In the 10th episode of Speaking of AI, LXT’s Phil Hall chats with Jeff Winter, digital transformation enthusiast and Hitachi Solutions’ Senior Director of Industry Strategy. In addition to his career as a leading technology and business strategist, Jeff is also the global, number one Industry 4.0 influencer, according to Analytica. Tune in to this episode to learn more about Jeff’s take on LXT’s recently published Path to AI Maturity report, generative AI, the current state of the AI evolution, and how companies can begin, or further, their AI and digital transformation.

Table of contents

Introducing influential technology commentator and Industry 4.0 leader, Jeff Winter

PHIL:

Today’s guest is a multi-award-winning technology expert who has held senior roles in strategy development and related areas at organizations, including Rockwell, Omron, Microsoft, and currently at Hitachi Solutions. He sits on multiple technical and advisory boards, working on strategies for IoT, manufacturing, automation. And according to Analytica, he is the global number one industry 4.0 influencer, quite an achievement. In his spare time, he’s a highly influential technology commentator with a huge audience, over 100K of dedicated industry insider followers. Please welcome today’s guest, Jeff Winter. It’s great to have you aboard, Jeff.

JEFF:

Thanks for having me here, this will be fun.

Where are companies getting it right and where are they struggling? What advice do you have for companies who want to capitalize on the transformative impact of AI?

PHIL:

Yeah. So in recent weeks, we released the third edition of LXT’s Path to AI Maturity report. For today’s discussion, it will be great to hear your take on the current stage of AI evolution and your interpretation of some of the key findings from the report. So, Jeff, over the course of your career, you’ve seen the evolution of technology and the impact of digital transformation.

With the massive paradigm shift we’re now seeing with generative AI, where are companies getting it right and where are they struggling? What advice do you have for companies who want to capitalize on the transformative impact of AI?

JEFF:

So I might take a slightly different spin on this question. I’m going to actually start with where companies are getting it wrong before we get into anything else. First, from a personal perspective, I don’t think most business leaders understand generative AI or AI in general. And I’m not an AI technical expert, but I can tell when someone really doesn’t understand what they’re talking about. Most business leaders have had no need to understand AI. Up until they were forced to with ChatGPT. The public pushed this on CEOs, making sure that nearly every company, at least publicly traded, had some sort of stance or answer on what they were going to do with AI. And that is why we see it as continually one of the top things talked about by companies according to IoT Analytics in there, what do CEOs talk about most each quarter?

And so whenever I hear anyone say, we need to use AI to fix it or to solve it or to improve it. That’s an immediate red flag for me because AI is such a broad and ubiquitous term that it actually has little meaning except as a useful buzzword. I mean, think about this. If someone asked you a question for your company’s strategy and you responded, oh, well, we’re just going to use the internet to solve that or to grow our company or to improve, or we’re going to use electricity to solve that. How silly does that sound? AI isn’t that much different. So in my experience, most executives can’t tell you the difference between machine learning, generative AI, and deep reinforcement learning. Three massively different forms of AI used in completely different applications.

So the first thing I’m going to say is start with educating yourself just on the bare basics so you understand really what AI is. The second thing I want to say is data management and quality. Many companies struggle with managing the sheer volume of data required for effective AI training, often facing issues with data quality and accessibility.

The push to use AI is just exposing how poor a company’s data quality really is and their data management practices and their data strategy. And third, I would say, is underestimating the cultural change required. Adopting AI is not just a technological shift, but a cultural one. Some organizations underestimate the need for change management, leading to kind of resistance and or underutilization of artificial intelligence capabilities. Your teams need to understand what AI is, how it is changing the industry and how it is changing their jobs, and how it can make them more effective.

So that leads into what advice would I give? Well, first is develop a clear AI strategy. But when I say this, I don’t mean a standalone AI strategy. I don’t want to see an AI strategy document. I want to see AI incorporated into your company’s business strategy. That’s where your AI strategy should live as part of your business strategy, not by itself. You should review and update your company’s entire business strategy to consider how AI can impact both positive and negative all aspects. You don’t need separate AI goals. You need business goals. And how to evaluate AI to understand how it can help those goals and how it can help achieve the strategy for those goals.

Second is embrace a culture of innovation. So encourage a culture that supports experimentation and learning, allowing for failure and continuous improvement in AI initiatives. Because if AI is used correctly, it helps automate some tasks and augment other tasks. So make sure your company encourages experimentation with AI as a tool to help improve both the company and improve each person’s individual job. Think of turning Tony Stark into Iron Man. Don’t think about having a replacement AI robot. Think about taking a person and making them significantly more powerful. There’s a big difference in that mindset. And lastly, I would say is, focus on scalability and sustainability. Ensure that AI implementations are scalable and sustainable considering long-term impacts and how they evolve with the technological landscape.

