The world witnessed a boom in the AI industry post-COVID-19. The application of AI systems in several industries has become profound.
In a recent PMI Pulse of the Profession® poll, over 80% of respondents feel that AI is heavily influencing their organizations and project management is not left behind.
Today, we ask ourselves: What is the role of artificial intelligence in project management? What is the impact of artificial intelligence on project management methodologies? How can AI systems support project managers?
These are no longer issues that talk about the future of project management but rather deal with the present.
In this article,
Introduction to Artificial Intelligence in Project Management
What you should know about Artificial Intelligence (AI)
How is Artificial Intelligence (AI) going to affect project management?
Will Artificial intelligence (AI) replace project managers?
Role of Artificial Intelligence in Project Management
AI Application #1: Predictive Analytics
AI Application #2: Resource Allocation and Optimization
AI Application #3: Risk Management and Mitigation
AI Application #4: Project Performance Tracking and Reporting
Introduction to Artificial Intelligence in Project Management
What you should know about Artificial Intelligence (AI)
The growth of the artificial intelligence industry has been a fascinating one.
Right from its inception in the mid-20th century, the advancement of AI systems has been fueled by an increase in computational power, availability of large datasets, and other technological breakthroughs.
AI systems leverage algorithms and data to simulate human intelligence and perform tasks that typically require human cognitive abilities.
Artificial intelligence’s growth has triggered a chain reaction that has led several industries to reconsider their future systems.
Organizations recognize the potential of AI in revolutionizing industrialization and are studying its impact and usefulness. In fact, according to an AI investment report in 2023, the investment in developing AI systems reached $94 billion in 2021, a 34% increase from the previous year.
How is Artificial Intelligence (AI) going to affect project management?
According to the Harvard Business Review, only 35% of projects today are completed successfully. One reason for this disappointing rate is the low level of maturity of project management technologies available.
Organizations are looking for ways to improve their project management efficiency, and the implementation of AI is included.
Project management software like Ayanza, Trello, Asana, and many more are integrating AI systems into their platforms or, instead, slowly becoming AI-based.
Artificial intelligence and project management will intersect to bring about transformative changes in the planning, monitoring and execution of project ventures.
Will Artificial intelligence (AI) replace project managers?
The trending idea of AI taking over the jobs of project managers seems likely, but then we should understand that AI systems have limitations too.
A survey by the Project Management Institute (PMI) highlights that as the use of AI in project management increases, so does the number of individuals managing those projects. It speculates that AI will assist and not replace project management jobs.
The position of project managers remains critical to project management, and the role of human intelligence and expertise cannot be overridden by artificial intelligence.
Given how fast and groundbreaking AI technologies are developing, we must explore what the future holds.
Let us look at some of the applicative benefits of AI in project management.
Role of Artificial Intelligence in Project Management
AI Application #1: Predictive Analytics
Artificial intelligence systems feature machine learning algorithms and advanced data analysis techniques, which can be helpful as a predictive analytics tool.
In a recent PMI webinar on AI Advances and Ethics, an expert opined that the actual value of AI is in the algorithms that support decision-making.
These algorithms, powered by an advanced data analysis system will be able to:
Provide data-driven insights:
As a step in providing insights for future actions, AI systems will scan vast amounts of historical project data. By calculating data relationships and extracting patterns, AI can highlight areas for improvement.
Predict project delays:
AI-powered predictive analytics can help project managers forecast project timelines more accurately.
By analyzing historical data and real-time project progress, AI models can identify factors contributing to delays, estimate the likelihood of future delays, and provide early warnings to project managers.
This information will help project managers to manage time more effectively.
Decision making support:
Artificial intelligence is modelled to simulate human intelligence. They try to imitate the human perspective of elements to relate efficiently with the natural world.
AI’s machine learning and cognitive abilities can help support decision-making processes.
When AI systems provide insights-based background work, they can further assist project managers in making better project decisions by simulating every available alternative while opting for the most optimal outcome.
By leveraging the predictive analytics capability of AI, project managers can make more informed decisions and optimize project outcomes.
AI Application #2: Resource Allocation and Optimization
The application of AI in project management transcends from improving project work methodologies to the ability to manage resources.
How can AI help in resource allocation and optimization in project management?
Well, here you go:
Analyzing allocation requirements:
AI systems can identify project requirements, considering task dependencies, skill sets, and project timelines.
By examining historical project data and patterns, AI can analyze resource needs and provide recommendations for allocating personnel, equipment, and materials appropriately.
