AI in Project Reporting: Less Effort, More Overview

April 25, 2025 – Reading time: 6 minutes

Microsoft Copilot streamlines project reporting by analyzing meeting notes and project plans to deliver concise, AI-generated updates. By using simple prompts, teams can efficiently extract key project statuses, challenges, and risks—reducing manual effort. With structured documentation in place, this AI-driven approach significantly boosts efficiency, especially in multi-project environments or those with tight reporting cycles.

Project reporting is one of the standard tasks of a project manager and is often time-consuming. Project resources that are actually needed for the operational management and planning of the project are tied up in regular reporting.

The effort required to create a report results from the analysis of past projects and amounts to at least one hour per week per project. Project management or team leads have to search for, collate and prepare information from various sources for the report. The progress in the project plan must be analyzed, as well as any problems and blocking points that have been addressed, which may have been noted in meeting notes.

All of this costs a lot of time, especially when the effort is continually added up through one to two-week reporting intervals. Accumulated over the year, this consumes a significant amount of time and energy that is actually needed for project management and project control. Especially for companies that work a lot on projects, the weekly reporting effort across the organizational structure adds up to a considerable sum. Consequently, any help and time savings in reporting deliver significant added value.

Artificial intelligence can support project managers, team leads and other roles that regularly prepare reports with the work steps in project reporting and thus reduce the time required. In principle, the approach we would like to present to you can be used for any reporting in projects and is therefore not limited to specific roles. Using Microsoft Copilot as an example, we will show you the prerequisites for using AI efficiently for project reporting, how you can establish AI-supported reporting in your organization and what specific benefits this will bring you.

The process used for AI-supported reporting differs only slightly from the process without AI support. In the latter, the report creator searches through the meeting notes from the previous days and transfers the most relevant information, such as progress, risks, challenges and the most important next steps, into a reporting template. The project plan is also considered and the most important information from this is also transferred to the reporting template. Another important source of information is the memory of the reporting creator, which is particularly useful if not all information is stored in writing. This information must be subsequently added to the template.

The process with AI support now looks very similar to the process without AI support. The special feature here is that the manual process of obtaining information is automated. The AI now takes over this task. It also compares the differences between the last reports with the information in the current report.

In our example, a reporting template in PowerPoint is used in which the results generated by the AI are reported. The categories, status, risks, next steps and challenges are listed in the template. The AI model (in our case Microsoft Copilot) supports the project management in developing the content by finding the right information and outputting it in precise form. In our example, we used the following prompt for Next Steps and entered it into Copilot:

„You are a project manager and report to management on the project. This document contains meeting minutes for the project. Each meeting is to be considered separately. The meetings are presented in chronological order. Report to me on the current next steps in the project. Summarize the information in precise and short bullet points.“

For challenges and risks, we use the same prompt and simply replace „next steps“ with „challenges“ or „risks“.

We use the following prompt for the status:

„You are a project manager and report to management on the project. This document contains meeting minutes for the project. Each meeting is to be considered separately. The meetings are presented in chronological order. Tell me which work packages are currently being worked on. Please only mention work packages that have not yet been completed. Summarize the information in precise and short bullet points.“

We use this prompt to summarize the current project status:

„Please give me a summary of the current status of the project. I am interested in quantitative information on the progress of the project, but also in qualitative information if tasks, the project or related objectives are at risk.“

We then receive a response from the AI for each prompt, which summarizes the required points in bullet points (Fig. 1). This information is then simply copied into the relevant template. The user is not restricted to the categories of the template shown here, as the AI can basically be used for any reporting template.

During the first implementation of our AI-supported reporting, meeting transcripts of the weekly meetings over two months and the respective project plan were managed for two projects. We estimate that an experienced project manager would need around 30 to 60 minutes to create a report in order to familiarize themselves sufficiently and record all challenges and risks for each work package. By using with the help of AI, the status of each work package can be determined in a structured manner within seconds. Especially if you use a pre-formulated prompt, as listed above, which you have adapted once to your needs. You can use the prompt to find out whether and when which work package has been completed. The AI can also tell you whether the respective work package is currently on schedule, is late or has been completed. For each statement, the AI also references the place in the document where the information is recorded. This standard function is very helpful for further reading or verifying the AI’s results if required.

It is also possible to interact further with the AI and ask questions, such as why a work package was completed late or is currently listed as late. The AI then uses the available data to try to find more detailed information that can be linked to the delay. It is important to mention once again at this point that the AI does not make any assumptions and, in case of doubt, simply states that there are no reasons (known to the AI) for the delay. Similar to this procedure, information on challenges and risks that arise can be worked out with AI support

AI support is therefore very helpful and time-saving, but also requires extensive meeting minutes that list all relevant information. So if you decide to use the AI-supported approach, we recommend establishing a high standard of minute-taking. This reduces the reporting effort and leaves more time for the strategic tasks of project management, especially in critical phases.

More complex tasks cannot yet be processed by AI. These include, for example, estimating the consequences of delays at the start of the project or the general assessment of the status of the work packages and the overall project.

To read the full version of this article, follow this link to the publication in Projektmagazin.

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