Simon Phillips & Ryan Darby

How to use AI to better manage programs and portfolios 

ABSTRACT

Project status reports have historically been used as the primary source of project control. However, for managing programs and portfolios these provide an incomplete view. The information is generally static and reveals minimal history. The project may be green today, but may historically have been flipping between different states. 

This hypothesis, that past project performance can support future insights, is being propelled by 2 separate factors: increasing workloads and the growth of AI. Project professionals, particularly in Program and Portfolio roles, are being asked to deliver increasing business value with the same resourcing. This is the perfect opportunity for AI to reduce the burden of analysing a large data source that changes over time, and to identify potential upcoming challenges. 

Using machine learning we will present a case study, focusing on detecting both patterns in underlying data and the relationships between control points that will build insights. We will also sign post the criteria needed for this system to be a success.

Attendees will take away a list of reasons current approaches are not working, as well as an understanding of what can be done to assist, and learnings into the reality of implementing such a machine learning approach.