EY’s AI Solutions for Industrial Manufacturing Leaders

In 2024, 41% of advanced manufacturing and mobility companies say that they aim to incorporate AI-driven products or services in their capital allocation processes. This is according to EY’s CEO Outlook Survey, from 2023. 

Alison Clark is Advanced Manufacturing & Mobility Leader for EY in the UK. She studied Business Administration and Marketing at Wolverhampton University, followed by an IBM Partner MBA at Harvard Business School. For 25 years, she has grown her understanding of digital innovations and data, to support customer engagement and assist employees moving from analog to digital.

Previously, Alison spoke with our sister title, Supply Chain Digital, to discuss the impact of AI on supply chains. Alison shared that empowered IT teams in the supply chain sector enable faster experimentation and more successful AI deployments.

“This seems simple enough, but it’s not, as it involves breaking organisational silos, overhauling legacy IT architectures and helping people navigate this change successfully,” she said. 

We caught up with Alison to discuss industrial manufacturing and how to get started on your AI journey today, amid all the uncertainty. 

Why manufacturing leaders encounter challenges when integrating AI

According to Alison, the reason industrial manufacturing leaders are increasingly facing problems when attempting to successfully integrate AI into their supply chain, talent and IT strategies, is down to three reasons: Legacy silos, structures and incentives.

“In the next 12 months, 90% of manufacturing companies will have fully integrated AI-driven product or service changes into their capital allocation process and are actively investing in AI-driven innovation,” says Alison.  “By 2030, 96% of companies are expected to increase manufacturing AI investment, and beyond 2030, 59% of industrial leaders see AI as significant or game-changing for the future of manufacturing.” 

Yet, 65% see data issues, such as access, format, integration, privacy and governance, as their main challenge for AI adoption in their companies. 

“There is also, perhaps, a higher level of cynicism in some divisions of manufacturing businesses; that this is just another tech fad,” she continues. “An ageing workforce may also believe that the real impact of AI will not have come to fruition in their professional lifetimes, and so, they’re disinterested in truly leaning into the wider opportunity.”

The technology to solve these data issues has been available for many years, but hasn’t been prioritised until it has a material impact on operations or market share. 

“This attitude towards data infrastructure investments, that have an opaque ROI in the short term but enable a step change in innovation in the long term, fits the profile of how decision making takes place within industrial companies,” says Alison. “A culture of careful continuous innovation elevated leaders that are more likely to maintain that status quo. At the same time stakeholders want to see innovation but, more often than not, are not willing to accept the risks and investments for the necessary experimentation to happen.”

For AI deployment, Alison argues that there is a big difference between developing one AI use case and having the right foundational capabilities and culture for innovation to flourish. 

“If that foundational base is not prioritised and embraced, companies will struggle to compete against those who benefit from AI driven innovation,” she adds. 

How AI can unlock value, according to EY’s latest report

EY’s latest paper ‘How can AI unlock value for the industrials’ details the challenges industrial companies face when deploying AI. 

Results showed that: 

• 45% of advanced manufacturing CEOs surveyed said that they believe AI is a force for good, which can have a good impact on business efficiency. 
• 60% of manufacturing leaders said that they are prioritising enhancing technology capabilities, sustainability integration and introducing new products and services in their capital allocation strategy. 
• 57% of manufacturing leaders admitted to investing in organic growth initiatives and M&A to improve technology, as well as expand their product or services.

It also offers a group of initiatives to start deploying AI in a way that addresses those challenges.

“First, we started by understanding the challenges to deployment,” says Alison. “Only by recognising the specific issues of a sector can we propose an effective solution.”

For industrial companies EY found the dimensions of differentiation to be around the supply chain, strategy, people and IT. 

“The crux of the challenges across these four dimensions has to do with manufacturers often having rigid structures, organisationally and of incentives, that leads the decision path to become the one with less risks to finance and operations in the short term,” Alison adds. “The consequence is holding off forward looking investments, with less transparent immediate ROI, until it may be too late.”

Secondly, EY suggested five initiatives capable of addressing and solving the challenges to deployment identified. The initiatives have two timelines: ‘Act now’ and ‘Evolve later’. 

"For ‘Act now’ recommendations demand little investment, cause minimal disruption, are flexible and are projected to show tangible outcomes in the near-term,” she says. “As momentum builds, these ‘Act now’ advisories subsequently develop into ‘Evolve later’ initiatives. In ‘Evolve later’, the requirements call for a top-down strategic coordination and capital allocation to grow the organisation’s AI and technology maturity.”

From Alison's point of view, most of the advice available out there seems to be geared towards high tech companies, but most manufacturing companies don’t fit that profile.

“Ultimately, we want to show industrial companies that there are ‘no-regret’ ways to get started on their AI journey today amidst all the uncertainty, ways that don’t require a step change and that continuously develop their AI/digital maturity in the right direction,” Alison shares.

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