AI? AI. OH! What It Could Mean for PMOs with Noel Pennington (51)
Chris: [00:00:00] In this episode of great Practices, I'm talking with Noel Pennington, director at Microsoft of Partner Strategy and Industry Cloud. Listen in, as Noel discusses the AI journey that most organizations take, the biggest challenges they're having and most important to you is what impact this could have on PMO leaders and project managers, and what you need to be doing right now to stay ahead of that curve.
Plus you'll learn what you can do to make sure you AI responsibly find out what AI won't be able to do in the PMO space, and you're gonna hear two very different experiences that Noel and I had when we tried to order food with ai. I.
[00:01:00]
We'd like to welcome you to this episode of great practices and no self-respecting podcast. Could not have an episode or two about ai. AI is in the zeitgeist, and every week there's exciting breakthroughs, new capabilities that AI is enabling. But what does this mean for project managers and PMO leaders?
Is AI good news for us, allowing us to do more with less? Or is it bad news for us allowing companies to do more? Without us. Well, that's what Noel Pennington. Our guest today is going to help us to understand. Noel is a director at Microsoft who leads the partner strategy for Microsoft Cloud for retail.
He's a [00:02:00] seasoned business development executive who is passionate about staying on top of the latest trends and directions businesses take, and AI is one of those trends. He is all over. So by the end of this episode, you'll be able to say, AI, ai o. That's how we can make the most out of it as PMO leaders.
Noel, welcome to Great practices.
Chris: first of all, can you tell us a little bit more about yourself and what you do?
Noel: Sure. I've been in retail for 25 years. In fact, Chris and I, you and I worked at Edgenet for a long period of time, which was a very interesting experience and point. We'll have to talk about that. Uh, but I've, you know, from a technology standpoint,
I worked for a large si cognizant. I worked for AWS, I worked for Microsoft. I always said I was gonna do the round term between AWS. Microsoft and Google, but I've been really happy at Microsoft and it's been really amazing. But I focus in on partner strategy at Microsoft, and what that is, is basically I work with our partners, some of our largest partners, typically the [00:03:00] top 20 or 30, and really kinda help them from a programmatic perspective.
I help them from a standpoint of go to market. Events, uh, skilling, uh, basically incentive dollars, kind of anything that they need when they're trying to figure out the intricacies of working with Microsoft. That also includes co-selling and creating pipeline to help them drive more business, especially from an AI perspective.
Sure.
Chris: Well, that is, uh, that's exactly what we're gonna be talking about today then. So glad to have you on and, uh, just being an expert in that area there. so, so based upon this experience then, what is that AI journey that you've seen most organizations take? What does that look like?
Noel: Yeah, so, so I always like to start, uh, from the process of, uh, people don't really maybe understand everything when it comes to a ai. So I like to spend a minute just talking about two pieces. one is called generative ai, and I think most people are, are used to generative ai. It basically is the AI that creates new content.
So for instance, Chris, you know, if [00:04:00] you wanted to write an email or you know, basically try to create an image or write some code that's called, that's generative ai. What that purpose is is basically to generate or summarize data. Uh, so basically has that content so it answers reports, designs. So that's generative ai.
The term that really is coming into fashion is something called agen ai. And that's AI that acts autonomously towards goals. Right now, the autonomous part sometimes is fearful for a lot of people because, even, today, even the experts don't know exactly how AI is coming up with some pieces, right?
They know the basics of it, but even like you talk to the experts and they're like. I wonder how it came up with that and there's hallucinations that still occur. so, but autonomous is gonna be like the future, right? I think that I was, uh, hearing something that said within the next several years, 14% of the, jobs will be done through [00:05:00] an AI bot or agen ai.
It takes actions to make decisions completes task with minimal human input. And that's really important because, you know, you still have that human input, uh, to make sure that it's doing correctly and doesn't necessarily go rogue, which we have seen in many instances. Um, so I wanna make sure that, you know, the audience understands the difference between, uh, generat AI and a gen
Chris: Thank you very much. Because, because you know what, and that is a, I love the foundation you set because here's the deal, man. We just all go around like, oh yeah, yeah, I know, I know what that is. And we just kinda shake our head when we hear those terms. We, we really don't, because it literally was just. It just came ab it just came about two weeks ago or something, you know, but we all pretend like we all pretend we know what it is.
But I appreciate very much you, you defining that for us, so, okay, good. Well we got that good framework there. So, so what, what is that journey then that you've seen, uh, that go down that path?
Noel: Well, I think the first part is, it's really interesting because when you think about it, it's the skepticism that that occurs. And, uh, you know, it's interesting, uh, [00:06:00] Chris, I was, um, I, I, I'm fortunate, I'm on a board for, uh, high school here in Nashville, Hillsborough High School. And, uh, I get to, you know, talk to a class that's ninth graders, right?
