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Using machine learning to build a solid pipeline of #Automation and #Analytics opportunities

Source | LinkedIn | Pascal BORNET | Digital McKinsey Leader – AI, Automation and Digital Innovation II LinkedIn Top Voice in Tech

Here is the link to the original post, including some amazing comments:

Case study: Increasing a portfolio of AI, Analytics and Automation use cases by a factor of 20 in three months


Three years ago, a bank facing fierce competition launched a company-wide digital transformation involving Analytics, Automation and Artificial Intelligence. The objective was to improve business efficiency, compliance, and customer experience.

After one year of hard work, using traditional approaches, the bank only identified a few piecemeal use cases for a few functions, estimated at a total of USD 8 million. As a result, the program was facing difficulties in getting enough recognition and exposure, mobilizing teams and management, and getting approval for funding.

To solve this, the bank’s leadership decided to launch a new assessment approach.


Traditionally very manual, and resource- and time-consuming, diagnosis and assessment methods used to employ armies of business analysts crunching data, and disturbing the organization with never-ending interviews and observations. These approaches started to be automated about 4 years ago. Nowadays, data is collected through out the organization, and machine learning is used to recognize, map patterns, and automatically identify areas of improvement.

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