Standard

IEEE WHITE PAPER

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Abstract

ABSTRACT Among the highly hyped AI related developments involving multiple stakeholders, there is still a lack of common understanding on relevance, requirements, impact, and applicability of AI-based solution for any given application. This is more pronounced in industrial AI applications where there is a substantial gap in understanding between the domain expert and data scientist. This leads to multiple iterations while conceptualizing, developing, deploying, and maintaining such solutions, often resulting in higher engineering efforts. This impacts scalability of such solutions. Standardization has an important role to play in such scenario. Among multiple ongoing standardization activities, establishing criteria to determine the level of maturity for implementing AI applications is very important to evaluate AI applicability in the initial phase. This can help all stakeholders to achieve a common understanding at a macro level before detailed explorations can be initiated, if required. This white paper touches upon some important focus areas that can be of help in establishing a framework for determining industrial AI maturity levels. This includes establishing both technical as well as organizational readiness and subcriteria therein for various AI maturity phases starting from exploration, experimentation, stabilization, expansion, and leading to AI enabled transformation. This sets the tone for setting up more detailed explorations to develop specific standards in this area, which will help in bridging gaps in expectations across multiple AI stakeholders, primarily the AI application providers and users/customers.

Document information

  • Standard from IEEE_AC
  • Published:
  • Version: 0
  • Document type: IS