AI Agents and Peak Academy: Brian Elms on Empowering Government Workers

Summary

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Podcast Summary

Podcast: Data-Smart City Pod
Episode: AI Agents and Peak Academy: Brian Elms on Empowering Government Workers
Date: January 21, 2026
Host: Stephen Goldsmith (A)
Guest: Brian Elms, CEO and Founder, Change Agents Training; Founding Director, Denver's Peak Academy (B)

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Episode Overview

In this episode, host Stephen Goldsmith interviews Brian Elms, a national leader in government innovation and the driving force behind Denver’s Peak Academy. The discussion explores how the Peak Academy empowers government workers, the role of generative AI in transforming public sector performance management, and ways to unlock productivity at every level of local government. The conversation is energetic, practical, and focused on real-world impacts. Notably, Elms shares insights on fostering agency among frontline workers, rethinking management, leveraging AI, and tackling the bane of “useless work.”

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Key Discussion Points & Insights

1. Origins and Impact of Peak Academy

• Peak Academy was created during Denver’s 2011 recession to innovate service delivery by investing directly in city employees’ problem-solving skills.

• Core Approach:

  • Train employees in process improvement
  • Support and coach staff through challenges
  • Encourage employees (the real subject matter experts) to identify and implement changes

• Results:

  • Over $50 million saved in government expenditures
  • Model replicated by 60+ governments
  • Empowerment leads to “unlocking potential” among public servants

• Quote:

  • "A lot of employees have the ability to do really cool things if we can unlock their potential." (B, 03:25)

• Memorable Moment:

  • Elms highlights the sheer fun and pride city workers experience when they’re given the power to improve – it "creates a flywheel effect" of innovation. (05:23)

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2. Empowerment from the Street to Middle Management

• Street-Level Problem Solving:

  • Workers like road crew or park staff are the experts; their frontline knowledge is essential for effective change.

  • Quote:

    • “Innovation is caught, it's not necessarily taught. Where excitement is caught, it's not taught.” (B, 05:59)

• Middle Management Resistance:

  • Middle managers face dual pressure from above and below and can be a bottleneck for innovation.

  • Strategies include training managers to create scoreboards, provide coaching, and shift from micromanaging toward a “coach, command, and counsel” model.

  • Quote:

    • “Mid level manager is the hardest place to be in government and it can also be the place that stifles a lot of innovation.” (B, 08:39)

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3. Structure of Peak Academy Training

• Training Model:
  • Introductory training: ~3-4 hours
  • Intensive “Black Belt” training: previously 40 hours, now condensed into 3 days
  • Ongoing follow-up sessions
• Focus:
  • Practical skills, applied directly to service improvement
  • Builds continuous improvement culture

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4. Modern Performance Management & Generative AI

• Challenges with Traditional Models:

  • Earlier methods relied on slow, feedback-heavy processes and manual data analysis (e.g., Excel 101).
  • Gap in knowing departmental processes and organizational “DNA.”

• Generative AI Opportunities:

  • Rapid, scalable business process mapping (example: 48 processes mapped in 3 days vs. a year previously).

  • Real-time insights allow for faster innovation cycles and broader staff access to data.

  • Quotes:

    • “Now you can do that with generative AI … your ability to create performance understanding is going to skyrocket.” (B, 11:26)
    • “I don't think you have to teach them how to do the data entry anymore or even the data analysis. You can use AI to do that with you and for you.” (B, 13:29)

• What Changes:

  • Shift from teaching basic data literacy to training staff to use tools and to critically evaluate AI outputs.

  • Importance of recognizing AI hallucinations (i.e., made-up or incorrect outputs).

  • Quote:

    • “You have to be able to recognize when generative AI is making things up. That's the real trick.” (B, 15:25)

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5. Integrating Human and AI Agents

• Human-Centered Innovation:

  • Training focuses on empowering workers to recognize and act upon opportunities for improvement.
  • Cultivating a shared language for identifying, articulating, and solving problems accelerates progress.

• Teaching Workers to Use AI:

  • Meet frontline staff where they are—start with simple tools before introducing complex AI tools.

  • Engage staff by demonstrating the value of data and encouraging iterative problem-solving.

