Lessons learnt about lessons learnt: now with AI?

Depending on how you cut it; there are five major hurdles to any lesson ever getting applied. In spite of the overwhelming odds, we should always try.

It is tempting to assume that we have no need to learn what went right or wrong in previous projects because our project is unique. Or people might assume that the information is just not out there, and they’re facing a new kind of question. Aptly, my post is inspired by a blog post from 13 years ago.

I am talking about a formal lessons learnt process, not the natural passing of bits of information in a piecemeal fashion. The first hurdle, therefore, is the willingness of a project team and an organisation to conduct formal lessons learnt.

Let’s first talk about the three steps in making the transfer; I quote directly from Nancy Dixon’s blog (linked below):

1. Sensemaking: The members of the project team jointly make sense of what they have learned.

2. Formatting: Designers assemble, translate, aggregate, and mine projects lessons in such a way that they are useful to different groups in the organization

3. Moving: KM professionals create both pull and push mechanism so that lessons are accessible to those who need them.

The final hurdle is the application of the lesson learnt - and, even more importantly, avoiding the misapplication.

With AI apps becoming more and more accessible, it is easy to imagine how businesses can create a very easy and effective lessons learnt repository - and even easier to imagine how far behind some will be in the coming months and years.