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Deciphering the Barriers of Implementing AI One by One

Understanding AI in the context of your own business, industry, application area, technology evolution is the foundation for any AI implementation in the enterprise.

  • Do not emulate or try to compete with the hyperscalars on AI. The market opportunity in AI is sufficiently bigger than what the hyperscalars are aiming for.
  • AI is an umbrella of different technologies (machine learning, deep learning, computer vision, NLP, machine reasoning) and depending on the specific industry and application, there is likely to be a unique technology combination.
  • AI application development is moving towards mobile application development with “drag and drop” interfaces, however enterprises should try and avoid “black box” approaches to “drag and drop” modules.
  • AI ownership within enterprises is most likely better off with a decentralized approach, with CEOs in the long run becoming the default Chief AI Officers.
  • Follow the right mix of “in-house + outsource” approach to implementations, based on circumstances.
  • Data is fuel for AI, and as an investment priority should be on top of the list.

Georgios Kipouros

Co-Founder and Head of Research, AI Business
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