  • Quote:

    • “What we do is we try to get people on the same vernacular for attacking a similar challenge. If I can do that, our ability to move faster through the problem solving exercise is exponential.” (B, 16:49)

  • Example: From mechanic to head of parks—growing internal talent through empowerment and support. (B, 19:25)

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6. Practical Advice for Leaders and Managers

• Three Core Recommendations:

  1. Learn: Stay curious and seek to understand how current processes really work.
  2. Let Your Team Fail: Give staff room to make mistakes and support them as they learn.
  3. Continuous Improvement: Always look for ways to reduce steps and eliminate “useless work.”
  • Quotes:
    • “Teach them where to find the useless work, rid the useless work, and then we can start doing, ‘How are we performing?’” (B, 21:56)
    • “Useless work sucks the joy out of your job.” (B, 22:55)

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Notable Quotes & Memorable Moments

• “Innovation is caught, it's not necessarily taught.” (B, 05:59)
• “Mid level manager is the hardest place to be in government and it can also be the place that stifles a lot of innovation.” (B, 08:39)
• “You have to be able to recognize when generative AI is making things up. That's the real trick.” (B, 15:25)
• “Teach them where to find the useless work, rid the useless work, and then we can start doing, ‘How are we performing?’” (B, 21:56)
• “Useless work sucks the joy out of your job.” (B, 22:55)
• “What motivates most public employees is not the money, it’s the ability to serve. And so what you've done is made it possible for people to serve better.” (A, 23:45)

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Timestamps for Key Segments

• 00:05 — Introduction and opening context
• 02:24 — What is Peak Academy and why it matters
• 05:23 — Empowering frontline city workers
• 07:14 — Addressing middle management resistance
• 09:26 — Structure of training programs
• 10:59 — Applying Peak Academy methods to AI/STAT programs
• 11:26 — How generative AI radically speeds up process mapping
• 13:05 — Shifting performance management with AI
• 14:54 — Data literacy in a world of AI-generated insights
• 16:22 — Training human agents alongside AI agents
• 18:41 — Practical strategies to empower frontline workers
• 21:05 — Three things every leader should do
• 22:55 — The problem with useless work
• 23:45 — Closing reflections and motivation in government

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Tone and Style

The episode is pragmatic, positive, and grounded in real-world experience. Both host and guest use vivid anecdotes, approachable language, and are clearly passionate about public service innovation. The conversation flows naturally, with laughter, awe, and respect evident throughout.

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Transcript

[A]

Thanks a lot for listening to DataSmart CityPod. Our goal is to provide you the most helpful information we can on how to innovate. We do that by featuring stories and interviews with people who are actually doing it. But we want to know what would be best for you. So if you have a second look at the show notes and fill out the form or go to bit ly datasmartpod, we're really interested in what we could do, what we could produce, who we could interview, what subjects we could cover that would help you do your jobs better. This is Stephen Goldsmith, professor of Urban Policy at the Bloomberg center for Cities at Harvard. Another episode of our podcast, Data Smart City Pod. I've asked today's guest to join me because he's nationally recognized as a leader in government innovation, in part because he's done really cool things, in part because he's been at it for a really long time. And he was the director of Denver's Peak Academy, which I think I've probably written about half a dozen times, and the CEO of Founders of Change Agents Training. Welcome, Brian Elms.

[B]

Thanks, Steven. It's so good to see you.

[A]

Now, just before we get started, I got a bunch of questions for you, but this says you're the founder of Change Agents Training. So we've been developing agents here, but they're not people. Are you changing generative AI agents or are you changing people as agents?

[B]

Well, funny you should ask. I think it's a twofold. Yes, we are building agents to help us with process discovery, and we do a lot of our work changing how employees interact with their services to try to improve them. And then we layer on some agents that are AI.

[A]

So let me reflect and ask you a question. I do not think that very many initiatives of government have had as much effect in US cities as what you did with the Peak Academy in Denver. It's been replicated everywhere and has produced enormous change. So I'm going to ask you mostly today about why it's not outdated. But let's start with what was Peak Academy and how did it unlock so much value?

[B]

Well, first of all, I have to collect myself hearing you say that we were so impactful. Yeah, I'm kind of floored. So let me collect myself first. Peak Academy started in 2011 in the city and county of Denver. It started because we were out of money. We were in a recession, and we needed to figure out how to keep helping people with less money, how to keep helping people with. With not enough people to provide the services it was designed to help improve our service delivery by investing in employees, teaching employees problem solving techniques, coaching them and supporting them when they ran into barriers or challenges for changing their service deliveries. So our entire focus was how do we train all of the employees in the city and county to improve their services one service at a time. And we learned very early on that a lot of employees have the ability to do really cool things if we can unlock their potential. And so that's what change agents does in the same way that Peak Academy still operates. So Peak Academy just celebrated their $50 million worth of savings in government. They help provide training and support for employees who provide day to day services to residents. And they try to figure out ways to do it better. And they ask the subject matter experts to take a step back, look at their process and try to improve that service. It is highly replicable. I mean, we have over 60 governments that run some form of Peak Academies today. It is a lot of fun. I think that's one of the big aspects to it because you get to have agency in the services that you're providing. And when an employee who does work for a living and they're doing their day to day job, when they feel like they have the power to make change, you can unlock their potential really quickly. And that's what it does. That's what change agents training does and that's what Peak Academy is still doing today.

