Context

  • High expectations from Top Management on AI to stimulate innovation and the transformation of R&D, but a high level of challenge to develop and deploy robust and sustainable solutions beyond proof of concept (POC)
  • Numerous digitization projects in the various R&D entities with a lack of consistency and coordination leading to limited ambition
  • A lack of alignment between R&D entities on the most relevant use cases to be addressed and the associated POC to be launched as a priority
  • Teams who “don’t speak exactly the same language”: business teams with a field-oriented vision vs. data scientists focused on algorithm performance. It leads to a potential lack of alignment with operational issues associated

Objective(s)

  • Leverage R&D data to optimize business efficiency

  • Generate new fields of innovation

It is necessary to manage resistance to change by promoting the development of teams’ skills, by facilitating communication between the various departments and by promoting deployment initial successes. Indeed, it is the support of all the employees in the digital transformation project that is a decisive factor for success.
Mathieu Pailler, Director, Innovation Practice Leader
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