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Episode: AI Use Case Series: AI in Project Management [AI Today Podcast]

Author: AI & Data Today
Duration: 00:10:55

Episode Shownotes

Enterprises are increasingly incorporating AI into many aspects of their daily business. And, with this, the project professional is increasingly using AI for a number of tasks. From AI chatbots, to predictive analytics, to generative AI applications that help automate project management duties such as project documentation, meeting minutes, or

report generation, the use of AI among project managers is growing. Continue reading AI Use Case Series: AI in Project Management [AI Today Podcast] at Cognilytica.

Full Transcript

00:00:00 Speaker_00
The AI Today podcast, powered by PMI, cuts through the hype and noise to identify what is really happening now in the world of artificial intelligence.

00:00:09 Speaker_00
Learn about emerging AI trends, best practices, and use cases on making AI work for you today with PMI hosts and expert guests.

00:00:20 Speaker_01
Hello and welcome to the AI Today podcast. I'm your host, Kathleen Molch.

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And I'm your host, Ron Schmelzer. And one of the things we've been revisiting in some of our recent podcasts is ways in which AI is being applied in different industries, our AI Use Case Series.

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And we did this many years ago, early on, mainly because we were trying to understand the impacts, the possible impacts, the different ways that AI could be used. And people were still very early in their AI journeys.

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Many people actually still are very early in their AI journeys, but it was more of

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trying to understand the possible ways in which AI could be used in these different industries, to understand the scope and scale of the kinds of problems we can solve with AI.

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Now that AI is basically in the hands of your ordinary individual, specifically generative AI, but so many aspects of AI, anybody can pick up a tool and start making use of it. Now the question is, I know that AI is, I can do anything with AI.

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The question is, what valuable things? What can I use in AI that's going to really drive benefit. So we decided to revisit the use case series. So that one, because things have changed in all these years.

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So there's many new things that AI is doing in all these industries, but also because we want to revisit what does AI mean for me today? How am I going to get value out of it today?

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And we're going to look at much deeper across maybe even a broader set of many more industries than we looked at before. And we're going to look at all the different ways that AI is impacting those industries.

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Exactly. And so if you haven't listened to our last podcast, revealing our big news, then I encourage you to do so. Our big acquisition news that we're so excited about.

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So for today's podcast, we thought that it would be fitting that we talk about how AI is being applied to project management. now that this is a PMI podcast. And for those that don't know, PMI is Project Management Institute.

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We'll link to that in the show notes. But we really wanted to spend some time today focusing on how AI is being applied in project management.

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If you listen to our podcast for a while, you know that we talk about how project managers can run and manage AI projects with the CPM AI methodology, but this is taking it from that other lens and saying, how are project managers using AI?

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And so PMI has put out this really great chart, and we'll link to it. It came out from a report where it's talking about examples of best fit applications for AI across automation, assistance, and augmentation for project management tasks.

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So these include things like meeting notes, summaries, or report generation, lesson learned summaries, large data set analysis, cost benefits analysis, risk analysis.

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And so we'll link to that so you can take a deeper look into that report and also this chart. But we wanted to spend some time to talk on this podcast about different ways that we're seeing AI being applied, that project managers are doing it.

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Yeah, and the perspective that PMI takes in looking at AI application is that the PMI is really there to encourage the growth and development of the profession of people who are doing project management for a living, project professionals.

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The way that AI needs to impact them is it needs to impact positively the things that they are doing. When they were looking at this, they looked at across two different axes, one which is, who does the work?

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The balance between things that people do and things that machines can do. The more that machines can do on their own, obviously, It's going to be automated because you don't need a human in the loop for that.

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And the more that humans need to do it, the more that the machine is augmenting their skill. And that's why they go from automation to augmentation on the who does the work.

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And at some point, there's like a balance between, you know, a pretty even split between what the machine is doing, what a human is doing, especially in things like analytics and things like that, where a machine can do some of it, but it can't understand the analytics.

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That's where the human steps in. And then the other axis they were looking at is the complication, like how complicated of a task is it to do? And things that have low complication are things like, you know, summarization of meeting notes.

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It's pretty easy. Just get the transcript, summarize it, figure out what the most pertinent points are.

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When you have something that doesn't have a lot of complexity, that means that there shouldn't be a lot of issues around things like, you know, accuracy or things like, you know, application, because hopefully it's an easy, repetitive task.

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Now, of course, meeting notes summarization can have accuracy issues, but maybe the impact on that is not so high. Now, on the other hand, if you're trying to do things like justify a project business case, or

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impact project decision making, that's a complex task, but it's also something that has a high level of dependency for accuracy and for good decisions.

