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Leaders of Analytics

Creating Customer Empathy at Scale with AI - Featuring Dr Kirk Borne

Oct 12, 2021
01:09:53

AI and machine learning are seen by many as capabilities with enormous potential for unlocking digital personalisation and customer empathy at scale.

Organisations that get this right are disrupting industries and leaving old-school competitors broke. Just think of what global businesses like Netflix, Amazon and Facebook have been able to achieve with data-driven personalisation.

Yet, for many organisations, the promise of AI seems elusive or at least very hard to achieve. Many businesses are not realising the full potential of their stores of data, simply because they don’t know how.

To help us understand the potential of AI and ML for Customer Experience Management, I recently spoke to my friend and co-author of Demystifying AI for the Enterprise, Dr Kirk Borne.

Kirk is a truly unique individual who combines his incredible intelligence with a real passion for his chosen vocation. Having graduated with a PhD in Astrophysics, he spent 20 years working at NASA, before moving into the academic and corporate worlds.

He spent 12 years as Professor of Astrophysics and Computational Science, where he created the world’s first data science undergraduate degree. He since moved into data science consulting where he has been an executive for the past 6 years.

Kirk has a social media following of well over 300,000 which is a testament to the huge amount of value he creates through content creation and knowledge sharing.

In this episode of Leaders of Analytics, we discuss:

  • What data science, AI and machine learning can bring to digital and analogue customer experiences
  • The most valuable applications of AI for customer experience management
  • How AI can be used to amplify the abilities of front-line staff
  • Leading applications of AI-driven customer experience
  • The technical and organisational challenges that must be overcome to move up the analytics maturity curve
  • The importance of ModelOps in operationalising data science

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