Practitioners Use AI at Execution Layer, Judgment Matters

Search Engine Journal and Duane Forrester's Substack summarise a Drexel University paper by Tim Gorichanaz that analysed 205 real-world ChatGPT use cases and produced a six-mode taxonomy of how people actually use conversational generative AI. The six modes the paper identifies are Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. Per the Drexel dataset as reported by Search Engine Journal, Writing comprised 47% of observed uses and Identifying comprised 10%. Search Engine Journal additionally reports the study's cases were drawn from Reddit and skew Anglophone, and cites a separate enterprise figure that 63% of organisations using generative AI apply it primarily to create text.

The taxonomy separates work that automates execution (drafting, summarising, translating) from work that requires human judgment (evaluating tradeoffs, forming strategy, nuanced critique). Industry-pattern observations: practitioners and organisations often optimise for near-term productivity gains by applying models to repeatable text tasks, which increases throughput but concentrates value in routine outputs rather than decision-making nodes.

For practitioners, concentration in the Writing and Identifying modes reduces the marginal upside of tooling improvements aimed solely at execution. Industry-pattern observations: when a workforce primarily applies AI to execution-layer tasks, strategic differentiation shifts to roles that integrate model outputs into higher-order judgment, such as framing problems, adjudicating model error modes, and synthesising ambiguous signals across domains.

Metrics and signals an observer should follow include broader usage surveys that break down modes beyond content creation, hiring and role postings that emphasise decision-support or interpretability skills, and tool features that surface provenance, counterfactual reasoning, or critique workflows rather than only faster drafting. Industry-pattern observations: rising demand for tooling and processes that make model outputs auditable and deliberative would indicate movement from execution-layer adoption toward judgment-layer workflows.