How A.I. Will Change Project Management
In today’s workplace, project management has gained momentum as a key component of commercial success. In the absence of a solid management system, a team would likely be in shambles, as there wouldn’t be an efficient system to track task assignments and team progress. According to the Standish Group, only 35% of projects are conducted successfully, and the Harvard Business Review attributes this to “the low level of maturity of technologies available for project management.”
As artificial intelligence (AI) becoming increasingly powerful in various areas of business, a burning question is how to use AI for project management. Project management apps such as Asana, monday.com, and Microsoft Copilot have greatly incorporated the power of AI into their tools, which have the potential to greatly enhance productivity. Other areas of business have also applied AI in creative ways: the Internet of Things, for instance, can help monitor crop growth in agriculture or the monitoring of the quality of goods in a factory.
Project management and AI may sound like two compatible fields, but interestingly, the crossover isn’t often discussed. We therefore explore some ways in which AI can be incorporated into your team’s project.
How is AI transforming project management today?
Prioritize projects and streamline progress
Advanced AI can help rank tasks and projects through data analytics, making sure that projects are in line with the overall business strategies of a company. It can create automated project reports, giving rapid updates on the status of the project via automatic, unsolicited reporting, thus keeping project managers constantly in the loop. Having a computer prioritize projects can also mean less human bias in the long run.
More efficient project lifecycle management and reports
With AI, the different phases of a project can be significantly streamlined, which empowers the generation of highly specialized project schedules and stakeholder reports. With historical data, AI can detect mistakes from the past and flag them out beforehand in all phases of the project. Project managers can make good use of Natural Language Processing (NLP) to extract insights from massive amounts of text data, such as project reports, emails, and client feedback– a task that would otherwise be laborious for human employees. This insight can further guide workers to improve their products and services.
AI-led testing enhancement
The introduction of automation in testing not only improves project quality but also generates extensive test scenarios that guarantee a more thorough project evaluation. This, however, is not easy–as projects grow in terms of complexity, AI would need to adapt to simulate “real-world” interactions in business.
How will the evolution of AI in project management look like?
1. Integration and automation
Integration and automation lie at the heart of AI development, and they are essentially software workflows that independently operate with preset parameters, needing little human intervention. In the realm of office-based project management, integration and automation can help with tasks such as transferring data from daily communications to systems of record, doing budget updates and scheduling, and monitoring human resources (recording work hours or flagging overtime work). In a marketing field, integration and automation can provide cognitive insight, such as predicting what customers are likely to purchase, or increase elements of personalization in business. While seemingly simple, integration and automation can significantly smoothen procedures, reduce costs and risks, allowing more time for managers to deal with higher-level tasks. Going forward, integration and automation will primarily enhance the features of a product – all while making internal and external business processes more efficient. It can also help a company understand clients more and make better decisions.
2. Chatbot assistants
AI chatbots can assist humans in real-time by responding to queries. With customer-oriented chatbots, projects such as Ultimate and IBM watsonx Assistant are designed to help companies maximize productivity and minimize costs 24/7.
AI chatbots are commonly known for assisting clients, but its uses can well extend to project management more broadly as well. With chat functions, bots can handle many trivial tasks during a project, performing data analysis, scheduling meetings, and compiling information from the Internet. This will allow the human capital in a project management team to be used more efficiently. This will allow the human capital in a project management team to be used more efficiently. In advanced AI-led project management, the AI can not only schedule meetings, but can also make decisions based on previous data of people’s work performances (e.g., freelancer Jane typically requires at least three weeks to finish this task, so the originally planned four weeks can likely be reduced). In the near future, there is potential to have chatbots streamline projects, while also effectively taking into account the working styles and schedules of different internal or external parties involved in a project.
3. Machine learning-based project management
Machine learning is a branch of AI, and can be described as machines imitating human intelligence and learning from experience. Its application is powerful and penetrates almost all fields of commerce – from medical diagnosis from images to what appears on your Netflix recommendations, machine learning plays a role in many of our daily activities.
Moving machine learning to project management, it can help by yielding predictive analytics on the basis of historical data. This is a new idea in project management and over the next decade, it could be set to sharpen decision-making by relying on AI-driven decisions. An example can be seen in the use of expert systems in the Space Shuttle Mission Control project. With machine learning, the expert system can predict system behaviors, thus making monitoring and repairs more efficient.
4. Autonomous project management
With AI playing a role in project management, some may question whether project management can perhaps become completely autonomous and require little-to-no human intervention. However, considering the potential risks of operations where robots are in complete control (see the case of self-driving cars as an example), as well as the budget needed to develop such completely autonomous projects, it is unlikely for project management to become fully autonomous in the coming two decades. The extent to which a project can be entirely autonomous also depends on the nature and complexity of the project itself.
Will AI replace project managers?
The advent of AI implies an upcoming revolution of the labor market, and many of us are concerned whether AI will have the ability to take our jobs and render us redundant. When it comes to project managers, however, it may seem a little out of the question for now. AI has the potential to heavily facilitate workflow, allowing the team to minimize the number of trivial tasks to take care of and focus mainly on tasks that require human insight and expertise. Advanced project management still requires critical thinking and innovation, and this can, largely, only be achieved through human experience. Although autonomous project management is taking shape as an idea, developing AI-led project management is highly complicated due to the intricacies of stakeholder communications, risk management, budgeting, and strategic thinking. As a result, for the foreseeable decades to come, human capital will still be key for project management success.
As AI continues to evolve into an augmentative tool, the most pressing task is to explore how AI can be integrated into projects. Our capacity truly lies in adapting it for the coming era. This requires not only effort (keeping abreast of AI developments) but also a certain degree of innovation and problem-solving, as there is no one-size-fits-all, quick formula for using artificial intelligence in project management.
Esme Lee is a science writer and editor in the UK, carrying a passion for tech copywriting. She has a background in educational neuroscience and holds a PhD from the University of Cambridge.