I would think that ninth graders would be all over the technology, right? Because it is the latest. It can help 'em with cowork, it can make money. There's lots of things it can do. And you know, I would talk to a class of 35 people and three would basically say, yeah, we're, you know, we're interested in, or we're doing some stuff.
And then it felt like 95% of 'em were very skeptical about it. Right. You know, they're, they're worried about the, you know, AI sort of stealing, I'm gonna put that as, as a word of. Oh, it's using other people's, it's sort of working off the back of other people's. And I use that example of like, okay, if you saw a Picasso painting right, and you copied it, right, are you stealing Picasso's painting?
So it's really interesting and I think that skepticism occurs all the way through the C level, right? So there's issues from a standpoint of like data [00:07:00] security and privacy. You know, is the AI basically gonna leak my data? Um, which, uh, Microsoft basically focuses on that a lot, uh, from a standpoint of making sure that your enterprise data is walled off, right?
Because it has to be walled off. 'cause that's your secret sauce. But, you know, the example of that, Chris, would be, um, let's say I'm basically doing a product launch. I want to basically be able to use my internal information, but also go to the web to see pricing, see what other competitors are doing, and Microsoft has an ability basically to make sure that the data is, your data stays there, but there's still lots of, lots of issues when you think about skepticism that occurs from that.
It's like one of the biggest things I've heard is internal investment, ai. From a standpoint of a workload is extremely expensive, so you have to make sure that it's meeting the return on investments from that perspective. And you have to weigh sort of the cost versus the benefit. Right? And I think you're gonna see a lot of that.
That basically comes up. But then there's the, the issue of like job loss. You know, we've all seen it, you know, Microsoft. [00:08:00] Laid off a lot of people and a lot of it from a standpoint of the, the, um, computer engineers, because a lot of our code has now been being written with ai. And so I think it's gonna change really the landscape when you think about it.
And there's so many instances, you know, we're talking, when I was talking to this class, um, when I was talking to them, they're like, oh, AI can't replace the teacher. And I'm like, well, maybe in 10 years it can. So. I think that skepticism is, is well-founded and you gotta make sure it's responsible. And there's so many pieces, uh, that are important on that.
And again, we can't get in into an hour. We can't really go through every single thing. But Chris, I think this is a high thing. You know, ROI, data privacy, um, job loss, those are all top of mind from a standpoint of the CEOs.
Chris: So where do you see, so like, let's say, okay, they, they, they move past apprehension. They move past skepticism. Um, then where do they go next? Like, what's the, what's the next step that you've seen most organizations take when it comes to ai?
Noel: Well, I think it starts with the basics, right? So, from my perspective, you know, how I use AI is [00:09:00] basically, you're probably like me. You get, you know. Hundreds of emails, maybe even 20 emails, right? And you know, going through each email and trying to figure out like what's important, it can be really hard, right?
So in the morning I open up copilot, which, you know, I work for Microsoft. So copilot is the piece that I go to and I ask for, for the top emails, maybe for my boss, maybe summarize those emails. What are the important beings that I have today? and then potentially also writing responses back to those using ai.
Now, I might crack the words to begin with, but then I want AI to make it sound a little more professional. Not that I'm not professional, Chris, but you know, AI could do a good
Chris: never questioned that Noel. I never did.
Noel: I might question that at times for myself, but you know, from, for me, right? And for, from, from that perspective, you know, just my daily sort of process has changed completely with ai.
Right. Um, as an example, we have this tool within Microsoft called Researcher. And if I want to understand sort of like how to do a specific go to market with a partner, I may go out because think about it. I feel like when you're thinking [00:10:00] about, um, program management, I bet that you have. 50 or a hundred emails on a specific piece that you're thinking about from a, you know, a program perspective, and then you have probably documentation and PowerPoints and all those things.
Can you imagine if I could just say, Hey, summarize all that for me, look at these nine PowerPoints and these, all these emails and, and there's probably things I forgot, right? So just that time saver is amazing, right? From my personal productivity.
Chris: So, so basically what you're saying is you go from skepticism to generative ai. Ai, which thank you again for the definition so we know what that is. And then the, I guess you would say the next step would be, I assume it's age agent ai, right?
Noel: Yeah, yeah. Well, think about it just from a personal perspective, Chris. You go on vacation, right, and you're basically looking for the best deal like everybody else. Imagine an ag AI that basically goes out. Does that analysis for you and continues to look for the best deal, right? Just think about how game changing that is for every aspect of your [00:11:00] personal life.
