Pace of change in AI, Data and Analytics

The ready availability, the burgeoning use of and almost constant discourse on AI is only the start. The pace and scale are increasing, for example, it took Netflix 3.5 years to reach one million users, ChatGPT on the other hand achieved this in just 5 days.  We cannot ignore it as a project professional – not if we want to stay informed, current and increasingly relevant in whatever area of the project world we work.

The umbrella term Artificial Intelligence (AI) is used by most people to cover a complex multi-layered landscape with no commonly agreed definitions or boundaries between e.g. where does AI, deep learning or machine learning start and end. Similarly for the roles involved such as data architect, data scientist or data analyst. These topics were only of interest to specialists or innovators until very recently.

Project professionals now need to understand, to varying degrees, the landscape to allow them to make informed decisions about what is and is not currently possible on their projects. To allow opportunities to be identified and seized. This is far from easy as even the increasing number of self-appointed experts in the AI landscape admit to struggling to keep up with the breadth of application, complexity and pace of development. One of the most prominent applications to date has been in and around planning and in particular around time and identifying and assessing the schedule – the credibility, predictability or risk associated with it. Multiple perspectives, ways of engaging with and services are now offered that require leaders to make decisions about the value added in delivering success.

In this half-day seminar we will aim to shine a light on the AI landscape through the lens of projects by asking experts to answer a number of pertinent questions, including:

•    What is AI and what it can do for us now and in the future?
•    What are the opportunities and threats in particular within projects?
•    What do we have to do as individuals and organisations to keep up?
•    Which roles will stay and which will go?
•    What can we learn from AI form outside of projects