did you get into the technology space and how did you start working with Lenovo? I know you’ve been with Lenovo for 16 years already.

Arthur Hu: Yes, so I was an engineering major for computer science and then after that I really wanted to apply some of that to the business world, and so I entered business consulting for a long time.  This is before computer science was cool, so there were way fewer computer science graduates back then, but I spent a lot of time in the business consulting world, and then from there Lenovo actually was one of the clients that I was able to serve, and at some point, I had the right opportunity to move from business consulting and actually delivering technology within a company. So, I made the move from consulting into Lenovo as you said 16 years ago.  At Lenovo I’ve had the chance to rotate through a number of roles. I joined Lenovo to do business transformation, and so at the end of that I was able to become the CIO in 2016, and then most recently with Lenovo creating  the solution and services group so that we’re really taking all of the hardware assets and portfolio that we have and making them available as a service so that we’re layering services  and solutions on top of the hardware  that we’re already very famous for,  and so I’m also the technology and delivery officer for that group as well.

Anna: Yeah, that’s quite interesting, and why was there such a shift from hardware development to focus on more offering solutions as a service, and how do you see the development in that scope? Do you see it will overcome the volumes of your offerings in Lenovo?

Arthur: We’re, first and foremost engage and listen to our customers, and so as we spend time more and more with them, it’s become an increasing trend where customers are actually looking for additional solutions beyond simply buying a hardware, like for example, a phone one time, or buying a PC, and then we see them three years later to see what else happens.  So that was one, which is we were hearing from our customers, they wanted more out of us.  They wanted us to deliver more, and services was a good way to engage them and provide them what they want. 

Another shift also is really about the notion of agility, being able to move more quickly.  When you provide and you think about things in a hardware, it's more in a physical sense, and of course, there’s always gonna be the physical world where we’re delivering some hardware on the ground, but by wrapping these in services, we’re able to be much more flexible.  So rather than having a capital expenditure to buy something that you own forever, you’re able to have operational or op-ex expenditure, and it’s a little bit more of pay-as-you-go model. So, we’re able to offer the flexibility, because then customers are able to either flex up if they suddenly have more demand, and then pay more, or flex down if they suddenly have less demand for some of the technology and IT services to pay less.  And that scalability and agility is really valuable for the business.

Anna: Can you tell more how does Lenovo implement AI in its solutions, and how do you adapt to it, and what exact types and use cases of AI do you have? 

Arthur: The first thing that is important to recognize is that it does take a long time. This is not one of the things where you snap your fingers, and then suddenly AI success happens. We’ve been at it for more than the better part of a decade, meaning we started with small use cases and pilots. And some of the times they actually didn’t go so well, so I’ll give you an example that was quite interesting.  One of the early AI pilots we did was around our supply chain. We’re one of the top 10 supply chain companies, according to Gartner and The World, because we have so many physical goods, factories, networks, suppliers. One of the things that we tried to do is how we increase the forecast accuracy of our demand. Because if we can plan that better, we can actually be more accurate.  So long story short, this would have been around 2015-2016. When we tried to do that, we found that it failed terribly, like really badly at first.  And then when we looked at why, it was not the technology. It’s not that the technology had any issue fundamentally with it.  It was because people were scared. People thought that by deploying the technology, we were trying to take their jobs, or if that they made the forecasting too good, that they would be redundant and no longer need it. And so, what would happen is people would actually cause the technology to fail, because AI is just training on data. If you make the data very poor quality, and it doesn’t reflect the real world, then you have a garbage in-garbage out problem.  And so, we actually had employees effectively sabotaging the data, because they said: «I don’t want to see this succeed, because if it succeeds, you probably take my job away, or you make me do something else».

Anna: So how does it change now? I think still people have a lot of fear that AI will substitute their jobs etc. So how do you encourage people to approach the innovation?

Arthur: Well, and it goes back to how we solved the problem when that pilot all those years ago, which is we actually sat down, we realized this was happening, and then we just had a conversation. And once we realized that there was an emotional fear factor that was causing it to fail, not the technology, then it became a question of communication and change management. And we just had a very real discussion that we clarified, but we had to emphasize the intent, is not to take jobs.

The «A» for Lenovo in AI is really around augmentation.  But augmentation means the base is you and me as a human being, all of us as human beings, and then taking the technology capabilities as a tool, just like you would an ax. Or any kind of tool you can imagine in modern life and making us better at what we do.  And that’s exactly what we found.  Once they understood that these models cannot operate independently, because we always need the human judgment for something so complex around the supply chain, where we have thousands of partners, we have millions of parts, we have such a complex network, you always need to be able to understand and see if something that’s coming out of the AI model is matching with reality.  So, I think that applies more broadly to the question you just had now, especially with generative AI. 