Optimizing resource allocation:
You have a budget limitation, project priorities, and various constraints. These factors must be carefully considered before resource allocation.
What does artificial intelligence do? AI systems will examine these factors and suggest resource allocation strategies that will consider the optimal utilization of available resources.
This will help develop an efficient resource management framework and significantly reduce the time spent working out one.
Handling complex resource scenarios:
Given the number of interdependent tasks and resource dependencies, managing resources can become complex.
AI can effectively handle these complexities with its ability to model and simulate.
A study by the Association For Project Management reveals that project managers believe that artificial intelligence is more beneficial to complex projects than simple ones.
Like how we use powerful hardware machines to accomplish more extensive and complex tasks, this is how AI systems benefit complex projects or any complex scenario that might be encountered.
Cost optimization
Artificial intelligence can assist in optimizing the cost of running projects and how resources are distributed.
AI systems can compare the selling price of several vendors, logistical requirements, the cost-effectiveness of alternative resources, and several other factors to give reports on financial plans.
AI Application #3: Risk Management and Mitigation
In a study published by Delloite, 81% of project managers revealed that they saw an improvement in risk management with the application of artificial intelligence.
Of course, there should be a noticeable leap in risk management with AI because it fills the gap for humans as we are sometimes shortsighted in risk assessment, according to a professor in a New York Times post.
Things could become complicated when risks act as dependencies and become progressive with the project— a really complex situation.
Here is how artificial intelligence can help project managers manage and mitigate risks.
Risk identification:
AI systems can examine project performance and relate with external data sources to identify potential risks during a project.
For instance, AI systems can identify the risk of unfavourable weather conditions in a house construction project plan.
It analyses historical data, external sources and compares them with present data to find anomalies in patterns. This helps the AI to understand when something is wrong or can be potentially compromised.
Real-time risk monitoring:
Integrating AI systems with several data sources and improving its machine learning algorithm facilitates better risk monitoring.
Artificial intelligence can continuously monitor project-related data in real time, allowing project managers to promptly detect and respond to risks.
Risk assessment and quantification:
AI can help project managers assess and quantify risks by analyzing historical data and utilizing probabilistic models.
By considering factors such as impact, likelihood, and interdependencies, AI systems can provide risk ratings and prioritize risks based on their potential severity.
This enables project managers to allocate resources and attention to critical risks more efficiently.
Intelligent automation:
This entails programming an AI system to carry out routine analysis of project progression and provide feedback on risk assessment.
This allows AI systems to routinely collect and analyze data or run specific management tasks that carry out particular risk assessments.
With such automation, project managers can cut the time spent on manual and repetitive assessment tasks and eliminate the potential for human error.
Natural Language Processing (NLP):
AI-powered NLP techniques can analyze unstructured data sources like project documentation, stakeholder communications, and social media.
Extracting and analyzing textual information can help AI identify emerging risks, sentiment analysis, and detect potential project issues that may not be explicitly reported.
AI Application #4: Project Performance Tracking and Reporting
With the analytic abilities of AI technologies, their role in performance tracking and reporting on project status cannot be overstated.
This allows project managers a deeper introspect into the status of the project and a better understanding of how every element of the project is performing.
Here’s how AI can be used as a tracking and reporting tool for project performance:
Automated data collection:
With AI systems, you can automate project data collection on task management tools, time-tracking systems, financial systems, and communication platforms.
This eliminates the need for manual data entry, reduces human error, and ensures the availability of up-to-date project information for performance tracking.
Real-time performance monitoring
With AI systems, continuous data collection, analysis, and reports on various aspects of a project can be done seamlessly.
AI systems will be able to track and identify anomalies in the project and provide instant information on every activity carried out.
This can provide any needed information on the key performance indicators (KPIs) at any given time, which project managers and stakeholders can use to closely and continuously monitor project progress.
Visualization and reporting
The power of AI visualization and reporting capabilities was demonstrated in an experiment carried out by Forbes.
Here, they asked project managers to provide a context initiative on a project plan for an artificial intelligence system. In return, the AI system generated a work breakdown structure (WBS) and schedule for the project.
The resultant output became better and more accurate with each step as the project managers provided more precise context.
When AI systems can access detailed data on project metrics, they can provide visual reports and dashboards clearly and intuitively.
Final Thoughts
According to a KPMG report, organizations implementing and investing in AI systems have seen a 15% leap in productivity.
AI systems are becoming more and more adapted for a reason.
The role of artificial intelligence in project management holds a beneficial position. It will assist project managers rather than replace them.
Its implementation will contribute to improved project outcomes and increase the rates of successful project delivery.