By Ricardo Lemos 
Artificial Intelligence is increasingly revolutionizing every industry you can imagine, and project management is no exception to this rule. According to PMI’s AI Innovators: Cracking the Code on Project Performance report, 81% of project managers admitted that their organization has already been impacted by AI.
The report also predicted that projects managed with AI will increase from 23% to 37% in the next three years. As the impact of AI grows, project managers will have to adapt. AI will change the way projects are managed, how strategy is implemented, tasks are executed, and decisions are made.
AI in project management:
1- Predictive Analysis
McKinsey conducted a study of 1,800 software projects and found that only 30% of projects were delivered on time. What’s worse is that 20% of these projects were able to meet the deadline because they removed certain features and functions from the project scope.
McKinsey’s study also found that date variance decreased when project managers used predictive models. There was a 30-40% reduction in defects per line, which improved code quality.
They concluded that by leveraging predictive analytics for planning and decision making, project managers can dramatically reduce project failures and increase project success rates.
Predictive Analytics can also help you with:
- Time management
- Team collaboration
- Better control over tasks
2- Risk Management
Every project has risks, interdependencies, and uncertainties associated with it. As a project manager, it is your duty to assess and respond to these risks efficiently, otherwise they can become the cause of project failure. With AI at your disposal, your job will become much easier. The AI system will alert you to potential risks by analyzing historical and real-time project data.
Most importantly, it increases visibility into your projects and can also predict potential project outcomes. For example, project managers can see whether a task will be completed ahead of schedule or not by analyzing how much time is being spent on a task. The same goes for projects.
3 – Project Estimates
There are times when you are not sure how long a project will take to complete or how much money you will have to spend on it. This is when you will have to rely on historical business data and project estimates, which can be unpredictable in most cases. AI and machine learning are great at analyzing large amounts of data and finding patterns so that they can provide useful insights that will help you with project estimation.
Having an AI tool to estimate a project can become a powerful tool in the increasingly competitive market, as it provides detailed time estimates for each task, which increases productivity. Its advanced algorithms break down tasks and predict durations, allowing for better planning and time management.
Given the increasing pressure on the modern estimator, what is more important when it comes to estimating: speed or accuracy ?
This is important because, according to market research, it takes approximately 40 hours (1 week) to prepare a small to medium-sized proposal, 90% of which is spent simply estimating the number of items in a project. The rest is spent collecting quotes and consolidating the information into a final bid number.
Below are some of the AI features that help professionals:
- Automated takeoff capabilities that use AI technology to count common items on drawings. What used to take days of work can now be done in hours.
- Advanced tools that allow you to create or import existing items and data from other software sources.
- Pool licensing, which allows licenses to be easily shared among multiple users within an enterprise.
- The fully integrated job manual includes all information about the work units so professionals can bid with confidence.
Therefore, we have seen that there are AI systems that help professionals to estimate with a greater degree of assertiveness and less risk. In this way, the limits of estimates are reduced to the point where they are even closer to the real values of the project. This makes it easier to compose the final cost of the project, making it leaner.
4- Knowledge-Based Systems
Gone are the days when AI was dumb and could only act based on the data you fed it. Today, AI-powered systems are smarter than ever and can learn new things over time. They can now understand the context of data, which means they can offer useful insights that can help you make the right decisions. What’s more, these knowledge-based systems can also support human learning, which can also make them more efficient at what they do.
Knowledge-based systems use machine learning and natural language generation to create documentation for the individual. Mark Broome, PMI’s chief data officer, was adamant: “Learning from a multitude of previously executed projects and associated project management artifacts will be used to train AI to effectively assist the project manager in all aspects of project management, including charter development, time and resource estimation, communications, and risk identification and management.”
5- Controlling the project with Machine Learning
Machine learning is at the heart of all AI technologies. It does a fantastic job of identifying patterns. Project managers can leverage the power of machine learning to study patterns in project schedules and highlight areas where you can speed up the project process. You can also use machine learning to assess risks and enable finance managers to provide a better offer to clients. This can increase revenue and profits for the company. It can also be used to automate approval workflows and eliminate friction.
6- Decision Support Systems
Two of the biggest questions most companies are asking are “Can AI-based decision support systems increase project success rates by reducing costs and errors?” or “Can these systems increase efficiency and prevent your project from going over schedule and over budget?” Fortunately, the answer to both questions is yes.
These decision support systems create intelligent processes using rules and logic and help automate your decision-making process. They can also help you make the right decisions in complex situations. Broome predicted that “As decisions need to be made throughout the project, project managers will rely on predictive models to evaluate options and select those that offer the highest probability of a positive outcome.”
Machine learning algorithms can also help companies visualize which features of their products customers are using and which ones they aren’t. This will allow project managers to make the right decisions about which features to improve and which ones to abandon.
7- Resource Management
Some AI-powered tools that companies are already adopting, such as predictive maintenance, can make projects more efficient. Task management tools are a prime example of this. They use an intelligent task assignment process to optimize resource utilization and maximize productivity without overwhelming your employees. Project managers can also automate routine and repetitive tasks with AI and free their teams to focus on more value-generating activities.
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