How AI Is Reshaping Project Management in an Era of Hyper-Collaboration

Hey guys. Thanks for the throw. We're here with newly appointed CEO for Planview, Matt Ziley, and we're talking about everything here from project management to value stream management to some good old fashioned collaboration.

But all this stuff is coming together, hopefully in the age of ai. Matt, welcome to the show. Thank you so much.

Good to be here. So Planview does have a pretty broad portfolio, and I think a lot of folks are using at least one, maybe two of those tools, but I'm not sure they got the whole portfolio installed. And I wonder if all these functions, maybe we'll see more convergence in the age of ai, but how do you see this all playing out?

Yeah, I think, uh, I think there's no doubt about it. I mean, I think even before the age of ai, the convergence was starting to happen, and the complexity that companies have always had is that they make decisions in different ways, even side of a single company. Different teams make different decisions in different ways.

They get work done in different ways. Uh, the last 5, 6, 7 years as companies have gone through pretty significant digital transformations, that's just been even more complicated because you've got teams inside of companies that are trying to develop software for the first time or create digital products. And so all of that complexity is there.

There's been this need in the market that we've been, uh, certainly fulfilling for a number of customers to bring that all together and do a single pane of glass so that companies have a complete view of what they're trying to get done, and they can make sure that everybody is executing against that strategy. But in the age of ai, uh, it, it, it's a multiplier effect unquestionably, because now that, that, that's still even at its best, a complicated picture when you've got large organizations, teams with different operating models, it's always been hard to still bring that together. AI does a great job of, of bringing simplicity, uh, to a lot of that complexity and really surfacing up where things are going awry or the data to make the best decision.

And so I think, uh, I think we're gonna see over the next 1, 2, 3 years. AI is an accelerant to companies achieving that, that true single pane of glass for their decision making, uh, and, and, and making sure that they have everybody kind of rowing in the same direction because of it. Do you think as a result we'll be able to maybe understand and surface the dependencies that exist between various projects?

'cause I think that's what often kills us is that nobody kind of realized that they were dependent upon somebody else doing something by a certain time, and then they fell behind and nobody kinda sent a message down the line. And so then, you know, for the one of a nail, the war was lost, Could, couldn't agree more. I mean, it's, it's a, it's a, it's the core of what every, I don't even wanna say large organization, every organization deals with that.

The only difference between small and large is that you can still, uh, get a small team in a room to kind of sort through it. But as your organization grows, it's those, those dependencies that, that hurt and slow things down and, and cause projects and, and initiatives not to be delivered. So it's a pretty big change we're going through there.

And, and it's an area that we spend a significant amount of time on as plan view, because the complexity has been less in the last few years about tracking dependencies and a lot more about knowing what to do about a dependency. Because when you track all the dependencies in an organization, it is far too complicated of a map for any one person to understand or take action on. And so we've invested a lot here.

It's a perfect application for AI because it can take often unstructured and sometimes conflicting data sets, bring those together, truly understand a map of what's happening. And then the trick becomes how do we bring that to the fore for people to understand and interpret and make decisions based on? And so it's a core application that, that we're focused on.

We call it the connected work graph. Uh, and it's really that intention of not just tracking all the dependencies, but turning that into, uh, uh, uh, a, a a view and an understanding that a person can take action on to, to limit the risks from those dependencies. Hmm.

How do we get the data to surface those insights? And I asked the question because, you know, back in the day there'd be somebody standing around with a clipboard collecting data and kind of looking over somebody's shoulder to figure out what was going on. And then they would create a report.

And you know, some people would read it and some people would not. But there was always this resentment because people were like, well, why do I gotta fill out, out all this data and do the entry? So can we automate the data collection these days?

'cause I think the data we need is in our platforms and our tools, it's already there, but we just need to pull it out, right? I think that's right. I think that's right.

I think, I think this is where a lot of companies are, are focused. We, we focus on trying to bring together the entire picture regardless of, of kind of where the data sits. But, you know, there, there are plenty of tools that people use to track their work today.

And a lot of those tools are making great strides to make it easier to get the insights from people into the tool and, uh, uh, leverage the fact that we have these, these LLMs now at their core that can very quickly translate the thoughts of a human into something structured for interpretation. And so a lot of that's happening at the kind of team tool level, which is, which is a big step in the right direction. Uh, but then the, the problem that we still have to solve is how do you make sense of it all?

And so I think we're gonna see over time the need for people to spend a lot of time on kind of classic data entry and so on, that's gonna continually decline. And even today it's pretty, pretty easy for most of us, even just using our voice to get data into, uh, into a system we might use so that that burden's gonna decline. The pressure on making sense of the data is going to continue to increase.

And so that's where we spend, uh, a good portion of our investment and time to make sure that when you have all these disparate data sources, they're coming together in a unified way and they can be used to make better decisions and, and take better actions. Mm-hmm. What is the level of maturity of the AI tools that we're using today?

