In Project Management & AI series from AISG project management group, this is another topic on How can AI help in IT project management?

Like in most domains, AI can help in project management domain too in several ways but let us focus at one of its techniques in this article called – Predictive Analytics.

What is Predictive Analytics? Predictive analytics is a process of using modelling and statistics to determine future performance based on current and historical data.

So how Predictive analytics can help project managers, is basically to forecast the probability of project success or failure based on available historical project data, project trend, and various project parameters such as timeline, complexity of the project, problem statement, bugs, resources, and so on. This can really help the project managers to identify potential roadblocks and adjust their strategies to achieve better outcomes.

Let us focus on one of the key project management domains, my favourite – Risk Management. Predictive analytics can help project managers to identify potential project risks and vulnerabilities in real-time. This can help project managers to take proactive measures to mitigate those risks, prevent delays and reduce the chances of project failure.

Take an example, A company is developing a new software application. The project involves multiple development phases, a cross-functional team, and a tight deadline, which is a most common scenario.

Typical AI Predictive Analytics steps are:

1. Data Collection: The AI system gathers historical data from previous software development projects, like the current project from the same company to achieve a better result, including project timelines, resource allocation, bug reports, team performance metrics, and external factors like market demand and changes in technology.
2. Data Preparation: The collected data is then cleaned and organized, with variables such as project duration, team size, skill set, number of bugs, and external events being structured for analysis.
3. Feature Extraction: The AI system identifies key features, such as the size of the development team, the complexity of the software, and the adherence to project timelines, which are potential indicators of project risks.
4. Algorithm Selection: The AI system applies, a machine learning algorithm, such as a decision tree or a neural network, to analyze the relationships between different features and the occurrence of risks.
5. Training the Model: With the AI model is trained on the historical data, learning patterns that correlate factors like tight deadlines, high bug counts, and inadequate resource allocation with project delays and failures.

Now the system is ready for prediction on the current project.

6. Predictive Modelling: As the current software development project progresses, the AI system analyzes real-time data related to team performance, bug reports, project timelines, and any deviations from the initial plan.
7. Risk Identification: The AI model generates predictions and probabilities for potential risks. For instance, it may highlight that if the team is consistently missing milestones and the number of reported bugs is increasing, there is a higher probability of missing the project deadline.
8. Mitigation Strategies: Based on the AI’s predictions, project managers can take proactive measures such as reallocating resources, adjusting timelines, or introducing additional testing to mitigate potential risks and prevent project delays.
9. Continuous Learning: The AI model continues to learn from ongoing project data, improving its predictions and adapting to changing project dynamics.

In this example, AI’s predictive analytics capabilities help project managers identify hidden risks in real-time by analyzing data patterns and correlations. This empowers them to make informed decisions to ensure the project stays on track and delivers successful outcomes.

I will cover in future, how AI can help the project managers in other project domains such as Resource management, Cost & Time management.  

Note: some of the information in this article was generated with the help of an AI language model.