Then turn it to the business life. I mean, it is pretty amazing on what you could do with that. And I think in the next, you know, there's Moore's Law, everybody knows Moore's Law every 18 months, basically, um, you see technology change. I don't think it's 18 months now anymore. I think it's more like, feels like 18 days or maybe even 18 hours Right.
Is what it feels like right now. It's amazing. Yeah, it is. Yeah.
Chris: Alright, so let's, let's pivot to the PMO space now. So, good, good foundation there now, and you, you mentioned it earlier, unfortunately, you know, people are losing their jobs and I mean, you know, you can attribute it to basically saying, okay, AI is taking over whatever that job was right now.
From your perspective, should PMO leaders program project managers, should they be concerned about losing their jobs? Is that something you see happening, or what does that look like from your perspective?
Noel: Yeah, I mean, unfortunately, you know, it was interesting because I was talking to a company. A startup and they're talking about sort of like the work that they [00:12:00] were doing and they, they were like, you know, we're responsible AI and you know, the biggest thing is we wanna make sure that, you know, job loss doesn't occur.
And I'm like, yeah, that's not realistic. Right. You know, I was talking to the same ninth grade class and I'll share kind of the things I told with them was, if you're not spending time understanding how AI can help your job, someone else is right. Whether it's the C level, basically trying to figure out how to make their.
Their organization's more efficient. Um, because, you know, AI's job is to reduce cost and cost is headcount. Let's be clear. Right. And that same example we're talking about a teacher. Right. You know, could a teacher be replaced with ai, maybe a robotic ai? Yeah. I think that's probably 10 years away or 15 years away.
One of the students gave me a really, you know, great thing they said, well, you know, governments won't, won't basically pay for it because of the expense, right? I go, well, lemme do the expense for you. The robot, let's say in 10 years is gonna cost you $18,000 and maybe it needs some maintenance. You know, once a year maybe that cost you [00:13:00] $1,800, eight, uh, an employee's gonna cost you what, 40, 50, $60,000 a year, right?
How, how do you do that cost be, uh, that cost benefit analysis in that example? So, you know, I think we, I think our generation right, you know, our age, um, it's gonna be less of an issue, right? I don't know that, you know, it's still gonna be an impact, right? But I think about my daughter, probably your daughter, you know, our, your children, right?
And I think about sort of like, you know, it's gonna be really interesting to see sort of how that works. I don't have a great answer for that. I, I can't tell you like what that's gonna look like, but it's gonna change the landscape, right?
Chris: And, and, you know, and I think that's, and it's like I've, I'm thinking about that. I'm like, man, I'm, you know, I'm kind of, I'm. I've got a couple more years left in my working career, but I'm thinking about myself. I'm like, oh man. It's like, this is amazing and it's cool and it's awesome, but I'm also kind of glad it came at this point in my career.
Y you know, [00:14:00] because it's, it will be rocking. I think it's gonna just rock a lot of things is what is ultimately gonna be happening now that we just can't even anticipate. And it's gonna be, it's gonna be tricky keeping up with that. Not that it can't be done. 'cause I think, you know, we've been through these types of things before.
Maybe not of this magnitude, but it is definitely going to be, uh, it's gonna be, it's gonna be a little bit of a turbulence there. I'm sure. So
how, how, how do you see AI being leveraged like in the PMO space? Like, what do you think that it could be able to do or would be able to do?
Noel: Well, you know, uh, what I do whenever I'm talking about, you know, you know, PMO isn't necessarily my strong suit. I can't tell you that I know everything about PMOI. I'm a generalist at Microsoft. I try my best to understand a little bit about everything so I can basically talk effectively about it. So what did I do?
I went to chat BT and I said, Hey. Like, you know, because we do own own parts of chat bt, so I feel like it's still a good thing for me. And I said, Hey, you know, if we look at the landscape of PMOs so I can talk, you know, I talk about the things that I thought about, but then it gave me some ideas of things that I [00:15:00] may maybe necessarily didn't think about.
So I think the first thing they talked about was strategic planning and alignment. Right? So I remember when I was doing project management, I think the big thing was, was like keeping roadmaps up to date and milestones, strategic objectives, and sort of like. Maintaining those and Right. Seeing sort of the changes to that, you know, creating that Gantt chart.
Um, you know, I use project I, I probably never used it to the extent that you've used it. Um, but you know, think about the fact that when you move one thing, the depend season within the Gantt chart, move accordingly. Right? But what is the GaN impact past the Gantt chart? Right? And one of the things I'm not thinking about, well, AI can look at that, right?
