Predictive Project Analytics Predictive Analytics AI for Project Management

AI for Project Management: Key Innovations Having an Impact

In his latest blog, Cora CTO Pat Henry takes a look at AI for Project Management, and how five key innovations are already starting to make a difference when it comes to managing project portfolios.

Artificial intelligence (AI) has opened up a brave new world when it comes to managing project portfolios. Here are five key innovations that are already starting to make a difference.

1. Aim

To leverage the vast amount of data available within your project portfolio management (PPM) platform to identify what attributes are important in predicting the success of a project. What is it that great project managers do?
Is there a way you could use the data within your PPM platform to predict the success of a project, or to prompt the user to perform some actions which could potentially turn the project’s RAG status from red or amber to green? Or are there any indicators that identify that the project is going to fail, and that we should cut our losses and move on?

2. Motivator

A lot of operational level data are gathered in your Cora PPM platform, from various logs, registers, workflows, uploaded documents, audit trails, etc. But they weren’t being analyzed holistically, on a project or portfolio level. Instead, we tend to rely on the project manager to interpret what all that data mean for their project. And, based upon their insights, we update the project RAG status, and the various registers, logs and report upwards if there are any issues, etc.
Building upon our in-house expertise in the area of AI and machine learning (ML), we saw a gap in the market for a system that could automate some of this manual analysis. By having insights on the project and portfolio status automatically generated, we knew we would be empowering the project manager on the ground to make necessary decisions faster. This then frees them up to spend more time on value-adding activities rather than admin.

3. Proof of concept

One area in which we are currently growing our offering is around financial information, cost books, financial forecasting, etc. As forecasting is an area that lends itself to AI and ML techniques. Specifically, we focused on the accuracy of the EAC (estimate at completion) forecast. Because forecasting costs accurately is vital for driving precise revenue estimates. So the EAC will stay the same over the course of the project.

To get an understanding of EAC deviation, we needed to identify the data points (levers) which impact EAC adjustments. This information can have a bi-fold impact: first, it can be used by AI and ML techniques to give percentage confidence about the predicted EAC deviation/adjustment. And it also can be used to promote process changes in our client companies, by informing them of the attributes which are more important and need to be tracked and monitored.
Over the course of four months, we used that problem to upskill our technical team on the intricacies of data extraction, cleaning, and the basics of AI and ML. The team got exposure to supervised and unsupervised learning algorithms, as well as reinforcement learning techniques, and learned the types of problems and data which were best suited to them.

4. Natural language processing

Another area of research and innovation within Cora’s platform is around natural language processing. Over $100bn worth of projects, in over 50 countries across the globe are managed using Cora PPM daily. At any one point in time, there are over 300,000 live projects operating in Cora PPM.
During the operation of these projects, a significant amount of free text is entered into the system, along with a large volume of documents, some of which also include handwritten notes. All this information is stored in its raw state. Project managers must log in to the system and manually select to view this information, before making their decision or updating the project status. The natural language processing service will parse, extract and annotate all of this raw data, enabling machine interpretation.

Having the ability to run sentiment analysis (looking at text someone enters and trying to gauge mood based on what is inputted) on a project or a portfolio will enable our clients to ensure that they are focusing their efforts on the correct projects and portfolios. The system will be able to raise early warnings where the perceived sentiment does not correlate to the reported project or portfolio status, and potentially escalate risks or issues.

5. Strategic portfolio management (SPM)

Currently, we are further developing our strategic portfolio management product, Cora SPM, which will build upon the strong foundations of Cora PPM. And we will apply advanced data analytics to convert operational-level data from project and portfolio management platforms (such as Cora PPM) into insights.

Cora SPM will have the capability to predict the success of projects, identify which projects should be included in a portfolio, and, ultimately, guide project and portfolio managers to ensure that they achieve repeated success.

Incorporating advanced data analytics and machine learning into the project and portfolio management space, when combined with our in-house domain expertise, will give Cora a significant competitive advantage, especially in our strategy realization offering.

Find out more about Cora’s project portfolio management software solution or scroll down to read more insights into the benefits of AI for Project Management.