And I asked the question 'cause it seems like all over the place, it's a little uneven, but we are moving to this agentic AI age. So how autonomous will an AI agent be in the context of, say, a project management application or value stream management or whatever it, it's, Yeah, I think, I think if I go back to 2025, I, I kind of characterize it as, as the, the world where, uh, build or buy was still a big decision that was, was being debated around ai. And, and it wasn't clear yet which tools were mature.

We've experienced the cells with, with things we use from, from other vendors. There's a wide range of effectiveness of AI tools today. And so people still have to do a lot of testing to make sure they work.

But when we get into 2026, I think what we're seeing is that companies are making the transition. They're a lot more comfortable using what I would call, you know, kind of built-in AI capabilities or packaged AI solutions instead of feeling that they need to build as much of their own AI solutions. And I think that's a benefit because one of the areas that we're certainly focused on is, is building and productizing the agents that we would expect our customers to use.

And so I think we're, we're over the hump on, on companies being comfortable with that approach. We engage with our customers directly on which agents we have and how they apply specifically to their use cases. There's still a lot of work that goes into that, but the benefit of that work goes directly to your question, which is when, in our case, when we partner with a company to take our agents and apply them to their use cases, we spend time, weeks, sometimes months, making sure that they get comfortable so that they can lean on the automation of agents and don't feel that it has to be, uh, highly overseen or governed and that it actually will provide scale to their organizations.

But the, we're in the year, this year 2026, where I think the way we'll get there is by proving out and doing proofs of concept to get people comfortable. I don't think we're at the point yet where people are gonna flip a switch, start deploying agents and fully rely on them. Maybe that's the, you know, what 2027 will become.

Um, but I think the big, the big kind of Rubicon we've crossed from 25 to 26 is, is on average companies are far more comfortable engaging in agentic discussions, uh, because they believe there are use cases they will be able to rely on heavily for agents. How smart can all this get? And I asked the question because, um, in an, in an ideal world, we discover that there's an issue before it has a catastrophic impact on our plan, and we reallocate resources to make sure that that issue goes away so that we can get back on track.

Will the AI agents not only surface when we have that issue, but will they negotiate with each other about how to reallocate those resources? 'cause in many cases, whatever the task is will be done by them anyway. I think, I think that's gonna be, I think that's what leading companies are gonna get to.

I think, I think we're at the point today where we can point to plenty of examples in our customer base where people will rely on plan view, uh, plan view's, agents and AI capabilities to elevate the issues that's already happening. And so it gives companies a lot of confidence that the, the flag is being raised whenever they see an issue so that, uh, any individual, uh, user can, can see those issues. They don't have to go find them.

They, they get, they get put front and center. So I think we're already in that world today, which is exciting. I don't think we're very far from the point where, uh, the agents will start to pro solve the problems as well.

And I think that is, uh, gonna vary by company, it's gonna vary by industry, it's gonna vary by the kind of degree of risk tolerance, but there are so many use cases we see already that are fairly low risk for an AI agent to not just identify the problem, but do the resource reallocation and, and companies are comfortable with that use case already. Now, I think there are some highly governed, highly regulated, uh, take financial services use cases where that is probably another year or two away. There's a, a lot of learning cycles we need to go through there, but it's not because the technology's not there to do it today, it's because, uh, the, the comfort level isn't quite there yet.

Hmm. Um, as you kinda put this all together, what becomes of the human project managers, because they are historically at least, or the backbone of many of the organizations that, you know, if it wasn't for them, most of this stuff wouldn't come to reality and everything we sold would probably not be delivered on time and everybody gets angry. So those project managers are kind of like the ones with the broad shoulders in these organizations, but how do they evolve?

Yeah, I think, I think we're starting to see already the, the elevation I would say of of not just project managers, right? In the world we live in, it might be anybody from a project manager to an agile coach, to an initiative owner in a transformation office. The, the, the way you described it is perfect.

The backbone of how work gets done in these organizations, tho those people are, are elevating. And the reason they're elevating is because the best ones now can multiply their impact. And so what I think is the big change is that every, every project manager knows that they're balancing what may be high value work with some lower value work.

And, and what's changing now is they can rely on automations and AI technologies to offload a lot of the lower value work and continue to elevate where they spend their time. Whether it's a higher number of projects, whether it's the bigger projects, whether it's more strategic initiatives. I think that's the evolution we're gonna, we're gonna go through and, you know, think we're, especially around technology delivery, the every five years, there's a pretty big change.

This may be the biggest change we face, uh, in in the last 20 or 30 years. It's not gonna come in the, in the, in a classic, you know, kind of replacing jobs productivity way. It's gonna come because it's gonna elevate these really effective people to have a bigger impact than they might have had two or three or four years ago, I think.

Mm-hmm. And to your point, a lot of these project managers, well it's a stressful gig no matter how you look at it, because they don't have a lot of control over the underlying resources, and yet they're accountable for the outcome often. So, um, do you think in the age of AI that maybe there will be less stress for those folks, less burnout, and they will feel like they have more control over their outcomes?

