The "learning by doing" assumption
A common belief in enterprise AI adoption is that capability develops through use. Give people tools, let them experiment, and competence will follow naturally. It is an appealing idea — but the data does not support it.
What the data shows
When training exposure and usage frequency are both tested as predictors of GenAI capability, only one is statistically significant: training.
Respondents with any formal training scored 80.4 on the SJT assessment, compared to 69.9 for untrained respondents — a 10.5-point gap (p=0.008). In the multivariate model, training was the only significant predictor. Usage frequency was not.
Why it matters
This finding directly challenges the assumption that frequent use builds capability. A daily user with no training is, on average, no more capable than someone who rarely uses GenAI. They have more practice — but practice without structure reinforces habits, both good and bad.
The implication for investment is clear: training budgets are not a nice-to-have. They are the primary lever for improving GenAI capability at scale. Organizations that rely on "learn by doing" are leaving a 10-point capability gap on the table. The GenAI Capability Pulse can quantify this gap for your specific teams and guide where training investment will have the highest impact.
What to do about it
- Invest in training, not just adoption: Training is associated with a measurable 10-point capability uplift. Adoption tools and licenses are necessary but not sufficient.
- Don't assume frequent users are competent: Usage is not a reliable proxy for capability — prioritize Verification and Data Handling training even for power users.
- Target rollout for scale: Start with daily and weekly users to improve outcomes where GenAI is used most, then broaden after baseline completion.
"Learning by doing" is not enough. Invest in structured training — it delivers a measurable 10-point capability uplift.
These findings are drawn from the GenAI Capability Pulse — a scenario-based assessment that measures what non-technical teams actually do with GenAI, not what they think they can do. If your organization is scaling GenAI adoption, start with a baseline.
Source: AGASI GenAI Capability Pulse (N=153). Regression includes training exposure and usage frequency as predictors. Estimated training difference: +10.5 points (p=0.008).