And think about sort of the risks and the mitigations and the misalignments. Then suggest that corrective action. Right? So again, think about a person using it and a person not using it. So you know, the fact is we're human right. And there's no way that I can look at every single thing I have, [00:16:00] especially when I'm thinking about a, a project I'm working on.
So, but AI can, it can look at everything and it's instantaneous, right? Because it has that information. It's, it's, it's gathering all that information and seeing sort of the misalignments that may occur. Then the impact on the road. Um, so that's one thing. I think just the, I don't know, is it feel like busy work a little bit, Chris, where you're basically moving the Gantt chart.
I mean, you know, you are better aligned to that than I am, but you know, if you had the system be able to give you the misalignments from the Gantt chart, I mean, does that help you? And you know, what, what do
Chris: A hundred percent. I mean 100%. Because then what it will allow you to do is it will free you up to focus on, oh, like risk, okay. It actually identified a risk. Okay, well let me go mitigate that risk. Let me get rid of that risk so it doesn't become an issue. You know? So it allows you, if you, if you do it the right way, I think it's gonna allow you to, to focus on those higher things that actually will make an impact versus.
You know, being a a, an email jockey or, you know, kind of moving things [00:17:00] around in a spreadsheet, you know, there's no doubt about that.
Noel: Yeah, I love it. I love the fact that you just, now you gave me, the next thing I was thinking about was sort of the intelligent scheduling and task management, right? So does Gant charts move? You need to query across multiple sort of calendars and also follow ups and reminders, right? So think about sort of the fact of like just if you have 20 people on a project, like trying to get them all aligned is really difficult, especially from just getting get on the calendar.
I know when I'm trying to get to our execs, like it is really hard basically working with their calendars, right? And so, you know, you have the AI that looks at that and then potentially gives you like, these are the more, most important people that are in the room that you should have the event because you've got 20 people.
You know, 20% or maybe it's 80% are not as important as that 20%.
Chris: Yeah.
Noel: So, and I think, you know, being able to get those follow-ups and reminders isn't something that, I'm sure I've missed it before, where I was like, oh, you know what? I need that one guy on that meeting and I forgot, and he was pivotal in my. My, [00:18:00] you know, my, um, you know, what I was thinking of.
So, you know, I think that's the second piece that's really sort of, you know, um, transformative when we think about ai. And, and I think it's interesting for those that are listening, you know, it's, it's, I think there are people that are very skeptical and worry about the job loss. But if I go back to my executive team and show how well I've done with the ai, even though the AI's doing the work, I'm using a tool just like any other tool.
I'm sure there's apprehensions when the first car came out, you know, moving from a force to a car, right? Think of how the apprehension of the, of the internet, right? Like, oh, you know, I don't know if I wanna send an email, I wanna have a phone call. And now you get to this point where, I dunno about you, Chris, but everything I do is on teams.
Like,
Chris: yeah, a hundred percent. Except this call, which is on Zoom.
Noel: you know, I'm not gonna hold it against you that you're on Zoom. So, you know,
Chris: Yeah.
Noel: zoom was amazing, you know, you know, all these platforms are amazing and it's really, you know, you think about sort of like how much [00:19:00] like Zoom as a company because of COVID, right? And you think about sort of all those pieces of that.
Um, or I guess it's more, it's, it's really kind of accelerated its path, but I think that's the second thing, right? This intelligent scheduling, this ability to do that, uh, and kind of, you know, where this is gonna go. So, I mean, Chris, any thoughts on that?
Chris: Well, you know, I think, and so I listen to a lot of podcasts myself, and I listen to this. Accounting podcast, you know, I mean, it sounds fascinating, right? But, but she's actually very engaging and it's, it's keep what you earn. And that's the name of the, that's the name of the podcast. And she's a CPA and she just talks about, you know, just kind of smart budgeting and all that kinda stuff.
But her point was, you know, the, the calculator was gonna put accountants outta business. The spreadsheet was gonna put accountants outta business. You know, now AI is gonna put accountants outta business. So there's always gonna be something that's gonna be coming around, and it is just a matter of how do you use that tool?
You know, and I, and I, and I think that's ultimately what it, what it comes down to, which is really kinda my next question then. So when you look at the PMO space, what are some of the [00:20:00] things that AI won't be able to do in that PMO space? Like, like what just could it not do? Yep.
Noel: Well, you know, when I think about this kind of third piece, when I think I was thinking about it was, you know, status updates, right? So it could create a stats update, right? And they do a pretty good job creating the status updates. But what I find is I do a lot of editing on those executive summaries in instances where, you know, the AI gets, look, let's be very clear, AI is based on data.
Right. If the data is not good, you know, junk in, junk out, that still exists regardless of what it's, we've seen that time and time again. Uh, you know, in the early two thou, or I guess in the middle 2000, I was doing some work, uh, with a large pet company and they were basically telling me about sort of like, oh, we wanna do this and this with our data, but their data was terrible, right?
