The recent emergence of ChatGPT, an AI-based language model, has caused a stir in both the consumer and enterprise world with the chatbot’s ability to provide detailed and articulate responses across a variety of knowledge topics. Developed by OpenAI, ChatGPT was released in late 2022 and can not only mimic human conversations, but write and debug computer code, compose stories, and answer test questions at a level above average human test-takers. These capabilities have brought up concerns, including academic integrity in education, cybersecurity, and job stability for a range of roles.
VDC Research decided to sit down with ChatGPT to ask a series of questions on the impact its technology will have on the frontline workforce. We also sat down with CEO and Founder of GridRaster, Rishi Ranjan, to discuss the progression of artificial intelligence and how models like ChatGPT are already revolutionizing critical use cases. GridRaster is a cloud platform provider that is enabling industrial and enterprise metaverse applications. This blog is an introduction to a series that VDC will be publishing on ChatGPT and AI in the enterprise.
One of the primary questions we asked ChatGPT was: Which industry will be impacted most by AI technologies, such as ChatGPT? Below is the bot’s reply:
Technologies like ChatGPT have the potential to impact many industries, but some may be more heavily impacted than others. Here are a few industries that may be particularly impacted:
It's important to note that while these industries may be impacted by technologies like ChatGPT, it is unlikely that they will be completely replaced by automation. Human workers will still be needed to perform many tasks that require emotional intelligence, creativity, and interpersonal skills.
These answers show the impacts that ChatGPT can have on a wide range of industries and the bot does not single out just one; however, as we go down the list that ChatGPT offers, we notice that the applications become more complex and require capabilities that exceed answering prompts and writing text. Healthcare, for example, is a prime example in which AI can be trained on healthcare data and then analyze medical images and aid healthcare professionals, alerting them to health conditions or allergies to certain medications.
Two industries that were not listed by ChatGPT are the bread-and-butter of GridRaster’s business: aerospace and automotive manufacturing. These industries are prime candidates for AI, because they have identified the ROI and allocated the funding for these technologies. Specifically, using AI-models like ChatGPT to help build virtual worlds in the metaverse to run simulations and increase productivity/efficiency metrics. When asked how ChatGPT can enhance the metaverse and help workers today, Ranjan stated two key use cases for the technology: “One is building a 3D world that replicates the real world… two is that enterprises have lots of data and they want to bring that data into the metaverse for analysis, running simulations and interacting with data more efficiently.”
There are issues with building these virtual worlds that have not been solved today. For instance, after building the virtual world, traditional AI cannot recognize when parts move and must be manually told that a part is in a different location. Ranjan notes the role of ChatGPT in this use case, “ChatGPT cannot help with this issue, but it can help with coding the virtual 3D world and running simulations. A lot of coding for the simulations is done manually, but ChatGPT can help reduce that time by 10X.” This ability to write code for businesses brings up the concern for the type of impact that ChatGPT and similar technologies will have on the workforce.
Ranjan mentions two categories of workers that will be affected by ChatGPT models in the enterprise: office workers and frontline workers. For the first category, he states, “The vast majority of the time developers are debugging the same code over and over again. Now, ChatGPT can write 95% of the code, so the developers only need to worry about 5% and fixing bugs, which leads to a larger focus on innovation and developing new code.” Frontline workers, on the other hand, are often not immersed in new technology, but are focused on manual work and conducting maintenance and repairs, in the case of manufacturing. Ranjan notes that workers will be able to interact with necessary data in a more natural way, “With ChatGPT, data will be more accessible to frontline workers, and they can share this data/collaborate in a natural way.” A primary example is having the AI provide alerts that increase safety measures when entering a hazardous environment or operating heavy equipment. In addition, workers can conduct training programs and simulations through the metaverse, reducing risk of accidents as a result.
Much of this technology is still in development and being tested by enterprises, but Ranjan estimates that in 3-5 years there will be a significant rise in adoption: “Today we are at the point where AI is helping with the manufacturing process and identifying parts of the shopfloor. Soon, they would like to map the entire depot/factory floor, so that everything can be seen together in a virtual world. The focus has moved to how to make these technologies cost-efficient.” VDC Research will continue this series of blogs in which we identify business use cases for ChatGPT and how this AI technology will impact frontline workforces.