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So what we're going to look at now is we're going to take a look at sort of all the different sorts of tasks.

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that project managers are doing and the ways in which we're applying AI to those tasks across these various different levels of machine and human involvement and low complexity and high complexity.

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And we'll start with one area, which is, well, one thing that project managers do on a regular basis is planning.

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And can we use AI to help with planning and resource management of the various things that project managers and project professionals are being called to do?

00:05:54 Speaker_01
Exactly. And so the answer obviously is yes, and we are seeing it being used. We're seeing AI in project planning for both resource allocation and scheduling as well.

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So, you know, projects often struggle with optimizing resource allocation and scheduling due to just complexity and variability of these project demands. And traditional resource allocation methods have had their challenges, right?

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They lead to resource waste and scheduling conflicts and also project delays.

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So we're seeing AI being used by project managers to help analyze these vast amounts of historical data quickly, predict future resource needs, and then also optimize schedules through the use of AI tools.

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And so this is a really great use case that we're already being seen.

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It's providing increased efficiency, cost savings, improved accuracy, and also enhanced flexibility because it's allowing for things like real-time adjustments now so that humans don't need to be going in there and doing all of that.

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Humans aren't really great at looking at very large data sets and processing that data and doing things very quickly, but machines are. So we've seen that really being a great use case.

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Also, AI is being used to help with choosing project priorities, right? You know, everybody always feels that their project is the most highest priority and everybody wants their project to be worked on right away.

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But project prioritization really is challenging due to just, you need to evaluate multiple different projects based on their potential ROI, their success rates, different needs, maybe the length of the project.

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And traditional methods can be subjective and also really time consuming. leading to suboptimal decisions.

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Also, humans have bias in us, and so maybe, you know, we like a certain team better than another, and so we'll start prioritizing those projects over, you know, maybe teams that are a little more difficult to work with, or whatever it is that humans do.

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So with the help of AI, it really helps make those data-driven recommendations, and You can have more objective and informed decision making, optimizing resource allocation, and just maximizing that overall profit value.

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So we're really seeing AI being used in some great ways around all of that resource optimization, just to help overall. But we're also seeing it being used in different ways. So we're seeing it being used for applying communication and collaboration.

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And one thing that we do like to talk about a lot is those soft skills of AI and how it's actually really being used. And so you can say, well, maybe I'm not a project professional. That's not my role.

00:08:36 Speaker_01
But maybe you're being tasked with some of those job requirements. We see that a lot. And a lot of our listeners have been telling us that a lot of conversations that we've been having when we speak, we hear that as well.

00:08:48 Speaker_01
So it, you know, it really is important. And those soft skills are pretty important. And so even if maybe this isn't your job, you can still apply some of these learnings.

00:08:59 Speaker_02
So more importantly, project managers have to communicate and collaborate to do what they do. So if you're a project professional, communication and collaboration is a key part of what you do. Of course, one thing you do is you have to report.

00:09:11 Speaker_02
You have to do reporting and you have to capture conversations. Because one of the things we have heard, one of the things that we have experienced is that Project professionals are in the middle.

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It's not like they're not necessarily the subject matter experts or the individual contributors who are doing the things, implementing, building the construction project or implementing the software product or doing the pharmaceutical development.

00:09:33 Speaker_02
And they're not also the strategic high-levels. They're not making decisions about what problems to solve. They're not making that strategic stuff at the C-level, right? They're in the middle. So, what do they do in the middle?

00:09:44 Speaker_02
They coordinate, collaborate. They ensure that the projects get done and get done with the degree of success. They pursue project success. So, they use AI to help with the required aspects of communication that they need.

00:09:57 Speaker_02
So, of course, we talked about things like meetings, because that's what project managers do. They meet. We know this from experience. Project professionals just love meetings because that's the life of a project professional.

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You need to capture whatever the insights are that are coming from the meeting, whatever the decisions have been made, and, of course, communicating them. Clearly, we know that we can use AI for this. AI is really great at

00:10:19 Speaker_02
you know, transcription, meaning summarization, capturing notes, capturing the action items, the to-dos, real-time transcriptions, content analysis, summarization, and, of course, integrating.

00:10:29 Speaker_02
You could take these notes and integrate them to your project tools, your planning tools, your spreadsheets, whatever it is. Of course, when you have a machine doing it, that means a person doesn't need to do it.

00:10:38 Speaker_02
This is the area of low complexity and high automation, right? Machines do a good job of this. A human doing meeting notes is actually kind of a rough task, right?

00:10:48 Speaker_02
So machines can save time, increase efficiency, improve the overall quality of the documentation, ensure accuracy, great stuff, right? Now, of course, there's more community than just meetings, right?

