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This article probably needs no introduction—we all know what AI is, the potential it has to disrupt almost every industry (including project management), and what ChatGPT is. I’ll spare you an intro in which I recap the hype train we’re all on.

In the interest of providing you with some useful information on how you might start using AI in your day to day project tasks (which so many LinkedIn-fluencers try and fail to do), here’s a roundup of ways project managers are using it right now.

9 Use Cases For AI In Project Management Right Now

There’s plenty of tools out there beyond just ChatGPT (although most of these use cases were tested in ChatGPT)—Notion has released an AI tool, as have Jira and ClickUp.

As you’ll see, in most of these examples, the project manager notes that they had to check and edit things themselves. At the moment, AI is another tool which needs human intervention—it can get things wrong (or sometimes make things up!), so it still needs a human touch.

1. Drafting Communications With Stakeholders

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Sometimes you need a little help wording that email or Slack message. Maybe you’ve already written the message and need some suggestions to improve your tone or cut it down. Either way, AI tools offer a fast shortcut to the 10 or 15 minutes you might waste away trying to edit it yourself. 

2. Turning Project Briefs Into Project Plans

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Once you’ve nailed down a project brief, building it out into tasks and phases can be a tedious task. AI is great for speeding up these manual, arduous processes. You’ll be able to slap the tasks and due dates into your project management software (after a thorough once-over, of course).

3. Generating Time Estimates

Sure, they might not be the most accurate, but your team members likely aren’t accurate 100% of the time either.

As mentioned here, it’s a starting point and as any good project manager knows, you should aim to under-promise and over-deliver. Whatever project estimate the AI gives you, keep this in mind.

4. Brainstorming Risks

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Start by giving the AI an overview of the project scope (make sure to remove any sensitive or proprietary details!) and ask it to generate some risks. Again, you can use this as a starting point.

It can probably give you a few you might not have thought of (especially if you’re new to managing risks), and you can use its output as a jumping off point to generate other ideas.

5. Brainstorming Ways To Reduce Budget


Stuck with a budget that feels too small? AI might have some ideas for making it work! You’ll enter an overview of your scope again, and ask it to generate some ideas for reducing your currently planned budget.

Don’t just follow its advice blindly, though! Make sure its suggestions make sense for your project, especially if you haven’t given it all the nitty gritty details.

6. Writing Project Documentation

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AI can write some project documentation for you, as long as you give it the right parameters. You’ll once again need to make sure not to share any sensitive details with it, but it will give you an outline that you can fill in with those sensitive details later. You’ll get a leg up on filling out your project documents with less hassle. 

7. Researching Project Background

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Get a head start on your background research—try asking it questions you might normally go to an SME for. Of course, you’ll still want to run what it says by a real person at some point, but starting with AI can save time.

And as this particular person mentioned, they still do end up speaking to an SME, which is important when working on projects you’re not an expert in.

8. Clarifying Technical Communications

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For project managers in more technical roles, part of the job is communicating all those technical bits to non-technical people, which can be a challenge.

AI can save you time by helping to simplify and clarify technical language, so you can keep regular communications with your stakeholders running smoothly. Paste in your text and ask it to simplify your language.

9. Assessing Project Health

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Even if you’re tracking the right KPIs for monitoring project health and progress, sometimes those KPIs don’t show that there’s a problem until it’s too late.

You need to find the leading indicators and warning signs—AI can help you there. If you feed it your list of KPIs and success metrics, you can ask to identify which of those are leading indicators.

What’s Next?

How have you been using AI as a project manager? We’d love to hear what use cases you’ve found—comment below!

As this technology is still new and rapidly emerging, I suspect there will be plenty more use cases for me to report on. Subscribe to The Digital Project Manager newsletter to stay in touch and keep up with everything happening with artificial intelligence in project management. 

By Nuala Turner

Nuala is the Editor of The Digital Project Manager. Her background is in content strategy, content production, and managing projects. She brings a strong editorial eye and a passion for connecting with experts in the field and teasing out their stories, as well as ensuring digital project managers are winning at work and smashing projects out of the park.