Reimagining Government with Priority Based Budgeting

Tyler Podcast Episode 95, Transcript

Our Tyler Technologies podcast explores a wide range of complex, timely, and important issues facing communities and the public sector. Expect approachable tech talk mixed with insights from subject matter experts and a bit of fun. Each episode highlights the people, places, and technology making a difference. Give the podcast a listen today and subscribe.

Show Notes

On this episode of The Tyler Tech Podcast, Chris Fabian, Senior Director of Product Strategy, ERP Budgeting at Tyler Technologies, explores the inception and evolution of Priority Based Budgeting, a solution transforming how communities allocate resources. Chris also dives into the exciting integration of AI in budgeting processes, and explores its future implications.

We also detail our latest white paper about the five main risks of legacy systems. You can download that here: Is Your Legacy Digital Infrastructure Putting You at Risk?

Learn more about the topics discussed in this episode with these resources:

• Application Sheet: Priority Based Budgeting
• Brochure: Enterprise ERP Financial Software
• Tyler in the News: The Future of Priority Based Budgeting
• Blog: Future-Proofing Government Through Technology Modernization

And you can listen to other episodes of the podcast at this link: www.tylertech.com/resources/podcast

Let us know what you think about the Tyler Tech Podcast in this survey!

Transcript

Chris Fabian: Our dream is that local governments don't suffer from resource scarcity, but they always have a mindset of we can do things differently in order to free up those resources and pursue the future. And if we can get that discipline, that is what the future looks like to us.

Josh Henderson: From Tyler Technologies, it's The Tyler Tech Podcast, your source for insightful conversations with thought leaders addressing the pressing issues in our communities. I'm Josh Henderson, and I'm on the corporate marketing team here at Tyler. Thanks for joining us.

In each episode, we dive into the essential topics shaping our society and shed light on the individuals, places, and technologies empowering the public sector. If you like what you hear, please consider giving us a five-star rating and review, subscribing on Apple, Spotify, or wherever you listen to podcasts, and recommending the show to others.

On today's episode, we're joined by Tyler's Senior Director of Product Strategy for ERP Budgeting, Chris Fabian.

From working in engineering to making significant impacts in local government with priority-based budgeting, Chris has a fascinating story to tell.

We get into a lot during this conversation, including Chris's career journey, the inception and evolution of Priority Based Budgeting, and how it helps communities reallocate resources effectively.

We'll also explore the integration of AI into this process and its implications for the future of government budgeting.

This episode is full of amazing insights, so without further ado, here's the conversation with Chris Fabian. We hope you enjoy.

Chris, thanks so much for joining me today on The Tyler Tech Podcast.

Chris Fabian: I'm happy to be here. This is pretty cool.

Josh Henderson: Awesome. Yeah. Great to have you here. So let's start things off by just if you could just tell me a little bit about yourself, your career journey, up to and including your role now here at Tyler.

Chris Fabian: You know, I started off well, I started off with pizza delivery, to be honest, but, as an engineer. And so, I definitely bring an engineering lens to the work that we're doing. It was in the midst of working in an engineering firm that I was working on water rate models, fleet analysis.

And inevitably, the mark of success would be, hey, we saved Laramie, Wyoming a million dollars, or we saved, Inglewood, Colorado ten thousand bucks. And I was always curious, what becomes of those resources, like, did our work amount to anything good?

And it was kind of following my nose on that path. All of us inevitably end up more central to understanding the overall budget of a city, to find out what becomes of saved money. Do they reallocate it to higher priorities?

And around the same time, there was a group of innovators who had written a book called Reinventing Government and The Price of Government. And they were fixated on budgeting questions around what are the outcomes we're really trying to accomplish, and can we reorganize the budget around these things? So, on a lark, I reached out to this particular group of individuals, David Osborne, Peter Hutchinson, and we partnered on some of the earliest implementations of what was known as budgeting for outcomes.