[A]

So let's. I'm going to stay with the original theme, finding out a little bit how you implemented it. But I have this photo, my favorite picture I've carried with me everywhere. And it's three fellows who worked for the Indianapolis AFSCME local who were representative of that local, not the only ones. And they're out on the street patching holes in the roads. And these fellows were asked, as were their colleagues, how would you unlock productivity? If you could change any of the rules, if you could make any suggestions? And when you talk about subject matter experts, that was not the mayor or the supervisor. These guys are the subject matter experts in asphalt. Right. And I won't go through all their suggestions, but how did Peak Academy kind of institutionalize my anecdote?

[B]

I think all cadence, Stephen. The amount of training that they do, the amount of follow up that they do, the amount of things that they're doing to try to create that flywheel effect that you're describing. How do you get the guy who fills a pothole on a daily basis look at it differently and continue to do it over and over and over again? Provide them the right tools, provide them the right setting and they'll flourish. We see it all the time. I mean, I'm doing a project in Alabama right now where we're looking at the DMV and re establishing how they do renewals. And because of what they're doing, they went from 90 minute wait times down to about 10, maybe even less than than that. In more instances, they figured out how to improve their business licensing process. So much so we have actually helped them generate more revenue than they ever have. It is very, you know, simple, practical ideas that snowball over and over and over. And when one employee makes a change, it affects another employee and they want to make a change. It is absolutely one of those things where it's like innovation is caught, it's not necessarily taught. Where excitement is caught, it's not taught. And when people feel like they can make change in a process, they just keep going, they don't stop.

[A]

So a lot of the business literature suggests that it's the middle managers who are most resistant. I found that to be a little true in Indianapolis and really true in New York City where there were lots and lots of managers. So discuss a little bit around how the original peak model dealt with not just empowerment of the men and women on the street, but the people who are watching them and supervising them.

[B]

Unlocking potential in both areas is a real key. So how do we help mid level managers get what they need to get done without getting into the morass of report writing? So how do we unlock them to enable their team members? And a lot of that has to do with giving them the proper tools to be able to do so. So instead of like checking up on them in a micromanagement fashion, how do you change your philosophy to create scoreboards for them or, or create incentives for them to keep going after and improving their work. The mid level management place, I would say is one of the hardest jobs to be in. And you get pressure from the people below you, and you get a ton of pressure from the people above you. And trying to figure out how to calm those two pressure valves is a tremendous asset. So if a mid level manager can calm the people at the top and calm the people below them, they can actually thrive. So what we try to show them is there are ways, there are skill sets that you need as a manager that can do that. Create standards, create scoreboards, learn how to coach, command and counsel your team. Move away from this catch them all sort of place where I'm just trying to catch up all the time. I'M just trying to catch my team doing something. Move away from that theory and into the coach command council theory where you're coaching your team, you're commanding your team when we're in a crisis and you're counseling their team when you're having challenges. Give them things to, you know, pursue. How do we make this a little bit better than we did yesterday? What do we need to do to fill one more pothole? What do we need to do to help one more client today and give them parameters to do? So super fun. But yeah, I would agree with you. Mid level manager is the hardest place to be in government and it also can be the place that, that stifles a lot of innovation.

[A]

So let me ask you one more peak 101 question. Tell me a little bit how it was structured. Did people get time off? How many sessions did they go to over what period of time? What did it look like?

[B]

There are multiple different training platforms that they, they offer. So they offer like an introductory training that does about three or four hours of, of training. They have a very intense training which they call their black belt, similar to like a lean process improvement program. When I was doing it was about 40 hours. They've dropped that down to three days and then they do follow up sessions.

