AI for Enterprises. Part 2: Metrics for Matriculates? [Extended version]

The recent wave of new opportunities to transform enterprise productivity, innovation and success leveraging artificial intelligence (AI) as part of digital transformation is driven by the tremendous advances in our ability to collect, operate on and extract knowledge from extremely large volumes of data. If not used with adequate care and caution this rapidly evolving opportunity currently comes with catastrophic implications for the enterprise that will be hard to course correct by throwing resources at.

In the first of this two part post on adoption of artificial intelligence in enterprises 'AI for Enterprises. Part 1: Where are we in tackling the popular adage GIGO?' posted in the MassTLC website and the extended version at ReSurfX website, we dealt with various aspects of data quality effects on digital transformation in the context of the popular adage garbage-in-garbage-out (GIGO). In this second installment (Part 2) of this post ‘AI for Enterprises: Metrics for Matriculates?’ also posted in the MassTLC website the theme centers on the power and pitfalls in the use of metrics from and for AI and machine learning (ML) based solutions. This is an extended form of the second part of the post and includes a powerful use case in the healthcare sector using ReSurfX solutions and the enterprise SaaS product ReSurfX::vysen. I highlight common problems in use of metrics and possible solutions that span beyond technical aspects to include enterprise architecture, culture and education appropriate to different organizational divisions and roles in leveraging these multi-disciplinary advances.