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October 19, 2023

Three ways gen AI is already changing manufacturing

By getting a real and rational look at where generative AI is at work today, manufacturers can better see where it will produce the best results for them.

As manufacturers explore the best use of generative AI for their business, many find themselves confronting an information problem. Namely, there’s too much of it, it’s often vague, and it often seems more like cheerleading than actionable advice.

Amid all the noise, the overarching message is loud and clear: Generative AI will change everything, so get going pronto.

We agree generative AI can unlock massive business value for manufacturers across every aspect of the business, from R&D to the supply chain to the employee and partner experience. Moreover, it is important for manufacturers to act ASAP, if they haven’t already. Even a relatively small-scale initiative will help create a framework for the leadership, governance and processes that must be in place as gen AI matures and expands.

But the key to acting both quickly and effectively lies in getting past vague information and drawing an accurate and rational picture of how generative AI could best be used.

Generative AI tools can be best understood by grouping them into three categories: conversational, referential and creative.

Armed with information about these categories, leaders can make informed decisions about where to focus their efforts.

Three ways to apply gen AI

Here's a closer look at each category, including real-world use cases.

  1. Conversational. Manufacturers rely on a large workforce to support customers’ after-sales service needs. Traditionally, it’s taken extensive training and years of experience for these employees to gain the expertise needed to diagnose and resolve issues.

    With generative AI’s ability to quickly process enormous volumes of data, a well-configured conversational gen AI tool can help new hires get up and running more quickly while also providing experienced after-sales professionals with fast and accurate insights.

    A conversational generative AI tool can take data from user manuals, service manuals and analyses of past diagnostic and resolution information, sift through it almost instantaneously, and diagnose service issues quickly. It can suggest accurate resolution steps for maintenance service personnel, and as the technicians work with the conversational gen AI tool, their feedback can further strengthen it.

    Use case:
    We’re working with a leading elevator manufacturer that’s using conversational generative AI to develop a maintenance advisory system. This internal tool will ingest thousands of pages of product specs, log data and FAQs. The result will be an application that can communicate with field technicians via natural language chat or a speech interface. The app will enable technicians to diagnose and resolve problems much more quickly, without having to consult other systems or experts.

  2. Referential. Many manufacturers have a large variety of documents—from manuals, to contracts, to installation and repair guides—that contain both standardized and non-standardized information and are customized for a seemingly infinite variety of jurisdictions, languages and trading partners. Using generative AI, they can develop a system that references, summarizes and interprets the information from all of these documents, pulls it into a desired format and language, and creates a new document that provides relevant insights for the end user.

    Referential uses of generative AI can also help speed and streamline contract management. Here, generative AI can be used to reference contract documents, track, analyze and automate compliance processes, and generate standardized and customized templates. By doing so, these systems can also help manufacturers get ahead of compliance issues and expedite remediation.

    Use case:
    Manufacturing companies already use contract management systems to draft, negotiate and manage their vendor contracts. Now, some software vendors have begun integrating referential generative AI into these systems. As a result, they can intelligently analyze contract data to pinpoint problematic clauses, identify those nearing expiration, and find potential compliance concerns.

    Referential gen AI-enabled contract management systems can verify supplier compliance with contract terms, including pricing and “Incoterms” (widely used terms of sale), by offering rapid and straightforward access to global contract databases. Additionally, generative AI can offer category managers valuable insights regarding resourcing decisions based on current market conditions.

  3. Creative. Manufacturers can also put generative AI to use in their research and design processes. By combining generative AI with research and customer data, they can create images and customized virtual 3D designs for different product models and iterate very quickly through a broad variety of design ideas, choosing which model best fits their needs.

    Generative AI can also be used to deliver personalized marketing campaigns by analyzing customer preferences and generating tailored content, from product recommendations to customized email newsletters that could potentially boost customer engagement and conversion rates.

    Use case:
    At Toyota Research Institute (TRI), car designers have used publicly available generative AI tools in the early stages of their creative process. Now, TRI has developed a technique for designers to incorporate design sketches and engineering constraints directly into the design process. By doing so, the company will reduce the number of iterations required to reach a final solution, as the system will ensure design ideas don’t run afoul of engineering realities.

Looking forward

Generative AI technology is finding its way into use cases that manufacturers can leverage according to their strengths and priorities. For example, a manufacturer with a large, dispersed pool of field maintenance technicians might start with conversational gen AI as a training tool and virtual expert. Conversely, a business that has identified transnational distribution as a weak point may zero in on referential generative AI, with its ability to improve contract management and compliance.

By analyzing their needs and the capabilities of gen AI, leaders have an opportunity to make considered decisions that will create both value and competitive advantage.

To learn more, visit the Manufacturing section of our website or contact us.

Cognizant Insights Team

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