Hot Take: Enablement in the Post-AI World - Are You Ready to Disrupt or Get Left Behind?

Let’s cut to the chase: traditional enablement systems, clinging to content-heavy, one-to-many approaches, are failing. The era of remembering as the cornerstone of learning is over. AI has rendered rote memorization obsolete. Instead of iterating on outdated systems, it’s time to radically rethink how we enable performance in the age of AI.

The critical shift? Post-AI enablement isn’t about knowledge retention—it’s about information application and critical analysis.

Legacy systems, both in education and corporate L&D, are slapping on “AI-powered” features without addressing the real issue: learners don’t need more content—they need frameworks and tools to analyze, apply, and create using AI. The days of static training decks or knowledge checks masquerading as learning outcomes are behind us. If your enablement strategy isn’t empowering teams to:

1️⃣ Critically analyze AI outputs for biases, sources and reliability 
2️⃣ Apply insights to solve real world challenges, and  
3️⃣ Create innovate solutions using AI as a co-pilot

...then you’re building for irrelevance.

The organizations thriving today are the ones adopting frameworks like the Post-AI Learning Taxonomy. They’re ditching “information transfer” for transformational learning models that emphasize application, collaboration, and disruption.

The results? Teams embracing these shifts are accelerating 30% faster than those clinging to the status quo.

It’s no longer about whether you use AI - but how you use AI to maximize the performance increases.

The bottom line: Enablement must disrupt itself. Stop iterating on outdated models and start equipping people with the skills that matter most in an AI-enhanced world.

What’s your strategy for thriving in the post-AI era?