00:10:59 Speaker_02
So project managers and project professionals need to create reports, monthly reports, regular reports. Machines are really good at that.

00:11:06 Speaker_02
Maybe they create them in a better, more visually appealing way, use better summarization language, maybe use different ways of representing data. And of course, it could be automated.

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And somebody may even request a report that can be done to their very specific needs without having to get the project professional in the middle of having to generate that. So data collection, data analysis,

00:11:26 Speaker_02
report generation, all those things we can use AI and generative AI specifically for, that of course speeds things up, improves the accuracy, increases efficiency, all that sort of good stuff.

00:11:36 Speaker_02
And there's lots of other forms of communication and collaboration. You can have AI systems doing things like summarizing chats and generating meeting invitations or

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conducting retrospective, you can even have the AI system conduct some sort of information gathering, surveying, trying to figure out what the status is and health of a project.

00:11:57 Speaker_02
You can even have the AI systems respond to questions that people may be asking of project managers, or basically do all sorts of chatbots to help with stakeholder gathering and with team brainstorming and with helping to make sure that tasks that have been started are followed up.

00:12:14 Speaker_02
and create action items and next plans. So these are one of a number of ways that AI systems are really helping to improve and perform the tasks of communication and collaboration that project professionals do, let's be honest, on a daily basis.

00:12:33 Speaker_01
Exactly. And we've also seen AI being used to help with decision support and also quality assurance. So, you know, Ron had said that these project managers do sit in the middle, but they need to be making decisions.

00:12:47 Speaker_01
And again, you know, helping improve recommendations. So we've seen

00:12:51 Speaker_01
AI being used to enhance data driven decision making because, as I mentioned earlier, AI is able to look at very large data sets quickly and then generate actionable and accurate recommendations.

00:13:04 Speaker_01
And so with the help of AI and predictive analytics, it can, you know, spot patterns and trends and correlations between data. that help provide those valuable insights and really just use AI as a tool to help with decision making.

00:13:19 Speaker_01
And we've also seen AI-driven automated testing for quality assurance so that AI helps enhance quality assurance because it can analyze code or predict potential issues and execute comprehensive test scenarios.

00:13:35 Speaker_01
So it can take historical data and it can help generalize and prioritize test cases and adopt to those changes. And then we've also seen AI being used to help with monitoring project outputs as well.

00:13:48 Speaker_01
So it's really just important, we've seen it with real time monitoring, it can provide that immediate feedback and then that helps improve quality. It helps with early issue detection as well so that issues don't,

00:14:01 Speaker_01
they can maybe bubble to the surface a little bit earlier and it can identify and address those quality issues early on in the process rather than getting a lot farther along in the process only to have that, you know, it be an issue and now it's suddenly a fire that you need to put out where before it could have been something that was easier to, you know, adjust and correct earlier in the process.

00:14:23 Speaker_01
We've also seen AI being used to analyze project trends and then present those findings in a digestible format.

00:14:31 Speaker_01
So we had said, you know, AI really is good at being able to generate different reports and it can generate reports for different levels as well. So now it can empower those project managers

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to help with analyzing complex project data and trends, and then transform that into easily understandable insights. And again, back to those communication skills, it can adjust and tailor the reports for different levels.

00:14:58 Speaker_01
So maybe stakeholders need to have one level of detail, executives need to have another level of detail. And, you know, maybe somebody else needs to have another level of detail.

00:15:07 Speaker_01
Now AI can help do that very quickly and really gear your reports towards all those different levels. Right.

00:15:15 Speaker_02
So of course, one of the things, so reporting, communication, collaboration, planning, these are all core parts of what project professionals do. The other thing they do is they have to keep an eye on things.

00:15:24 Speaker_02
You got to do the project monitoring and you have to let people know the status of things. And so of course we can use AI for all sorts of project monitoring and reporting tasks. We talked a little bit about the whole setup of the project.

00:15:37 Speaker_02
You know, when you start a new project, there's a whole bunch of things you need to do to gather the information, to get inputs, to get the data.

00:15:44 Speaker_02
we could have AI systems help with that information gathering step, generate project templates, maybe issue out surveys or forms, and then collect it from all the necessary parties.

00:15:54 Speaker_02
Because creating these project templates can be a very repetitive and a time-consuming task, and that requires attention to detail and consistency.

00:16:03 Speaker_02
So we can use AI to do all sorts of things from automation of the creation of the templates, to handling the data analysis, to doing standardization, to personalizing perhaps,

00:16:13 Speaker_02
Even some of those surveys, and that of course really improves efficiency, improves effectiveness, improves consistency, and of course makes you better to be prepared because you have all the data you need.