And on the one hand, it was really exciting because here we were in a territory where not many people dare to tread, which is, you know, the highly protective, realm of departmental budgets. And at the same time, what I came to realize was, well, this was a great concept, there was maybe need for more work to make it applicable to everyday communities across the country. So, with an engineer's mind, I got really curious about how could we create a system to replicate a process and actually make it a lot truer to this idea of aligning budgets with outcomes, overall. So, I had a small management consulting firm. I'd, left my job in county government and went out on my own to try to bring this process to life, partnered with ICMA and GFOA in the early days and got some of our first organizations.

We were using spreadsheets at the time and just kind of rolling up our sleeves and seeing if we can help these communities.

It is very fulfilling work, but we also realized that in order for this to sustain, we're going to need some technology.

So that led to the development of software by about 2015, 2016, all the way up until we got to work with Tyler.

We started to notice that so many of our clients were also Tyler clients and wanting to make a sustained difference year after year. And therefore, that's, I reached out to Tyler to see if we can make something happen. And here we are with the dream of really scaling this budgeting approach across many more communities.

So, taking it from, you know, cutting edge and innovative to regular that everybody does it, and that's just the way the budgeting is done.

Josh Henderson: Yeah. Wow. And such an interesting path and, like, just to to kind of see how things kind of led into other things isreally, really interesting, almost like serendipitous in some sort of way.

So that then became Priority Based Budgeting, which is what it's now as in this in the current state of things. So, you kind of touched on this a little bit, but tell me a little bit more about what inspired priority based budgeting and, you know, how it's evolved since it was incepted initially? And then furthermore, I know there's an AI integration, and how that's enhanced its effectiveness.

Chris Fabian: Great question. So, priority based budgeting has definitely evolved. And the original inspiration was you look at a at a city's budget or a county or school district, and it's, you know, cliche. It's a thick document.

It's full of details. If you watch any budget hearing of a community, inevitably, you'll see an elected official trying to scour those line items and figure out, you know, why are we spending more on postage than we did last year? You know, what's the storyline there? And they're trying to do a great job.

My own elected officials looked at that same budget document, and they said, like, what page do I turn to to find out how much we're investing in the safety and well-being of our residents? How much of our budget goes to economic vitality and job creation?

And we could say, well, here's the line items for the public works department, and here are the line items for the police department. You quickly come to appreciate that there's no place to turn to to answer that question. And it's obvious now looking back, but at the time, we wondered, you know, what a great question to ask, and how do we come up with an answer like that? So, the inspiration was, how can we reflect this seemingly complex budget document around what the community actually gets?

So, they're putting money into government, and what are they getting out? And that was the inspiration. Where it evolved, you know, in the early days, it was the great recession when we were implementing. So, a lot of communities were doing this work because they needed to find a sensible way to balance the budget.

And so, what are we going to keep when we don't have enough resources to spend on everything?

And that was exciting period of time. We had one community, San Jose, say, the way that governments typically do budget reductions and balance the budget is known as across the board budget cuts. So, they'll say, trying to be fair to all departments, we're all in this together. We’ve got to reduce the budget.

So, every department gets a five percent or a ten percent budget cut, and you all figure it out and come back to us with an answer. If you think about it, that's there is a degree of equity in that. You're trying to treat everybody fair. But San Jose said, you know, if a family did that, if they had a ten percent reduction to their household income and they tried to do an across the board budget cut, it would look like reducing your mortgage payment by the same percentage as your dining out tab, by the same percentage as your Netflix subscription.

And, of course, we wouldn't do it that way. So why do we do it in local government? Why do we do these across the board cuts? And the realization was it's because we don't have another way. We don't have our options in front of us. We don't have things in priority order to say these programs are actually of a lower priority, and we can do them differently.

