NICK WALKER:  Welcome to Manage This, the podcast by project managers for project managers.  Every other week we get together to talk about the things that matter to you as a professional project manager.  And it doesn’t really matter whether you’re a PM veteran or someone simply exploring what the field is all about.  We’re here to offer some ideas, some perspective, and draw on the experiences of others who have been down that road and have realized success.

I’m your host, Nick Walker, and with me are two who are still on that road, Andy Crowe and Bill Yates.

ANDY CROWE:  Thanks, Nick.  We’ve had so much interest in the topic of artificial intelligence within project management, and we’ve got somebody here who knows a lot about AI who’s going to be processing that with us.

NICK WALKER:  Our guest here in the studio is Chris Benson.  He’s an artificial intelligence machine learning strategist, a solution architect, and a keynote speaker who specializes in deep learning.  That’s the computation technology that is driving the artificial intelligence revolution.

Chris is the co-host of the Practical AI podcast, produced by Changelog Media, designed to make artificial intelligence practical, productive, and accessible to everyone.  He’s the founder and organizer of the Atlanta Deep Learning Meetup, one of the largest AI communities in the world, with nearly 2,000 members.  Chris, it’s great to have you here on our podcast.

CHRIS BENSON:  Thank you very much.  Happy to be here.

NICK WALKER:  Could we start off by just defining for our listeners what artificial intelligence is?

CHRIS BENSON:  So artificial intelligence means a lot of different things to a lot of different people.  In my view it’s really a marketing word more than it is anything else because over the years the definition of artificial intelligence has changed and evolved.  So what you might have thought of in the 1980s is vastly different from what it is in 2018.  So before I define it, I want to point out I was in a group of artificial intelligence experts that Adobe was hosting about six weeks ago.  And in doing that, they asked us all that same question; and all 10 of us gave 10 different answers.

ANDY CROWE:  Well, and the joke is, if you ask two economists for a definition, you get three answers.

CHRIS BENSON:  Absolutely.

ANDY CROWE:  Same idea, huh.

CHRIS BENSON:  Yup.  So it was very much that.  So I wanted to note that.  Take what I say with a grain of salt.

ANDY CROWE:  What do you think it is, yeah.

CHRIS BENSON:  So what I think it is, is a narrow definition.  I would consider that in 2018 artificial intelligence is synonymous with deep learning, which is the application of deep neural networks.

ANDY CROWE:  Interesting.  Well, learning is certainly a part of AI that I think that’s almost a universal component that goes across most definitions.  Most definitions talk about the ability to imitate intelligence and things like that, imitate human intellect.  But that ability to learn and grow as a neural network is an interesting part of it.  So how do machines learn?

CHRIS BENSON:  So there’s different techniques.  And those all broadly fall under the definition of machine learning.  The thing that separates deep learning, which is how I’m defining AI, from the rest is that it can take an enormous number of inputs – we call them “features” in data science – and process them in a highly nonlinear manner and give inferences, which are essentially probabilistic predictions on what the answer might be.

For instance, to make it real:  If you have machine vision, and you are putting a cat in front of the camera, and it will come back and identify that it thinks it’s a cat.  It might come back 97 percent.  But the difference is these technologies aren’t going to come back with 100 percent.  They’re probabilistic technologies.  But they can make these identifications using a model that is many orders of magnitude more complicated, and therefore in some ways more capable, than previous models of machine learning.

ANDY CROWE:  I have a funny comment to that end.  About two weeks ago I looked across the street, and I saw something, and it’s funny what your brain does when it doesn’t have something that fits a pattern or that makes sense.  And I saw a cat coming across a street.  It was a couple hours before dark.  And I looked at it, and this cat was enormous, and it was walking funny, but my brain’s telling me, well, it’s a cat.  And I called my wife over to see.  It was a giant raccoon coming across the street.

CHRIS BENSON:  Oh, okay.

ANDY CROWE:  And, yeah, now that’s a daily ritual.  That raccoon crosses the street.  But it’s interesting that you say that, that it’s not 100 percent certain, because I was pretty certain, and I was wrong.