[A]

Now let me, let me try to get some free advice from you. Generally what professors do of learn from whoever they're talking to and tell the next person, we don't actually do anything. We just got to talk about it. So let me ask you for some advice. We've been working with half a dozen of the largest cities in the country on how to modernize performance management or STAT programs. And the theory is that the original model had, you know, a good leader and a smart data analyst wonk in the background. And the leader would cross examine his or her management and then they would be supported by the analysts. The iteration of the questions and answers. Part of innovation is iteration and the innovation would be two weeks later they'd come back. So we've been trying to figure out with generative AI two things. How do you speed the iteration and how do you broaden with natural language the number of people who have access to the data? So let's take Peak and apply it to unleashing the power of generative AI across the city enterprise. How would you do that?

[B]

The biggest challenge I had, Stephen, was I did not know what was going on in some of the departments. And so we're tasked to figure out the performance in these departments, but we don't really know how they do what they do. We're sort of guessing how they do what they do. And back then I had no database to tell me. What technology are they using, what's the hierarchy structure, what are the processes that they're going about? And I think now you can do that with generative AI. You can get that database fulfilled where you have the DNA of the organization. If you have the DNA of the organization, your ability to create performance understanding is going to skyrocket. So if I knew how they did what they did or what systems they were using, my ability to help them find better ways to do it or empower their team members to find those spaces would be insanity. You can do that now and you can do that quickly. We just did a project in Australia where we helped them understand about 48 processes documented high level information, task level, how they do it. We did 48 processes in three days. If I were to do that back then, back in 2012, 2013, 2014, that project would have taken me a year to get those 40 processes. I can now do those 40 processes discovery wise and provide them actionable suggestions based all on their feedback to us and our feedback to them using generative AI.

[A]

So you're making some interesting points on business process reengineering and process mapping and your ability to do that quickly, which is really quite fascinating actually. How would you get the deputy sanitation manager of Denver to be able to use today the same analysis that you and your team used 10 years ago?

[B]

Well, we wouldn't have to do what we were doing, which is Excel 101, how to read your performance dashboard. That should have change. I also feel like there are parts of the performance management structure, Steven, that were really heavy touch. You know, we, we had multiple analysts looking at multiple services all at the same time. And like you said, it would take about two weeks for us to get feedback. I think that feedback loop slows down tremendously. I also think that you need a really good analyst who can sit with that director and show them this. I don't think you have to take teach them how to do the data entry anymore or even the data analysis. You can use AI to do that with you and for you. So I think that would change the other piece. Steven. We did a lot of analysis on things that we didn't have a lot of power and control over. And we spent weeks, if not months, if not years trying to unpack those things. We now can do a deep dive in. Okay, what's happening on this intersection that is causing These potholes to appear rapidly. That's something we would not have been able to do in the old version because I would have just been looking at how many potholes did you fill? How long did it take you to fill them? Were we able to get a better pavement standard going on? But now I could say, hey, that intersection is causing problems both at 911, at 311 and in all these other areas. And I would be able to do a deeper dive with that team. And I don't know that I would have to sit with the director of that department and explain how we did the analysis. The analysis would be a little more obvious because it's more transparent.

[A]

What would data literacy training look like in a Peak Academy?

[B]

What we found, Stephen, when we first started Peak is we were doing Excel 101. We then brought people into how do you use Excel a little bit better and how do you use this data? Because I think there isn't as much data literacy needed when it's going to spit it out to you.

[A]

Well, we need to teach them how to be semi literate so they can use those tools and they have to

[B]

be also be able to recognize when generative AI is making things up. That's the real trick. And that's a challenge that I have in my work is where is it hallucinating and where is it making stuff up?

[A]

I just for fun in my class last year, went over to the Boston Open Data site and I grabbed street data and I played a kind of a game using an agent in the Harvard sandbox. I said, I'm the transportation director on the north side of Boston. How does the number of potholes I have compared to the south side? And then I said, how does my demographics compare? And it went on for a while until I got over my head and had to stop. But going back to the beginning, you're the CEO and founder of Change Agents Training. So how would you train agents to use agents to solve city problems?

[B]

Well, we've built our own agents on problems that we've seen. So we're not necessarily training our clients how to do that. We're still training our clients on how to visualize their processes, how to show people where opportunities are available, how to help them get to the place where they can see a problem. One of the challenges that Peak Academy does really well and what we do in change agents is we try to change the language of change. We try to change the language of problems. How do you see it, how do you say it, and how do you solve it and what we do is we try to get people on the same vernacular for attacking a similar challenge. If I can do that. Our ability to move faster through the problem solving exercise is exponential. If they're all using different language for how they see something, if they're all using different languages for how to solve something, we're going to be talking through each other. I've never thought about creating a product where I teach people how to make agents. That would be. That's a. That's a cool idea. I'm hoping I can steal that from you someday and let you know how successful it is. But I still look at how do I make humans who mow grass for a living better at their jobs? How can I help someone who works in a park and every day goes out to fill, you know, fix a backflow preventer, but they can't find it, they don't know where it is, they don't know why it's broken. That's who I work with the most, is helping those agents feel empowered enough to make change. That's my goal.