And so I think that AI does a good job when it has good data. But let's be clear, Chris. Data is not good, right? I mean, that still is an issue and [00:21:00] AI can help you with that, but I think you, you can't basically send an executive summary without really spending time really understanding it. It doesn't understand the tonality of that.
Maybe get some of that Right. The tonality perspective. So, you know, it's really interesting. I'll be writing an email and, uh, you know, one of the options that I have for copilot for the, the applications I use is it'll gimme insight into like. The tonality is informative. Oh, this tonality is, you know, you know, you're sounding very kind or it's very complimentary.
but a lot of times when I'm reading through it, I was like, oh, that tonality doesn't work for me. So, you know, ai, it doesn't have the emotion right. That attaches to this. And I think that that piece, that human interaction is important.
Chris: and as you're, as you're just talking, as you're talking through this and, and I'm. I'm interviewing you, but it's just helping me understand this even better. 'cause I'm trying to figure this out too. To be honest with you, and here's been my experience so far, is it misses nuance. You, you know, you've, you've got to [00:22:00] understand the nuance, which is like, it may get 97% of the way there, but just that last 3%, man, if you don't understand what that nuance is like, especially in an executive summary, that could be devastating.
That could absolutely be devastating if there's, you know, not understanding that piece. And then to your point, you know, I think it is, it is about, um, relationships and it's about influence. I think that that is what people at this point can still do. Um, that, that I don't believe, I, I don't know. I can't see how a AI could do that, you know, as far as that goes.
So, I mean, that's just been kind, kind of my experience, I think is, I'm just trying to figure all of this out myself.
Noel: Yeah. You know, it's interesting Chris, 'cause 'cause I, I think, you know, and I don't, I don't know that it's 3%, I think it's more like 20% Right. But I think that it's 80% there. I think that the, the, uh, the fact is, is that the AI is really informing right, those users. But you know, there are times when you're thinking about those people that have gone over and above.
You know, sort of the program [00:23:00] management piece of that and AI's not gonna necessarily understand that this was an over and above sort situation. Um, and so, you know, it was interesting going back to that, that, that piece where I was talking to that the, the kids about sort of like, you know, can you replace a teacher with a, a robot at some point?
And, you know, one of the other teachers said, I have a motion the robot won't. Yeah. That's today. I agree. Let's be very clear, uh, with quantum computing and all the pieces that we think about from that perspective, will it get there? I, I, you know, I think so, right? I mean, I think that, you know, I'm not, I'm not suggesting like, you know, we're going to a Terminator type of moment.
I think it is one of those things where, you know, we do have to understand that this is a, this is something that is smarter than. You know, front of all, a few, you know, humankind. When we think about sort of the intelligence of that, there's several examples I can give where, you know, AI basically found breast cancer four years before the radiologist would've found.[00:24:00]
So to me that's amazing. Right? That's amazing. Um, but there are instances where AI does a terrible job. And an example of that was, um, I think there was a, the airline, and people can look this up, it's been a while since I read the story, but it was talking about sort of like someone was calling an AI chat bot.
'cause they basically were flying and I think it was bereavement flight and the AI just did it. Got it completely wrong. Right. So you know that brand management and those kind of things are also another thing that's from a skepticism standpoint. You have to be careful with. Because I don't know about you, Chris.
You know, so it was interesting. I was, uh, ordering a pizza, a pizza from a very small pizza shop. Right. Um, and they had an AI bot on the other line. It was really clear, you know, after, for the first two minutes, I was a little suspect about it, and after the first three minutes I was like, oh, this is an AI chat bot.
Like, but yeah, I couldn't tell. Like I think the normal person wouldn't be able to tell, because you know, you heard the clicking and you heard like background noise. Um, [00:25:00] and that's a little scary to me sometimes because I'm like, you know, can you know, like, that's gonna be really interesting as basically we go more and more to AI assistance, which I think is great, but it also goes to that job, you know, you know, no one's gonna lose a job because of that,
Chris: Yeah, well, let me, let me, let me tell you my experience, Noel, that I had with, uh, with AI trying to order food. You know, not too long ago, I, I, uh, I went through a drive-through and I just wanted a half sweet half unsweet tea. That's all I wanted, man, large, and it was blowing that thing's mind. It could not, it could not figure that out.
And then finally somebody jumped in, you know, and said, you know, ah, this stupid, you know, and all kind of stuff. So I, I think there's a little work to be done. It'll, it'll figure it out, but, but that just blew its mind.