And so that was inspiring back in 2008, 2009 to have a sensible alternative to across-the-board budget cuts. We always wondered after the recession goes away and there's more of a time of abundance, will anybody need priority-based budgeting? And it turns out, we didn't know at the time. You know, we thought, well, we'll play this out and see what happens. But after the great recession, we did return to some degree of economic prosperity.

And yet, communities were constantly showing us that if they got really ambitious and tried to be clear about the needs of their community, overall, we need money for our climate action plan. We need far more money for infrastructure. We need money for building out a sense of a safe, and smart city. Many of the values of Tyler. We’re always in a degree of resource scarcity.

And therefore, priority-based budgeting is a handy lens, so to speak to ensure that we can thoughtfully question where all the money goes today and ensure that we can find ways to reallocate it towards the underfunded priorities that always are in need of additional resourcing.

Josh Henderson: Right. Right. It sounds like I mean, not that the recession not that we want to paint the recession in any sort of light, but the fact that it allowed for communities to kind of, like, understand that resourcefulness, in a certain way and then kind of carry that on in the future is really, really great necessity, I feel like.

Chris Fabian: I like your word resourcefulness.

We've thought about that over the years. What does it mean to be resourceful is knowing where you're as you think of MacGyver. Right? You think of, like, how are you going to be resourceful? We're going to do the best with what we have, and that's what priority-based budgeting can be as well. It's we have finite resources, and we have to somehow come up with a combination of using these resources towards programs that have the highest degree of impact, and there is a solution there.

Josh Henderson: Absolutely. Absolutely. Now can you walk us through obviously, it's been implemented in in in several areas around the country. Walk us through the sort of the key steps involved in doing the implementation with local government.

Chris Fabian: Yeah. So, there's a recipe. There's key data that we have to build in order to unleash the opportunities for resource reallocation.

And so, most organizations when they start, they have their line item data. And we have to go through a transformation to say, we're going to take those line items. From the line items, we're going to try to understand what programs and services your departments provide. Because usually, you know, if you walk into any city across the country and said, can you give me a list of the services kind of old school yellow pages style, of what you provide?

No one has that list, so we have to create a program inventory.

We take the line-item information and allocate it to those programs to find out not only what it is that we do, but how much it costs us to do what we do. So, there's a pricing approach. Then we systematically score all of these programs relative to priority criteria. So, we say, how does this program help us achieve a safer society? How does it help us achieve economic vitality, transportation, and well-being or whatever that local government believes the role of local government is for their community overall.

In addition to other key reasons for providing programs like degree of mandate, is this program required? That's a good reason to provide something.

Are we the only possible service provider of this program? Meaning, if we stopped providing it, would we leave a certain population without the benefit of that particular program, or are there other service providers?

Other examples are population served, demand, cost recovery.

So, we're learning all of these things about the programs that they provide in order to then prioritize, score, and rank them and drive the opportunity. Literally drive, what is the future for every program that they provide. Is this program cybersecurity, as an example, that's a program in an IT department, is usually on a growth trajectory. Like, if it's five years from now and we have not invested more heavily in cybersecurity, a local government would say that seems antithetical to what we're trying to accomplish.

Whereas other programs might be on a sunset trajectory. They're low priority. Demand is going down.

Somebody else provides them. They're not recovering their costs. Let's thoughtfully question, should we remain in this business, or can we reallocate resources away from that particular program?

So that's how the process looks. It's data creation to serve as a means to an end, resource reallocation opportunities and ultimately a budget process.

Josh Henderson: Yeah. Thanks for breaking that all down for us. I feel like there's so much thought involved in that and that probably, I mean, it's helpful for a community who's looking to implement. Right? Where it's like somebody's coming in and they're putting resources of their own into getting this stuff implemented. And to have that thoughtfulness put into it, I think, is probably a huge value add for these communities.

Chris Fabian: So, I like, thanks for saying that.