CHRIS BENSON:  Yes.  I mean, there’s an analogy to be made there is that, saying this very loosely, neural network technologies are essentially modeled after the brain, a mammal’s brain.  Not just a human brain, but any mammal’s brain.  The cerebral cortex, specifically.  And so, with that said, you can take sometimes tens of thousands of inputs into that.  And, yes, we make mistakes.  And just as we make those mistakes, today’s neural networks make those kinds of mistakes all the time before the training gets right.

BILL YATES:  I think it’s worth noting that your wife pointed that out to you, that mistake.

ANDY CROWE:  She did not.  I self-corrected.

BILL YATES:  You self-corrected.

CHRIS BENSON:  He got there before she could get to it.

ANDY CROWE:  As we talk this through, project managers are looking at this idea of AI.  And a lot of people believe that it may have an earlier impact on project management than some of the other domains.  Which is interesting to me.  I’m not sure I agree with that.  What do you think?

CHRIS BENSON:  So I don’t know if it’s having an earlier impact because talking with people all the time about this, I see it having an impact everywhere, in just about every industry on the planet.  And matter of fact, I haven’t been able to come up with an industry that I don’t think will be impacted in the years ahead.  Some maybe sooner than others, but you’re already seeing it across medicine and transportation, financial, you know, security, you name it.  It’s already starting to have a place.  Machine vision’s everywhere.  Natural language processing is everywhere.  These technologies are becoming pervasive.  We’re all using it every day, every time you’re doing Google searches or using your email or whatever.  So it’s already affecting our lives in a profound way.

BILL YATES:  That’s true.  Even in my home, you know, I think of my friends Siri and Alexa.  iTunes is getting smarter; whatever streaming service has these recommendations and suggestions.  It’s as if they can see inside of me.

CHRIS BENSON:  They literally know more about you than you consciously do yourself in many ways because everything that you do is data for them, and it is constantly crunching that data behind the scenes.

ANDY CROWE:  Well, so now that gives us an interesting transition because project managers are also supposed to predict, to some degree.  That’s an important part of our job.  It’s not all of our job by any stretch, but it’s an important part, is to look at things going on and to spot some signal in the noise, if you will, or some trend that maybe the team doesn’t even consciously know yet.  Maybe the customer hasn’t picked up on this.  Maybe the developers don’t know.  But the PM sees it.  That seems like a pretty natural fit for AI.

CHRIS BENSON:  It’s a very natural fit, especially so – and there’s a question that I’d like to even pop in before that, and that is, what will AI do well for us in general?  And that is today very specific problems that are highly complex.  So if you have many, many different inputs that come into a problem, but you’re narrowing the scope of what it’s trying to accomplish to something that’s very specific, then in many cases we’re seeing AI technologies that are improving upon even human experts.  And I would say that that is likely one of those.

ANDY CROWE:  So that’s interesting to me that AI is sort of tuned toward very specific and very complex problems.  The human brain is amazing at general things.  Not everybody can make change, you know, for a $5 bill.  And so it’s kind of funny that the human brain can do a lot of broad things, but not everyone is really good at super complex things.

CHRIS BENSON:  Yeah, you’re making a great point there.  And that’s that we should not think of today’s neural networks as analogous to an entire brain.  So you could think of it as a very small collection of neurons in your brain that has been trained through your own activity to do a very specific task or identify something.  That’s what today’s neural network would be.  So if you were going to get to a level of complexity in dealing with daily life, where you’re doing that kind of generalization, that would be like having lots and lots and lots of deep neural networks that are all put together to sort of simulate what your brain is doing.

ANDY CROWE:  Well, and psychologists tell us those neural networks sometimes compete, as well.

BILL YATES:  Right.

CHRIS BENSON:  They do.

ANDY CROWE:  And that’s interesting from my standpoint, that then you have some kind of function that prioritizes those things and knows which ones to listen to and which ones to tune out.  That’s fascinating.

