Artificial intelligence (AI) is fast becoming mainstream in business, with various industry leaders curious about how it can impact work at every level. AI has already gained a stronghold in various business practices, from customer service to marketing, and HR and project management is not left out.
Project management has evolved in various ways since the use of basic software to track workflows. AI is fast becoming a core part of project management by boosting productivity and prediction. It makes sense that every project management out there is curious about how to implement AI into multiple workflows and make the best use of the technology.
In this post, we will explore 4 use cases of how AI is transforming project management and how you can get ahead of this growing trend.
- 1 Why is AI important in project management?
- 2 4 Use cases of AI in project management
- 3 Final Thoughts
Why is AI important in project management?
AI is gaining ground in different facets of project management, from project planning to execution, monitoring, control and closure. Project managers can leverage AI’s automation capabilities and sophisticated analytics to make timely decisions and improve performance.
AI has definitely pushed project management from just project management tools like Asana and other Jira competitors to faster automation and more insightful analytics.
Here are some other reasons why AI is important in project management
More data to analyze
A lot of data is involved in project management workflows from multiple systems, tools and stakeholders. With AI solutions like machine learning, these big data sets can easily be analyzed quickly to reveal powerful insights.
More complex projects
Certain projects can be difficult to handle with basic project management tools. AI aids fast collaboration between important parties and teams across the globe. This results in fast decision-making and implementation.
AI enables predictive analytics, making it easier for project managers to forecast risks, project loopholes, and potential outcomes. Project managers can identify patterns in real-time data to improve planning and execution techniques.
Stand out from competition
Innovative companies are already leveraging AI technology in project management procedures to gain a competitive advantage in the industry. Not exploring AI can make you fall behind industry expectations.
4 Use cases of AI in project management
AI keeps pushing the boundaries of what’s possible in project management. Let’s explore four specific use cases of how AI is impacting project management.
Project managers generate schedules to visualize and organize project requirements. They use static scheduling templates that only contain project milestones, tasks and phases to do this. By leveraging and analyzing real-time data, AI can create very accurate and complex schedules in seconds.
For this experiment, project managers can ask the AI tool to generate a work schedule based on a specific context. Within seconds, a full schedule and work breakdown structure will be generated. With more practice, project managers can provide more detailed context to help the AI to provide better schedules. For example, your prompt could be “Generate a schedule for a worldwide CRM implementation project with an eight-week timeline starting from November 2023 using project management best practices”. In seconds, the AI would revert with a complete schedule showing all requirements and estimated task duration.
With this tool, project managers can save time by generating custom schedules for multiple projects without sacrificing accuracy. This could lead to better resource allocation and more successful projects.
When allocating resources to different phases of a project, project managers often have to input the costs into each cell manually. AI, particularly ChatGPT, can suggest and spread the total cost of the project using an S-curve, thereby automating the process fully. You can also automate the process to enable the AI to spread the cost by different timeframes, whether weeks, months or years, depending on what the business needs.
This use case can save a lot of time for project managers who must present a cost estimation draft to team leaders. It creates a good structure that the team can build on. For a use case like this, how detailed and tailored your cost estimation experience with AI will be will be determined by how much information you’re willing to share with the technology. Since AI builds off the information you provide, the more detailed and comprehensive your prompts are, the more tailored and accurate the financial forecasting will be.
Advanced testing of systems and software
Before a major project begins, project managers must run several tests on the systems and software that will be used to avoid setbacks and ensure everything works optimally when the project is ready to go live. With AI, project managers will soon be able to automate multiple advanced tests of systems and software to help the team spot defects early and implement self-correcting processes. This will drastically reduce the multiple hours spent on testing and the number of necessary reworks, allowing project managers to deliver bug-free solutions in record time.
For example, when trying to launch a new product, the product development and project management team would work together to manually test every product element before it’s launched. With AI, these tests can be automated in advance, with every element and scenario analyzed for bugs, possible user or design issues, etc. This way, the product development team can fix issues before launch.
Risk logs generation
Risk management is a crucial part of project management. When you identify potential risky elements in the early stages of a project, it’s easier to eliminate them to ensure successful project completion. Risks can be found in different elements of a project, from scheduling to resource availability and allocation; sometimes, the project goal itself can be risky.
Experienced project managers would have to assess every element or stage of the project, identify potential risks and document them. They can also analyze how these risks could impact the project’s success.
While this strategy works, project managers only identify and analyze these risks based on their wealth of experience with past similar projects. The issue is that each project is different and requires fresh insight. AI helps in this case by analyzing potential risks based on the information and context provided for each project.
Project managers can easily create a comprehensive risk log by explaining the purpose or context of the project to the AI and asking the technology to explore and share potential risks. You can take it further and ask the AI to provide possible mitigation strategies and solutions for said risks. Even though you may not use all the information the AI provides, it is an excellent starting point for your risk management process.
Project management is one of the major sectors that can benefit immensely from AI. This technology already helps project management automate and manage several processes, and it will only get smarter and better. With more in-depth analytics, risk management, schedule generation and cost estimation, project managers can save multiple hours and get a great head start on projects.
If you are not utilizing the power of AI, it’s important to begin the process to see how this technology can fit into your existing project management strategy. The best way to begin is to isolate tasks or project elements that AI can improve and track any improvements. The key is to ensure you’re not left behind regarding leveraging AI in project management.