Guest: Forrest Zeisler, Co-Founder and CTO at Jobber
Host: Seth Earley, CEO at Earley Information Science
Published on: December 22, 2025
In this episode of the Earley AI Podcast, host Seth Earley sits down with Forrest Zeisler, co-founder and Chief Technology Officer at Jobber. With years of experience building technology for service professionals, Forrest Zeisler has played a pivotal role in empowering small businesses—from landscapers and plumbers to cleaners and contractors—to harness AI and automation for streamlined operations and growth.
Discover how Forrest Zeisler and his team scaled Jobber from three customers to over 300,000, delivering more than $100 billion in services, and learn how their journey demonstrates the transformative impact AI can have on businesses of all sizes.
Key Takeaways:
Small businesses can benefit enormously from AI, especially for reducing administrative tasks and boosting productivity.
Adopting new technology isn't just about features—it's about building trust and reliability for the end user.
Jobber’s growth began with direct customer conversations, leading to a highly configurable platform supporting over 55 industry verticals.
The journey from manual onboarding and white-glove service to sophisticated self-serve and AI-driven automations took years of iteration and customer feedback.
Integrating AI isn’t just about chatbots or flashy features; the real impact comes from making technology disappear in the background, allowing users to focus on their craft.
Reliable automation, rooted in real customer behavior and best practices, is key to driving widespread adoption of AI across industries.
Building trust with AI systems should mirror how you onboard new employees: review, supervise, and gradually increase autonomy as reliability is proven.
Orchestrating multiple AI models and agents allows platforms like Jobber to deliver context-aware, intelligent assistance that feels human and personalized.
Insightful Quotes:
"AI is beginning to simplify that work and reduce administrative overhead and reduce those efforts and help small companies provide more consistent and more efficient and more reliable results." - Seth Earley
“Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.” - Forrest Zeisler
"Trust is hard to build and easy to lose. Moving from a compelling prototype to something customers rely on takes time. You have to crawl before you walk and walk before you run. Weak foundations will not support your vision." - Forrest Zeisler
Tune in for a behind-the-scenes look at building scalable, reliable AI for small business—and the lessons you can apply whether you're an entrepreneur or driving digital transformation in a larger enterprise.
Links
LinkedIn: https://www.linkedin.com/in/forrestzeisler/
Website: https://www.getjobber.com
Ways to Tune In:
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Podcast Transcript: Building Trustworthy AI for Small Business and Enterprise Scale
Transcript introduction
This transcript captures a conversation between Seth Earley and Forrest Zeisler about scaling AI solutions from 3 customers to 300,000, exploring lessons learned in building reliable automation, the evolution from white-glove service to agentic systems, and practical strategies for earning user trust in AI implementations.
Transcript
Seth Earley: Welcome to today's Earley AI podcast. I'm your host, Seth Earley. In each episode, we explore how artificial intelligence is transforming organizations and how they manage information, improve customer experience, improve employee experience, and create business value. Today, we're going to be focusing on how AI is empowering small businesses and home service professionals. This is an area that is not well covered in the industry. A lot of AI is about enterprise organizations. And these are the teams that are managing, scheduling, invoicing, communicating with clients, doing their daily operations. They don't have technical teams. They don't have large teams of people to help them do this stuff. So AI is beginning to simplify that work and reduce administrative overhead, reduce those efforts, and help small companies provide more consistent and more efficient and more reliable results. Joining me today is Forrest Zeisler. He's co-founder and chief technology officer at Jobber, and Forrest has spent years building technology for service professionals, including landscapers, electricians, cleaners, contractors. His experience creating these tools that make complex systems simple gives him a very unique view on how AI can support small teams and help them grow. Forrest, welcome to the show.
Forrest Zeisler: Thanks so much for having me.
Seth Earley: So, let's start off with some misconceptions around AI, especially related to smaller businesses. What do people not get around small and mid-sized businesses when it comes to AI? Many people think that personal productivity tools are one thing—writing emails, summarizing results, handling meetings—but beyond that, there's not a lot that people are doing in terms of small businesses, because they just don't have the technical capacity. A lot of people think that advanced AI is really for large organizations with those advanced teams, with those technical teams. How do you see that perception changing, and what's the reality of service-based businesses today?
Forrest Zeisler: I think that's a misconception that I'm here trying to change. First off, no one is going to benefit more from AI than these small service businesses. Painters, landscapers, window washers have nothing to fear from AI. AI is not going to start installing furnaces or cutting down trees. But there's so much administrative work that happens within these businesses, that is a tax on their productivity. When they're sending out invoices, when they're dealing with payroll, when they're doing the hundreds of administrative tasks that they are distracted by every day, they're not getting paid for that work. That is directly coming out of them delivering their craft, performing their trade. And AI is perfect for automating those types of tasks so they can focus on what's important.
