How Generative AI Can Redefine Manufacturing – Bankwatch

The technology can transform knowledge management from static systems into dynamic, intelligent ecosystems and provide context-aware conversational assistance

How Generative AI Can Redefine Manufacturing

As global manufacturers navigate the complexities of modern production, generative AI can offer powerful tools to enhance efficiency, increase transparency, reduce costs, and drive innovation.

In Deloitte’s Future of Manufacturing study—which gathered sentiment from 600 respondents across U.S. manufacturers—38% report that they are piloting generative AI. While 24% of respondents indicate that they had adopted generative AI use case(s) in at least one of their facilities, 10% say they have implemented it across their broader networks. A majority (63%) of respondents also indicate that AI and machine learning (ML) is a top investment priorities in the next 24 months, ranking higher than digital twin, the omniverse, and the metaverse.

Following are three AI capabilities that are promising for manufacturers.

Data Extraction, Simplification

Automated content generation and summarization—along with personalized knowledge delivery—can enable generative AI to transform knowledge management from static systems into dynamic, intelligent ecosystems. This is done with rapid analysis of large volumes of data to identify patterns and key insights. The process can empower the workforce with the right knowledge at the right time, enhancing efficiency and decision-making.

Workers can quickly access digitized formats of standard operating procedures, manuals, logs, batch records, and other documents to help improve operations, resolve queries, and make faster decisions. What’s more, tacit and historical knowledge from technicians can be captured to customize trainings based on specific needs and styles. This can help improve the effectiveness of existing training programs and facilitate faster onboarding.

Furthermore, generative AI models can complement traditional AI prediction models to enhance data analysis by providing richer, more comprehensive insights. This can help manufacturers to improve operations, optimize production planning, minimize out of stocks, predict equipment failures, and analyze product defects.

Context-Aware Conversational Assistance

With its ability to understand human language, generative AI can provide context-aware conversational assistance. That means, smart systems can comprehend the meaning of user inputs and adapt responses based on user preferences, creating natural and meaningful conversations. This capability can significantly aid manufacturers by providing workers visibility for performing root cause analysis, enabling them to take the most appropriate mitigation actions.

For example, if a user asks about optimizing a production line, generative AI-powered applications can consider current production schedules, production constraints, resource availability, and past optimizations to provide insightful and actionable recommendations for improvement. Operators, supervisors, and managers can use the conversational interface to track production and inventory in real time, helping solve exception-based situations. Generative AI-powered applications can also assist in quickly analyzing the root cause of shop floor incidents and suggest potentially preventive and corrective measures.

Multimodal Proficiency

Generative AI models demonstrate increased versatility in handling varied data formats. Users can provide input in various modalities, including text, images, audio, code, video, and 3D models. Correspondingly, these models can generate outputs across a similar range of modalities.

For instance, maintenance workers could input queries using various methods, such as text prompts with error code or audio messages with details, even uploading images of malfunctioning parts into a generative AI-based maintenance chatbot. The chatbot could use data from different sources such as sensor readings, maintenance logs, technician reports, and visual information to diagnose equipment faults. The chatbot could then generate multimodal outputs, such as text-based troubleshooting instructions, visual aids illustrating repair procedures, and even audio overlays with step-by-step guidance for the technicians.

By integrating a variety of data such as sensor readings, visual inspections, audio, and maintenance logs, generative AI can enhance machinery fault detection, which can help spot potential issues missed by traditional methods.

Further, generative AI could be instrumental in developing immersive learning experiences for workers by creating training modules, process documentation, and standard operating procedures from raw text, picture, and video feeds. Even the learning modules themselves can be a combination of textual guides, video snippets and real-time simulations, using adaptive quizzes and personalized feedback to enhance the impact.

Managing Manufacturing Challenges

As companies adopt generative AI, proactively addressing challenges related to data privacy, security, availability, and quality is paramount. Furthermore, the potential for generative AI model “hallucinations” and the ever-changing regulatory landscape require careful and ongoing attention. It can be helpful to implement a framework that incorporates these considerations and supports responsible and sustainable generative AI deployment.

Some ways to address these concerns include:

Minimize data privacy risks when using public large language models (LLMs) by enforcing zero-retention policies with service providers and simultaneously implementing stringent access controls within the organization’s own systems to restrict data access and modification privileges related to generative AI applications. This dual approach helps to protect sensitive information both externally and internally.
Use explainable AI (XAI) techniques, change management, and other forms of trust-building approaches.
Implement guardrails for the ethical, secure, transparent, and reliable use of generative AI models.
Establish a generative AI quality assurance team and develop a broad observability platform, incorporating tracing and evaluation capabilities for LLM applications. This platform should support both application-specific benchmarks and real-time performance evaluations, facilitating robust reporting and continuous improvement. Application evaluation frameworks can benchmark the results against bias, accuracy, coherence and relevance.
Use high-quality data, effective prompt engineering, strong grounding, fine-tuned generative AI models, and continuous evaluation to minimize hallucination.

Start Small, Plan Big

Generative AI isn’t just a technological advancement—it’s a paradigm shift that could redefine the manufacturing landscape. It holds the potential to drive significant efficiencies, create new opportunities, and solve many age-old industry challenges.

While manufacturers have historically been cautious of adopting process automation, the industry is already moving toward agentic AI wherein intelligent, discoverable, and trustworthy AI agents could independently accomplish tasks and make decisions. That likely means the adoption of generative AI has become table stakes.

Large-scale adoption of generative AI will likely depend on identification and adoption of high-value use cases. By starting with small, strategic implementations, manufacturers can build a solid foundation for broader transformations.

—by Tim Gaus, principal, Deloitte Consulting LLP

Published on Jun 20, 2025 at 3:00 PM

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Courtesy WSJ https://deloitte.wsj.com/riskandcompliance/how-generative-ai-can-redefine-manufacturing-522e058a?mod=Deloitte_riskcomp_wsjarticle_Native_SSFY26