EY Report Shows AI Being Adopted in Advanced Manufacturing
A report by EY has highlighted the degree to which AI technologies are being rapidly adopted by industrial sectors, as manufacturers utilise traditional AI, like machine learning (ML) in operations technology (OT) to streamline operations. Almost half of mobility and advanced manufacturing companies have fully integrated AI-driven service or product changes into their capital allocation process. The same percentage is actively investing in AI-driven automation.
When it comes to manufacturers and their investment in AI, the numbers don’t lie:
- Around 45% of advanced manufacturing CEOs surveyed believe AI is a force for good that can have a positive impact on business efficiency and innovation.
- 59% believe AI will be significant for manufacturing’s future beyond 2030.
- 57% of manufacturing leaders pursue investment in organic growth initiatives and M&A to improve technology and expand their product and service offerings.
- Over 60% of manufacturing leaders are prioritising enhancing technology capabilities, sustainability integration, and introducing new products and services in their capital allocation strategy.
- By 2030, 96% of companies are expected to increase manufacturing AI investment.
Integrating AI has distinctive challenges and roadblocks. Some of the biggest revolve around organisational infrastructure and mindsets, particularly in the manufacturing sector, as AI requires a dramatic overhaul of traditional skills and working methods.
“Many big companies lack the knowledge base or agility to create and scale generative A.I. solutions on their own," said Carmine Di Sibio, EY’s Global Chairman and CEO. “Entrepreneurs are well placed to help–bringing agility, innovation, and specialised knowledge and enabling enterprises to leverage generative A.I. in a way that aligns with their business objectives.”
De Sibio and EY recognise this, which is why they’ve included in their report a five-initiative approach to make manufacturing ‘AI-ready’. Here’s our breakdown.
EY’s five initiatives to make manufacturing ‘AI-ready’
05: Initiative: Create a data architecture assessment and upgraded roadmap
Manufacturers should begin their relationship with AI by creating a cohesive data strategy. Traditional AI and GenAI have different data forms that must be handled coherently. This data can then inform the OT-focused point solutions manufacturers deploy, allowing them to execute a plan with phased upgrades according to feasibility, impact and ROI
The next step is a ‘ knowledge graph’. This means training large language models (LLMs) on operating procedures and best practices, codifying information that typically resides only in employees' heads. Data architecture should also be used to evaluate AI, with specific attention paid to process design, dependencies, security and data quality. Governance review is needed to address AI legacy risks like bias, data privacy and cybersecurity.
04: Initiative: Establish an “AI value realisation office” and evolve into a control tower
Manufacturers need a unit that streamlines resources and experimentation around AI and ties it to business outcomes, realising benefits, conducting risk management and optimising resources.
This unit is a value realisation office that must evolve into what EY calls a ‘fully-fledged control tower’ where decision-making and oversight concerning AI come from the top of organisations.
The control tower is a C-suite business unit, tasked with steering cross-organisational initiatives and AI strategy. It has the authority to coordinate resources across different business functions and orchestrate reskilling needs and ecosystem strategies
Leveraging easier-to-use technologies like GenAI and low-code or no-code can lower technical barriers to experimentation, enabling non-technical people to participate earlier. All of which increases buy-in, develops expertise, improves agility and enables activities that create value.
03: Initiative: Develop AI ecosystem partnerships
With AI partnerships, like any, it’s critical to vet partners, set performance standards and manage partnership costs.
AI solutions also require adaptivity and connection to central systems. Partners add integration and management costs, impacting the orchestration of the technology stack. If AI partners underperform it can cause greater harm than traditional partners.
Companies must compare AI capabilities, maturity and ecosystems against emerging best practices when choosing partners for AI projects. A great way to build experience for larger AI projects is by establishing early partnerships with multiple entities to embark on small pilot project opportunities.
02: Initiative: Explore future scenarios to align the approach to AI
Manufacturers often struggle to align AI use cases with overall strategy and vision. They can more effectively prioritise initiatives and allocate resources by identifying scenarios for AI impacts.
Future-back planning will help manufacturers identify the business impact of AI, exploring regulatory, macroeconomic and resource constraints, linking AI activities to business value.
Transitioning the value realisation office into a control tower will help link the top-down scenario planning and capital allocation.
01: Initiative: Develop a workforce reskilling plan
Workers need to reskill to gain AI proficiency and enhance competencies that will grow more valuable with the rise of AI, like content integration, quality assurance and customer engagement
Assessment is needed of what tasks AI will take over and what worker competencies are required, especially for blue-collar work where this has been less explored. The solution is creating a continuous learning culture that empowers employees to adapt to constantly changing skills needs.
AI’s benefits to advanced manufacturing depend upon organisations engaging in foundational work. AI isn’t just a technology upgrade, it’s a cultural and organisational upgrade, one that companies with the EY report can begin embarking on.
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