From ‘data-driven’ to ‘data-informed’ design — grounding AI for design in knowledge, context and decisions

Engineering design is increasingly influenced by abundant data and advances in machine learning, yet the common narrative of “data-driven design” mischaracterizes how design knowledge works. Unlike perception-focused AI domains that rely on large labeled datasets and stable mappings, engineering design is data sparse, knowledge rich, decision centric, and context dependent. This article reframes AI for design as data informed rather than data driven, arguing that effective decisions require integrating knowledge, constraints, mechanisms, semantics, and evolving intent, which data alone cannot supply. Drawing on the classical Data-Information-Knowledge (DIK) hierarchy, the article proposes three first principles for grounding AI in design: (i) DIK distinction, preserving the differences between raw data, processed information, and actionable knowledge; (ii) Domain problems, emphasizing alignment with domain objectives, constraints, and causal mechanisms; and (iii) Design context, embedding AI within the interpretive, intent-laden nature of design reasoning. An operational framework and end-to-end pipeline are introduced to align AI tools with decision needs, avoid dataset-first framing, and evaluate systems by decision quality rather than predictive accuracy. Positioning data as evidence rather than authority, the article shows how elevating contextual grounding, feasibility logic, and designer intent enables AI to move beyond pattern recognition and support rigorous, interpretable, knowledge-aligned design decisions.