The Head of Fujitsu Research Talks about the Future of Generative AI
Article｜2023-09-21
7 minute read
Technology has evolved at an overwhelming rate in recent years. Especially in the past year, the evolution of generative AI has become a hot topic, including ChatGPT, which leverages large language models (LLMs). Not a day goes by that we don't hear about generative AI, such as ChatGPT, Dall-E2, and Stable Diffusion, which generate images.
Currently, companies are actively considering the use of generative AI, and it is expected to produce significant results in improving operational efficiency and productivity at a wide range of levels, from the individual business level to the organizational and management levels. The use of these exploding technologies will be key to the competitiveness and sustainability of future companies. At the same time, many companies are struggling with how to implement such advanced technologies.
Seishi Okamoto, Executive Vice President of Fujitsu, who leads Fujitsu Research, told us about some of the ways in which companies are working with generative AI.
Business Scenarios and Challenges of Generative AI
"Fujitsu conducted a survey on the business use of generative AI based on customer feedback from various industries. As per the survey, the business scenario of generative AI can be classified into three categories based on the required data range, and we have also found that customers expect a wide range of use cases," says Okamoto.
The first case is to improve the efficiency of individual operations by utilizing internal information within a limited scope of work. For example, it can be applied to various tasks such as generating feedback to customers in customer support with natural sentences, automatically generating ad designs with simple ideas, automatically generating documents for loan and contract reviews, and automatically generating code for software development.
The second case is "automation of organizational operations" using a wider range of internal and competitor information. There are a variety of business scenarios such as increased productivity and automation in marketing, budgeting and staffing planning, effective marketing schedules, documentation and content generation in new product/service planning, store opening planning, production and procurement planning.
The third case is "management support," which utilizes an even wider range of internal and market information. "We believe that we will be able to support the development of operational stories, faster documentation, and greater efficiency in business operations such as management strategy, business model generation, medium- and long-term planning, portfolio optimization, and investment and M&A," he says.
On the other hand, there are significant challenges to the business use of generative AI. He lists three issues that he thinks are particularly important.
"First of all, it is not clear how to apply it and how to operate it in actual business. For example, a construction client told us that they are unsure of the best ways to improve the accuracy of their answers, such as changing the external information and prompts provided."
"The second is the problem of computational resources. A retail customer complained that the computational resources required for generative AI are hard to understand and the cost is unclear."
"The third issue is information security and privacy. A real estate customer says that it is too early to use it because it is unclear whether the sales team working closely with customers can determine copyright violations and accuracy."
