# Practitioners Use AI at Execution Layer, Judgment Matters > Source: > Published: 2026-05-28 14:35:34.561291+00:00 # Practitioners Use AI at Execution Layer, Judgment Matters Search Engine Journal and Duane Forrester's Substack summarise a Drexel University study by Tim Gorichanaz that analysed **205** real-world ChatGPT use cases and identified six usage modes: **Writing**, **Deciding**, **Identifying**, **Ideating**, **Talking**, and **Critiquing**. The study found **Writing** accounted for **47%** of observed cases and **Identifying** about **10%**, and Search Engine Journal reports the dataset came from Reddit and skews Anglophone. Search Engine Journal also cites a figure that **63%** of organisations using generative AI apply it primarily to create text. Editorial analysis: this concentration on drafting and factual synthesis means many practitioners are using AI at an execution layer rather than the higher-value judgment layer, with implications for career differentiation and where teams extract strategic leverage. ### What happened 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. ### Editorial analysis - technical context 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. ### Context and significance 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. ### What to watch 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. ## Scoring Rationale The Drexel study surfaces a widely observable pattern in practitioner AI use that affects how teams capture value. It is notable for user-research insights relevant to practitioners but not a frontier technical breakthrough, and it is recent (within days), yielding a modest downward freshness adjustment. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)