Generative AI (Gen AI) holds unprecedented promise and potential for companies in industries like manufacturing, offering immense value like optimizing production lines, reducing waste, improving supply chain logistics, and more. McKinsey research indicates that Gen AI applications stand to add up to $4.4 trillion to the global economy annually. However, for those interested in adopting this technology, it’s imperative to understand the both the benefits and drawbacks of Gen AI in smart manufacturing.

In this interview, we catch up with Jeff Winter, Industry Strategy Leader for manufacturing for Hitachi Solutions, and Smart Manufacturing Advisor at CESMII, to learn more about how organisations can systematically evaluate processes and operations to pinpoint where Gen AI can create the most impact. Read as he shares valuable insights into emerging trends and potential disruptions within smart manufacturing, along with a comprehensive perspective on harnessing the full potential of Gen AI for sustainable growth and innovation.

Maryam Irfan, IQPC: Can you tell us a little bit about yourself and the work that you do?

Jeff Winter: I am the Senior Director of Industry Strategy for Manufacturing at Hitachi Solutions. Essentially, my role involves guiding Hitachi towards becoming a best-in-class digital transformation provider. To do this successfully, I stay involved with all existing and new industry developments, and actively participate in numerous industry associations, academic groups, advisory panels, standards bodies, and research teams. This allows me to stay abreast of the latest advancements and share valuable insights with others.

Maryam Irfan, IQPC: How do you envision Gen AI contributing to the evolution of industry 4.0 and digital transformation, especially in comparison to other technologies?

Jeff Winter: Gen AI is a critical part of what we refer to as Industry 4.0 and represents an integral aspect of the ongoing digital transformation journey for manufacturers. It helps automate and simplify complex tasks, to help enhance decision-making processes, collaboration, and innovation. Unlike traditional AI systems, which mainly analyze and classify data, Gen AI creates new datasets, designs, and algorithms, simulating human-like creativity and reasoning.

From a people perspective in smart manufacturing, Gen AI has the potential to revolutionize how workers engage with company systems, transforming how they access and input information. From a process perspective, it can automate design processes, generate optimal models for machinery parts, and reduce time and costs associated with various tasks. What sets Gen AI apart from other Industry 4.0 technologies are its simplicity and applicability.

From a simplicity standpoint, things like ChatGPT have brought Gen AI to the forefront of accessibility. With minimal to no training required and zero investment, anyone can utilize this technology, making it easily adoptable. In contrast, technologies like blockchain are extremely disruptive but often require extensive time and financial investment to implement effectively. With Gen AI, if your company already has good data available, you can begin implementing GPT models within hours, not years, making it exceptionally quick to integrate into existing systems.

In terms of applicability, Gen AI has the potential to impact every function across industries, from the shop floor to the CEO's office. Few technologies possess such versatility, and this is primarily due to its nature as a tool meant for generating solutions.

CHECK OUT: Jeff's Thoughts on The Connected Worker Summit Series

Maryam Irfan, IQPC: How can organisations systematically evaluate processes and operations to pinpoint where generative AI can provide the most value?

Jeff Winter: Organizations can systematically evaluate processes for AI integration using a fairly basic framework. First, you need to conduct a thorough audit of existing processes to pinpoint areas with high potential for AI impact. Consider industry-specific applications that align with strategic goals and have high ROI projections.

Second, involve cross-functional teams in discussions to identify what I like to call the "Golden Use Case" – applications where AI can provide a real competitive advantage. For example, healthcare firms may focus on personalized treatment algorithms, while retail businesses might prioritize customer experience personalization, and manufacturing could concentrate on customer service, field service, or maintenance technician support.

Third, conduct feasibility studies for these use cases, with a focus on data availability, normalization, contextualization, and indexing. Also, consider operational readiness; consult AI experts to assess the complexity and scalability of potential solutions.

Fourth, use a scoring matrix to prioritize identified AI opportunities based on criteria like feasibility, strategic alignment, ROI, and competitive differentiation potential. This prevents analysis paralysis when faced with numerous potential use cases.

Fifth, conduct pilot projects for the top-scoring use cases, measuring performance against a set of KPIs and comparing results with traditional methods. Finally, based on the pilot results, plan for full-scale implementation of successful use cases, constantly monitoring and refining AI models as you learn more.

Maryam Irfan, IQPC: Have you seen any successes so far and what key factors contributed to that success?

Jeff Winter: Since the technology is relatively new, there aren't as many full production case studies or use cases available as the world would like. However, many companies have successful pilots that are now transitioning to full scale.

For example, CarMax, the used car retailer, utilized the power of Azure, OpenAI, and ChatGPT to revolutionize their customer experience and content generation process. They used Gen AI to streamline content generation on their car research pages, which not only boosted SEO but also improved the overall customer experience.

Initially, they aimed to summarize customer reviews for approximately 5,000 car pages - a task which would have taken 11 years with their current staff. With Gen AI, CarMax achieved this goal in just a couple of months with an impressive 80% editorial approval rate for the content created by the AI model. This strategic move earned them a spot in the 2023 CIO 100 awards list, showcasing an innovative use of AI.

Maryam Irfan, IQPC: How do you see the role of Gen AI evolving in the next few years and what trends should businesses watch out for?

Jeff Winter: In the next few years, Gen AI is poised to significantly reshape the labor landscape across most sectors. The McKinsey Global Institute report indicates that Gen AI will lead to an increase in automation adoption by approximately 15-40% by 2030, significantly altering labor demand and associated work activities. Roles previously considered automation-resistant can now be impacted, highlighting the dynamic nature of this technology.

Businesses should prepare for a shift in labor demand and consider reskilling programs, especially focusing on social, emotional, and technological skills. Companies in sectors with high physical labor, such as various parts of manufacturing, need to rethink their workforce strategies. Given the dynamic nature of Gen AI, businesses should maintain agility to adapt to rapid technological changes. Consider how disruptive ChatGPT has been, despite being available for just a little over a year.

Once companies start rolling out their own internal versions of ChatGPT, let's call it ‘ChatPlus’, linked directly to their real-time data, consumers would likely prefer interfacing with the company's ChatGPT model over a person, as it would be faster and better at answering any questions about the company or its products. This effect will be even more profound when natural language processing gets fully integrated. That will be a game changer in the way that we interact with companies.

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