Noel: Yeah. You know, it's funny 'cause you know more and more of those, the, you know, the, those companies are doing it because they wanna reduce. The, um, the, the problems that you have when ordering food, right. Get it right. but sometimes when things get it wrong, and there are [00:26:00] plenty of examples of this where it gets it wrong and we still see it, you know, day in, day out.
Uh, but I think, you know, it's, it's, it's learning as we go. Um, and as again, as quantum computing basically, and those shifts become more and more prevalent and more and more, you know, more affordable, it's gonna be real interesting to see where it goes.
Chris: So let me ask you this then. So we, you know, I, here's the deal. It's a lot of uncertainty, you know, I mean, that's just the reality of what this is. So if you were a PMO leader or just in business anywhere, what. Would you be doing right now? What are you doing right now to ride this wave, you know, and not ideally get washed out by this wave?
Like what can people be doing right now to just really make sure, 'cause there's still gonna be people around. I, I assume so, so what can you, what can you do to be around.
Noel: Well, uh, so, so the first thing is, is that, um, I would start at the baseline, right? Get your people to start understanding ai. And for those that are afraid of ai, you need to have the right conversation with them to say, what are you afraid of? [00:27:00] Right? I think when people first start using ai, there's a skepticism that's not necess you.
So, so we'll go to, I forget the chasm. What was it? The like crossing the chasm. So it was that book about early adopters versus, the people in the middle, then laggards, right? You can't be a laggard in this because it's changing so quickly. Right. Not saying you have to be an early adopter. Right.
Because again, Chris, as we talked about, there are many issues when you're thinking about AI from standpoint of the skepticism that exists within an organization. One. To the data, the data privacy issues, especially from a regulated, uh, regulated, uh, piece. So, you know, how retail looks at AI versus how healthcare looks at AI is so completely different, right?
Um, you know, uh, and I think the regulated versus non-regulated is also another important piece as you basically look at project management, because those pieces, and then of course you get the GDPR. All the other issues that exist. So, you know, if you think about like healthcare, right? As an example, [00:28:00] if you're, if you're working on PMO, um, you aren't allowed necessarily to look at someone's information, which could be transformative when you think about sort of being able to do analysis across, an organization to make things more effective.
Right. Whether that's cancer treatment or whether that's, um, treatment from standpoint of just having, you know, I have my regular sort of one-on-one with a, a patient and maybe I misdiagnose something or I basically. Introduce a new drug that I didn't think about that potentially interacted with a drug they're already taking.
Uh, Microsoft has a a, um, a, uh, tool called Copilot Dragon, which is for healthcare, and it basically listens, um, in a session to what doctors or nurses are saying. And then it captures all those pieces do, does transcription and potentially it could see, oh, you prescribed this drug, but there's interaction over here.
Chris: that's crazy.
Noel: And so, you know, the fact is, is that if, if I was gonna get [00:29:00] started, I would go out and say, um, Microsoft has written a really good, um, report. It's called Responsible ai. And if anybody needs to get it, just, just again connected on LinkedIn, I'm happy to share it. But I think you have to start with the responsibility of ai, right?
You have put guards around it in your organization, understand what's effective, what's not effective, and again, just do the basic things. Have your team understand how to write an email more effectively, how do you search more effectively? Because, Chris, I'm about you, but I create a lot of freaking PowerPoints.
Um, and so, you know, you know, going back and going, you know what, I created a really amazing slide, and then I have like, I don't know, like 900 slides I gotta look at and I just can't do that. So, you know, co-op really helps me through that process. So again, I think the personal productive productivity is where to start.
As you build on that, then introduce it potentially into your first, you know, next project. Maybe you have AI sort of help you in that next project. If you're not using AI [00:30:00] and in this day and age, I mean, if you're not using ai, like that boat is, that train has left the station. Like if you're not using it, I think that it's, it's gonna be the downfalls of, of businesses.
Chris: Well, and I, and I think, I think you started out the answer to that question just perfectly. Don't be a laggard. You, you know, I mean, it's like just, you know, you can't turn your head the other way and say, this isn't happening 'cause it's happening. So, you know, em, embrace it. Educate yourself on it. Use it.
You know, there's, like you're saying, just put your toes in the water and then next thing you know, like literally I've got this, I've got a little. Post-it note on my desk and it says, how can AI help me do this? So like, this is on my monitor and that's always, every time it will jog me as I'm like, I'm trying to slog through something.
I'm like, what am I doing? Let me, let me just see if AI can help me do this. And guess what, every single time it has. So I just think you just gotta, you just, it's just a great tool and just don't be a laggard. I think that's great advice [00:31:00] right there.
