Executive Summary
As AI technologies advance rapidly, the number of AI project procurements
continues to grow. Yet the traditional Function Point (FP) method shows clear
limitations: in AI projects such as chatbots or Retrieval-Augmented Generation
(RAG), the user interface may appear simple while massive data processing and
complex computational workflows operate underneath. FP cannot capture the true
scale of these hidden engineering efforts, creating a risk that AI project budgets
will be underestimated and ultimately unrealistic.
To address this gap, this report shifts the focus from AI model development to
AI Application Service Construction (Engineering) and proposes a framework for
scope estimation. It recommends adopting the international SNAP (Software
Non-functional Assessment Process) standard to quantify the technical
complexity involved in back-end operations—such as data preprocessing,
embedding generation, and vector-store construction—that FP cannot measure.
For a sound compensation system to take root, this report suggests key
directions: discovering automated measurement tools for AI technical scope,
accumulating AI project data, and fostering the AI engineering company
ecosystem. Ultimately, these efforts will contribute to enhancing the accuracy and
transparency of AI project cost estimation.