Digital Transformation by Thomas M. Siebel
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Best Practices in Enterprise AI Application Development

 

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Compared to earlier generations of enterprise software such as CRM or ERP, the requirements for developing enterprise AI applications and deploying them at scale can be daunting. AI applications involve ingesting, aggregating, and processing massive volumes of disparate data from numerous sources, as well as training, and tuning of sophisticated machine learning models.

Enterprises that succeed at AI follow a set of best practices for implementing this new class of enterprise software. Based on experience in working on some of the largest AI implementations globally, C3.ai has developed a complete, end-to-end application development methodology that codifies these best practices.

This paper provides an overview of this methodology. By following these best practices, organizations can minimize risk and significantly increase their likelihood of achieving meaningful and sustained results.

Contents of this paper include guidance in how to:

  • Plan your AI development project – how to ideate a use case and build a roadmap
  • Specify the application – how to create a technical product specification addressing User Interface, Data Model, AI/Machine Learning, Application Logic, and Integration Architecture
  • Build the application – how to manage application development, testing and tuning, release, and deployment into production
  • Operate the application – key considerations for support, measurement, and improvement, including gathering user feedback

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