And you see so many: some of them are saying, this will destroy the world. Some of them say, this will save the world. And so, it’s really confusing for a lot of people, because you see all these headlines, and what should you make about it?  There will continue to be a time of exploration, where it finds its fit in the world.  What are the use cases where it’s actually going to be most useful?

Once people understand that it’s really about augmentation, I think they can reframe it, because then it becomes much less of a you or me versus the machine, who’s gonna win? Because that’s kind of a zero-sum mindset.  And I think that also ignores the history of all disruptive technologies, whether you think about fire, wheels, the loom, the steam engine, all of these things were disruptive technologies, but over time, they ended up actually creating more economic opportunity, and I think this will be the same. 

So, if people can understand and take some comfort from history that this has happened, then I think we can get on with the business of how do actually we manage this, how do we explore more and channel it to the way we want it.  It’s a tool, we use it to shape the world the way we would like, and it’s not something that it’s being done to us, where we’re just being shaped by it unknowingly.  So, I think being conscious about it and saying we can manage this, we can control it, it’s a tool for us to direct, and not the other way around. 

The question people should be asking is not, will this take my job?  The answer is no, this will not take your job. However, someone who knows how to use these tools a lot better than you, they may take your job.

So, for example, if you are making clothes, if you just are hand knitting every piece of clothing by yourself, and someone has a sewing machine, probably the sewing machine doesn’t take your job, but the person who’s using the sewing machine can take your business, because now they’re much more productive.  And so, I think it’s a little bit of reframing with people using the technology, and then it makes a lot more sense away from, or in addition, to help go through some of the breathless hype that you see a lot of people saying. So, to get away from the extremes and being more practical about it.

Anna: So, in what other base do you implement AI besides that they are taking decisions, like the proof of progress?

Arthur: There's no part of the company that it hasn’t been touched.  So, pick any part of the company, marketing. So, for example, what we’ll do in marketing now is we’ll be able to use the feedback from our campaigns, because we’ll launch campaigns, and as a global brand, we will have hundreds of millions of users around the world.  We’ll have hundreds of millions of impressions, and we can take all that data that comes back, who clicks on what, which ones lead to purchases, which ones lead to recommendations, which ones lead to good reviews, and we’re able to much better understand our customers so that we can serve them better. 

If you think about the machine itself, the machine also generates a ton of data, right? Things are in the process. It says I’m overheating.  Maybe it says you don’t have enough memory.  Maybe it says it looks like the power plug is gonna fail.  And so, we’re able to take across all the devices that we have that have agreed to share telemetry or technical data with us and use that to understand how people are using our devices.  And that’s incredibly valuable because it helps us in our customer satisfaction, and it helps us actually engineer our products better.  We can tell from the data, oh, that button’s in the wrong place, or people seem to really struggle using this feature. And we’re able to take that from, because you can’t manually analyze that.  There’s just too much data.  By applying these techniques at scale, you can start to cluster these patterns and learn from what people are essentially telling you, not because they’re speaking to us, but they’re via the voice box. But they’re speaking to us through the data and the AI, and some of the algorithms that we’re deploying are helping.  So quality, engineering, and prediction. So, I think it’s really about finding patterns, but in each domain. 

Anna: And as well, besides AI, you implement many other innovations and technologies, such as digital twins. The concept of Metaverse as well became super popular in the recent years. Can you tell more how you embrace Metaverse technologies, and as well, with your partner, NVIDIA, on the development of data storage solutions for the Metaverse? Can you tell more about that?

 Arthur: We really think about it as mixed reality.  There’s a lot of names for this. AR, so the augmented reality, and the virtual reality are specific technologies.  Mixed reality, or XR, is another way of thinking about it, but it’s the same thing.  Metaverse is an application of that. And so, we have a think reality XR platform, and it’s our way of providing an end-to-end solution. It has APIs, it has a software developer kit, it has the digital, or the actual gear, the equipment, and we provide it, because we are fundamentally a provider of computing and AI, storage and networking, and so we take these and make these building blocks available for people to then build solutions on.

If you have AI devices, whether that’s four degrees of freedom, right, in 2D, or six degrees of freedom, if you have the software tools to be able to create applications.  For example, we partner with a company called ThoughtWorks in this space, but we also have solutions that are around smart education, that’s a common use case that we have actually an external offering for. 

