1. Identify and address resistance to AI
Prepare for organizational dynamics you may not have anticipated, because AI-enabled learning will surface capability gaps and cultural patterns that were previously hidden. Implementation often reveals complexity as technology adoption intersects with power dynamics, generational differences, and cultural attitudes toward learning. Some leaders embrace data-driven feedback and become champions. Others question the value or find reasons why "this won't work for us."
At Syngenta and across organizations I've worked with, success comes from anticipating this resistance. Before piloting AI-enabled learning, have explicit conversations with senior leaders about what success looks like, how progress will be measured, and what happens when data reveals development needs at all levels. Create psychological safety for leaders to be learners. Otherwise, a pilot may succeed technically but struggle to scale organizationally.
2. Align learning features to business outcomes
Start with a focused pilot built around one critical capability where language directly impacts business outcomes. Use AI features that genuinely enhance learning, such as speech analysis, conversation practice, and personalized journeys, always integrated with qualified human instructors who provide context and cultural nuance.
3. Measure success in a nuanced way
Measure behavior changes and business impact, not just completion rates. Following the pilot, lessons should be extracted systematically, considering: What value did this create? For whom? Under what conditions?
What works in one region or context may not translate directly to another. A feature that drives engagement in North America, for example, may require adaptation in Asia-Pacific. The organizations advancing fastest take a systematic approach to learning and build evidence-based business cases. AI-enabled learning requires the same rigor as any business-critical investment.