What do AI model training and video-based training for employees/customers have in common?

2025-12-01

What do AI model training and video-based training for employees/customers have in common? A lot more than you’d think. At first glance, they seem like two separate worlds—one driven by data science, the other by human behavior and learning. But both share a fundamental goal: learning to perform better over time. AI training involves feeding models with labeled data, fine-tuning, and continuously evaluating performance to achieve desired outcomes. Human training through video content is about delivering the right knowledge, in the right format, at the right time—also aiming for improved outcomes like performance, satisfaction, or retention. And here’s where it gets interesting:

Both need curated input—garbage in, garbage out. Whether it’s low-quality data or poorly designed video, the learning suffers.
Both benefit from iteration. AI models improve with feedback loops; humans retain more with repetition and reinforcement.
Both rely on contextual relevance. Just like AI struggles with data out of distribution, learners struggle when content lacks relevance to their roles or goals. But the biggest difference? AI learns through algorithms. Humans learn through emotion, motivation, and connection. The future of training—whether for machines or people—is adaptive, data-driven, and contextual. And if we get both right, we unlock exponential impact. That’s why I built Lupo.ai , to democratize knowledge transfer through video and help companies/organizations train their people so they perform better over time and unlock their full potential.