One of the things that we’re talking to people about is getting the best advantage out of using AI for project and work management. We see many of you wanting to “skip to the front of the line” and jump in to do really elaborate things with the AI technology and the available tools. There is a lot of great stuff with AI generators in the marketplace right now, and that's fantastic. But one thing for you to ponder, what if you could realize immediate value now just by doing some of the operational components of project management (like automating meeting minutes) as you build towards a larger strategy to take advantage of the big things that AI can do.
Some of these features are things that you can find in Microsoft CoPilot and in products like we have, Teams4PM, but you can also build in some of the features on your own using existing technologies, especially in the Microsoft sphere. You can use Azure AI, the GPTs, the CoPilot capabilities and extensions built into it.
I urge you to consider your objective. There are limitless things you can do now to see immediate value and make the project manager or team member's job easier. You can build automated issues and risks and decision lists, benefit analysis, etc. just out of Teams meetings and transcripts. So, when you do that, you're now building a foundation for a lot more that you can do with AI in the future. For example, imagine you’re creating automatic risk registries from meeting minutes. When you present these to the project manager, they can quickly identify whether a particular item poses a risk or not. These are things that you might not have done as robustly before because it was harder to do, or you forgot to do it. So now you're building better data.
Let’s say you're working across multiple work streams in a consolidated area, like within Teams, and you have different tools that you're using for the actual project management. Pull everything together where the work is, within Teams, and start automating these operational features and capabilities. So, in the case of risk, now you've got risk registries across different types of work across all kinds of projects, but it's in a standard format. Now you have a data structure that you can mine for risk analysis. As you build this bigger body of data, you then can have AI look at all of it and do a preemptive risk analysis, or even identify if there's a risk you're not seeing as a human coming into this type of project. Because you have this bigger data set, AI can go and look at it and be more effective.
So, just something for consideration -as you build towards your big goals, work on checking off some of those small goals too.