Tuesday, April 21 | 1:30pm ET
AI pilots are everywhere. Proven impact is not.
For learning leaders, AI creates a very specific challenge. You’re expected to build capability and drive adoption, but also show how those efforts translate into business performance. AI pilots get launched, tools get tested, and early interest builds. But when it comes to demonstrating real business impact, things stall. Success metrics are unclear, results are difficult to validate, and initiatives struggle to move beyond isolated experiments.
The issue isn’t the technology. It’s how the pilot is designed.
In this session, we’ll walk through the exact approach used to take AI initiatives from idea → validated results → scalable execution, without getting stuck in endless pilot cycles.
We’ll cover how to align stakeholders early, design pilots that generate credible evidence (not just demos), and avoid common pitfalls that cause AI initiatives to stall. Real-world examples and frameworks will illustrate how to capture outcomes, communicate value, and build momentum for broader adoption.
Whether you’re just starting with AI or trying to move beyond isolated experiments, this session will give you a clear roadmap for turning AI pilots into performance.
What You’ll Learn
- A repeatable path for scaling successful AI initiatives
- A clear framework for structuring AI pilots that produce real evidence
- How to define success metrics tied to business outcomes, not activity
- How to move from idea → hypothesis → pilot → validated impact
- Execution best practices for building workflows that support practice, coaching, and continuous improvement