[A]

Just to follow up quickly. Let's talk about that grass cutter in the park. One way I hear your answer is you're going to work with her so that she can do her job better. Another way to think about this is to ask the question this way. What can you teach her about how she can use the data and tools available or maybe her boss to be the coach that you are. Right. How do we use the generative AI to unlock the potential to do things better? How do we get there?

[B]

Getting that agent to understand one that they can make improvements is going to be the biggest barrier. So I don't just cut grass for a living. I am my own person and I can actually solve these problems. That's the first barrier. Not all grass cutters believe that that's their job. There is a thought process that they have that you're going to have to unlock that there aren't all those people in the world who care that there is the ability to do this. And one of the challenges that we have in both Change Agents training and our other company, Riverwork, is not everyone wants this much information. Not everyone wants to act on it. So what we try to do is meet them where they are and get them to believe that there's a different way of doing it. That's the first piece. The second piece would be to sit down and show them what their data would look like. You're totally right. You could use AI to, to measure how Much rain they've gotten, how much nitrogen they need to put in the soil, the downtime of the mower, you could totally do that and improve that service. I think you would want to challenge them to see how far they would go without saying, oh, you're not going to go this far because you don't know how to use an AI agent. I want you to go as far as you possibly can using a marker and a post it note. And then if you're excited by that, let me pull you to this next realm that you're talking about. And can I get a individual who always thought of themselves as a mechanic to become the leader of the mechanics, the manager of the mechanics, the department director? That's a challenge that you want to grab them where they are and see if you can pull them through. There are so many anecdotes where I can tell you, I started working with a mower and now they're the head of parks.

[A]

That was a great answer. You know, I'm from the school of we have good people working in bad systems, not bad people. So your whole approach and your dedication has been towards changing the systems to help people work better. We've got an audience of state and local officials listening. Name one place they should start in order to unlock the productivity of their workforces and in order to be more responsive to their citizens.

[B]

There are three things that I tell every leader and supervisor. So one, learn. Be very curious about how you do things. Constantly try to learn. Two, let your team members fail. Let them make mistakes and help them when they fall apart, try to keep pulling them through this world. Three, live in this space where you're constantly improving. Steve, if I asked you, how do you get a travel approval when you were mayor, you would say, hey, I asked my assistant. And then a couple weeks later I'm traveling. Or you don't even know there's a travel approval process. But if you asked me how did I get travel approved through peak, it would take me about 17 to 18 steps to do it. Most managers, most leaders have no idea how long and complicated it is to do a simple service. So constantly improving, it's not just good enough that there's 17 steps right now. Let's make this 15 steps. Next year, let's make this 12 steps in a week. The challenge that we have, Steven, there's so much useless work that we perform, and we are in this constant cycle of that useless work. I love doing performance. I love helping people get to that space. But if your team is doing useless work they don't want to do. Performance management. So your challenge is teach them where to find the useless work, rid the useless work, and then we can start doing. How are we performing?

[A]

That was a great answer. You didn't just call me useless, did you? That was. I just want to make sure.

[B]

I mean, you useless work sucks the joy out of your job.

[A]

Yeah. No. I want to close with another anecdote. I was sitting in New York City City hall working for Mike Bloomberg when somebody decided because we didn't have enough money that there'd be no out of town travel unless approved by me. Me. We have 300,000 employees. I thought this is, this is like, what is this process and how possibly would I do it? I was not very good at it. But any rate, on that closing note, let me just back up and say I have long admired what you, Brian, have done. And it's because it liberates publicly minded officials to do a better job for the residents. And the literature shows that what motivates most public employees is not the money, it's the ability to serve. And so what you've done is made it possible for people to serve better. And for that, we thank you and for your time today on the podcast, thank you very much. Brian Elms, floored again.

[B]

Stephen. Thank you so much. Coming from you, that means the world. If you like this podcast, please Visit us at datasmartcities.org and find us on itunes, Spotify, or wherever you get your podcasts. This podcast was produced by me, Betsy Gardner. Thanks for listening.