Infographic: Sizing up the economics of AI

Companies that take a multi-use case approach to getting started with Enterprise AI report nearly 3X the return from AI investment than siloed concepts
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An Accenture research report shows that, falling to scale up Artificial Intelligence initiatives might put 75% of organizations in existential crisis. The research is based on a global survey of 1,500 C-level executives from 16 industries. According to the study, an overwhelming majority of 84% CXOs believe that they must implement AI to achieve growth objectives. Yet, only 16% respondents said that they are successfully leveraging AI as per plan. As a result, compared to the laggards, a tiny group of top performing companies are achieving three times more returns from their AI investment.

In order to succeed, organizations are looking to simplify best practices around sourcing and storing of data across different formats, deployment of diverse AI models, ethical solutions for compliance and performance monitoring. So that decisions made by machines can be explained, visualized and audited. Another challenge is to initiate and develop high quality analytics and predictions around a small data set. Unlike Google, Facebook, Citi or Wal-Mart not every organization has the luxury of cleaned and accessible large data sets, so they would try to attain accuracy with limited data in the first phase.

 

To scale it up, organizations need able and experienced Data Scientists and AI engineers to build machine learning models and knowledge extracting applications. Here is an inforgraphic that outlines the costs and how organizations can best manage them.

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