CHRIS BENSON:  Yeah, our own brain creates all sorts of noisy signals because each little piece of our brain is being trained for specific things, and they don’t always go together well.  And so just like that, that’s actually in robotics right now we’re seeing that, is that to get a robot to do a set of things that it’s assigned to do typically requires a whole bunch of different neural networks, one for every task, and they don’t always agree.  And so you have strategy components that have to address what to listen to.  And that translates out into all sorts of other industries, as well.  It’s not just robotics.

ANDY CROWE:  I’ve done a project years ago with statistical process control, and there were elements of that that are kind of echoing in what you’re saying, interestingly enough.  There was an order to it, but there were things that were competing, and you had to sort of use almost fuzzy math to get to the right solution.  It’s deeper than we want to go into here.  So one of the things, Chris, ideally project managers try and keep the team focused.  Do you see any applications for AI in that?

CHRIS BENSON:  Sure.  So the way I would assess that and go down the path of developing a strategy for project managers, is I would look at the engagements that they’re in, look at all the different things that are hitting that project manager.  There’s so much information they have to consume and sort through.  And so, if I was going to do that for project managers, I would think, what are all those features?  What are those inputs that the project manager, the human project manager is having to deal with?  And what is the output that the project manager is getting?  Where do they go wrong?

And I would get the data that represented each of those points, and I would put it through a model and train that model and try to get the equivalent of the right answers coming out of that.  And I think that there’s a role, a very powerful role for AI bots to help project managers in the days ahead to ensure that things are going better than they would without that partnership, which is a really key term here is that many jobs, including project management in my view, are likely to be partnerships between humans and AI.

BILL YATES:  Yeah, that makes sense.  You know, the idea of having something that helps the PM, that assists, and especially reduces the cognitive load, I know that’s one of the benefits of AI is reducing the cognitive load.  That’s great.  I mean, I think of that as, okay, if I can take some of these tasks that are very complex, maybe they are narrow scope, but they’re a specific problem that have a lot of data associated with them.  If I can outsource that, right, if I can offload that, then I’m freed up to see the big picture, as Andy says.  Now I can use my brain to really engage the “How is my entire project environment going?”

CHRIS BENSON:  Yeah, I think that’s very likely with project management and with many other jobs, as well, where things that we don’t do well as humans, where we struggle to account for so many things in the spur of the moment, you can have a machine that is going through that for you, giving you the output and letting your human creativity apply itself with that right answer.

ANDY CROWE:  You know, my car has a lot of collision avoidance sensors and software on it.  And so if I’m approaching something too quickly, it will be very quick to let me know.  I also have a Harley.  And my Harley has none of that; right?  You are 100 percent the collision avoidance yourself.

BILL YATES:  Well, that’s why it’s so loud.  People look out for you.

ANDY CROWE:  Loud pipes save lives.

CHRIS BENSON:  It’s functional.

ANDY CROWE:  But the experience between those two is very, very different because the car has an autopilot feature.  It will drive itself.  And I engage that pretty regularly.  The Harley has no autopilot whatsoever.  So when I’m driving it, when I’m riding that motorcycle, I have to pay incredible attention.  So what Bill said resonated with me about reducing the cognitive load.  The cognitive load, maybe it should be the same in both.

We’ve just passed a distracted driving law here in Georgia that says you can’t touch your cell phone while you’re driving except in really limited instances.  But maybe the cognitive load should be the same.  But it does allow me a sense where I have to pay much, much more attention on the motorcycle than I do the car.  I wonder if that’s where we’re headed with some of this?  I wonder if there’s going to be sort of a heads-up display for project managers that shows you things that you might not even recognize as problems long before they are.

CHRIS BENSON:  Possibly so.  I mean, it comes down to either a partnership where you’re collaborating and where you do have that heads-up display; and then to some degree, sort of like talking about your car, there’s delegation, where you’re completely offloading a set of tasks because it doesn’t make sense for you the project manager to have to attend to that, too, and allow you to focus on the places where you can add the most value in that relationship.  And so I think you’ll see both.  I think you’ll see some tasks delegating, and I think you’ll see some with that heads-up display and the bots that are interacting with you, doing this collaboration.  So the nature of work itself is changing right now.