You mentioned automation and augmentation, could you dig a little bit more into the distinction between the two?

PHIL:

That’s great. And I’m sure people will take a lot away from that. I certainly do. Could you dig a little bit into that distinction between AI for automation versus AI for augmentation? Some maybe use case examples.

JEFF:

Sure, and this is the biggest way I like to break up how to think about AI. Because AI is a tool that helps make decisions. That’s what it is. So you can either use it to help you make a decision, that’s augmenting. And you can use that, for example, for predictive analytics, where you’re analyzing a bunch of data and providing a forecast for you to make a decision based off of the information that the AI provided.

The other is automation. This is where you’re handing over the wheel to the AI where it’s making the decision for you. This is used, think of it, you know, common terms is like a self-driving car is an example. But in terms of like the manufacturing industry, which is where I mainly play, this is about having AI do real-time control of production processes where you’re not intervening at all. It’s figuring out the best way to optimize the line, for example, to produce the highest yield.

What initially piqued your interest in LXT’s AI maturity reports?

PHIL:

Very cool. Okay, so you’ve been a frequent and insightful commentator on LXT’s AI maturity reports. Do you recall what it was that initially caught your eye? Do you think we’ve been asking the right questions?

JEFF:

So good question and the simple answer is I was Googling AI maturity statistics and ran across LXT’s report and I instantly liked the way that they organized the report and especially how they overlaid it to popular models out there that I already knew, like the Gartner model, which helps to kind of anchor your understanding. Now, some of the questions in the report are things that I just haven’t seen any other places, especially business drivers by industry, types of AI used by industry, and my personal favorite types of data used by industry. So do I think that they’re asking the right questions? Yes. And I would say the only thing I would ask for more of is ask more questions expanding your survey pool because the information is great.

Companies are stating that they have reached higher levels of AI maturity in recent reports, what do you think is driving these companies’ self-perceived growth?

PHIL:

Great, and I know you have actually given us some guidance on upgrading the questions, augmenting them in the more recent edition, and Jodie has really appreciated that. So, Jodie Ruby, for anyone that isn’t familiar with her, her name’s not on the cover of the AI report, but she’s the author, she’s the driver behind this. So thanks for your help with that.

Since we released our initial AI maturity report in 2022, we’ve seen some massive shifts in AI awareness and maturity. Back in 2022, just 40% of companies said they’d reached the higher levels of AI maturity. And our latest research has 72% of companies claiming, and bear in mind that when I say claiming, this is self-perception, it’s not objective reality. They’re claiming that…They are now at higher levels of AI maturity. Is this consistent with what you see and what do you think is driving it?

JEFF:

So this is an interesting one because like you said, it’s self-reported, which in fairness is the only way to really conduct a survey. But I would be curious to see how this actually compares against companies, their actual maturity rather than kind of a self-proclaimed perceived maturity. But doing that would require an expert to go in and assess their company. And that’s a massive undertaking. But most reports you see out there in most forms of technology adoption, and even my main field, Industry 4.0, most companies claim that they are higher than their peers. And that’s just not possible statistically if most companies are saying they’re better than their peers, kind of like the studies you see out there of everyone thinks that they’re a better driver than everyone else out there. It’s just not possible.

Now, the proliferation of ChatGPT has single-handedly increased everyone’s interest in the whole field of AI. I can’t think of a single company that isn’t investing in AI or exploring it across the company. That level of awareness, just focusing on figuring out what to do with AI has absolutely increased dramatically. So you can’t even attend a conference or read an industry or tech-related magazine and not have AI be just the key focus, regardless of what industry you attend or what conference you attend.

And because of this, almost everyone is out there learning about the technology, learning about the products that are out there, and learning about the applications. And once you know something better, you immediately think your maturity is automatically higher on the subject, regardless if you’re actually using it or not, you perceive your maturity is higher. But I would say very few, if any companies are actually objectively a fully AI-mature company, even if some of them think they are.

What steps should companies that are still experimenting with AI take to ensure that they don’t get left behind?

PHIL:

Yeah, your answer doesn’t surprise me, but it’s great to have that encapsulated in a summary like that. That’s, yeah. Okay, so if these reported maturity levels were indicative of actual maturity in the marketplace, what steps do you think that companies that are still just experimenting with AI need to take in order to ensure that they don’t get left behind?

JEFF:

So to put it simply, I would say companies that are dabbling in AI, they need to shift from playing with the technology to integrating it into their core operations. AI is one of those technologies that should be the central nervous system of your entire company, because it is one of few technologies out there that really impacts every role in the company, from the frontline worker to the CEO across every single function. And there aren’t that many technologies out there that can claim that. And so the technology is there. And I want people to know that, and it works.

