How AI will transform industrials

In this episode, Alison Clark, EY UK Advanced Manufacturing and Mobility Leader, explores the roadmap to overcome challenges and leverage AI’s potential in industrials.

Key takeaways:

Companies face several challenges in adopting AI, including supply chain complexities, strategic alignment, talent scarcity and the need for robust IT infrastructure. Addressing these challenges is critical for successful AI integration.
Effective AI implementation thrives on robust data and efficient data distribution systems. It is vital to address data misalignment, elevate forecasting capabilities and manage inventory effectively for successful AI deployment.
The adoption of AI should be a strategic decision, not a trend-driven response. Companies must approach AI with a strategy that aligns with their business objectives.

For your convenience, full text transcript of this podcast is also available.

Welcome to the EY Advanced Manufacturing and Mobility Business Minute podcast series, where EY professionals explore the critical business issues impacting our industry today.

Moderator

For today's session, we're going to be discussing the evolving landscape of artificial intelligence (AI) and its transformative role in the industrial sector. The stats speak for themselves. Forty-nine percent of advanced manufacturing and mobility firms have fully integrated AI-driven changes into their capital allocation processes. Within the next year, 41% will join these front-runners, leaving only 10% on the fence. And by 2030, it's projected that a whopping 96% of companies will take steps to boost their manufacturing and investment.¹ Here with us today to discuss what this means for the future of AI in the manufacturing industry is EY UK Advanced Manufacturing and Mobility Leader Alison Clark. Alison, thank you for joining us today.
So, when we look at the world today, there are a few things that have been touched by AI. The emergence and swift adoption of generative AI (GenAI) is making industrial leaders take notice of the technology’s immense potential to transform and disrupt. What stands out to you about what we have seen so far with the growth of AI in the industry?

Alison Clark

So, it's quite a difficult question to answer, to be honest, because it does seem to change every day. There's definitely a change in the dynamic in people's receptiveness to GenAI, and that's helping give manufacturing a new lease of life in some areas. And it's not just about now, though. It's about where this is going to go and how this could change the game for some industrial leaders. The certainly high numbers, nearly 60% of them that are saying this is really impactful.² But this is an industry that will face some other challenges with that, because we need them to move fast, because industry and industrial organizations have been held back for quite a number of years. COVID-19 did not help around uncertainty and particularly constraints in supply chain, but there's also constraints in their strategy, what this means for their people and how they really ensure that they adopt this, leveraging IT across the board.
The most important part of AI, and it is dependent on robust data. And it is quite difficult to make sure in industrial organizations with legacy technology that they're bringing all of their supply chain into a coordinated and data-driven, actionable environment. And that means they need data that talks to each other, that is then AI-compatible. Historically, in industrial organizations, supply chains have been really complex, and therefore what we've got to try and help them do is bring them together to be able to forecast better, and make sure that the data they need to do forecasting and management of a more flexible supply chain is available and the data is digitized. Because often, they will have a combination of digital and analog data, some of which they will be very dependent on. Then, taking a proactive stance to this around things like inventory management can be reliant on the data. And if the data just isn't organized or robust enough or accessible enough, that can really stop and hinder that progress.
The other big thing in this market, particularly, is that for AI to really have meaning and impact, it's got to be adopted right the way across the board, it’s got to be consolidated, and you've got to have responsibility and advocacy from the C-suite. If you're not organizing these initiatives, which have been around for the last 10-20 years, AI isn't a new technology. It's the scale and speed at which it's able to operate now. What can have happened is that some of these solutions have been fragmented and exist in different parts of the organization. To take a really strong step forward, you got to bring it all together, make sure that it's aligned and make sure that there is a real visibility of what's going on.
We also are going to need, in this industry, a different skill set. AI needs people. It isn't the belief that AI is going to replace people just, it may come in 30-40 years, but really just at the moment what we need is the people that understand it and can deploy it. There is scarcity of talent across all sorts of areas in industrial organizations, engineering, and technology as a whole. And we've got to harness and tackle the skills shortage across all of the organization elements, and make this an attractive industry for people to want to go work in. There's a gap at the moment between the skills that are available and the skills that are needed, and that will definitely hamper industry's ability to leverage and accelerate this.
The final bit that is relevant is the infrastructure that AI is built on. And again, this can be challenging in industrial organizations because they have different types of technology that don't necessarily exist in other industries. And that means the priority has been around keeping the manufacturing organization running. So, what we want to try and see is this become a priority in the delivery of that product, so it becomes a very, important element of that technology landscape. There aren’t unlimited budgets in industrial organizations for IT. So, it is going to still be a prioritization, but making sure that we've got the right talent and the right people and the right infrastructure – are the basics that we've all got to align around.

Moderator

Thanks for that very insightful breakdown into the four key areas of supply chain strategy, IT and people. But now I’d like to share some stats and get your thoughts on how industrial leaders are incorporating AI into their respective business models. So, according to our survey, around 45% of our advanced manufacturing CEOs view AI as a positive force, and over 60% are focusing on enhancing technology capabilities and sustainability integration for new products and services in their capital allocation.¹ At the same time, 57% are investing in gross initiatives to expand their offerings. And given these findings, what do you think this implies for the industry as a whole and how countries are approaching AI?

