Consulting Solutions

Six stage gates to a successful AI governance

Successful AI governance

Responsible use of should start with a detailed assessment of the key risks posed by [1], followed by a good understanding of the principles that should be followed [2], and then the governance of from a top-down and end-to-end perspective [3].

Author and Copyright: Anand Rao, Global Artificial Intelligence Lead, PWC, via

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningWe have discussed these in our previous articles [1, 2, 3]. In this article, we focus on the first line of defense and dive into the nine-step data science process [4] of value scoping, value discovery, value delivery, and value stewardship and highlight the dimensions of governance.


Given the focus on governance we look to answer the key questions on who is making what decision based on what rationale along the nine-step process. Although we have nine steps in the process, we consider only those points in the process where we are making a key decision. The figure below shows our nine step process, the key outcomes for the nine steps, and the six stage gates where we are making a major decision. These decisions are:

  1. Is it worth having an solution or not?
  2. How do we design (build or buy or rent) the solution?
  3. Does the model meet our expectations?
  4. Do we deploy the model into production?
  5. Is the model ready to be transitioned for ‘business-as-usual’ operation?
  6. Should the model continue as-is, retrained, redesigned, or retired?

Stage Gate 1: Is it worth having an solution or not?

This stage gate occurs in the first step of business and data understanding of the value scoping phase of the project. This is undoubtedly the most critical step as it determines if we want to go ahead with having an solution or not. We should understand five key areas to make this determination: […]

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