I like where you're going. So, some of our communities take the data, and they put it online for their residents as well. And part of the reason that they're doing that is, and this gets a little bit into transparency. They want to showcase, here's what we're doing for you in the realm of safety and well-being, and here's what we're providing for you in the realm of economic vitality and job creation so that residents can really understand what the government is trying to invest in to bring about these key outcomes.

And the other reason to, one of your points that they put it online is to demonstrate to their residents who, in this era, might be a little skeptical of government, that they're really trying to take a rigorous, thoughtful look at the budget and examine where they might have options for partnerships, where they could reallocate resources and do everything possible before they ask the residents for new money. And we really love that. The rigor and the thoughtfulness behind prioritization is a testament to what the community is willing to do to prove to their taxpayers that they're trying to be good stewards.

Josh Henderson: Stay tuned. We'll be right back with more of The Tyler Tech Podcast.

I hope you're enjoying listening to this episode of The Tyler Tech Podcast.

I'm here with my colleague Jade Champion to talk about the importance of tech modernization.

Are you struggling to maintain your legacy systems? It might be time for a change.

Jade Champion: That's right, Josh. We just released a white paper that outlines the five main risks of legacy systems and the benefits of modernizing your digital infrastructure.

Josh Henderson: From security weaknesses to inefficiencies and high maintenance costs, legacy systems can really hold back government agencies. So, what are some of the benefits of future proofing with an updated tech stack?

Jade Champion: Modern cloud-based solutions help to streamline processes, protect against cyber threats, improve the resident experience, meet compliance requirements, and provide more scalability.

Josh Henderson: Are you ready to leave your legacy systems behind and improve your digital services?

Check out our show notes for resources to help you get started and reach out to us at podcas@ttylertech.com to connect with a Tyler expert today. Now let's get back to The Tyler Tech Podcast.

Josh Henderson: I'm curious about equity initiatives when it comes to priority-based budgeting. So how does priority-based budgeting support equity initiatives and and climate action plans? And can you maybe provide specific examples of how AI driven priority-based budgeting has been used to fund these types of programs?

Chris Fabian: The city of Pittsburgh, Pennsylvania approached us right before the outbreak of COVID. And what was taking place? They were intensely focused on a climate action plan, which looked to them like a list of projects, like converting their fleet to electric vehicles or looking at more energy efficient ways to run their buildings and facilities, overall. There's an intersection between climate and equity for climate justice as well.

So, they had a host of things they really wanted to do with a price tag because achieving climate justice is not free. And then they had causality defined if they invested in these types of projects, they know they're going to get a better outcome, but we just need the resources to be able to do it. And so, our approach was let's implement priority-based budgeting in order, you know, specifically to find all the different ways that we could partner, run our programs more efficiently, raise revenue in an entrepreneurial way, spin off programs into nonprofits to free up as much money as we possibly can in order to redirect towards funding this unfunded climate action plan.

The Bloomberg stories, from Bloomberg Philanthropies, this is part of their American cities climate challenge program, was that cities don't necessarily believe that they can fund key priorities like addressing climate action. And yet, here's Pittsburgh reallocating $41 million dollars towards this, unfunded climate action plan in the midst of COVID when they're having to reduce services in other areas that it was that much of a priority. That's how they're going to be able to do it. We had a positive success story with Pittsburgh.

And what came next? Bloomberg Philanthropy started the city budgeting for equity and recovery process in the midst of COVID, realizing that the groups in our society who are most disadvantaged already were getting disproportionately impacted by COVID. And for those communities who are really trying to instill a sense of equity in their communities had developed an equity action plan involving each programmatic change they would make to expand sidewalks, install more streetlights or lighting in the parks, and economic opportunity and access. Similarly, all of these initiatives required resources to actually implement. And so, city budgeting for equity looked like, again, scouring your current budget to systematically uncover any partnership opportunity, any way to stop providing a service or let another partner provide it, generate new revenue if it meant that we could free up and reallocate resources to fund their equity objectives.

