Docebo

Knowing which skills exist isn’t the same as knowing how they’re applied. Merck’s Kason Morris on closing that distance, in the 2027 Skills Intelligence Report.

Most L&D leaders know exactly where their workforce falls short. The 2027 Skills Intelligence Report looks at what’s stopping them from fixing it.

Conflicting priorities, missing tools, and governance. See the full findings in the 2027 Skills Intelligence Report.

Before you buy another platform, look at the people already on your team. Ben Eubanks shares why internal development wins on cost and speed.

Egle Vinauskaite on why buying AI tools isn’t enough on its own. More expert views in the 2027 Skills Intelligence Report.

Treating AI rollout as a regular tool deployment is a category error. In their new Skills Intelligence Report, Docebo found that: ➡️ for almost 30% of their respondents, AI tooling budgets grew faster than AI enablement. ➡️ 7% said the enablement budget was cut to fund AI tooling. ➡️ 47% said their headcount budget was redirected to other priorities, AI tools among them. Read that again (I needed to a few times). I know AI likes to use the phrase *stopped me in my tracks*, but I’m writing this myself, and this phrase does apply here 🙃 All the promised AI riches assume business transformation, but the way organisations fund and approach enablement often treats it as a straightforward technology rollout. It’s not. More sophisticated uses of AI often mean people have to develop their own use cases, rethink their day-to-day roles and ways of working, and develop complementary skills and expertise. They also have to navigate the potential upheaval around professional identity, the value of their expertise, job security, and ethical concerns, to name a few. Then there’s the next layer of the onion: the environment created by leaders over years and translated by managers into the daily work experience. When I work on these questions with my own clients, the t-words (tools & training) are a given. The bigger challenge keeping them up at night is everything around the t’s: changing work and thinking habits, solving job design and coordination, making it rational for people to integrate AI into their context, keeping work sustainable, preparing leaders to lead their teams through this turmoil for years to come. The transformation that AI requires is a different category of work. Investing crazy money in tooling but not in enablement is madness 😶