10 ways Generative AI is delivering real value to the industrial sector

10 ways Generative AI is delivering real value to the industrial sector
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In the complex world of industrial operations, staying competitive means constantly finding ways to streamline processes, optimize resources, and uncover new opportunities. Generative AI offers a practical path forward, with applications that span the entire value chain. From customer interactions to product development, these ten use cases demonstrate how AI is already delivering real-world results.

1. Automated interaction logging and summarization

Marketing, Sales, and Service are continuously in contact with (potential) customers, and need to keep track of all previous interactions to effectively engage with them. Generative AI can automatically log & summarize calls, emails, and chat messages, to continuously provide an up-to-date overview of customer status.

This can easily be automated - typically within just a few days of development time – by combining speech-to-text tooling like Deepgram, to generates the text transcript, and GenAI tooling like OpenAI, to summarize the transcript into any desired format. This use case reduces time spent on summarizing calls by up to 100%; proven to result in 10-15% efficiency gain at many clients. Furthermore, Gen AI call summarization also improves quality and consistency of summaries across agents, generating valuable data for further analysis.

2. Automated personalized replies for Sales & Service

Customer service agents and Sales teams spend a considerable portion of their time addressing frequently asked questions. This workload can be significantly reduced by adding your company's data sources to Generative AI tooling. Such integration facilitates draft responses to common inquiries, including complaints and payment discussions. To ensure the quality of responses, it is crucial to keep agents in the loop, allowing them to review and approve AI-generated messages before sending. Implementing this approach has shown the potential to dramatically increase response speeds and decrease the time spent on communications by up to 30%.

3. Automated service visit preparation

Technicians spend considerable amount of time preparing customer visits. A large part of this can be optimized with Generative AI. For example, prior to the visit the technician can receive a summary of a customer request and obtain a customized visit checklist based on the request or complaint from the customer – all read to the technician using a text-to-speech model. Furthermore, the technician can get suggestion for relevant questions to ask to customer during the visit. Many of our clients are reducing preparation time per visit by up to 5 minutes with this automation.

4. Automated service visit logging

After every visit, technicians need to log the output of their visit, which can be a time-consuming task and something that tends to be forgotten. Once finished, a technician can log performed actions automatically & handsfree using speech-to-text models. Once generated, the output is automatically updated in the CRM system and potential follow-ups are created. This can completely remove time spent on after-visit logging – as logging is now performed while driving to the next client.

5. Improved knowledge base search (for clients & service)

Sales reps often find it time-consuming and challenging to retrieve customer-specific information. Besides that, it can be difficult to respond adequately during customer conversations. Combining Retrieval-Augmented Generation (RAG) with Generative AI tooling, and internal data sources (like best practice sales guides), can enable you to develop a personal assistant for sales representatives. This tool can provide immediate answers to queries about customer-specific deals (e.g., "What are the specific pricing agreements for customer X?") or offer advice on handling customer issues (e.g., "How should you respond to an upset customer due to late delivery?" or “Which additional products can I sell to this customer?” This improves sales rep efficiency, customer satisfaction, and potentially revenue as well.

6. Improved (replacement) parts & product search

Maintenance and procurement teams often spend considerable time searching for suitable replacement parts and products. This process can be significantly improved by integrating Generative AI tools. By analyzing images or descriptions of needed parts from customers, the AI can search internal databases and external suppliers to find exact matches or compatible alternatives. This use case can be implemented within a few weeks and has been shown to improve the accuracy of part identification, and streamline procurement processes, ensuring timely maintenance and operational efficiency.

7. GenAI-driven product innovation

The industrial space is rapidly evolving with incumbents launching new products. To accelerate product innovation, Generative AI can be used to analyze online search data, sell-out data, or market reports to uncover changing customer behavior and needs, best-selling products, and consumer insights. This approach ensures you are always informed about the latest trends, helping your products align with current market demands and preventing delays in market entry.

8. Automated market analysis for new product applications

Industrial companies can accelerate product development and sales by leveraging Generative AI tools. These tools analyze diverse text data, such as patents, news, and social media, to identify new product applications and market opportunities. For example, AI can analyze patents to find companies developing products potentially leveraging your solutions. This approach helps uncover market opportunities and speeds up the transition from research to market-ready products.

9. Natural-language-based reporting and data analysis

Business analysts often need to create detailed analysis, reports and dashboards on trends. This task can be simplified by using Generative AI: while current reporting tools often required coding skills – Gen AI enables any employee to describe the output they need (e.g., sales activity of last 5 years), and have Gen AI generate the insight for them. This use case improves the speed and accuracy of reporting and data analysis, and provides consistent and actionable insights from your data.

10. Automated local product compliance checks

Global companies often expend significant effort ensuring their products comply with various and ever-changing local regulations. Generative AI is highly effective at summarizing and examining large volumes of legal documentation, making it a valuable tool for quickly determining product compliance with local standards. By integrating Generative AI with legal documentation, legal departments can save significant amounts of time, allowing them to concentrate on more strategic tasks.

The integration of Generative AI into industrial workflows isn't about replacing humans; it's about empowering them. By automating routine tasks, streamlining processes, and uncovering hidden insights, companies can free up their workforce to focus on higher-value activities. The examples in this article are just the beginning. The potential for generative AI in the industrial sector is vast, and the time to explore its benefits is now.

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Matti van Engelen
Practice Lead Digital & AI

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