The forefront of operational efficiency through generative AI
Generative AI technology has passed the proof-of-concept (PoC) phase and is entering the stage of full-scale implementation as a powerful efficiency tool in actual work. In various industries, success stories are being reported one after another that eliminate bottlenecks in daily work and dramatically improve white-collar productivity.
One of the typical usage examples is the automation of document creation and summarization. In-house FAQ systems that instantly create high-precision minutes from recorded meeting data, or extract and summarize the necessary information from a huge collection of manuals and regulations, are becoming standard tools in many companies. This reduces the huge amount of time spent searching for information and creating materials, allowing you to focus on more creative, high-value-added work.
Examples of use in development and creative departments
Generative AI is also becoming an indispensable “copilot” in the field of IT engineers and developers. Companies that have introduced coding support tools such as GitHub Copilot are reporting that their development speed has improved by several percentage points due to automatic code completion, bug discovery, and test code generation. As technical barriers have been lowered, the movement toward “citizen development,” in which non-engineers create simple tools themselves, is accelerating.
Changes are also occurring in the design and creative fields. Approaches such as creating a large number of package design proposals for new products using image-generating AI and then drawing inspiration from them to improve them, and leaving the composition and slide design of presentation materials to AI are becoming widespread.
As can be seen from these examples, the key to successfully improving work efficiency through generative AI lies in the ability to design appropriate prompts (instructions) for asking ``what should AI do?’’ and the flexibility to rebuild existing work flows based on AI. Going forward, each company will need to urgently build its own efficiency model that combines its own know-how with AI.