[2024 Latest] Automating Requirements Definition and Prototyping with Generative AI

"Outsourcing costs for system development are too high," "Even after conveying our requirements to the vendor, it takes months to see results"—the root of these challenges faced by many companies lies in the analog "requirements definition" process, which relies heavily on human-to-human communication. In our consulting practice, we frequently see stagnation in this upstream process significantly lowering the overall ROI of projects. However, the evolution of current generative AI technology and no-code tools is fundamentally changing this structure. This article explains the forefront of "in-house solutions" that leverage LLMs to generate prototypes directly from business requirements, breaking away from vendor dependency.

1. The "Requirements Definition Quagmire" Caused by Traditional Vendor Dependency

In our actual consulting engagements, we often encounter new business and DX projects that have been stalled for months at the "requirements definition" stage. The business side communicates in vague terms, engineers interpret them, and voluminous documentation is created. This process not only incurs high consulting fees but also consumes even more time just for reviewing the completed documents.

A common tragedy on the ground is that "requirements definition documents created at a cost of tens of millions of yen become obsolete the moment the development phase begins." This happens because static media like documents cannot verify the actual user experience or consistency with business flows. This long lead time is the primary factor that strengthens vendor dependency and robs companies of their agility.

As shown in the graph above, an approach utilizing generative AI can shorten prototyping from months to just a few days. This enables an overwhelmingly efficient feedback loop where you can "see something working first and then make adjustments."

2. Instant Generation of "Living Specifications" via Generative AI × No-Code

In our consulting practice, we utilize LLMs (Large Language Models) not just as simple text generation tools, but as "bridges that convert business requirements into data models." Specifically, by simply inputting business requirements described in natural language, the AI automatically generates the database design (ER diagrams), API definitions, and even screen transition logic required for no-code tools.

For example, even in the context of In-house EC Construction and Growth Support, requirements such as complex inventory synchronization or point allocation logic based on customer ranks are immediately translated into no-code prototypes using AI. This allows clients to deepen discussions while looking at "actual screens" rather than just "words," bringing misunderstandings close to zero.

The greatest advantage of this method is that the generated prototype serves as the base for the production environment. Instead of a traditional "disposable prototype," it functions as a continuously evolving "living specification," allowing for a dramatic reduction in development costs.

3. ROI Brought by Frontline-Led In-House Solutions

The ultimate goal should be to build a system where business department personnel can mass-produce applications themselves, rather than relying entirely on IT departments or external vendors. In our actual consulting, by introducing automated requirements definition using AI, we have achieved a system that reduces development costs to less than 50% of traditional levels while allowing frontline improvement requests to be reflected on the same day.

Breaking away from vendor dependency is not just about cost reduction. It means gaining the "organizational adaptability" to respond immediately to market changes and customer feedback. In today's business environment, this difference in speed translates directly into a difference in competitiveness. Prototyping methods to accelerate frontline-led in-house development are no longer an option but an essential strategy.