GenAI Efficiencies in Manufacturing, Smart Farming, and Beyond – with Dr. Steffen Hoffmann of Bosch

Traditional manufacturing faces significant challenges due to its reliance on legacy processes, manual quality control, and limited adaptability to evolving market demands. 

Research from the Universities of Liverpool and Bristol in 2022, respectively, highlights that legacy manufacturing equipment and processes often lack the advanced sensing, real-time monitoring, and data analytics capabilities found in modern, digitally retrofitted or new systems. 

This limitation makes it difficult to detect and address defects early, leading to inefficiencies and quality issues. The research notes that process plants “tend to have long lifespans of up to 20 years and some of the available machines today may not have the capabilities to communicate with other factory units.”

Research published earlier this year by the Asia Pacific Academy of Science explicitly discusses how AI can help manufacturing by enabling system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material property modeling. These applications allow manufacturers to optimize their operations, enhance decision-making, and foster innovation. The paper highlights that AI can facilitate real-time monitoring and predictive maintenance. 

Emerj Editorial Director Matthew DeMello recently had a conversation with Dr. Steffen Hoffmann, Managing Director of Bosch UK at Bosch, to discuss how Bosch is embedding AI across its operations, not to replace humans, but to enhance efficiency while maintaining a strong ethical stance that ensures human oversight in every AI-driven decision.

He highlights Bosch’s dual approach of democratizing AI across the workforce and enforcing ethical guardrails, demonstrating the adoption of scalable, responsible AI. 

This article examines two actionable insights from their conversation for agriculture and life sciences leaders driving AI development at their organizations:

• Transforming precision in agriculture: Deploying AI for targeted interventions, such as using image recognition to identify and treat specific weed patches, to reduce chemical use, lower costs, and enhance crop yield sustainability.
• Boosting efficiency with Humans-in-the-Loop: Leveraging AI to automate repetitive tasks such as defect detection in manufacturing to free up human workers for complex problem-solving surrounding system supervision. 

Listen to the full episode below:

Guest: Dr. Steffen Hoffmann, Managing Director of Bosch UK, Bosch

Expertise: Leadership & Management, Automotive, Engineering

Brief Recognition: Dr. Hoffmann has been with the Bosch business for over 32 years, starting his career in 1992 in Germany as a management trainee and subsequently holding several executive commercial roles in various countries, including Germany, Japan, and southern Africa. 

Transforming Precision in Agriculture

Steffen opens the podcast by mentioning the need for organisations to identify AI applications that deliver a clear and measurable benefit. Then, they must ensure they have the right conditions in place to apply those solutions, which include access to financing, technical skills, and the capacity to integrate AI into existing systems. 

He cites examples to illustrate how AI can create a meaningful impact in agriculture. In the UK, one project, he says, addresses the challenge of black grass, a difficult-to-detect weed that harms crops. 

The AI solution utilizes image recognition to identify patches of black grass and then selectively sprays only the affected areas, thereby reducing chemical usage, lowering costs, and preserving the rest of the crop. 

He also references an earlier project that utilized optical sensors and a pneumatic piston to push weeds back into the soil, demonstrating how targeted, AI-enabled interventions can enhance both efficiency and sustainability in farming.

He points out that while generative AI is currently at the forefront of public conversation, the use of AI and intelligent systems has a longer history across various industries. For example, in the automotive sector, they developed an electronic transmission control unit that learned from individual driving styles to make gear shifts smoother over time. 

Similarly, in the home appliance division, washing machines were equipped with algorithms that adapted to a household’s usage patterns, optimizing wash cycles accordingly. 

Steffen also highlights an internal HR tool called “Rob” as a strong example of an AI application in administrative functions.

How the “ROB” Digital Assistant Works, as described by Dr. Hoffman on the ‘AI in Business’ podcast: 

1. Submit Your Issue
   
   • The user presents a case, issue, or problem drawn from the HR domain.
2. Retrieve Relevant Information
   
   • The algorithm automatically gathers all pertinent information related to the case, including:
     • Relevant labor laws
     • Internal HR policies
     • Procedures
     • Guidelines
3. Enter a Query
   
   • The user types their specific question or request into the chat interface.
4. Generate an Instant Response
   
   • The system provides an immediate answer that incorporates all retrieved context and documentation.
5. Sense User Context and Emotions
   
   • ROB detects the context of the conversation and senses the user’s emotional state.
6. Deliver a Tailored Solution
   
   • The assistant formulates and shares a solution specific to the user’s case.

Steffen explains that the “ROB” digital assistant is currently an internal tool within their organization. However, he emphasizes that its capabilities could easily be extended to external support environments, such as call centers. He envisions that when a user calls in with a technical issue related to a power tool or home appliance, a system like ROB could be employed exactly the same way it is used in HR.

Boosting Efficiency with Humans-in-the-Loop

He clarifies that the goal of AI at Bosch is not to replace humans, but to enhance human efficiency by offloading simple, repetitive tasks. It allows employees to focus on more complex, value-added work. 

Steffen further shares an example from manufacturing to illustrate how AI is improving quality control processes. At two Bosch plants in Germany, AI is being used to inspect fuel injection components, highly complex parts used in engines. The AI system detects variations in the manufactured components and identifies error patterns, which are then flagged for human inspectors to review. 

The targeted approach streamlines the inspection process, making it easier to spot non-compliant parts early. As a result, one of the plants, Steffen says, saw a 15% reduction in cycle times during production ramp-up.

Ultimately, he explains that Bosch is intentionally creating a company-wide culture where AI is viewed as a tool accessible to everyone, not just specialists. To support this, they have developed a basic AI training program that has already reached 65,000 associates. 

At the same time, the company also maintains a strong expert base, with over 5,000 AI professionals who have collectively registered 1,500 patents in the past five years and are currently working on more than 100 generative AI use cases.

He also emphasizes the importance of ethics in AI deployment, particularly in regulated industries such as finance:

“One principle that we have is there always has to be a human arbiter. We are not allowing a machine decision that has effects on other people. There’s always a human in between.

So now you might say, ‘Okay, that limits the AI, or limits AI to efficiency increases,’ but that is for the moment, good enough. 

If you apply that principle, I would believe that even in more, more regulated industries, like you mentioned, financial industry will see a lot of good use cases of AI.”

– Dr. Steffen Hoffmann, Managing Director of Bosch UK at Bosch