00:16:25 Speaker_02
We talked a little bit about project status and project status reports and progress monitoring and reporting.

00:16:30 Speaker_02
We can use AI systems to do all sorts of stuff, automatically look into the tools, integrate, collect data from various sources, do some sort of predictive analysis, do some trends.

00:16:41 Speaker_02
aggregate the data, figure out what the progress looks like, and of course, communicate that progress automatically to the people who need to know. Saving time, improving accuracy, improving transparency, increasing the efficiency.

00:16:55 Speaker_02
We have all sorts of things we can do with AI to do some sort of analysis on project progress tracking. We can compare projects to other projects. to see if it's spotting some sort of pattern that may have taken other projects off the rails.

00:17:09 Speaker_02
And so we can say, oh, this project that you're doing is seeming very similar to this other project that went off the rails.

00:17:15 Speaker_02
And we have to say, a lot of times projects go off the rails for things that have nothing to do with the technology, has to do with the culture, you know, communication or lack thereof. And

00:17:25 Speaker_02
you know, the ability for people to stay on task and focus and things like that. So people are people and they tend to act the very same ways. So sometimes what gets one project off the rails can happen to another.

00:17:37 Speaker_02
And having an AI system provide that analysis can helpfully provide some guidance. And that means that we can also use AI to do things like identify and address bottlenecks. provide some AI insights into what might be holding up projects.

00:17:50 Speaker_02
And of course, we can use that sort of information to do some calculations, automatically calculate estimated delivery times, or whatever the calculations may be. We could figure out what's called schedule variance, SV, or cost performance indexes.

00:18:05 Speaker_02
We can constantly use AI to analyze this project data to provide real estimates on cost and schedule. You know, really help with that.

00:18:14 Speaker_02
And of course, if we're doing it automatically, we can improve accuracy, save time, do early warning on things, enhance our decision making, increase our efficiency.

00:18:23 Speaker_02
And, you know, there's many other things we can do with AI from this project monitoring and reporting system. We think of like what we're doing with AI in predictive health.

00:18:32 Speaker_02
or predictive finance or an education, we're going to apply that very same technology and get those very same results when it comes to AI with projects and get some of those benefits to help keep these projects running as healthy as possible.

00:18:47 Speaker_02
And we want to think of it from that perspective.

00:18:50 Speaker_01
Exactly. We've also seen AI being used to help with expense forecasting. And we've also seen it with budget optimization and, you know, really just kind of that overall area.

00:19:03 Speaker_01
And then we've also seen AI recommendations for training and skill development as well. You know, we always talk about being lifelong learners, having this growth And so this isn't just for that project professional, it's really for everybody.

00:19:15 Speaker_01
But we have seen it be used in the project management profession in particular.

00:19:21 Speaker_01
AI can just improve training and skill development because it provides you those hyper personalized recommendations based on your individual needs and your individual goals and your individual skill gaps as well.

00:19:32 Speaker_01
So that's why there's this idea of hyper personalized education we've seen and we're really excited about that. We also see AI-enhanced learning programs for project teams as well.

00:19:44 Speaker_01
So maybe you have a team that needs help in certain areas, and now you can provide that personalized and adaptive training experience for the team. So we've been seeing AI used in really just a lot of different ways for that project professional.

00:20:00 Speaker_01
If you're a project professional, we'd love for you to reach out to us and tell us if you're using AI for any of these you know, use cases that we talked about, or maybe some that we didn't talk about. So definitely start a discussion.

00:20:13 Speaker_01
We always share our podcasts on LinkedIn, and we would love to hear from you and how you are using and adopting and applying AI to your everyday lives, both personally and professionally.

00:20:24 Speaker_01
And so this is, you know, we're in the middle of our use case series. And so we're going to be covering a number of different industries because it's really important to understand how others are using AI so that you can learn from it.

00:20:36 Speaker_01
Maybe you need to make a case for your organization to start using AI, or maybe it's giving you some ideas and it might not be your industry. but it's pretty easily translatable.

00:20:47 Speaker_01
So definitely, if you haven't done so already, subscribe to AI Today so you can get notified of all of our upcoming episodes, rate our podcast. We always love five-star ratings.

00:20:55 Speaker_01
You can rate us on your favorite podcast platform and definitely just stay engaged. Like this episode and want to hear more? With hundreds of episodes and over 3 million downloads, check out more AI Today podcasts at aitoday.live.

00:21:09 Speaker_01
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00:21:22 Speaker_01
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00:21:35 Speaker_01
Check it out at aitoday.live slash list. Music by Matsu Gravas. As always, thanks for listening to AI Today, and we'll catch you at the next podcast.