So, we called it budgeting for equity, and more and more communities that have been going down this particular path, communities like Salt Lake City, Columbia, South Carolina, Austin, Texas who are extremely excited about equity making bold commitments. And on the climate side, communities like Kansas City, Missouri who have adopted policies to reach net zero emissions in their communities but don't have the budget allocated to back up the policy commitment. They're deploying priority-based budgeting to align those resources to achieve those goals. So consistently, from equity to climate and other initiatives like safer communities or smart cities. It's a similar approach.

What are the actions that they want to take? What are the costs of those actions? And how do we get resources realigned from the budget in order to fund those.

Josh Henderson: That's great. Some great use cases. And, obviously, to have a city, you know, like Pittsburgh kind of lead the way in that, and then you have that example for other cities to kind of take that that stance as well. So, it's really, really great.

Chris Fabian: And you asked about AI, and I wanted to touch on that. So, you had a reaction earlier, which I really liked, which was, you know, just thinking about a lot of the data that we're putting together, and it produces a very thoughtful look at the budget. If you look at our earliest days, even back to the spreadsheet era, we have one city manager, Sam Anselm. He was in Joplin, Missouri. He writes on his LinkedIn profile, survivor of the spreadsheet era of priority-based budgeting, and it's hilarious. And he's a current implementer, in his current community in Missouri.

And the earliest implementers, all the departments had to list out their programs. We work with them kind of like if you ever seen office space, you've got the Bob saying, what do you do? And so, they list out their programs, then the departments would have to take their budget and allocate their cost to these programs. Then they would score them all in order to then look for opportunities for cost savings and reallocations.

And it was a lot of work. It's thoughtful, but it's it was a lift.

You fast forward about a decade into this, and we had developed such an amount of data for how certain positions get allocated to programs. You take an accounting technician one.

What are the programs that every accounting technician one has been allocated to across the hundreds of organizations who have done PBB for ten years? Every fleet mechanic one, every patrol officer or dispatcher.

And intersecting with modern technology, it occurred to us that one major way that we could help our implementers save time for implementation and get to the benefit of priority-based budgeting was to predict data on their behalf. So, we read in general ledger line-item data by position, by object from their chart of accounts. From that, our large language model predicts back for them programs that we see evidence that they're providing. So, we give them, here's the programs that we think you're doing based on your own data.

We provide them cost allocation. So, the price tag of those programs from a prediction and scores in order to group those programs most ripe for new revenue enhancement, sourcing opportunities with the data in the prediction model says, basically, nine out of ten communities raise enough revenue to cover the cost of this program. You're not. This looks like an opportunity or the vast majority of priority-based budgeting implementers for this program outsource it.

It looks like there's an opportunity in your community, we would encourage you to pursue that. So, it ends up providing a, a playbook, if you will, for each department in every program to say every single program has an opportunity for reallocation, new revenue generation, and it's all predicted from the AI, from our approach to machine learning. Now, departments look at that data, and they provide quality control.

They're looking at the programs and saying, well, I see why you predicted that. We do it a little bit different here. So, they will tweak.

They will provide their know how, their validation, which provides even better data. And then we take that data, put it back into our model so that we continue to learn. So, every implementer of priority based budgeting helps reinforce or enhance our prediction capabilities with machine learning.

Josh Henderson: Yeah. Yeah. No. It's I'm glad that you added on there. The predictive analysis is also come through by humans.

Right? It's not necessarily like, oh, we're just taking it as is. We're actually going in, and we're, like, analyzing that analysis.

Chris Fabian: I'll give you an example. You're spot on. So, in Fort Worth, City of Fort Worth, Texas, their police department goes through the predictive analytics.

And we teased out all of the various programs that we believe that they provide. And one thing that sticks out to a human is you're a big police department in the city of Fort Worth, and yet we don't see any evidence of a 911 or dispatch operation.

