Agentic AI: Driving Autonomy In Asset-Intensive Industries

Official government figures are less dire, with the Bureau of Labor Statistics reporting that the median tenure of a manufacturing worker dropped from 5.9 years in 2014 to 4.9 years a decade later. But regardless, manufacturing workers are coming to work each day with a lot less expertise than those who worked similar jobs decades ago. As the AI hype cycle cools and business pressure mounts, now is the time to become practical.

In other words, organisations will likely be at least two years behind the competition, at risk of losing money on unnecessary asset replacements, and possibly suffering more frequent unplanned downtime and wasted energy. All this while falling further behind on productivity and sustainability – not something many businesses want to be seen doing in 2025. As AI solutions gain traction in manufacturing, they signal a broader movement—one in which intelligent tools amplify human expertise, making specialized knowledge more accessible and operations more efficient. From automating quality control and predicting equipment failure to optimizing supply chains and enabling intelligent surface preparation through these tools, AI is ushering in a new era of precision, efficiency, and adaptability. As manufacturers adopt AI-driven systems to optimize operations and reduce downtime, these technologies are becoming central to the evolution of Industry 4.0, where data and automation reshape how products are made and maintained. This guide by 10X Engineered Materials covers use cases, benefits and the future of AI in manufacturing.

How AI is improving quality control and reducing defects

The result is more uniform surface finishes and minimized abrasive use—two critical factors for quality and cost efficiency in aerospace, automotive, and heavy equipment maintenance industries. In abrasive blasting operations, where components like nozzles, hoses, and compressors degrade with use, predictive models can anticipate when performance thresholds are nearing critical limits. This knowledge can reduce system downtime by 35%, lower maintenance costs by 28%, and provide an additional layer of worker safety.

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• By integrating machine learning algorithms and vision systems into operations, companies can unlock new levels of precision and efficiency previously unattainable.
• Danone has a partnership with the Singapore Economic Development Board (EDB), with a joint initiative around ‘digital health’ since 2018.
• But regardless, manufacturing workers are coming to work each day with a lot less expertise than those who worked similar jobs decades ago.
• IT leaders should update their data and AI governance frameworks to cover field operations use cases that require sharing sensitive data with third-party LLMs and AI agents.
• As AI integrates into machines and control panels, workers will learn to operate and troubleshoot systems using new interfaces like augmented reality, natural learning commands, and AI-driven human-machine interfaces.
• Even when SaaS platforms announce agentic experiences, data teams should evaluate whether data volume and quality on the platform are sufficient to support the AI models.

Using large language models and machine learning, GenAI platforms moved beyond the responses of chatbots to evolve and grow (“learn”) from their interactions with people. However, despite the apparent benefits, many companies still resist adopting these “no-brainer” solutions. Instead, they continue using spreadsheets that merely “tick the box” or they replace assets on a fixed schedule or after breakdowns, when IoT based tracking would be more effective. This is costing them dearly – in downtime, energy waste, and missed opportunities for improvement. A year ago, Danone signed with US software giant Microsoft to “explore” a multi-year collaboration to integrate AI throughout its operations. Ask about how AI can provide value, then learn how to present the concepts without drumming up fears of job loss.

For example, take a service provider like a facility management or catering company managing critical assets such as refrigerators and freezers in hospitals or catering environments. Additional review is needed when there are data privacy and regulatory concerns. IT leaders should update their data and AI governance frameworks to cover field operations use cases that require sharing sensitive data with third-party LLMs and AI agents. Some field operations perform a set of common tasks at different locations and must adapt to local conditions and requirements. For field operations that perform a wider variety of work types in highly differentiated conditions, AI agentic experiences partner with field engineers to provide real-time information and guidance.

Agentic AI: Your Personal Assistant

• And there’s no doubt that the organisations that will truly thrive in the coming years are not the ones waiting for perfect conditions or more case studies.
• Before the generative AI boom kicked off in late 2022, Siemens had more than 1,500 AI experts on staff.
• Managing these assets and ensuring their functionality or industry compliance is still, in many cases, tracked using manual processes – relying on spreadsheets, which are time-consuming and prone to error.
• This level of understanding allows them to identify subtle process shifts and nonlinear relationships that SPC might overlook.

Focus on projects with clear, tangible metrics and ROI to assess the benefits. As with all AI projects, ensuring the system has plentiful, clean data is necessary. Governance and standards must be in place to ensure boundaries around AI use.

Consider a high-throughput line where abrasive blasting is one of several value-added steps. AI can optimize upstream and downstream processes to synchronize with blasting throughput, ensuring balanced workloads and minimal idle time. It can also correlate variables such as abrasive type, pressure, and humidity to final finish quality, recommending adjustments to maximize yield. Instead, it extends their abilities through autonomous, guided actions.

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AI-based systems go further by continuously learning from live process inputs and adapting thresholds dynamically. This level of understanding allows them to identify subtle process shifts and nonlinear relationships that SPC might overlook. Artificial intelligence (AI) is no longer a future-facing concept in manufacturing. It is a present-day force accelerating productivity, precision, and decision-making across the industrial sector.

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Before the influence of generative AI, the company had an annual productivity gain target of 3% to 4% across the business. But Koerte says with AI, the hope is to double that aspirational range. Across the broader workforce, there are 460 distinct AI use cases in production today, a figure that excludes the unique AI chatbots that workers can create to perform tasks through a secure system that’s called SiemensGPT.