Another one that’s interesting, that we not only use the Metaverse and these mixed reality technologies as a use case within Lenovo, we also sell it outside, is around training, so a very specific kind of training.  It’s a bit of industrial education, and so when there are complex machinery that needs to be done, sometimes you need to spend months in a classroom with a physical piece of equipment, but when you use an augmented reality solution, you’re able to overlay technical information with what’s happening on the very complicated piece of equipment.  It could be a server rack that's using our latest supercomputer cooling, for example.  So how do we install that, how do we fix that, what are all these parts? And so that’s something we use internally to make our own technology and our own techs who service and provide these services more productive. It’s also something we sell externally. This technology is useful in industrial scenarios where people need to be in the field. And so, there’s a lot of applications here where the mixed reality also generates simply different possibilities that you aren’t able to have in kind of just the purely physical world.

Anna: And as well Lenovo adopted some blockchain solutions, for example for the supply chain.  Can you tell more about this and do you have any other plans to leverage this technology for other solutions for other use cases?

Arthur: Yeah, blockchain is really interesting as a technology.  And I think also it went through a hype cycle.  So, we have deployed blockchain with partners around areas where we really want to have that shared visibility across a common set of transactions, as well as the traceability and the way to authenticate.

And so, what we’ve done and used in blockchain primarily is around our supply chain so that we can look deeper into what is the sourcing and the providence of our supplies as they make our way. 

I think there are more use cases in the future.  One of the things that’s interesting about blockchain for us as I found is that we want to make sure we are following the business problem and not the technology.  And I’ll give you a good example.  This comes probably from seven or eight years ago when there was even more hype around it and it was newer, where almost all of my teams suddenly said they wanted to deploy blockchain for everything because it was everyone wanted to experiment, which is really good.

Anna: I guess it was like 2017 or something.

Arthur: Yes, that’s right.  And so, people were very excited. Everyone wanted to experiment with blockchain. And suddenly when I was doing our architecture reviews internally about what technology we were going to adopt, I found everyone wanted to use blockchain.  And I said: «This doesn’t sound right because it’s not possible that it’s all the things». So, to my point about following the business problems, there was one application they want to use blockchain.  And I said, why? And it turned out in that particular case, which was a marketing database application.  They could have actually, and what we ended up doing is just, if they had a regular relational database, just a regular old database that's been around 40-50-year-old technology, it would have actually solved the problem faster.  It would have been a better system. And so, I think it’s a good tale just to remind us, technologies need to find their proper use case. And so, for blockchain, I think absolutely where there’s areas of immutability. You really have to find the use cases where it matches the characteristics of the technology well.  And otherwise, for example, if you try to use blockchain, because blockchains due to their public nature, are not very fast performing.  And so, if you need a high end, if you want to run something that has a million transactions a second, that's not gonna be a good use case for blockchain just because of the technology.  But if you need immutability, if you need the traceability, if you need the ability to digitally verify anything that’s ever happened since the beginning of when you set up a transaction, then it’s a good use case. 

I think we continue to be on the lookout. 

Anna: Can you share some challenges as well for Lenovo and how to overcome those?

Arthur: One of the biggest things we’re working on is what is called our services-led transformation.  People still remember Lenovo and think of us as the ThinkPad company from all those years ago. And we are, and we continue to be. And proudly so. But what a lot of people don’t know is that more than 30% of Lenovo’s total revenue is driven by AI solutions. And so, we are substantively evolved and improved company. We have PCs and we have phones, and we have all this data center and infrastructure solutions. And now we’re putting on top of those services and solutions.

It takes a lot to get a company to shift from a very proud heritage of world-class hardware development and extending that, the hardware plus the software and the solutions, and the services that go with that.

And so that mindset, it's really almost like building a new company from scratch because a lot of the capabilities that were fit for selling the hardware, we don’t have nearly as mature for selling solution and services. So that’s one of the biggest challenges but also the biggest, exciting thing we have because it’s what we talk about to our investors.  It’s what our customers are interested in.  And that’s really guiding the way for our investments.

Anna: Yeah, I see it’s more from the branding side and how consumers perceive you.  And so as well, can you share maybe some upcoming news of Lenovo or as well any exciting partnerships in mind you have?

Arthur: As for partnerships, we've always have many things in the pipeline.  I think what to look for as you watch Lenovo is those two things. One is to have us and see us continue to evolve that are non-PC businesses, that we continue to lead in PCs and that our infrastructure businesses and our services and solutions really wanna grow. Those are the things to watch for Lenovo.

And then partnerships are part of that that we pursue. For example, an area that we’re very excited about is our AI innovators program.  And what we’ve done there: we've set up kind of and brought in dozens of partners, startups, medium-sized companies, large companies, and we’re working with them to create the industry solutions around virtual assistance, around smarter prediction, about more empathetic user interactions. And so, look in that space, because I think there’s a lot of innovation that's happening there.  And I think I’m excited about the innovation and announcements that will be coming from that space.

Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.