Noel: Yeah. You know, Chris, I, I, I think that just, you know, if you're not using it, there's so many free and, tools that are available to you. Some new costs, you know, you can start with chat to. BT and chat tv d does a good job for you. And it's 20 bucks a month, and I promise you that, uh, 20 bucks a month is gonna, like, it's what one, it feels like it's one coffee at Starbucks is what it feels like at this point.
Um, and I, I just think that, you know, that 20 bucks a month will make you s even if it pay for it personally,
I think it's gonna, you know, make it worth it for, for, you know, anybody that's not using ai.
Chris: a hundred percent.
Noel: Yeah.
Chris: Noel, this has been an awesome conversation. If there's one great practice that you wanted our listeners to remember from today's episode, what, what would it be?
Noel: Well, um, the two things I'd say is you have to understand the ROI before you start this. Um, it is expensive. It's an expensive task. You gotta make sure that you're lined up well. You make sure you understand what the [00:32:00] benefits are gonna be versus what the cost is gonna be. Because it's why Nvidia is like the most, I think it's the, the, the most valuable company in the world right now.
It's like $3 trillion, something along those lines. Um, that, that's the reason. It's like their chips are powering everything. Uh, the second piece of that is responsibility. I'm a huge proponent on responsibility. So let's do this, right? Let's make sure that the AI you you're using is responsible. So if you're regulated, you have to think about your users that are out there.
If you're non-regulated, you gotta think about the transactions that occur. So if I'm a re, if I'm a retailer, you know, I, I really have to think about sort of like, what are the impacts if I use customer data, right? So, you know, I think that responsibility still exists. Um, you know, you can put some governance around that, which is extremely important.
And that's a people thing. You know, you, you can't have the AI regulated itself, right? You need a person regulated. So those are the two things I think as we lead. And that really should be considered.
Chris: So again, to your point, individually, [00:33:00] uh, AI is not expensive. $20 a month, you know, at the most, right? Enterprise wise, it is very expensive, and you need to really think through that and make sure that is, and then to your point, AI responsibly, you know, because there, there's no talent, man, where this thing could go.
You could, you could veer off the road in a heartbeat.
Noel: yeah. Absolutely, Chris. Yep.
Chris: All right, Noel. Well, again, what is the best way, like if people wanted to continue this conversation with you, get more of your insight into all of this, what's the best way for people to reach you if they wanna discuss this further?
Noel: Yeah. So they can just get, uh, go to LinkedIn first, uh, per, if you type Noel Pennington or Noel Pennington, you know, I get both of 'em at Microsoft. Um, NOEL. Uh, last name, P-E-N-N-I-N-G-T-O-N. Uh, you can send me a message. I'm usually really good about responding to email, especially, um, responses because, you know, occasionally I will use AI to respond back.
Makes my life easier, um, because I do get a lot of, um, not, not just, you know, people like this, but also lots of partners that reached out to me on LinkedIn because [00:34:00] they aren't sure of my email address, which I won't tell run my address 'cause I don't, you know, I can't get in day with email. But I promise you if you send me a note, I'm, I'm happy to, to kinda give you some guidance there.
And also the tools that Microsoft use that perspective
Chris: All right, well thanks for being on today, Noel. This was a great
Noel: really appreciate. Chris, it.
Chris: All right, man. Talk soon. Well, that was another great episode of great practices, and we certainly do appreciate Noel joining us today, and what were some of the great practices and insights that came from today's episode? Well, I like that. Just outta the gate, he gave us a definition of generative a.
Versus Ag agentic ai, you know, I mean, these words are pretty new into our vocabulary and, uh, we kind of go around like, oh, yes, I, I know exactly what that means when, Hmm, maybe we don't not quite know the details around what's the difference between the two? Loved it. Generative AI was generating or [00:35:00] summarizing data, so it's gonna go out, it's gonna go find that data, it's gonna collate it.
Put it together, parse it, and then just serve it up to you on a golden platter. So that's the generative ai. Then he talked about the age agentic ai. That's the AI that is working autonomously towards goals. So it may not have necessarily the data that's supporting it, but. Figuring things out and it's understanding what to go and what the next best step is to do on its own.
So that's ultimately where most companies are beginning to go now is working towards that Ag agentic ai. So what was the path that he talked about that most companies are going down or have gone down when it comes to implementing ai? We first of all talked about the fact that. Initially, there's usually skepticism.
Uh, leaders are gonna be concerned about data security and privacy. Data leaks is the ROI worth it. Uh, what is the impact that this is going to [00:36:00] have on jobs within an organization? So there's that skeptical time period, but then people get past that and then they find that they are now moving and working in the basics, which would be that generative AI side of things.
