DIP-AI: A Discovery Framework for AI Innovation Projects

TL;DR

DIP-AI is a tailored discovery framework designed to enhance problem identification in AI innovation projects, integrating standards and design thinking to improve project quality and stakeholder satisfaction.

Contribution

The paper introduces DIP-AI, a novel framework combining ISO standards and Design Thinking to support early-stage problem discovery in AI projects.

Findings

DIP-AI is perceived as relevant and useful by industry and academia.

Participants found DIP-AI facilitated problem discovery effectively.

The framework promotes higher quality AI project outcomes.

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.