BILL YATES:  The idea of reducing the cognitive load, when I was thinking about this conversation we were going to have – I’m reading a book right now called “Choke,” and it’s about human performance and how to not choke, whether it’s a math test or you’re shooting a free throw, whatever it may be.  And one of the examples they gave focused on this idea of reducing the cognitive load.  So if we go into a test or go into a high-pressure situation, the more we can focus, the better, so reducing the cognitive load.

They give an example of a math test.  And if someone tried to answer the question in their head, then they’re using a lot more of their brain than they need to be.  If the question was written out horizontally, again, I’m not – that’s not naturally how I solve a math problem.  So if you could rewrite it vertically, then, okay, now this reduced the cognitive load for me.  I’m able to focus.  So again, I can see how for the PM, if I’m able to focus on just that which I really need to see, and block out some of those other things where somebody else is presenting that for me, then I’m really able to provide the most value that I have.

CHRIS BENSON:  Absolutely, in that way.  And that really comes down to the nature of that partnership you’re going to have.  You want your PM adding value where they have that unique capability that the machine doesn’t necessarily have.  And the things that are more rote, the things that require lots of data crunching really fast, instead of trying to go through those calculations in your head, let it do it, give you the output, and you keep focusing, you the PM keep focusing on your creative approach to that.

ANDY CROWE:  I think a lot of people, a lot of the fear comes in at this point, though, Chris, because if you go back, I don’t know, 50, 60 years go in American business, you had a typing pool.  And executives dictated their thoughts into a Dictaphone, and somebody magically typed that up in an appropriate format and sent it back.  That doesn’t exist anymore.  I mean, you can say Siri does it now.  But everybody types for themselves.  You know, you just learn to type.  And I don’t know anybody who has a Dictaphone.  There may still be somebody out there who does it that way.

I think a lot of people are afraid the same thing’s going to happen in PM, and it’s going to work like this, that before you had to have a project manager to organize, to schedule, to prioritize, and to drive these tasks, to track performance, to monitor and control, to take care of all this stuff.  A lot of people are afraid that some kind of machine learning is going to take so much of the heavy lifting out of it that executives will just do it themselves, just like they type themselves now.

CHRIS BENSON:  So I would take a different approach to that myself.  I would suggest that, instead of a project manager assuming that the heavy lifting, all those kind of fundamental get-through-it processes that they do, instead of worrying about that being what’s going to make you valuable, I would say take advantage of this.  Allow the AI to help you supercharge you as a project manager so that you’re actually more valuable than you ever were to your organization before.

ANDY CROWE:  And this is why I enjoy talking with you so much.  I absolutely agree with that.  I think it’s going to empower us to do more things in less time.  I don’t think it’s going to replace anybody.  We’ll see.

BILL YATES:  Yeah, I agree, I agree.  I’m thinking – I think of 10 resources.  Let’s say a project manager’s managing 10 resources on the team.  And let’s say I’ve got a bot.  I have something that is helping me track those 10 to figure out who gives me the best estimates?  Who’s not good at estimating?

ANDY CROWE:  And maybe adjust those estimates for them.

BILL YATES:  Yeah, yeah, yeah, yeah.

CHRIS BENSON:  Absolutely.  If you have that data coming in over time, if you know that one of your sources isn’t giving you the best estimates, there you go.  That’s data.  That’s what a model does.  And it accounts for that.  It turns out they can turn in whatever they turn in, and you train a model to give the corrected version that is right on time.

BILL YATES:  Right.

CHRIS BENSON:  Right on budget.

BILL YATES:  Yeah.  Andy, one of the issues that I see, and I think back to the projects that I actively managed, it goes back to one of the first things that Chris said, features and inputs.  How are we tracking this data?  How much data do we actually capture from project to project that we can feed into these models?

