Transforming Manufacturing with Generative and Agentic AI: Overcoming Workforce Challenges and Boosting Efficiency – AEM

By Gregg Wartgow, Special to the Association of Equipment Manufacturers —

Artificial Intelligence (AI) has been a hot topic over the past few years, evolving from a buzzword to a tangible force in the manufacturing arena. Surprisingly, while AI technologies are now considered mainstream, they still represent a relatively new frontier for many companies. Today, manufacturers are not just experimenting with AI; they are integrating it across their operations to enhance efficiency and optimize costs.

Two distinct categories of AI are particularly noteworthy: Generative AI and Agentic AI. Generative AI leverages existing data to create new content—everything from text and code to images and reports. On the other hand, Agentic AI utilizes reasoning capabilities to tackle problem-solving tasks, making it a game changer in various applications.

AI as a Workforce Solution

As discussions around AI evolve, it’s crucial to address the elephant in the room: the potential for workforce displacement. While some roles may be replaced by AI, the question arises—could this not also represent an opportunity for growth?

Many industries struggled during the pandemic, resulting in a significant loss of experienced personnel. During this time, the average tenure at manufacturing companies dramatically decreased, dropping from 20 years in 2019 to just three years by 2023. This loss creates challenges, as workers grapple with uncertainties and questions without seasoned mentors to guide them.

As Danny Smith, a principal strategist for Artificial Intelligence at Amazon Web Services, highlighted at the AEM’s 2025 Annual Conference, the skills gap in manufacturing is expanding, yet it hasn’t stirred the same level of anxiety among CEOs as it once did. He noted that executives are seeing AI not just as a replacement for people but as a means to enhance productivity. This shift allows companies to achieve more with their existing workforce while adapting to changing dynamics.

Generative AI Takes a Huge Leap Forward

Smith emphasized that today’s Generative AI represents a significant leap forward from traditional Machine Learning methods. For instance, typical computer vision classification has been likened to a “black box,” where machines categorize images based on training data but without transparency regarding their reasoning processes. This lack of insight can create inefficiencies and frustration among engineers.

In contrast, Generative AI can understand context, enabling it to not only recognize what it has been trained on but also predict future outcomes. Through innovations like “zero-shot visual inspection,” machines can identify new objects and patterns they’ve never encountered before, thereby enhancing the quality control process by flagging defects like cracks or other anomalies in components.

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Examples of AI in Play Today

Both Generative and Agentic AI are already making waves across manufacturing sectors. Smith shared various practical applications of AI that improve operational efficiency:

• Product Engineering: AI aids in generative design, compliance with standards, and warranty analytics, making the product development process more robust.
• Smart Manufacturing: Applications facilitate guided maintenance, quality diagnostics, and the onboarding of new employees, thus supporting a smoother operational workflow.
• Digital Customer Experience: AI enhances call center operations, marketing efforts, and field service diagnostics, ensuring a more seamless customer interface.

Several quantitative benefits signal the effectiveness of AI integration:

• Sales assistants using AI have freed up an average of four hours each week for sales reps to focus on high-value tasks.
• Engineering assistants have returned two to three hours to engineers for critical design work.
• Root cause assistants expedited field service diagnostics by 24%.
• Manufacturing maintenance chatbots have dramatically reduced average downtime by 75%.

Smith highlighted specific success stories demonstrating the advantages of Generative and Agentic AI:

RFP Responses: One company employed Agentic AI to automate and enhance their response to RFPs. Previously a time-consuming process, the integration streamlined proposals by cross-referencing divisions and incorporating a wider array of solutions. Though the win rate stayed constant, they noticed an increase of $200,000 in their annual deal size.

Production Meeting Planning: Quick meetings at the start of shifts that used to consume two hours of preparation can now be efficiently managed through AI-generated data requests, significantly reducing prep time for leads.

SOP Creation: Using internal AI models allows companies to transform cumbersome manuals into applications by analyzing content, creating user-friendly resources that are easy to update.

Data Analysis: By allowing managers to engage directly with their data through AI inquiries, organizations can extract valuable insights into profitability trends, thus empowering informed decision-making.

Automating Workflows: A small Texas manufacturer transitioned from a manual order acceptance system to an automated one utilizing Agentic AI, optimizing the order process by 97%, reserving only 3% for human oversight where necessary.

Embarking on a Path Toward AI

The aforementioned instances illustrate just a few of the myriad ways that companies are currently leveraging Generative and Agentic AI. Smith offered guiding thoughts for organizations eager to harness AI to overcome challenges in workforce management, engineering, sales, production, and administrative tasks.

“Leadership must be open to the new possibilities AI offers and should not create barriers to its success,” he advised. “Empowering teams to explore what AI can accomplish is vital; individual insights can pinpoint where AI’s value is most pronounced.”

To kick-start this journey, Smith emphasizes the importance of collaborating with vendors to fill resource gaps while identifying use cases promising the best return on investment. “Look for areas with existing quality data and skilled personnel,” he suggested.

Lastly, embracing AI entails a degree of change management. Effectively integrating Generative and Agentic AI into operations can augment teams, enhancing their productivity and efficiency while maximizing the overall potential of the organization.