An L&D AI Recommendation Engine

What Data Does It Need To Be Performance-Based?

It needs data about the Performance and the Performers.

In 1986, in my 16th CAD – Curriculum Architecture Design project since 1982 – I saw more variance in both the Performance Requirements in one Job title and more variance in the existing knowledge and skills of the target audience than I had experienced to date.

Nowadays, when I hear of AI Recommendation Engines in L&D – I worry that it is a misguided approach – that could be improved with better data.

I’m thinking GIGO rather than GIGO – “Good In Good Out” versus “Garbage In Garbage Out” – as the saying usually goes.

What I see is L&D, like T&D before it, looking for the easy path – and avoiding conducting Analysis – yet still.

AI won’t enable recommendations based on what everyone else is doing and finding helpful (whatever helpful might mean and how that might be measured).

There was also this constant churn in my client organization – including reorganization (4 SBUs became 5), 300 people were added while I was working with them in those first 3 years, and new products were being created and older products buried. The organization managed 500,000 products. The stakes were SKY HIGH.

A Short Video

The value of AI in L&D lies in the data – and not some dance around that need.


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