DIP-AI: A Discovery Framework for AI Innovation Projects

TL;DR

DIP-AI addresses a critical gap in AI project development by providing a structured discovery framework that integrates ISO/IEC 12207 and 5338 with Design Thinking to guide early problem identification and feasibility analysis. Through an evaluative industry–academia case study, the framework’s canvas and construction process were shown to enhance problem discovery, stakeholder alignment, and communication, culminating in a deployable MVP and positive acceptance among participants. While perceived usefulness was strong, usability improvements such as a glossary and tool support were recommended to facilitate broader adoption. The work advances requirements engineering for AI by offering a concrete, repeatable discovery pathway that can improve project quality and stakeholder satisfaction in real-world AI innovation programs.

Abstract

Despite the increasing development of Artificial Intelligence (AI) systems, Requirements Engineering (RE) activities face challenges in this new data-intensive paradigm. We identified a lack of support for problem discovery within AI innovation projects. To address this, we propose and evaluate DIP-AI, a discovery framework tailored to guide early-stage exploration in such initiatives. Based on a literature review, our solution proposal combines elements of ISO 12207, 5338, and Design Thinking to support the discovery of AI innovation projects, aiming at promoting higher quality deliveries and stakeholder satisfaction. We evaluated DIP-AI in an industry-academia collaboration (IAC) case study of an AI innovation project, in which participants applied DIP-AI to the discovery phase in practice and provided their perceptions about the approach's problem discovery capability, acceptance, and suggestions. The results indicate that DIP-AI is relevant and useful, particularly in facilitating problem discovery in AI projects. This research contributes to academia by sharing DIP-AI as a framework for AI problem discovery. For industry, we discuss the use of this framework in a real IAC program that develops AI innovation projects.

Figures (5)

• Figure 1: Transfer Technology Model adapted by pfleeger.
• Figure 2: DIP-AI Canvas.
• Figure 3: Participants in the Case Study Context and the Use of DIP-AI in the background.
• Figure 4: DIP-AI completed by participants in the case study.
• Figure 5: Heat Map of Participant's Perception (TAM 3-based)