Authored By: Kamran Sathar, Head of Project Services, Korus Group Australia.

It’s the talk of the town and with good reason. Artificial Intelligence (AI) has made an impact across many industries with its promise to change the way businesses operate in the near future. Listening to Professor Paul Boudreau’s presentation was extremely insightful. I would like to share some takeaways which inspired my approach to project management.

What Is Artificial Intelligence (AI)?

In short, AI is using software that performs iterations in loops based on calculus and builds a model from data. This model is driven by data to continually learn and evolve. There are three types of learning.

1. Supervised: uses labelled data sets

2. Unsupervised data: a large volume of unlabeled data sets that have sufficient features for the model to determine its label

3. Reinforcement: repetition and self-correcting

The two AI components for project management are that facilitate the learnings above are machine learning, based on historical data and natural language processing which processes text to find meaning and response. These components are explored further within Professor Boudreau’s presentation with references to Microsoft’s Power Bi as one of the available tools to perform data correlation and learning.

How Do We Get Started?

Depending on the nature of your industry, there are small steps to start this journey. No matter the size of the organization, classifying your information to derive meaning will better prepare your business to leverage the tools of tomorrow.

1. Compile your historical data and start evaluating by volume, structure and relevance. If the volume of data is small, begin capturing information with relevant features. These could be how many risks eventuated on a project, how many risks didn’t occur, how much was spent on tasks, how difficult were the change orders, budget for each change order. The process is trial and error in identifying what relevant tags of data are important for you and your business.

AI in Project Management

2. Perform data analytics such as Pareto charts to identify the relative importance and frequency of events or issues. Additionally, generic check sheets are a good starting point if data collection is consistent from the same reference point – i.e. items within weekly project meetings.

We tested this through the Pareto chart below mapping the frequency of RFIs raised (left Y axis) within projects against WBS packages (X axis) and against the percentage contribution towards the total number of entries logged (right Y axis). The results suggest that RFIs relating to the first 3 categories form 80% of the queries and collisions onsite. However, upon reviewing the circumstances, it can be noted that the bottom 3 categories generally attract a low frequency across projects within the RFI register as these are items that enter the construction site towards the end of the build.

We then supplemented this dataset with the entries within the snag register to mitigate this variable (see below). The results show a jump in the frequency of base build and furniture items, and indicate that 4 categories result in 80% of logged entries. The exercise illustrates the continued process of refining information to derive meaning and can form powerful insights such as:

  • Identifying that the dataset may be characteristic of specific scope or conditions.
  • Correlating the entries against the risks identified at the commencement of the project.
  • Allocating resources effectively by creating automated alerts and priority tagging
  • Improving the functionality of an implemented virtual assistant.
  • Informing a prediction of where risks lie prior to commencement of a project through the presence or absence of key scope requests
  • Pinpoint specific milestone dates where coordination between stakeholders is most critical
  • Indicating a need to further classify the data into more specific categories
  • Identifying areas for further research and analysis that are required to enable a model creation

3. The compounding effect of collecting relevant information will strengthen your ability towards leveraging AI to improve project success. At a baseline, the practice will refine the art of asking the right questions. Automation can be built around interpreting and applying constraints to the datasets and eventually building a model that perpetually improves classification and predictions.

I resonated with Professor Boudreau’s concluding note that AI will inevitably be more efficient in making projects successful, which is the ultimate goal for our clients. Our vision at Korus Australia is to offer our clients a tailored lean cross-sectional team that is core to your business, who are passionate about your business’s future and celebrate your project successes. We will continue to learn and apply innovative thinking to how we deliver projects collectively with you. Please reach out if this read was interesting to you or your business.

In my next blog piece, I will be sharing insights from my attendance of PMI’s webinar on how AI will transform project management by Antonio Nieto and Ricardo Vargas including six aspects of how artificial intelligence will disrupt the field.


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Citations: The Power of Artificial Intelligence for Project Management (2021) YouTube. YouTube. Available here

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