Clark

I think what it implies is that there's choice. And in the industrial organizations that I work with and that we work with, that choice becomes about prioritization and the limitations that can be applied to that, which again relate to talent, data structures, robust, interoperable systems, so systems that can talk to each other.
Talent will be the biggest issue that a lot of organizations will face and it's not a new one in this sector. Talent shortages have been an issue for a while. And when you add technology talent into that, it just exacerbates the situation a little bit. Having a real focus on investment in talent and skills, and enhancing skills in this new technology space is definitely something that is attractive to some of these organizations. If you go to the opposite end of trying to work out how you can restructure and reorganize to be able to embrace these new technologies, there are a number of COOs that we see looking at new working models completely. So, this talent challenge that will be here for a while doesn't completely stop them from doing what they want to do. But retention and aging workforce are definitely something that is very relevant and prevalent in this industry.
We've been talking about data a lot. I mean, I've been working in this sector for over 20 years, and data has been on the periphery, if you like. It hasn't been the main purpose to drive data and certainly first-party data, because a lot of these organizations are business to business and haven't had the same approach to data that some of the consumer organizations have had. So, the adoption and the primacy of data, which is now really at the heart of some of these new technologies, the access, availability, format, and also privacy and governance of data is something that now these organizations have to really get hold of. And they're not data companies. They're not technology companies. They are manufacturing and industrial companies. So, it's a different suite of capabilities that has now got to run alongside their traditional, as I said earlier, engineering and manufacturing capabilities.
The other thing that suddenly starts to become very relevant when you start to adopt pure digital, cloud and AI technologies, is the risks around that data and the risks around your infrastructure become different, and your exposure to cyber risks can be significant indeed. So, not only are you having to invest and review the capabilities and your skills in your workforce and the technology, but you’ve also got to then run a risk profile around all of that, which is no longer just about physical risk. It's about cybersecurity risk. So, you got to bring it all together in some roadmap plan, that is relevant to your business. And make sure that your business and your business objectives are not distracted by the availability of technology, which can often be the case.
We get a bit overexcited about this technology, and we forget that, actually, what we're supposed to be doing is building cars or building aircraft or making catalysts. These are things that these organizations do. That's their primary objective. And the technology should be there to enhance the way and the effectiveness of them doing that, not replace it.

Moderator

Thank you, Alison, that makes a lot of sense. So, we’ve talked about the proliferation of AI and now some of the organizational needs require to leverage the value of AI. Can you talk about specific actions industrial leaders can take to ensure that their companies are AI-ready?

Clark

What we've tried to do is simplify this approach into five key initiatives with a recognition of this very specific industrial sector. We've set them up in order to try and help people accelerate in their – embrace in their – deployment of AI, but also that there's a balance between things that are short-, medium- and long term so that you can get some results quickly to prove the capability, but then also look to the long term. So, they're not just throwaway or pilot things that people don't buy into.
The multi-speed approach means that we can start to help these organizations build momentum, and AI then becomes part of their strategy. It becomes purposeful and, most importantly, actually has a commercial impact. And this is the first step. So, this is where you start as far as my experience in the work that we've done, is that you need to consolidate. I mentioned this earlier. You will find that there will be a lot of activity using AI technology, a lot of initiatives, some of which you won't even realize are using AI because it will be built in. And what we think you should be doing is starting to consolidate that so that you could truly measure the impact of this technology. And you really want it to have a commercial impact, either drive cost efficiency, drive speed, accuracy or indeed drive new revenue. So, we call that something like a value realization office. It just gives it this package and these edges that then mean that you can build value cases or business cases off the back of it.
And sometimes, it's quite difficult to tangibly make a return on investment if things are very fragmented. So, we want to bring it together. And we want to combine these quick wins that I talked about earlier, as well as the idea that you're gathering a capability, unlearning and upskilling as we go. We then want to move toward that being some transparently held capability, and you could call this a control tower or anything that shows that the investment is driving a change and is driving a business outcome. But what you want is a commonality of managing that and driving those across organizational initiatives to make sure that we're maximizing and optimizing the value that we're getting from this investment.
Often what I've seen in some of these cases, with some of this exciting new technology, and GenAI falls into this space particularly, is that people want to deploy it for the sake of deploying it. In my view and in my experience, what we want is for people to be using this new technology to solve their business problems, not creating or making new problems to solve with the new technology, because then we're standing still, really. So, we want to make sure that we're identifying AI impact and benefit, and a way to do that is really to set a north star or an objective. At EY, we call it future-back planning, where we say this is where we're trying to get to and we're going to break that down into a number and a series of different activities. But instead of just working from where we are and trying to project forward, we're going to set a direction of travel. What it does is open up a clear link between your technology and particularly AI, and how you allocate resources and how you prioritize against that overall plan. Now, over time, organizations evolve and hopefully get better. And so, you should gather some momentum and acceleration toward being able to do this.
This top-down future-back planning means that you can adjust and get better at things as time goes on, with the view that you're still traveling in the same direction. And actually, it means you can stop things as well as start them. And it may be that some of the skills that we identified during that process then become redundant because we've replaced them with new skills. But the real value of this is having a plan that everybody across the organization can sign up to. And the deployment of this technology becomes very, very purposeful.

