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Where and How Should GenAI Be Used for Industry?


To scale GenAI use up, industrial organizations will need to make deployments easy to use and integrate the technology into normal workflows

The arrival of ChatGPT on the market brought the power and potential of Generative AI (GenAI) into view. It seems organizations of all types have embraced the technology and are using it. However, it is one thing to provide answers to simple questions (prompts). The real issue is how does an organization use it to safely and efficiently to yield the highest impact at scale without disrupting operations?

Before tackling that question, it is important to put the use of ChatGPT and GenAI into perspective. ChatGPT has seen rapid user growth since its launch, reaching 100 million active users in its first two months, setting a record for the fastest user growth for an application. As of March 2024, ChatGPT already had around 180.5 million users, with OpenAI’s website receiving about 1.6 billion visits per month.

Now, industrial organizations are starting to jump on the tidal wave. Why the great interest in the technology by industry? A 2023 survey found that 25% of US companies saved between $50K to $70K using ChatGPT, while 11% saved over $100K.

How are ChatGPT and GenAI used?

ChatGPT and GenAI are making a significant impact in various industrial applications. In the industrial manufacturing industry, the technology is driving advancements in several key areas. Some use cases include:

  • Enabling optimizations in planning, predictive maintenance schedules, risk mitigation, and enhancing communication efficiency.
  • Leveraging GenAI for quality control by detecting anomalies in data, thus improving decision-making, reducing costs, and enhancing customer satisfaction.
  • Providing quick responses to common issues, allowing for faster diagnosis and personalized recommendations, and fostering stronger relationships between manufacturers and their customers.

Additionally, different groups within industrial industries are using ChatGPT and GenAI to improve operations. For example, sales and marketing use the technology for keyword analysis, copywriting simplification, automated customer feedback, and A/B testing. Others are using its capabilities for transcription, scheduling, and summarizing reports. Software developers are using ChatGPT and GenAI to code, automate quality assurance testing, and maintain system documentation.

These examples illustrate the versatile capabilities of ChatGPT and GenAI, highlighting their potential to revolutionize industrial applications by enhancing efficiency, improving customer experiences, and modernizing traditional processes.

See also: How Gen AI is improving Aker BPs data management practices

Detailed applications for industrial organizations

GenAI is finding use in industrial organizations in numerous application areas. For example, there are opportunities across operations, process engineering, and maintenance. A common use is for operators to use GenAI to access documentation when they’re out in the field. Or allowing process engineers to have a single workspace to visualize all of their drawings, process data, and work orders and be able to work through troubleshooting or root cause analysis faster. Maintenance can benefit by being able to better optimize and prioritize their work orders just by layering some analytics on top of all the work orders that are currently being collected.

Such applications are only possible when an organization makes its data safely available for use in GenAI models and applications. That requires breaking down traditional data silos found in most industrial industry organizations. But that, in turn, introduces new issues.

Simply put, the application of ChatGPT and GenAI in industrial manufacturing comes with challenges, including cybersecurity risks, ethical concerns due to automation, and the need for workforce training to integrate AI technologies effectively​​.

Issues to consider when implementing and using GenAI

The hype around ChatGPT and GenAI is forcing organizations to evaluate the technology. There is a FOMO (fear of missing out) factor driving even the most conservative users of new technology to at least look at what is possible.

Rushing in without a plan of action is not wise. Some steps to follow to decide if the technology is right for an organization include:

  • Meet with various stakeholders to scope out what the organization expects to get out of using GenAI.
  • Determine whether the organization has enough or the right data for GenAI to make an impact.
  • Scope out low-hanging opportunities where the organization can start using GenAI and quickly demonstrate its value to the company.
  • Find areas where, once GenAI is introduced, its use can scale.

Following these steps, an organization can get a sense of whether GenAI can help and where GenAI will deliver its greatest impact.

See also: Overcoming Gen AI Adoption Obstacles Across Process Industries

A final word

According to the August 2023 PwC Pulse survey, 65% of leaders in the industrial sector say they are either already training employees on new technologies including AI and GenAI or have a plan in place to do so.

To scale up GenAI use, it will need to be adopted throughout the organization. Like any new technology, for that to happen organizations will need to ensure their deployments of the technology are easy to use. In that way, the technology will be available to users with different digital skills. Additionally, the solutions must be integrated into normal workflows.

Doing these things will help an organization reap the greatest value from GenAI.

Salvatore Salamone

About Salvatore Salamone

Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.

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