Will Artificial Intelligence (AI) redefine manufacturing, or is it just another tech buzzword? At Hannover Messe 2025, ABI Research analysts saw AI and Generative Artificial Intelligence (Gen AI) on full display. Practical applications are demonstrating efficiency gains, robust autonomous capabilities, and operational resilience. Their new cousin, Agentic AI, has also emerged as a key facilitator for digitally transforming the factory floor. Agentic AI is the brains behind fine-tuned copilots that enhance anything from inventory management to industry-specific Product Lifecycle Management (PLM). However, manufacturers remain skeptical about Agentic AI, and vital infrastructure considerations are still in discussion.
From predictive analytics to self-managing workflows, nearly all technology vendors are integrating AI-based technologies for improved manufacturing outcomes. As ABI Research’s Hannover Messe 2025: Sustainable Industry Needs a Sustainable Business Model whitepaper reveals, the manufacturing sector is shifting from lofty AI promises to real-world implementations. This post dives into how AI, Gen AI, and Agentic AI deliver tangible outcomes, the adoption hurdles they face, and how to choose the right technology supplier to support smart manufacturing aspirations.
Practical AI Applications for Manufacturing: From Data to Decisions
AI is transforming manufacturing by turning raw data into smarter decisions. Gen AI tools for predictive analytics, root cause analysis, and quality inspection are boosting throughput and slashing defects. Siemens is leading in this space with smaller AI models tailored for specific tasks, such as autonomous quality checks and ensuring precision without heavy compute demands. “A key, according to Siemens, is designing smaller AI models that execute specific functions and interact with each other autonomously,” says Senior Research Director Ryan Martin.
Amazon’s Q platform takes a different AI approach than Siemens. It enables workers to create custom apps without coding. For example, Amazon showed us how the Q platform can pull insights from financial filings to guide production shifts. While this may be a limited use case, it demonstrates the potential for industrial AI to scale across other manufacturing areas.
These solutions reflect Hannover Messe’s focus on business outcomes over environmental goals, prioritizing measurable gains like cost savings and efficiency. For smart factories, AI’s power lies in making data actionable, paving the way for leaner operations.
Gen AI and Agentic AI Are the Next Frontier, but Questions Remain
Gen AI and the cutting-edge variant, Agentic AI, are pushing manufacturing toward true autonomy. Gen AI amplifies human input into economic wins, automating tasks like engineering and supply chain optimization. Again, Siemens provides a standout example of AI innovation in the manufacturing sector. Its Industrial Copilot streamlines workflows in the TIA Portal, helping engineers work faster and smarter.
Agentic AI takes things a step further with specialized copilots for high-value tasks. “Specialized AI agents, such as copilots for operations, supply chain, and industry-specific PLM, will enable manufacturers to pick and choose which copilots to deploy based on high-value use cases,” notes Industry Analyst James Iversen. These lean models cut compute costs and energy use, aligning with sustainable practices.
Yet, adoption lags behind as vendors need more case studies to win over skeptical manufacturers. Gen AI and Agentic AI hold immense potential, but trust hinges on proven results. A hindrance to Agentic AI implementations, as noted by Principal Analyst Leo Gerg, is the uncertainty around how it will be deployed. Through various discussions with Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft, and other hyperscalers, Gergs identified a common theme: nobody is absolutely sure where agent orchestration will happen. Will it take place in the public cloud? At the edge? On-premises? There must be clarity regarding Agentic AI deployment and training location before its adoption proliferates across the manufacturing space.
Top Challenges in AI Adoption for Manufacturers
Rolling out innovative AI technologies isn’t without its roadblocks. For example, manufacturers must first tackle data maturity, as “it is an iterative process from digitizing data to running operations based on Agentic AI,” explains Distinguished Analyst Michael Larner. Siloed systems hinder manufacturers’ ability to collect and organize data, stalling the deployment of advanced analytics.
Corporate cultural rifts further add complexity to AI implementation—mandates from the boardroom don’t always align with shop floor priorities. “Many factories are considered siloed organizations and decide on their investment priorities unilaterally,” Larner adds. He stresses the need for executives and facility managers to be on the same page when dictating AI investments. A successful alignment of ideas and strategies will necessitate deft change management skills.
Hannover Messe also underscored the need for Information Technology (IT)/Operational Technology (OT) convergence to make AI tools effective. Without IT/OT convergence, even cutting-edge solutions risk falling short of the production and quality Key Performance Indicators (KPIs) manufacturers aim to meet. Industry Analyst James Prestwood noted the importance of Unified Name Space (UNS) in making this happen. “UNS is a hot topic, and there is a unanimous understanding that convergence would be fundamental to actually leveraging AI, Gen AI, and analytics capabilities in the most impactful way. Prestwood continues, “While not a new topic by any means, demonstrating how Manufacturing Execution System (MES)/Manufacturing Operations Management (MOM) offerings support this data convergence and usability was a core element of vendors’ messaging this year. “
Choosing the Right AI Partner: What to Look For
Selecting the right technology supplier is essential for harnessing and using AI to its full potential in manufacturing. Hannover Messe 2025 spotlighted a shift to practical solutions that tackle real factory challenges—data silos, complex workflows, and global pressures. The right partner must empower predictive analytics, support autonomous Agentic AI workflows, and navigate data governance, aligning with the need for actionable data, operational efficiency, and digital security. Here’s what manufacturers should prioritize when choosing AI partners:
- Scalable Cloud Solutions: Seek technology vendors that are offering flexible, cloud-based platforms to streamline AI deployment. For instance, the AWS-Rockwell collaboration delivers Software-as-a-Service (SaaS) tools such as DataMosaix and Flix, enabling effective scaling and flexibility of deployment for real-time insights. These scalable platforms enable manufacturers to introduce new AI applications at the pace they want to.
- Targeted AI Innovation: Choose suppliers with specialized AI models for manufacturing tasks, such as predictive maintenance, quality control, or supply chain optimization. Outcome-based solutions maximize immediate impact without overtaxing resources.
- Data Sovereignty Expertise: Prioritize partners tackling geopolitical data concerns, as data control, accessibility, and governance have been a pillar of AI infrastructure planning. Sovereign cloud providers like GCP and IONOS ensure regulatory compliance and protect proprietary data, essential when training Large Language Models (LLMs).
AI, Gen AI, and Agentic AI are redefining the manufacturing industry. These advanced tools drive efficiency, improve product quality, and boost throughput, while minimizing the need for human intervention. To uncover how top technology companies are fueling this digital transformation, download ABI Research’s free whitepaper, Hannover Messe 2025: Sustainable Industry Needs A Sustainable Business Model. In addition to identifying the most advanced AI use cases in manufacturing, the paper evaluates the growing role of Software-Defined Automation (SDA), the latest developments in circular technologies, and must-know wireless connectivity advancements.
