We’ve seen the same pattern play out project after project: the plan needs to come together fast, the monthly report must be ready by the deadline, and risks to be caught before they turn into surprises. This is especially true for someone leading their first project: they need a lot of support here, and there isn’t always time to give it. We don’t think AI solves this on its own, but based on our own experience, it can take on a large share of the repetitive, manual work, if you know where it’s worth using.
What AI can do for you
We’ve seen these tasks come up in almost every project we’ve worked on. That’s why we recommend them as the first places to use AI:
- Project plan drafting: AI builds the structure and a first draft based on your chosen criteria and best practices.
- Monthly reporting: AI compiles a status update from project data. It’s quick, and the format stays consistent.
- Risk logging and dependency identification: AI surfaces risks and dependencies systematically, not just when someone has time.
- Stakeholder communication drafting: AI writes a draft message. The project manager finishes it.
- Onboarding support for a new project manager: AI guides newcomers through shared processes and ways of working.
In our experience, AI saves time on these tasks and makes the quality more consistent. The key is to train it on your organization’s own criteria, not generic assumptions.
A practical example: the monthly report with AI
We’ve seen this situation many times. The steering committee meets on Friday. Before that, the project manager has to prepare a monthly status report covering the project’s five key areas.
Without AI, this often takes half a day. The project manager digs through the project management tool, spreadsheets and emails, then writes the report from a blank page.
When we’ve tried AI for this ourselves, the process has looked like this:
- Gathering the data: AI pulls information from task management, the schedule and budget tracking.
- First draft: AI writes a status overview and lists schedule deviations and open risks. The draft is ready in the organization’s own report template.
- Review: The project manager goes through the draft in 15–20 minutes and fixes any misreadings. They add their own view on how serious the deviations are and how they’ll affect the project.
- Send: The report goes to the steering committee. Preparation took an hour instead of half a day.
The same principle works for the risk log. AI can go through the project’s communications and schedule. It can flag things like a supplier’s schedule slipping or a double-booked resource. Without AI, the project manager might only catch these later. Still, we recommend that the project manager always decides which risks are real and how to respond.
AI doesn’t make better decisions. It gives the project manager more time to make them.
What’s worth keeping for yourself
A project manager’s job isn’t just gathering information. It’s also making decisions and leading people. We don’t recommend handing these to AI:
- Prioritization and change decisions: what gets done, what doesn’t, and why.
- Difficult conversations: with stakeholders and the team, and building trust.
- Exceptions and conflicts: handling the unexpected and resolving disagreements.
- Ownership of the outcome: someone has to check and approve anything AI produces before it goes out.
In our experience, AI doesn’t replace the project manager. It frees up time from reporting and compiling. That time goes to the work that needs a person: decisions, conversations and supporting the team.
Curious which tasks in your projects you could hand to AI first? We’re happy to help you map them out. Just get in touch.