Claim
Agentic AI tools diffuse rapidly across non-engineering knowledge work when skills, meaning shareable workflow templates, exist to reduce the prompt-engineering barrier. Without skills, adoption stays concentrated in the roles that built the tool.
Mechanism
Engineers adopt agentic tools first because they can construct effective prompts from existing technical context. Skills encode that prompt engineering work into reusable templates that non-technical users can deploy without understanding the underlying mechanics. Once skills exist, the adoption floor drops: a legal professional or researcher can trigger a complex agentic workflow by selecting a template rather than writing from scratch. Organizational diffusion then follows the skills catalog rather than individual technical capacity.
Conditions
Holds when: shareable workflow templates exist and are discoverable by non-technical users. Works best in organizations with mixed-function teams where at least one technical person can author skills that others consume. Measurable within a quarter of deployment.
Fails when: the agentic tool lacks a skills or template layer, forcing every user to construct prompts independently. Also slower in highly regulated environments where workflow templates must pass legal review before distribution.
Evidence
Johnston and Holtz studied Codex deployment across OpenAI's own workforce from December 2024 through early 2026, covering engineering, legal, research, and general knowledge workers:
- Total Codex users grew 5 times during the first half of 2026.
- Legal staff produced 13 times more output tokens per user.
- Researchers produced 50 times more output tokens per user.
- Codex usage in 8-hour-complexity tasks grew 10 times.
- 26.6% of active users used the skills functionality, and this cohort drove the majority of cross-functional diffusion.
- Codex largely replaced ChatGPT as the default AI tool for non-engineering roles inside OpenAI.
The researchers found that skills-sharing was the key mechanism separating roles that scaled rapidly from roles that plateaued at initial adoption levels.
Signals
- Agentic tool usage in legal and research roles rises after skills are published by engineering or product teams.
- A small cohort of power users authoring skills precedes a broader wave of adoption.
- Output tokens per user grows faster than user count, indicating depth of use rather than shallow adoption.
Counter-evidence
This study covers a single organization (OpenAI), which is not a typical knowledge-work firm. OpenAI staff are unusually AI-literate, which may lower the skills-authoring barrier beyond what most organizations can achieve. The 50x researcher output metric reflects tokens, not task quality or business outcomes; more tokens do not automatically mean better research. The diffusion pattern may not replicate in organizations where skills must pass compliance or legal review before they can be shared.
Cross-references
- (none in current corpus)