AI Project Management:
What it is, why and how to use it
A guide to understanding the uses of Artificial Intelligence (AI) in project management in 2023.
AI is no longer just a trendy term in the business sphere anymore, it's actively used, and project management is no exception. But – how is AI for project management being used, and what are the drawbacks and benefits?
In this comprehensive guide, we’ll cover the various ways AI is being applied in project management in 2023, the types of AI being used, and some tangible examples of its practical application.
What is AI Project Management?
AI project management is the practice of leveraging Artificial Intelligence (AI) in project management tools, jobs to be done, or roles. AI is commonly used to improve the efficiency, accuracy, and organization of project management-related tasks and can be found in many different aspects of the field and across many industries.
For software teams in particular, 72% of them report actively exploring AI tools. This may look like tools for creating and managing tasks or facilitating agile methodologies.
How can AI help with project management?
Because AI is still a very new technology, its capabilities and use cases are changing, practically by the day. As of 2023, no single AI project management solution can manage your projects without significant human intervention. Most solutions are meant to be an extension of human skills rather than a replacement for them.
According to our survey results, most software teams are looking to use AI in project management for planning, estimation, and prioritization, and backlog refinement.
Let’s take a look at how the current technology can and is being applied to project management:
AI task creation and text summarization
AI can potentially revolutionize the most tedious (and not-so-fun) aspect of project management – task creation. Think about it – the majority of the time you spend managing projects is usually involving steps like:
- Adding tasks to the PM system and embedding them under larger projects
- Detailing descriptions and requirements
- Assigning tasks to team members
- Setting due dates
- Control Chart reports let you see how long issues take from start to finish.
- Determining effort of tasks
Enter AI, our handy assistant. It can step in to offer various support mechanisms, including but not limited to:
- Suggesting task metadata
- Summarizing, generating, or reformatting task data and descriptions
- Editing task descriptions
- Organizing data
- Predicting project end dates
With AI, we get a glimpse into the future, helping us stay ahead in the game.AI's learning capabilities from historical data also prove to be a game-changer. Teams with a wealth of data at their disposal can leverage AI to make more accurate suggestions based on historical outcomes. This is because AI has the ability to learn from previous user behaviors and data inputs, enhancing its efficacy and accuracy in task creation.As humans, we’re not always the best on reflecting on past outcomes with perfect accuracy or without bias. With AI-powered recommendations, we can make more accurate unbiased decisions.
Finally, let's look at some real-world AI examples:
- Zenhub's AI labels: Zenhub AI will scan all existing labels and suggest the best ones based on your Issue title or description, improving data cleanliness and conserving time.
- Notion AI: This tool lends a hand with summarizing or writing task descriptions, effectively saving teams time and enabling a focus on critical tasks.
AI data hygiene and consistency
When you start a project, you don’t always have the foresight you have mid-way through or at the end. That’s why learning from patterns and mistakes made previously can help ensure smoother sailing in the future. This is something AI is particularly good at. Regarding applying this to project management, here are some things AI may be able to predict:
- AI-suggested due dates based on your team’s previous performance may ensure that project proposals are more realistic.
- Suggested story point estimations or “effort level” may help teams better understand how complex tasks within a project are to get a better grip on resourcing and timing.
- Predicted project blockers and risks can help teams prevent things from stalling where they have previously stalled or notify team members of at-risk deadlines.
- Resource requirements: With the right data, AI could help determine the number of people, types of skill sets, and hours required to complete a project within a certain timeframe.
- Wrike’s AI project risk prediction, which identifies whether or not a project is at risk of being incomplete.
Note: There are other ways you can achieve the same predictive results without AI. For example, Zenhub project end-date predictions can predict whether or not a project is at risk using historical data. Still, the tech behind this is not considered true artificial intelligence. Check it out here.
Types of AI in project management
The following are examples of types of artificial intelligence and how they might be applied to project management. This, of course, is not an exhaustive list of all possibilities, and AI technologies can also be combined and tailored to meet specific project management needs.
The benefits of AI in project management
AI is intended to augment human capabilities instead of replacing them. For developers currently using AI, reducing busywork and improving the accuracy of work are the top two benefits they are looking to get from AI use.
Improved Decision Making: AI can analyze vast amounts of data and historical project information to provide project managers with data-driven insights. This enables more informed and less biased decision-making, leading to better project planning and execution strategies.
Efficient Resource Allocation: AI algorithms can optimize resource allocation based on project requirements, team capabilities, and timelines. This helps in maximizing resource utilization and minimizing waste, ultimately improving project efficiency.
Risk Identification and Mitigation: AI can analyze historical project data and external factors to identify potential risks early in the project lifecycle. This allows project managers to proactively implement risk mitigation strategies and avoid costly delays or failures.
Automated Task Management: AI can automate repetitive and time-consuming tasks, such as data entry, document processing, and scheduling. This streamlines project workflows, freeing up valuable time for project teams to focus on higher-value tasks.
Continuous Improvement: AI can learn from project data and outcomes, enabling continuous improvement in project management processes. It helps in identifying best practices, learning from mistakes, and refining project strategies over time.
Predictive Analytics: AI-powered predictive analytics can forecast project outcomes and potential roadblocks, allowing project managers to take proactive measures and keep the project on track.
Drawbacks of using AI in project management
While AI can be a game-changer in project management, it's not without its speed bumps. Let's talk about some of the most common roadblocks you might encounter when incorporating AI into your project management strategy:
Misinformation and Bias: AI is only as good as the intel it's given. If it's fed data that's slanted or just flat-out wrong, your AI system might start making decisions that miss the mark. This can lead to project outcomes that leave you scratching your head.
Errors and Lack of Accountability: Even AI can have an off day. And when complex tasks are at play, it's not immune to making mistakes. The tricky part? Figuring out where things went wrong can be like finding a needle in a haystack.
Security Concerns: AI needs to know the 411 to do its job. But if your security isn't up to snuff, that could lead to your sensitive project data falling into the wrong hands, and that's a risk no project can afford.
Lack of Interpretability: Sometimes, the way AI works can feel like it's all smoke and mirrors. If project managers can't understand how AI makes its predictions or recommendations, it can throw a wrench into the works.
Dependency on Quality and Quantity of Data: When it comes to AI in project management, garbage in equals garbage out. If your data is subpar or you just don't have enough of it, your AI's performance might take a hit.
Integration and Adoption Challenges: Merging AI into your existing project management toolkit can be a tall order. Not to mention, people's resistance to change and unfamiliarity with AI can make the adoption process slow-going. Read our blog, Will artificial intelligence end agile as we know it? for more discussion on this topic.
Social Implications: With AI in the picture, the landscape of work might look a bit different. The social and ethical implications of this change need to be thoughtfully addressed. Check out our blog Will AI Replace Project Managers? for our thoughts on this.
Overreliance on AI Recommendations: It's easy to get swept up in AI's insights, but it's important not to lose sight of the human element. When we rely too heavily on AI, we risk overlooking the invaluable intuition and expertise that only humans can bring to the table.
AI project management: future outlook
Here's the deal – artificial intelligence is a game-changer in the realm of project management, no doubt about that. It's like adding a sprinkle of magic dust to your decision-making, a dash of efficiency to your resource allocation, and a spoonful of foresight to your risk mitigation. But remember, it's not all rainbows and unicorns. There are some drawbacks to consider – biases, security risks, adoption challenges –the works! As we venture further into this AI wonderland, it's crucial that we don the explorer's hat, navigate its potential pitfalls, and keep a steady hand on the wheel.
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