Moderator

That's interesting. We've discussed the formation of the so-called value realization office and the importance of aligning AI use cases with the overall strategy of the organization. These initial steps seem to start a virtual circle to lay some groundwork for an industry or industrial company to become AI-ready. But previously, we were also talking about key challenges around talent and data. Do any of the other initiatives address these challenges?

Clark

Of course. The final step is really to bring together an ecosystem of partners. This is a complex area. It's a complex, sophisticated technology. We've said there's a shortage of skills around this. And so, it's really important that as an organization, you bring together complementary capabilities that have a maturity and experience, that can fill in some of the gaps in skills that your workforce don't necessarily have. There's a lot of technology that companies have spent years and billions of dollars in investing in this space. So, it's not necessary for all organizations to start at the beginning.
What we want to encourage is where there are accelerators or where there is out-of-the-box capability, that our clients and organizations can adopt those so that they can leap forward as quickly as possible, and bring in that ecosystem together with partners that you trust with this ability to be responsible for certain areas that align with your requirements so it isn't just a question of a group of organizations that are great at the same thing, or it's not all of the most advanced AI providers or technology in the world. What you want is a group that is complementary, fits together, and brings value to you. And that takes a little bit of time, and it's worth investing in making sure that those are the right group of partners, ultimately, to make your ecosystem stronger and more efficient.

Moderator

It's interesting to see how data strategy and workforce adaptation both play crucial roles in successful AI deployment, but also on the overall readiness of the AI organization. Now to complete the picture, you mentioned there were five initiatives. Could you please explain the remaining initiative?

Clark

What I would add to everything we've talked about so far is that the organizations, particularly in this industrial sector, well, they want to accelerate. They have to forge their own journey. Although the technology is a trend, it isn't something that you can necessarily copy and paste. This really has to be seen as an even better if technology so that you're using it to help your organizations with your own challenges and objectives. Every organization has a different path, has a different way to go to market, different set of customers, different challenges, different supply chains. So, although there's a roadmap that you can follow that we can help our clients and businesses develop, it will look different for every organization.

Moderator

Thank you for your insights, Alison. So, for our audience, it's clear that AI is a strategic move that requires a solid approach, both from creating a value realization office to tying it to strategy, to looking at data and the people skills required to building an effective AI ecosystem of partners. It's a comprehensive shift. It touches upon technology, organization and culture. But ultimately, AI will allow businesses to do more in terms of how resilient they become, how efficient they become and how predictively they can support their business. Do you have any final takeaways from us?

Clark

The most important thing is that this is a journey that's unique to the organization. Although the technology is available, and we can leverage learnings and accelerate, adopting the same solutions that some of your competitors or peers are doing may not always be the right path. As long as we stay focused, and we support our customers and our clients to stay focused on their objectives, your specific business objectives, and that means if you're making cars, you focus on that, if you're making airplanes, you focus on that. It isn't about what did somebody else do somewhere that I can then pick up and copy and paste. The other point I would make is that this is going to be a longer-term commitment. I know that we have had technology that has come up in the past, that everybody's got very excited about and hoped that it's the answer to everything, every question that we could possibly expect. This is a truly fantastic opportunity to leverage a new suite of tools, but do it with value and outcome in mind so that you stay focused with your business objectives at the forefront.

Moderator

I couldn’t agree more. Alison, this has been a fascinating discussion. How can our listeners find out more about this topic?

Clark

We've got some work that we've done and an article that is on ey.com called “How can AI add more value for industrials?” So, I'd love people to go and read the article, and visit ey.com. There's a lot more information and insights on all of the points that we've discussed in some detail around the five initiatives we talked about. And as you can probably tell, I enjoy talking about this topic. So please, I'm happy for further discussions for anyone to reach out and can have further conversations on this as appropriate.

Moderator

Thank you for joining us today and for this interesting discussion on this so relevant topic nowadays.

Clark

Thanks for having me.
Thanks for listening to today's EY Advanced Manufacturing and Mobility Business podcast. We hope you found it engaging and informative. To listen to other business minute podcasts, you can find them at ey.com/ammpodcasts.

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Presenters

Alison Clark

EY UK Advanced Manufacturing and Mobility Leader

Podcast

Episode 32

Duration

20m 52s

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EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.

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How AI will transform industrials

Key takeaways:

Moderator

Alison Clark

Moderator

Clark

Moderator

Clark

Moderator

Clark

Moderator

Clark

Moderator

Clark

Moderator

Clark

Moderator

Clark

Presenters

Alison Clark

In this series

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