[Implementation Case] AI Service Launched in 2 Months! Speed and a Small Start Create New Business Potential
Table of contents
- Introduction: Challenges in AI implementation and solutions
- Overall Overview: Project Success Factors
- Issues and Background: Current Status and Issues of AI Implementation
- Solutions: Development approaches and measures
- Implementation Results: Project Impact and Results
- Summary: Putting ideas into practice in a short space of time
Introduction: Challenges in AI implementation and solutions
'We want to use AI in a new business, but development takes too long...' 'The requirements haven't been finalized, and the project isn't moving forward...'
There are many companies that have such concerns. Company S, which we will introduce here, was also facing exactly this issue.
However, by taking a small-start approach of 'first making something that works,' they were able to complete a prototype of their AI service in just two months, and quickly bring it to market.
Let's take a closer look at how this was achieved.
Overall Overview: Project Success Factors
The greatest feature of this case is that a new business idea was realized as an AI service in just two months.
It is not uncommon for AI implementation to take several months to over a year. However, Company S was able to successfully develop a prototype in a short period of time by focusing on the following three points:
- 1Speed of initial response: Business validation can be carried out early, and feedback can be gathered quickly from both inside and outside the company.
- 2Flexible requirement changes: Brush up on ideas while creating something that 'just works'
- 3Utilizing AI technology (RAG x LLM): Automatically generate highly accurate content with minimal input
What is an MVP (Minimum Viable Product)?
A prototype that implements only the minimum necessary functions. It is useful in the early stages of a new business where testing and improvement are repeated.
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