Automation and Artificial Intelligence in the Type Design Process: Insights from an Industry Survey

Abstract

This article investigates how automation (both deterministic and artificial intelligence–based) is integrated into professional type design practice, a field with exacting standards of craft and which relies on specific and long-established working methods. Drawing on an online survey conducted in early 2025, alongside detailed follow-up interviews with select type practitioners, we map current practices and attitudes, as well as the perceived risks and opportunities in the field of automation for type design in general, and the implementation of artificial intelligence (AI) in particular. Data analysis established that deterministic, rule-based automation is ­near-­ubiquitous in type designers’ workflows, and is already used for a variety of tasks such as interpolation, glyphset expansion, and various font engineering tasks. In contrast, AI tools have currently only been adopted by a minority of practitioners, and are largely being used for adjacent tasks such as writing code, gathering project documentation, or generating proofing strings. The majority of respondents expressed strong resistance to automating what they identify as the creative core of their work (e.g., sketching, drafting a basic alphabet, marking proofs), but show willingness to delegate the most labor-intensive, technical operations to software, with kerning repeatedly identified as the leading candidate for further automation—provided that human oversight and decision making remain throughout the process. Ethical concerns (such as training data provenance, lack of transparency, and environmental costs) lead to a cautious attitude towards generative AI, a position also fueled by some expressed anxieties about corporate concentration. We argue that sustainable and worthwhile innovation in typeface design should prioritize assistive tools that are transparent and encourage human decision-making, in order to optimize routine work without compromising iterative practices through which designers acquire judgement. Such tools would ideally balance streamlined workflows with the acquisition and reinforcement of highly specific skills, which in turn enable designers to preserve qualitative typographic standards.