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KM AI course: Case study- World bank

1 May 2021
Dr. Moria Levy

May 18th, Session 7. Today we are hosting Margot Brow, Senior Advisor Knowledge Management, Office of the Chief Operating Officer, International Finance Corporation (sister organization to World Bank). We all heard about World Bank's knowledge management program, being one of the pioneers in this discipline, opening the doors for many others. Dr. Vincent Ribiere, one of our course leaders, led this interview.

So what did World Bank KM team do? They nailed down the core activity of the bank and its unmet needs: Reusing existing knowledge when leading a new project. World Bank has run, during the years, over 22,000 projects. Manually sorting all the information from the various projects and offering the top-ten projects to be considered, together with the most relevant information and knowledge in each, is a 2 months job.

And here started the journey: Understanding that AI can help. Starting to gather data sources representing various aspects.of information and knowledge on projects, from strategic organizational resources (project descriptions and key project documents; country profiles; World Bank advisory services & analytics; delivery challenges' papers and more); from external resources (similar non-World Bank lending projects; endless open databases and defined libraries); and even from SAP (key team members in the project).

The KM and AI team started programming through tools as R, python and SAS, combining advanced unsupervised learning algorithms together with manual knowledge curation. The challenge: to offer each project, as part of the organizational project workflow, the most relevant projects and knowledge that are important to consider for their current project, together with a package of relevant documents and lists of people to consider speaking with.

Bottom line: Grand success. 5 minutes work of AI and very relevant suggestions, that in any other manner would be buried or ignored.

Take aways:

  1. Work gradually. Start without technology expenses.

  2. Team together people that understand the business, the organization, KM and IT.

  3. Embed the knowledge in the business project processes.

  4. Think outside the box (data sources).

  5. Invest in communicating. They won't come by themselves.

What next? many plans. For example, shortening and helping better onboarding. WOW! We indeed are waiting to hear the details of success next year.

 This post was initially published in LinkedIn

Margot Brown interviewed by Dr. Vincent Ribiere
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