Product knowledge training is about methodically educating employees, partners, and customers about the ins and outs of a company's products or services. For employees and partners, it's the essential working knowledge they need to confidently sell, support, and deliver the product. For customers, it's the know-how they need to adopt it smoothly and get the most value from it.
Librarians have been actively collaborating and talking about it almost every day, whether it's creating tutorials and digital learning objectives or thinking about the conversations to have with instructors. It can feel like cognitive dissonance to be actively working with AI on a regular basis and also saying we're constantly thinking about the harms and the biases.
Bias risks: AI can amplify inequalities, like mislabeling non-native English writing as AI-generated. Privacy concerns: Schools face rising cyberattacks, and data misuse risks are high. Accountability: Human oversight is crucial to prevent over-reliance on AI.
I have zero sense of direction, so when I travel, I rely heavily on my Google Maps app. Before I take a road trip, I mark the points where I need to go, where I will stop for breaks, and how long it will take to get to my destination. A content strategy is the same premise. Content is created to guide users on where they need to go, the stops they need to make along the way, and the destination they should arrive at.
Choosing the right training content isn't about how much training content you offer. It's about how well that content fits the job it needs to do. The real training challenge is not content. It's fit We often hear that teams need more training. But when we dig deeper, the problem is rarely a lack of courses. It's a lack of focus. Training often fails because different needs get lumped together into one giant learning initiative. For instance, it's impossible to use the same approach for teaching introduction to Python as for harassment prevention.
