Dataiku is dedicated to leveraging the latest industry insights to help our clients stay competitive. This blog post explores how current trends in the economy, workforce, and technology may shape the manufacturing sector's approach to analytics and AI in 2025 and beyond.
Setting the Stage: Economic and Geopolitical Landscape
The manufacturing sector is set to face a multifaceted economic landscape in 2025. Reports from organizations such as the World Economic Forum and the European Central Bank, forecast that global economic recovery will likely remain uneven. While many regions may experience a gradual economic upswing due to easing inflation and interest rates, manufacturing in Europe and North America is expected to remain under pressure from rising energy costs and shifting supply chain dynamics.
AI technologies offer manufacturers a powerful means to navigate these challenges. In an environment where competition is high, AI's role will likely need to continue to extend beyond operational efficiency, helping manufacturers maintain a strategic edge by adapting swiftly to market conditions. To ensure sustainable scaling of AI within companies it will become increasingly important to practice reuse and capitalization of existing use cases, in order to keep costs in control while still reaping the benefits of the technology.
Workforce Trends and AI's Potential
Shifting demographics, labor shortages, and evolving skill requirements continue to reshape the manufacturing workforce. With many skilled manufacturing employees retiring and fewer young workers entering the field, the sector faces a significant skills gap. Studies from the U.S. Bureau of Labor Statistics and the OECD highlight these demographic shifts, pointing to the need for upskilling and innovative approaches to workforce development.
As a response, 2025 will likely see a continued increase of citizen data scientists at manufacturing companies (or rather more an enablement of domain experts) as AutoML and low-code applications continue to advance. AI democratization empowers non-data experts to engage in data-driven problem-solving without extensive technical training. This trend enables manufacturing professionals — such as process engineers and quality control specialists — to identify bottlenecks, monitor production quality, and proactively improve workflows through user-friendly AI tools. AI-powered training programs and interfaces will also surely continue to support these efforts by helping employees transition into new roles within an increasingly digital workspace, as stated by the World Economic Forum in their 2024 Global Risks Report.
Moreover, the transition to Industry 5.0, which is set to accelerate in the coming year, places industry employees at the center of a collaborative approach to AI. As defined by the European Commission, in this framework, human expertise is complemented by AI, creating a working environment that leverages both machine efficiency and human insight to tackle complex manufacturing challenges. Industry 5.0 aligns AI advancements with human-centric processes, ultimately creating more resilient operations that can better respond to global workforce trends.
Technological Trends in AI for Manufacturing
AI technology is evolving rapidly, offering existing and new applications that can drive manufacturing forward in 2025.
1. Selected Use Cases Begin to Move Beyond Pilots
Generative AI for Product Design: Generative AI enables R&D and design teams to accelerate product design improvements for performance, sustainability, and customization. This is especially beneficial for high-value industries like automotive and aerospace, where custom designs and lightweight materials are in demand.
Generative AI streamlines design processes, reduces time to market, and minimizes the need for physical prototypes by generating and simulating various design iterations. In 2025, we may begin to see this use case overtake established Generative AI use cases such as conversational AI (chatbots) and content creation, generating even greater ROI, as suggested by a recent survey by MIT Technology Review and Microsoft.
AI-Enhanced Digital Twins: The adoption of digital twins has been transforming manufacturing over the past decade by providing a virtual environment to test, optimize, and monitor physical assets in real time. Digital twins also provide a comprehensive data foundation that companies can use for further AI use cases. A new development expected to pick up momentum in the coming year is the direct integration of digital twins with AI methods such as reinforcement learning and Generative AI.
In addition to enhancing simulation capabilities, AI-driven digital twins enable predictive insights by continually learning from real-time data, allowing manufacturers to anticipate potential failures or optimize operations dynamically. AI enhancement is anticipated to create a proactive decision-making environment, where digital twins can autonomously adjust to changing conditions and fine-tune processes with minimal human intervention.
2. Widely Adopted Use Cases Continue to Provide Value
Operational Efficiency and Quality Control: AI-enabled operational efficiency use cases continue to gain traction, helping manufacturers improve sustainability by reducing waste and energy consumption. AI applications around quality control, predictive maintenance and production parameter optimization have become integral to reducing downtime and ensuring consistent product quality. These well-known "classical" AI manufacturing use cases are becoming more and more integrated into daily operations among competitive companies, as they seek to balance high-quality output with cost efficiency, as highlighted in a recent industry study by Bain.
3. Fringe Use Cases Yet to Reach Maturity
Challenges in Integrating Internet of Things (IoT), Edge Computing, and AI: Although IoT, edge computing, and AI have each reached a high level of technical maturity, their combined application in manufacturing is still in its early stages. Integrating these technologies into a unified system remains a complex challenge, hindered by factors such as compatibility issues, high infrastructure costs, and cybersecurity concerns as highlighted at the recent leading IoT conference Embedded World.
While early adopters are beginning to see the benefits of this integration, broader implementation will require further advances in interoperability and cost-effective solutions. Once these integration challenges are addressed, the combined benefits of IoT, edge, and AI could unlock new levels of real-time data processing and analytics, setting the stage for highly autonomous and resilient manufacturing environments.
Similarly to the approaching trend of integration of the above mentioned technologies, large manufacturing organizations are already today facing challenges in managing complex and hybrid infrastructures. Here, orchestration tools such as the unified monitoring capabilities of Dataiku are becoming increasingly valuable.
Continuous AI Progression
Continuing the trend of recent years, in 2025 leveraging AI, including Generative AI, will be a greater necessity for manufacturing companies seeking to remain competitive. From adapting to economic pressures to bridging workforce gaps and integrating advanced technologies, AI will play a central role in shaping the future of manufacturing.
Dataiku offers manufacturing companies the tools to democratize AI and empower employees across all levels, enabling them to drive meaningful improvements in efficiency, quality, and responsiveness. For manufacturers ready to embrace the future, Dataiku provides the foundation to make AI a core asset in a dynamic and challenging industry landscape.