ANDY CROWE:  But you know what, this is the other part of that is that we believe we know what’s important to track, and we may not.  There may be so many factors that we can’t even really process.  And so I look at it that way.  I find – here’s what I find.  I find that the AI on my phone, I’ve got an iPhone, is creepy.  It seems to know where I’m going to go, sometimes before I know where I’m going to go.  And it always just makes me a little uncomfortable when it tells me how long it’s going to take to get there, and I hadn’t even actively decided that I was going there yet.  So it picked up on some pattern, maybe every Monday I go by Whole Foods or whatever it is.  It figures out something.  So when I look at that, it makes me aware, okay, we are creatures of habit.  We do things habitually.  And there are actionable estimates that we can give on that.

BILL YATES:  Right.

ANDY CROWE:  It’s interesting.  So you know what, right now in the Agile world we look at story points, and we look at difficulty, and we play planning poker, and we do things like that, which is sort of almost heuristic.  I think AI could be a terrific help if you know how to describe something to the AI, if you can describe it meaningfully.

CHRIS BENSON:  So you’re raising a great point there about two things that I’d like to note.  One is about the data for the models that you’re going to create to help you, and one is about the human interface with that model.  And that is that, even though Siri in your case knows you better than you do in terms of where you’re going, the way that it’s choosing to interact with you today is a little bit creepy, as you say.  So there’s a lot of work being done in that area in terms of, aside from the data and showing what the right answer is, how do you communicate the right answer in such a way that it is truly seamless and doesn’t give you that weirded out feeling that you’re getting currently.

ANDY CROWE:  Some of that’s just going to be training me, I guess, you know, over the years.  And my children probably don’t feel that way about it, but I do.

CHRIS BENSON:  Probably.  And we have to be flexible and grow with this.  But the other side is the data.  And you raised a great point when you said we may not know what needs to go into the model.  That’s a field called “feature engineering” in AI, with the feature being the input.  And that is a big thing.  And right now it is collaboratively done between human experts and the model because, when you go through, AI will weight the importance of those different inputs.  And so to some degree you can shortcut things by taking a human expert that hopefully knows the right answer and can get there sooner.  But if not, you can feed a model lots of different data points, and the things that aren’t so important it will weight with very little importance, and the things that are important it will weight accordingly, as well.

ANDY CROWE:  It’s the same thing we did in manufacturing, learning how to describe three-dimensional objects in a way that made sense and could be CNC created.

CHRIS BENSON:  Yes.

ANDY CROWE:  So it’s a similar thing we’ve got here, whether we’re talking about manufacturing, whether we’re talking about construction or software engineering.  Thinking about how we want to describe things, that’s going to be a big deal.

Bill, I’ve got a question for you.  And Chris, I’d like to hear you weigh in on this, too.  So we have traditional waterfall project managers, and we have sort of the newer Agile project management.  I firmly believe that one is not better than the other, that they’re two ways to answer the same question.  But I do think that one may be impacted more than the other by this.

BILL YATES:  Huh.

ANDY CROWE:  What do you think?

BILL YATES:  Well, I would see how – I think it would fit more friendly with Agile, just to start out with, because of the frequency, as you noted, the velocity, the user stories, counting up points, estimating.  Estimating is such a regular part of what they do.

ANDY CROWE:  Right.

BILL YATES:  And the retrospectives happen.

ANDY CROWE:  Estimating, refactoring, and reprioritizing; another short iteration or burst, yeah.

BILL YATES:  Right.

CHRIS BENSON:  Yeah.  It allows you to have that feedback loop from your AI helpers, whether they be bots or whatever other tools that you’ve created to feed into that Agile process, as well.  So I think you get the benefit of that AI supercharge right there.

ANDY CROWE:  I think we’re not all that far off from having AI bots on the Slack channel, and you don’t know or think about who’s your coworker and who’s the bot.

CHRIS BENSON:  Actually, that’s already happening.

