Define-ML: An Approach to Ideate Machine Learning-Enabled Systems

TL;DR

Define-ML is a structured framework extending Lean Inception to better support early-stage ideation of machine learning-enabled systems by integrating data and technical constraints, improving alignment and feasibility.

Contribution

This paper introduces Define-ML, a novel extension of Lean Inception that systematically incorporates ML-specific considerations into product ideation.

Findings

Participants found Define-ML effective for clarifying data concerns.

The approach reduced ideation ambiguity and improved alignment.

Participants expressed strong intent to adopt Define-ML.

Abstract

[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependencies, technical feasibility, and alignment between business objectives and probabilistic system behavior. Traditional ideation methods like Lean Inception lack structured support for these ML considerations, which can result in misaligned product visions and unrealistic expectations. [Goal] This paper presents Define-ML, a framework that extends Lean Inception with tailored activities - Data Source Mapping, Feature-to-Data Source Mapping, and ML Mapping - to systematically integrate data and technical constraints into early-stage ML product ideation. [Method] We developed and validated Define-ML following the Technology Transfer Model, conducting both static validation (with a toy problem) and dynamic validation (in a real-world industrial case study). The analysis combined quantitative surveys with qualitative feedback, assessing utility, ease of use, and intent of adoption. [Results] Participants found Define-ML effective for clarifying data concerns, aligning ML capabilities with business goals, and fostering cross-functional collaboration. The approach's structured activities reduced ideation ambiguity, though some noted a learning curve for ML-specific components, which can be mitigated by expert facilitation. All participants expressed the intention to adopt Define-ML. [Conclusion] Define-ML provides an openly available, validated approach for ML product ideation, building on Lean Inception's agility while aligning features with available data and increasing awareness of technical feasibility.