But the biggest killer of AI projects isn’t the tech itself. It’s the resistance from people within the organization, which is a cultural hurdle. Companies must, they have to nurture a culture that is ready and eager to adopt AI, making it clear that AI is a tool to help everyone work better, not a threat to their jobs. There’s several statistics out there that show that the more educated people are on AI, the more open they are to using it in both their personal and professional lives.

People that don’t understand something naturally fear it. And because of how fast ChatGPT has grown, and therefore everyone’s interest in the whole field of AI, you’re getting a lot of resistance from a lot of people. So experimentation is great, but it has to evolve into practical applications. And for that, the whole team’s mindset has to be in sync with the AI-driven direction that the company is heading.

Do you think generative AI is more important than other branches of AI or is this a “drink the Kool-Aid” moment due to the hype around Gen AI and ChatGPT?

PHIL:

Yeah, and I like what you said earlier as well about when you’re in this experimental phase, don’t be afraid of failing. You’ll be more powerful with your experimentation if you take a few risks with the knowledge that failure is one of the possibilities. Okay, so 70% of companies state that generative AI is more important than other branches of AI, and 11% say it’s much more important. Is this consistent with what you see in the real world, or is this just a huge “drink the Kool-Aid” moment?

JEFF:

So from my perspective, no, generative AI isn’t more important than other branches of AI. Rather, it’s one piece of a much larger puzzle. If we consider machine learning as the foundation of AI, it’s like the engine in a car. It’s been powering the AI vehicle for years, mostly out of sight and silently working under the hood. Most companies are already riding in this car without realizing the engine’s complexity because it’s so well integrated into their systems.

In fact, I wish I had the stat right now, but I just saw a statistic today by Statista that shows that machine learning is like a whopping 65%, or something like that, of all AI use. Most people don’t realize how that is still the majority use of AI is machine learning.

Now, generative AI on the other hand is kind of like the car’s flashy dashboard and infotainment system. It’s what people see and interact with. It’s fun and it’s engaging, allowing the average person to play with features, enjoy the ride, and even customize their experience. But without that engine, the underlying machine learning algorithms crunching the numbers, making predictions, and analyzing data,  the dashboard just wouldn’t have much to display.

So in business operations, even the most impressive generative AI applications are typically powered by the heavy lifting done by machine learning algorithms behind the scenes. So as a good example, if you create a ChatGPT-like interface and you ask a question like, what’s the demand forecasting prediction for supply chain? All the generative AI is doing is pulling that information and typing it out in layman’s terms for you to read. The actual prediction is done by machine learning.

PHIL:

Yeah, yeah, we actually saw something quite analogous to this with the evolution of speech technologies where really speech technologies, there’s typically three major components. So there’s the recognition piece, the acoustic piece. There’s the language modeling where it’s doing the language understanding piece. And then there’s the text to speech, which is the generation of actual responses. And again, that’s the piece that people saw. And that was the piece that people judged the entire technology by. Whereas the recognition and the understanding piece was where the real heavy lifting was being done. But people’s reaction was to the interface that they could see, or in this case hear. Yeah, so it’s not exactly without parallels.

What do you think are the top use cases of generative AI? Does it match what companies report in LXT’s Path to AI Maturity?

PHIL:

OK, respondents to the report indicated that the three top uses of generative AI are creating documentation, 38%, improving decision-making, 36%, and marketing, 35%. How does this align with your view, and what do you think is missing from this short list?

JEFF:

So this is probably the question that most people want to know the answer to. Where are people using generative AI and what value are they getting out of it for where they’re using it? Now I’ve read dozens of reports answering this question and they all basically have different answers. Why? Because it all depends on who answers the question and the questions that are actually there or the answers to the question. So generative AI can be used by… Everyone, like I said, remember from the frontline worker to the CEO in every function, every industry. That’s a wide range of people.

So depending on who you ask, you will get different answers. And with that, like I was saying is the answers are usually pre-given by the survey. There’s very few surveys that have free-form answers. They almost always have a select from this list. And those lists are rarely the same.

In the case of what you just said, creating documentation, is an activity, decision-making is an activity, marketing is a department. So you could argue, you know, they’re not exactly mutually exclusive with it. So the irony here though, is that generative AI, if used properly, would be able to actually handle survey responses that are free form and using natural language processing and machine learning could easily cluster them into answer groups.

