Ever feel like you’re juggling a dozen functions at once, hoping you won’t drop the ball on any task (or the whole project itself)? Well, what if I told you there’s a tech-savvy assistant to make project management a whole lot easier?
You’ve landed on one of the (actually good) uses of AI! In fact, in a Capterra survey, 93% of companies that have invested in AI in project management reported positive ROI.
From keeping deadlines in check to making smart decisions faster than you can say “Gantt chart,” here are the top eight AI use cases in project management.
1. Automated task scheduling
Imagine having a virtual assistant continuously analyzing project requirements, team availability, and task dependencies to create a perfectly productive schedule. With AI project management tools, you can say goodbye to hours spent juggling spreadsheets and calendars. Instead, you focus on strategic planning and decision-making while the AI takes care of the nitty-gritty details.
These AI-based scheduling tools also prioritize tasks based on their importance, deadlines, and dependencies. With clear priorities in place, you tackle the most critical tasks first, maximizing productivity and minimizing project risks.
2. Project forecasting and budgeting
AI is great for creating comprehensive reports for you to easily communicate key findings to stakeholders in a clear and actionable format.
You can use AI to analyze historical project data and external factors to forecast future outcomes (just make sure you double-check the outputs). From predicting timelines and resource requirements to forecasting budgetary needs, AI gives you the insights you need to plan and execute your projects more effectively.
AI budgeting tools spot potential budget overruns before they happen and highlight areas where you can save costs. Plus, with real-time monitoring of project finances through AI algorithms, you can make adjustments on the fly, ensuring you stay on track financially.
3. Productivity and collaboration
Coveo reported that knowledge workers spend nearly three hours of an average workday searching for information needed to do their jobs. Finding the right document or information can turn into a vicious circle of lost productivity — and that’s where AI tools (like dedicated chatbots) make that process a whole lot easier.
By using natural language processing (NLP) techniques, AI project management platforms can index and search through project data, helping you quickly find and sort relevant information. This capability enhances productivity by making sure your team has access to the knowledge they need to make informed decisions and complete their tasks effectively.
Virtual collaboration spaces with AI capabilities serve as one-stop hubs for team communication, sharing documents, and coordinating projects. They enhance collaboration by sorting and prioritizing information and enabling live conversations.
Related: 4 Best Practices for Collaborative Project Management
4. Creativity enhancement
Imagine you're designing a building with the help of AI generative design software. All you need to do is provide inputs about what you want the building to achieve (like strength, energy efficiency, and cost-effectiveness) as well as any constraints (such as available materials or local building codes).
Then, the AI tool would generate several design options, tweaking and adjusting them based on the input parameters. Then, after several rounds of refinements, you might arrive at better solutions that may not have been immediately obvious through traditional design methods.
Or, better yet, say you’re brainstorming with your team. The focus should be on generating as many ideas as possible (something AI can help with), but then, what do you do to sort all that information into actionable next steps?
Mural AI can help you with just that — by selecting a group of sticky notes, you can have Mural analyze the content and arrange them into groups.
5. Risk management
Artificial intelligence in project management involves the analysis of project data, like past performance and team dynamics, to find patterns that indicate potential risks. You can find out if certain tasks always run late or if there’s a history of resource shortages. Knowing this helps you catch problems early before they become big headaches.
You can also play out different scenarios to see what might happen, such as simulating a budget overrun or a supply chain disruption to see how it'd affect the project. Scenario planning helps you prepare for the unexpected and come up with strategies to handle potential risks.
Related: Explore Mural’s risk wall template to determine which ideas have the highest probability of success
6. Intelligent decision support
Artificial intelligence does more than just crunching numbers.
AI tools for project management analyze historical project data, market trends, and external factors to find patterns and correlations that are clear and obvious to you. This analysis gives you a clear picture of what’s working and what’s not, helping you make informed decisions backed by evidence. No wonder a survey by the Project Management Institute found that decision support is the second most popular way project managers use AI, just behind reporting.
Another role of AI in project management is to help you optimize resource allocation. By looking at factors like skills, availability, and costs, AI tools suggest the best way to divvy up resources to get the job done efficiently.
Related: Group decision-making: 9 frameworks for success
7. Performance monitoring and evaluation
AI keeps track of how your project is progressing and highlights areas where you can improve. AI project management tools automatically track key metrics like project progress, milestones, and budget expenditures, giving you a real-time snapshot of how things are going.
But AI doesn’t stop at collecting data — it’s also good at making sense of it. For example, your AI tool might notice that certain tasks always take longer than expected or that a particular team member consistently outperforms others. Using these insights, you can modify your process steps.
AI-driven performance monitoring also includes qualitative metrics. Through sentiment analysis and NLP, AI tools analyze project-related feedback, like customer reviews or project documentation, to gauge overall response and identify areas for improvement.
8. Quality assurance and control
AI tools automatically detect anomalies in project data, such as irregularities in software code, project schedules, or financial data, signaling potential quality issues. This proactive approach helps you address quality issues early on before they escalate into costly problems.
In Agile project management, AI supports quality assurance by helping your team prioritize testing efforts and focus on high-risk areas. This way, you can maintain quality standards even in fast-paced development cycles.
Collaborate and work smarter with Mural AI
Still wondering how to use AI in project management? Mural AI is here to support all your visual collaboration needs. Our generative AI works where and when you need it to help you get the most out of your projects.
Mural AI is the assistant who speeds up those tedious tasks, the contributor who sparks brilliant ideas, and the coach who guides your team toward success. It also integrates with Microsoft 365 Copilot to streamline your daily tasks using simple, natural language prompts.
Whether you’re a designer summarizing brainstorming sessions or a product manager retrieving project kickoff templates, Mural AI has you covered.
Use Mural AI — available with our Enterprise plan — today to collaborate at the speed of thought.
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