As the buzz surrounding generative AI (gen AI) in manufacturing transitions from excitement to a more measured and pragmatic evaluation, manufacturers are increasingly looking at how to implement these technologies to maximize business value. Investments in AI and machine learning are expected to continue growing in 2025, but with a focus on high-return, strategically targeted applications that directly impact efficiency and productivity.
The Growing Role of Generative AI in Manufacturing
In 2025, AI and generative AI will be integral to the future of manufacturing, but the shift is moving from broad experimentation to a more calculated approach. As manufacturers look to increase efficiency, reduce costs, and drive innovation, AI adoption will be more strategic, with a primary focus on ROI. According to Deloitte’s 2024 survey, 55% of industrial product manufacturers are already using generative AI, and more than 40% plan to increase investments in the next three years. However, manufacturers are increasingly aligning their AI initiatives with broader digital transformation strategies to ensure they meet specific business objectives.
The Data Foundation: A Prerequisite for Successful AI Adoption
For AI to be effective, manufacturers need quality data. Unfortunately, data challenges, including issues with data quality and contextualization, continue to be significant barriers to AI adoption. However, a growing focus on data lifecycle management will help address these challenges. A 2024 Deloitte survey revealed that three-quarters of manufacturers are increasing their investment in data management to support generative AI strategies. By focusing on areas where a solid data foundation already exists, such as customer service or product design, manufacturers can begin to see measurable benefits without needing to overhaul their entire data infrastructure.
AI in Customer Service: Enhancing the Buying and Support Journey
One of the most immediate areas where generative AI is making an impact in manufacturing is in customer service applications. These systems are often based on digital, language-rich data like call records, technical documents, warranty claims, and product manuals, which don’t require extensive data modernization. As customer expectations shift, 74% of manufacturers in Deloitte’s 2024 survey indicated that they are already leveraging or planning to use gen AI to improve customer experience. Applications include virtual chatbots to guide customers through product features or AI-based service manuals that combine augmented reality for remote assistance in maintenance. These tools offer manufacturers a high ROI by enhancing customer interactions, improving efficiency, and reducing operational costs.
Accelerating Product Innovation with AI
Another promising application of generative AI is in product design and innovation. Manufacturers can leverage AI to mine historical design data and engineering archives for new product opportunities, identifying potential improvements on existing products. By 2028, it’s estimated that 50% of large manufacturers will use AI to evaluate engineering archives and find new ways to innovate. This approach allows manufacturers to make the most out of legacy products while accelerating the development of new solutions. Connecting these use cases to critical business priorities, such as customer experience or product innovation, can also help secure the necessary internal support and funding for AI initiatives.
Targeted AI Investments for 2025: Building the Right Strategy
In 2025, manufacturers must be strategic about AI investments, focusing on areas that offer clear, high-ROI opportunities. For example, generative AI can improve efficiency, reduce costs, and increase productivity—key priorities for manufacturers dealing with economic uncertainty. According to recent surveys, AI and machine learning technologies have the most significant impact on business outcomes, with gen AI delivering the largest ROI after cloud and SaaS technologies. However, to maximize these benefits, manufacturers need a clear AI and data strategy that includes governance, risk management, and an operating model to ensure that AI is being used effectively across the organization. Despite the benefits, a 2024 survey found that only 51.6% of manufacturers have a corporate AI strategy in place, indicating a gap in preparedness for AI adoption.
The Importance of Data Organization for Long-Term AI Success
A key to unlocking the full potential of generative AI in manufacturing is a well-structured data foundation. Manufacturers must prioritize organizing and managing data to create a seamless environment for AI deployment. By improving data accuracy and accessibility, companies will ensure they are ready for long-term investments in AI technologies. The future success of AI in manufacturing will depend not just on technology itself, but also on how well data is integrated and managed across the organization.
Conclusion
As manufacturers gear up for 2025, the focus will be on prioritizing AI investments that drive tangible business outcomes. From enhancing customer service with AI-driven support tools to accelerating product innovation and improving operational efficiency, generative AI offers manufacturers high-ROI opportunities. By aligning AI initiatives with broader business strategies and investing in data lifecycle management, manufacturers can ensure that their AI projects are successful and sustainable, setting the stage for future growth and innovation.