ANDY CROWE:  So we’re not far off from it.  I was right.

CHRIS BENSON:  As an avid Slack user, I have seen these.  And there are some little things that I’ve seen developers do putting together such.

ANDY CROWE:  I, like economists, am really good at predicting the past.

CHRIS BENSON:  There you go.

BILL YATES:  Yup.  I think a lot what we’ll see with software, my guess is with project management software, scheduling / estimating software, we’re going to see more and more integration of AI in that.  So that it’s almost like, Andy, I almost see it as there’s a little smart feature built into the tool that says, are you sure you want to put that estimate in?  Are you sure those are the only risks you’ve identified?

ANDY CROWE:  Are you certain you’ve described this or understand it adequately to take the next step.

BILL YATES:  Right, right.

ANDY CROWE:  Is it decomposed to the proper level yet?

BILL YATES:  Right, right.

CHRIS BENSON:  And if it has access to your historical data from all these other projects that you’ve been working on, it may come back and let you know, you know, we’re not accounting for this.  We’re not accounting for that.  Because the data that that AI model is using is essentially a proxy for reality.  And so if you feed it the data that is the reality of all these past projects, then you can get an amazing helper for you.

ANDY CROWE:  When it’s going to get interesting is when that particular project management AI starts talking to the strategy AI for the corporation and saying, “I’m not sure this is really strategically aligned with where we’re going.”  Or, “Hey, the finance AI component bot doesn’t think we have funding for this, and we’re going to have to change the prioritization of the components that we’re developing in order to match the funding capabilities.”

CHRIS BENSON:  I think you’re exactly right because, you know, going back, not only do I see it impacting every industry, but really every function in every industry.  And so you made a comment a few minutes ago about not really knowing if that person on Slack is in fact a human or an AI bot, and I think that’s exactly right.

ANDY CROWE:  So we’re all relatively young.  Are we going to live to see the scenario I just threw out?

CHRIS BENSON:  I think it’s already starting to happen.  I think over the next few years there’s so much work being done in that space that, yes, I think we’ll experience it.

ANDY CROWE:  I worked with a CFO who was very much like a bot.  So I’m not sure he had a soul.

CHRIS BENSON:  That may be the very first function in your organization that you can’t tell the difference anymore.

BILL YATES:  That’s true.  That’s true.  There are so many practical applications, though, for project managers.  I mean, to me, Andy, if I get over the whole idea of “The Matrix,” with capital M “Matrix,” if I can get beyond that, if I think of something that’s looking over my shoulder at the plans that I’m building and managing with a team, and it says, “Hey, Bill, check it out.  You’ve got this many stakeholders identified in your contact list, but you’ve only got this number of risks in your risk register.  You may want to look at that again.  You guys may need to go a little deeper with your analysis.”

ANDY CROWE:  Hey, listen, having a bot to help you remember to follow your communications plan.

BILL YATES:  Sure, oh, yeah, yeah.

ANDY CROWE:  Or to follow some policy within the organization.  That would be significant.  I’ve got a friend who works for Chevron.  His computer forces him every few minutes to take a particular kind of break, just ergonomic break – stand up, stretch your wrists, do this.  And they take it very seriously.  And I wouldn’t quite go so far as to call it AI, but it’s sort of a forced compliance within the software and hardware that it locks them out.  They have to do it.

BILL YATES:  Wow.  My Fitbit does not have that much control over me.

ANDY CROWE:  But it buzzes when you reach your goal; right?  Or it lights up or something.

BILL YATES:  Right, it makes me feel something.  Sure, sure, sure.

CHRIS BENSON:  We’re not looking for AI to be our overlords.  We’re looking for that partnership.

BILL YATES:  Okay.

CHRIS BENSON:  And that’s where we’re going to see that benefit.

ANDY CROWE:  So it’s the difference between sort of “Minority Report,” where there was still a lot of looking over shoulders and technology involved in your day-to-day lives, and “The Matrix,” where no, no, your new overlords are here, and you’re here to serve their needs.  Interesting.  Chris, I’ve got another question for you.  Do you see areas where AI is going to struggle within project management?

