Editorial summary. This is our text summary of an article published by training-magazine. Charts, figures, and the author’s full voice are at the original — read it there .
Editorial verdict
Methodologically credible but vendor-affiliated. The 10-year longitudinal dataset and 1,100+ respondents lend weight to the core finding that high-tech and high-touch integration outperforms either alone — but Wilson Learning's co-authorship creates a conflict of interest that warrants scrutiny of conclusions favoring relationship-based development products.
Executive summary
This article presents findings from the 10th annual Leadership Development Survey, a longitudinal study conducted jointly by Training magazine and Wilson Learning, drawing on responses from more than 9,000 L&D practitioners over a decade, including 1,115 respondents in the most recent cycle. The central argument is that effective leadership development requires an integrated combination of high-technology tools — particularly generative AI — and high-touch human practices such as executive involvement, manager coaching, and applied learning experiences. Neither approach alone is sufficient. Key findings include: generative AI adoption among surveyed organizations more than doubled from 23 percent to 49 percent year-over-year; high-performing organizations are significantly more likely to use both AI and relational development methods; overall leadership development effectiveness has improved only modestly from 49 percent in 2017 to 54 percent in the current survey period. Six outcome-based indicators are used to differentiate high-performing from low-performing organizations. The article concludes that technology-enabled scale, when coupled with executive commitment, coaching, character development, and real-world application, yields the highest reported effectiveness. Investment in leadership development as a share of training budgets has remained near 25 percent outside of COVID-period spikes.
Key insights
- 1Generative AI adoption in leadership development more than doubled in one year — from 23 percent in 2024 to 49 percent in the 2026 survey cycle — yet AI adoption alone does not account for performance differences between high- and low-performing organizations.
- 2High-performing organizations report executive involvement in leadership development activities at a rate exceeding 80 percent, compared to approximately 40 percent in low-performing organizations — a two-to-one gap that the survey identifies as one of the strongest differentiators.
- 3Overall leadership development effectiveness has increased only modestly over the 10-year study period, rising from 49 percent in 2017 to 54 percent currently, suggesting that neither successive waves of eLearning, virtual learning, nor AI adoption have produced transformative aggregate gains.
Practical takeaways
- Organizations aiming to improve leadership development effectiveness are observed — in this dataset — to combine generative AI for personalization and scale with high-touch elements such as coaching, mentoring, executive sponsorship, and applied learning assignments, rather than relying on technology platforms in isolation.
- The survey data indicate that governance structures, AI literacy investment, and incremental implementation — starting with contained tasks before scaling to simulations and media content — are practices associated with higher-performing organizations in their generative AI adoption.
Frameworks mentioned
360-Degree Review
A multi-source feedback method explicitly cited as a learning methodology used more frequently in high-performing leadership development organizations.
Action Learning
An applied learning methodology involving real organizational challenges, cited as a high-touch development practice associated with higher-performing organizations.
References
- Training magazine and Wilson Learning (2026).10th Annual Leadership Development Survey.
Source & Provenance
training-magazine
Michael Leimbach, Ph.D.
May 6, 2026
Industry Report
United States
Original source metadata is preserved. AI analysis is generated separately.
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