How To Be a Better Executive In The Age of AI. | Devin Kearns | CustomAI Studio

The Gist

Executives fail to adopt AI effectively because they rely on delegation, lack daily practice, and fall for polished vendor demos instead of building a hands-on operating model. Closing the productivity gap requires leadership teams to align on governance, block dedicated experimentation time, and upskill their workforce into power users.

Quick Overview

Executives lag behind individual contributors in AI usage because they treat AI as a delegation task rather than a hands-on practice. Devin Kearns breaks down three structural reasons leaders fall behind: the delegation reflex, calendar neglect, and the illusion of flawless vendor demos. Organizations achieve an eight-times output multiplier only when leadership implements structured operating models and turns operators into super users.

Key Points: OpenAI research from August 2026 shows that individual contributors use AI significantly more than managers, directors, and executives. Frontier firms achieve 8.3 times the output per user compared to 2.6 times at mid-tier organizations. Executives suffer from the delegation reflex, expecting others to do the work while failing to build personal intuition. Calendars packed with meetings leave zero hours for executives to actually try, test, and fail with AI tools. Polished vendor demos hide the messy middle of rate limits, schema drift, and authentication errors. Deloitte research indicates that while employee AI use jumped by 50 percent, only one in five companies has a mature operating model. Super users who are properly trained experience five times the productivity of standard users.

Context: Devin Kearns runs CustomAI Studio and implements agentic AI systems for businesses. Observing repeated disconnects in leadership strategy meetings over eighteen months, he analyzes why enterprise AI adoption stalls despite widespread tool availability.

Detailed Analysis

Executive teams constantly ask where they stand with AI, yet leadership is consistently the least capable group at utilizing the technology. While employees on the front lines use AI daily to build expertise, executives rely on a delegation reflex, treating AI as something to hand off rather than master themselves. Their calendars are entirely booked with information-gathering meetings, leaving no time for hands-on experimentation. Furthermore, executives are easily swayed by flawless vendor demos, failing to anticipate real-world obstacles like schema drift and rate limits. To bridge this gap, leadership must establish clear operating models, block dedicated practice time, and transform everyday operators into super users.