I’ll give you an example.  Soft skills have become a lot more important in the past few years.  They’ve taken on a big focus within the project management community.  It used to be project management was very much a left-brain, cerebral, almost an engineering function.  And then they started realizing, okay, people are not robots.  They’re people.  They have needs.  And the way you communicate with them, the way you talk to them, the way you try and motivate and inspire them makes a huge difference to the success of the project.  Is AI going to struggle there?  Or will it learn to basically emulate some of that?

CHRIS BENSON:  So Andy, I think you have answered the question that you’ve just asked me to some degree, and that is that soft skills are definitely going to be the area that AI will not be dominant in in the next few years.  As you pointed out with Siri earlier, it is still weirding you out a little bit.  And those interactions are vital to productivity.  And so AI will be helpful as a bunch of small components that are very task-specific.  And you, the human project manager, will be the ringmaster who is managing all those components which are designed to help you in very specific ways.  But it’s your creativity and your human brain which will drive things forward.

ANDY CROWE:  The Ringmaster, just like Sauron.  Well, Bill, it sounds like it’s time for us to start developing some soft skills because that’s going to…

BILL YATES:  Yup, it’s coming.

ANDY CROWE:  That’s going to keep us from the high tide the longest.

BILL YATES:  That’s right.  That’s where we can add value.  But, you know, I can think of it.  We could – I could see AI helping me as a project manager by tracking hours per team member and saying, hey, here are those that are putting in overtime, and this is the fourth week in a row that these two have.  You know, pay special attention to them.  Go check with Susan and George.  They may need some time off.

ANDY CROWE:  Send them an edible arrangement.

BILL YATES:  Right.  And it places the order for me.

CHRIS BENSON:  And it may even be able to take it a step farther and suggest reasons why you’re seeing those changes over the last few weeks or something to where it doesn’t just tell you that they’re happening, but it kind of gives you a sense of why and what you might want to do about it.

NICK WALKER:  What a great discussion, Chris.  Thanks so much for being with us to share your insight.

CHRIS BENSON:  Thank you very much for having me.  I had a great time.

ANDY CROWE:  Hey, Chris, thank you so much for coming.  And we always like for our guests to leave with a little something in hand.  And it’s going to be hard for a bot to replace this.  It’s a Manage This coffee cup.  And this is going to make you the envy of all of your friends and associates.

CHRIS BENSON:  That’s the perfect gift.  I love my coffee, and now I can look at my Manage This logo all the time.

ANDY CROWE:  Yeah.  And let me ask you something for our listeners who might want to get in touch with you.  You also have a podcast.  Can you tell us what’s the best way to reach you, and how can we find out more about this podcast?

CHRIS BENSON:  Sure.  So the podcast is called Practical AI.  It’s produced by an organization that software developers in particular may very well know called the Changelog, and it is one of their podcasts.  And you can find that at Changelog.com/practicalai.  But to either get there or to reach me in social media, the easiest thing is to just go to ChrisBenson.com.  It’s my website, and there are links to any way you want to reach me, including the podcast.

ANDY CROWE:  Thank you, Chris.

CHRIS BENSON:  Thank you very much.

NICK WALKER:  We want to remind our listeners about the extra benefit we have for you here on Manage This.  As you collect your PDUs, your Professional Development Units, you can add a few to them just by listening to this podcast.  To claim your free PDUs, go to Velociteach.com and select Manage This Podcast from the top of the page.  Click the button that says Claim PDUs and just click through the steps.

That’s it for us here on Manage This.  We hope you’ll tune back in for our next podcast.  In the meantime, you can visit us at Velociteach.com/managethis to subscribe to this podcast, to see a transcript of the show, or to contact us.  And tweet us at @manage_this if you have any questions about our podcasts or about project management certifications.

That’s all for this episode.  We thank you for joining us.  Until next time, keep calm and Manage This.