And then the third thing I would say here is that the big distinction between how companies use generative AI and how people use generative AI are very different and they need to be very much called out. For example, I use ChatGPT every day. I use Microsoft Copilot every day, which uses generative AI, and I use my company Hitachi Solutions Enterprise Chat every day. And those are all at a personal level. All different uses, all generative AI. But then there are company level uses. For example, a marketing department using it, like in what you had, or a company creating an internal ChatGPT to assist supply chain or to create an external chatbot for customer service. Very different. And it can be viewed from two different perspectives, like I said. The company creating it or the person using it,  even if it’s the same use case or application.

PHIL:

Yeah, and when I actually, when I looked at that particular, the responses to that particular question, so here I picked out the three that were the highest scoring, but what I actually found was that the range between the highest scoring and the lowest scoring was not particularly wide. And so in effect, all of the options that were presented to respondents got some reasonable level of uptake and that suggested to me that maybe people just don’t know yet.  As you said, it depends on who you ask and probably what they’re thinking about on that particular day.  It’s a very context-driven. But I did get an impression that it suggested A, that people aren’t sure, and B, that it could be applied to everything. So it could be being the operative part of that.

Data from the report shows what companies are investing in. Where do you think companies should be focusing investment when it comes to rolling out AI?

PHIL:

And the last question on the report here, our data shows that when it comes to budgets for AI, companies are investing most in strategy development and training data, followed by controls, compliance, hardware. Where do you think companies should be focusing investment when it comes to rolling out AI?

JEFF:

So I would say companies should definitely have a clear data strategy before diving into AI. And that kind of partially relates to some of the things you talked about in there.

This means investing in the process of cleaning and organizing their data because no matter how advanced the AI is, it can’t work its magic with messy data. So before dreaming of an AI-driven successful application or use case, it’s critical to roll up your sleeves and scrub the data until it sparkles. Ensure that data is complete, accurate, normalized, contextualized, and it’s just, it’s the real deal. It’s clean data. Clean and prepared is the best thing for AI.

And so it’s one of the few jokes I kind of give in the industry for AI, but the moment someone says that they have an AI strategy and I go, great, can I see your data strategy? And they don’t have one. It’s hard to say that they actually have an AI strategy. So that is one of the most important things I say to figure out is your data strategy. It should answer what data should you be collecting for what purpose. What are you going to do with it? How are you going to collect it? And then AI, how it fits in to take advantage of it so that you can drive action.

PHIL:

Right, so if you don’t have an AI data strategy, you probably don’t have an AI strategy, is that a summary?

JEFF:

If you don’t have a data strategy that goes over what data you should be collecting, for what purpose, how you’re going to collect it, and what you’re going to do with it, it’s hard to have any AI strategy.

Where could companies use AI easily without even knowing anything about their company, their size, their industry, their maturity that has a fairly high success rate?

PHIL:

Great. My closing question. If you were running this interview, what’s the big question that I should have asked you but maybe didn’t?

JEFF:

Oh, I’d have to think about that one. I might actually have to pause and think about that one. I don’t know if I had, I didn’t have that one planned. It’s a good question. How about this? Where could companies use AI easily without even knowing anything about their company, their size, their industry, their maturity that has a fairly high success rate? That’s probably a question that I think should be asked more by companies, not just which type of AI use case has the highest ROI because some of those have the highest ROI, but are also the most expensive to implement. And it depends on your company’s data strategy. So a good example of one that I think is pretty useful for a lot of different industries, not all of them, is taking advantage of your documents. Generative AI specifically is very good at being able to, with natural language processing, is able to take those documents and do stuff with it. And that stuff is where generative AI comes in.

Let’s say you have thousands of contracts in PDF form. You can have it analyze those contracts to look for similarities or abnormalities in future contracts that come in. You could have, if you’re a public company, evaluate companies’ 10K reports that are in PDF form. If you’re a manufacturer and you have user manuals, you might have thousands of user manuals that you could just immediately load in and use those in your generative AI model.

So I would argue it doesn’t even matter what industry you’re in. We’re doing it right now with companies in the real estate industry where they’re We’re doing it right now with companies in the real estate industry where they’re loading in thousands of different real estate listings to be able to pull in that information, make sense of it and summarize it, and determine trends. So there’s a lot of different ways that you can do this just with the PDFs that you have because PDFs have now become standard nature, been around for a decade or more that just become, everyone has hundreds of PDFs on their computer. Take advantage of those. That’s an easy use case that doesn’t matter your maturity or where you’re at.

PHIL:

That’s a great question, a great answer. And I’m really glad that I put you on the spot with that one because I think people will take some real value away from that.

JEFF:

And you did put me on the spot.

PHIL:

Jeff, great talking to you. I’ve been looking forward to this for quite some time. Jodie has sung your praises for a very long time and it’s been a real pleasure to actually meet you in person and talk to you about these contemporary issues. Thanks again for taking part. We really appreciate it.

JEFF:

Thanks for having me here, this was fun.

PHIL:

Great.