Why did we predict that is because we looked at all the positions. We the algorithms look at all the positions, and they say, we don't see any positions that lend us to believe that you offer dispatch and that to a normal human is weird for a police department. To which Fort Worth replied, we are in a contract. So, it's a contracted services line item.

You never would have seen that, and we work with Tarrant County for a regional dispatch overall. And so that's where human interaction to say, we understand why the prediction resulted in this conclusion. Here's a tweak to get a little closer to how we do things. And now we feed that back into our dataset to say, we just learned something for new evidence of how you might provide this program overall.

Josh Henderson: Yeah. Yeah. That's great. That's a great anecdote.

I mean, we could talk I feel like we're going to talk about this for hours, and I know you've done a lot of talking here at Connect this these past couple days. And we'll talk again on the podcast in the future, I'm sure. Yeah. But lastly, I just wanted to kind of look ahead a little bit.

I know we none of us can predict the future. That's just impossible, especially when it comes to technology. None of us knows where it's going at this point. We try we try to we try to guess, but it's kind of impossible.

But how do you see something that you helped create? How do you see PBB sort of evolving, you know, over the next five or ten years?

And then, again, you know, how do you anticipate AI and AI integration being part of that?

Chris Fabian: One of the primary reasons I was so thrilled to join Tyler is because here you have an excited, loyal, innovative base of local governments who are, who you've built so much trust and credibility with, and they have their line-item data within the ERP products.

We get excited about a future where it's not extraordinary for organizations to implement priority-based budgeting, but it's commonplace that everybody gets a first prediction. Every organization can see evidence of programs that they provide. They can get a diagnostic of how aligned they are with their strategic plan priorities as elected officials change. And that they,it becomes more normal, not controversial, to see agility with resource allocation for local governments to quickly understand where they could pivot, where they could partner, where they could generate new resources.

And it doesn't just come up when there's a budget deficit to solve, but it's what we do all the time in order to see that we have liquidity, resource liquidity in our budgets to pursue priorities with ambition. We have big problems to solve, from climate, equity as we've talked about, homelessness and housing, to safer societies and consistent economic well-being among many, many others. And our dream is that local governments don't suffer from resource scarcity, but they always have a mindset of we can do things differently in order to free up those resources and pursue the future. And if we can get that discipline, that is what the future looks like to us. More partnerships within the community.

So, we see local governments partnering with nonprofits and private sector and their school districts overall. We believe that priority-based budgeting can be a very powerful catalyst for understanding who's doing what in a community and where they could come together and partner.

And the use of AI will only accelerate where those opportunities exist. So, that's a great future to dream of.

Josh Henderson: That's the perfect place to end the conversation.

Chris, thank you so much for this chat. This was really, really informative. I'm sure our listeners will get a lot out of it, and I can't wait to have you back on the podcast.

Chris Fabian: Right on. Thank you so much. I really enjoyed being here and, loving the Connect conference.

Josh Henderson: I hope you enjoyed this conversation with Chris Fabian.

If you'd like to learn more about the benefits of Priority Based Budgeting and other topics discussed throughout the episode, check out our show notes for more resources. A modern digital infrastructure allows the public sector to adapt in the digital age. From operational agility to data management and analytics, as well as cost savings, security, compliance, and scalability, governments are poised to take advantage of innovative technology to create more efficiencies in their systems and better support and connect their communities.

Tyler creates solutions made exclusively for the public sector and has experts with government experience ready to support you on this journey. I hope you're excited about what the future holds and will reach out to us at podcast@tylertech.com to connect with the subject matter expert if you'd like to learn more. And whether it's budgeting or modernizing your digital infrastructure or something else entirely, we want to hear from you about what you'd enjoy hearing more of and how we can make The Tyler Tech Podcast even better. Fill out our audience survey in the show notes today to let us know how you heard about the show and your ideas for future episodes.

And don't forget to subscribe to the show and rate and review the podcast.

For Tyler Technologies, I'm Josh Henderson. Thanks for joining The Tyler Tech Podcast.