So using that to. You know, do research. Maybe, uh, extrapolate out a whole bunch of information from spreadsheets or summarizing and clarifying emails. So that's exactly where that generative AI can help companies, um, you know, just really understand things faster, make things, uh, get done faster, then. It moves to that third step, which is that AG agentic ai.
So he gave that example of, you know, just this AI that would really be able to plan out a vacation, make the reservations, give recommendations on where to go and what to do and all that kind of thing. So that's ultimately the path that most companies go down. You know, ending up in that age agentic ai, [00:37:00] which, you know, really kind of makes the experience that much better and faster for everybody.
Now what about the PMO space? That is ultimately what we're talking about today, is should people be concerned about, positions in the PMO or project manager loss? Well, his point was, if you don't understand how AI can help your job, somebody else is and somebody else does. So you've got to make sure that you are implementing that in your day-to-day work.
The reality that he said was AI is a tool that is used to reduce cost and a big cost in companies is headcount. So we've gotta make sure that we are utilizing and leveraging AI to help us do our jobs better. So what were some of the things that he mentioned that, AI could be leveraged in the PMO space?
Well, it could help with strategic planning and alignments. [00:38:00] That'd be one great area. You know, being able to understand what the company's goals and objectives are, and then wrap all of this around it. Make sure that the roadmaps and the milestones and the Gantt charts, the project plans, all of these things are coming together in order to support, uh, those strategic initiatives.
Then when we're actually managing the projects themselves, that's where the intelligent scheduling task management comes into play. Uh, you know, finding openings on calendars follow ups and reminders. You know, just kind of making sure that the right people are at the right meetings, you're talking about the right things, and just really making sure that, uh, the right work is getting done.
That's another area that we could use AI to leverage in that PMO space. But the flip side was there's certain things that at this point, AI is not going to be able to do. And the fact is, is that AI is based on data and if the [00:39:00] data isn't good, you got. Bad data end, you're gonna get bad data out. So AI isn't necessarily able to understand, uh, tonality.
That was one of the things that he mentioned, uh, Mrs. Nuance, you know, like he gave the example, especially in executive summaries, we may not have all of the nuance or the details or just those finer points that really, you know, make for an excellent executive summary. Uh, those are the types of things that.
Are gonna absolutely have to have somebody looking at it. He said that ai, you know, basically could get us 80% of the way there, but you still have to finish it off by just having that, you know, set of eyes, looking over that and making sure that everything is just so and accurate. He gave great advice to PMO leaders and project managers on
What to do in order to ride this wave and not get washed out? Uh, number one is we need to understand ai. If we're a leader in an [00:40:00] organization, we need to make sure that we're having conversations with our people, that they're understanding. We need to understand maybe what it is that they're concerned about, what are they afraid of, and really help educate people on what AI can certainly do in the PMO and project management space.
He also brought out the point that you cannot be a laggard. That is, things are just changing so quickly. You don't have to be an early adopter. You don't have to be on that bleeding edge, but at the same time, don't find yourself on the tail end of things just trying to catch up where everybody's just moving forward so quickly and fast, and then you're just beginning to say, yeah, I really should kind of.
Pick up and understand what this AI thing is all about. You cannot be there. Like you said, that train has already left the station and uh, we need to be on board and moving forward with that. So, great advice when it came to just, this is how easy it could be just. Do an email, [00:41:00] ask AI to help you write an email or do some type of search, or use it as some type of productivity assistance.
Just dip your toes into the water, uh, in order to start the experience of using it. If you haven't used it already, do not be a laggard. And he really wrapped things up with those two great practices is as business leaders, you need to make sure that you understand the ROI of ai, um, because it may be, or it is expensive, you know, at an executive level and at an organizational level.
So you've gotta make sure that you understand how are you calculating, the return that you're getting back on such an investment. And then his whole advice As far as. Responsibly AIing, or I guess I would say AI responsibly. so make sure that, you know, we understand what regulations are in place, what's not regulated, transactional, if there's transactional data, what's the [00:42:00] impact?
If you're using customers data in order to generate results and come up with information from that, AI can't regulate itself. So you need to make sure that you are, . In a responsible place when it comes to implementing ai. Well, we'd like to thank Noel for being on great practices again today. We got a lot out from that conversation.
Well, do you have a great practice you'd like to share? you can just email chris dot [email protected]. That's C-H-R-I-S dot [email protected] and uh, somebody will get in touch with you very shortly.
Also be sure not to miss a single episode by subscribing to great practices on your favorite podcast platform. And if you like what you hear, we've had great guests on, we've got many more coming up. Uh, be sure to share this with your manager, colleagues, and any others you think would benefit. So thanks again for listening again today to this episode and keep putting great [00:43:00] practices into practice.