I hope so. I hope so, because I think, I think what we're certainly gonna see is they're gonna have more information, and it's not information that they have to go weed through. It's more information that AI is going to help surface insights, really actionable insights from.

And so I think there's generally gonna be a sense that, that most, uh, uh, project managers and initiative owners are going to have a better understanding of the risks and the dynamics in the work that they're trying to get done. And that isn't always inherently comfortable, but I think it's a good step towards just having that, that complete view of what is really happening and that's gonna give them the opportunity to go and, and take action in the right areas. And so I think over time, we, we will see that, that it, the, the stress should reduce because the risks are going to reduce.

And I think those are the two things that correlate most strongly in a, you know, in a project manager's world, for example, now, there's still always gonna be the challenges of how do you rally a a group of people that you may not have direct control over to, to address these issues. I'm not sure that that that is quite, uh, gonna go away yet. But I think if we can, if we can reduce the, the stress from foreseen and unforeseen risks, it's a big step in the right direction.

Um, do you think ultimately if we get all this right, that our business forecast will be more accurate because we'll have more visibility into what's actually being done to drive the next product or drive the next innovation and we'll just have a better handle on things in general? And maybe the CFO should care more about this? There's, there's no doubt we're, we're seeing it today because the, the best, uh, transformation organizations, right?

They are very good at setting out objectives and key results and tying every bit of work that they're leading back to those key results. But to your point, the, the variability comes in, in whether they execute on time, whether they actually achieve those key results. And, and a lot of that comes from surprises and risks and, you know, people mischaracterizing the, the health of any given project or initiative.

And so I think the more that AI takes on those responsibilities of assessing the, the true state of the state and elevating to the rest of the organization, providing visibility to where the risks are and how healthy any initiative is, how we're tracking against the business results, it can only lead to, uh, improved likelihood of achieving those business results. And so I think we see plenty of, of our customers today that are already kind of at that, at that stage and very effective in in how they do that. Um, but there are many organizations that aren't there yet, right?

They still kind of manage, uh, individual projects versus are they actually hitting the, the key results they're trying to, to achieve. So what's your best advice to organizations about how to get ready for all this? 'cause I think end of the day it comes down to the data and a lot of that data today may be, isn't as organized as it should be.

Yep. I think that's right. I think, I think the, the biggest value that that AI provides is it works really well today with unstructured data.

And so I think a big part of this for a lot of organizations is, is to get started. And, and I think that they'll, in, in a lot of cases, they're surprised by how quickly they can find valuable use cases. So it's almost a bit of, you know, if we, if we think about AI as this one blob of technologies, well that, that's really hard to apply to any business problem.

And so the key is getting in and discovering what are the one or two or three business problems for us. The ones we help with at planview tend to be, you know, are, are we aligning the right capital and resources to projects? Are we executing on those projects?

Do we set the right strategies and initiatives? And so I think when we, when we even pull those apart and find the one or two or three that any individual company is really challenged by or struggling with today, we can find very discreet solutions for AI that don't require a rewiring of their entire enterprise architecture. And I think that's a bit of the sentiments in some companies sometimes is they feel like to get the benefit from ai, they really have to completely rebuild their underlying data lakes or data lake houses and their infrastructure.

And I think that might be a good North star for some companies. It's not necessary to start seeing value from AI today. And so the biggest, the biggest message is to, to to dive into some really high value use cases, make those successful, and build a roadmap of those over the course of the next one, two or three years.

Alright, so in the coming year, what should folks expect from planview? There's, uh, there's a lot, uh, happening here. Obviously we're talking a lot about AI for good reason and what, what we've, uh, I think discovered and what we're building towards is, is truly the, the rebuilding, the rewiring of a process that has existed in most companies for the better part of two or three decades.

It might be a project process, it might be an agile process, it might be a product, uh, operating model type of approach. And we, we've dealt with that complexity for a long time, that every organization has multiple operating models. When we look ahead, what, what we're gonna be able to deliver to customers is gonna continue to be the application of AI at the highest level of defining the right strategies, deploying the work down to every team, and making sure that you adapt and adjust, uh, when, when you, when the needs rise, when, when the market changes.

And when you're in a world that is changing faster now than ever before, which is something we've said every year for the last five or 10 years, but is truer now than ever before, companies can look to plan view to completely accelerate that cycle, leveraging AI solutions. And, and in our belief, the ability to make the best decisions faster is the thing that will determine the success or failure of most companies in the next two or three or four years. And ensure they're not left behind by competitors that might be evolving faster than them.

And so our investments in, in bringing AI to that application space, they're, they're here and now and, and I think, uh, will, will be the, the theme for us for 2026. All right. Well, as Benjamin Franklin once put it, you know, even in the age of AI failing, the plan is planning to fail.

Hey Matt, thanks being on the show. Thanks so much, Mike. And back to you guys in the studio.