When it comes to the mindset, I've been blown away by how open these small businesses are, these entrepreneurs are, to adopting technology. They're not anti-AI or anti-technology, they're anti-wasting time. They don't have time to burn cycles playing with things. A lot of technology, especially on the front edge of the early adopter curve—you and I both know you can spend 5 hours automating a 5-minute task, or you can go through all this change management and get a tool, and discover it only works 80% of the time. And then you have to go through all the trouble of changing back. And they just want to focus on reliable, high-confidence steps in their business so they can stay focused. They don't have time to waste. If you can show them that something is going to actually help them, and give them confidence, and build that trust, they are more than happy to adopt technology. Anything that gives them the edge, they'll take on.
Seth Earley: Refresh my memory again of how many customers you have, because when we first spoke, I was thinking, well, you're just focusing on small businesses and what are the lessons learned? But you've really scaled this in a significant way. So, how many customers do you have at Jobber?
Forrest Zeisler: Over 300,000 customers. They're all definitely smaller compared to the enterprise customers or businesses that you usually speak to. But at scale, we've delivered over $100 billion worth of services through our platform now.
Seth Earley: Wow, unbelievable. When you start thinking about how you're taking these complicated workflows, and you're applying AI in a way that feels natural to users, help me understand how you've derived these, and how you've integrated this. How are you applying AI in this way where people don't have technical expertise, but they need reliable outcomes, they need reliable value from automation? It's almost like you're putting AI in the background. How have you kind of gotten to that point? What's been the process for doing this? Because you have very, very different businesses who you're supporting on Jobber. Talk a little bit about that evolution and how you've arrived at that.
Forrest Zeisler: Do you want the long answer or the short answer?
Seth Earley: Give me the long answer. We have some time.
Forrest Zeisler: I think the first step is understanding that when we started Jobber, we started with just three customers. We went door to door talking to home service professionals, asking them what problems they had. We sat down with them, we watched them work, we understood their pain points. And from those conversations, we started building. We didn't start with a vision of what the product should be. We started with understanding what our customers needed. And that customer-first approach has been the foundation of everything we've built.
In the early days, everything was white-glove. We would manually configure accounts. We would spend hours on the phone with customers, walking them through setup. We would customize workflows for individual businesses. It didn't scale, but it taught us an incredible amount about what our customers actually needed versus what we thought they needed. Over time, we've been able to take those learnings and codify them into the product. We support over 55 different industry verticals now. And the way we've done that is by making the platform highly configurable while still being simple to use.
When it comes to AI specifically, we started experimenting heavily around 2017. But those were labs and betas. None of them were production-ready. We learned many ways not to build the lightbulb. Around 2021, things really started to take off. We were able to move quickly because of those earlier learnings. GPT-3 made a big difference. We shifted from trying to build our own in-house models to using something that was better than what we could build ourselves.
Seth Earley: What went wrong in those early experiments?
Forrest Zeisler: Reliability was the biggest issue. Customers adopt technology that reliably saves time and money. If something works most of the time but fails unpredictably, they will not trust it. And once you lose that trust, it's very hard to get it back. The market also was not ready. At the time, AI felt very sci-fi. People weren't comfortable with it yet. But now, the landscape has changed dramatically.
Seth Earley: How large is the organization now?
Forrest Zeisler: About 1,100 employees, with roughly a third in research and development. We still maintain a strong sales and service presence. Even when customers self-serve, we like to offer help. That consultative approach improves conversion, retention, and adoption.
Seth Earley: What are the biggest lessons learned from this journey?
Forrest Zeisler: You have to crawl before you walk and walk before you run. Weak foundations will not support your vision. It will take longer and cost more than you expect. Trust is hard to build and easy to lose. Moving from a compelling prototype to something customers rely on takes time. You need to prove value consistently before customers will give you more responsibility.
Seth Earley: Where are you now in the AI journey?
Forrest Zeisler: We moved from informational bots to agentic systems that can take action. One example is our AI receptionist. Many calls come in after hours and go unanswered, so we built strong voice technology in a controlled environment. We've handled over 300,000 calls so far. From there, we expanded agentic capabilities across the core product with audit logging and safeguards. Now you can talk to Jobber while walking through a job, and it builds quotes in real time. The next challenge is confidence in autonomous actions when the user is not present.
Seth Earley: Trust becomes the central issue.
Forrest Zeisler: Exactly. Trust is built gradually, just like with a human employee. Review first, then suggest, then draft, then act. Context matters enormously. We need to understand not just what to do, but when to do it and how much autonomy the user is comfortable with at each stage.
Seth Earley: What does agentic AI enable that traditional automation cannot?
Forrest Zeisler: Traditional automation handles the happy path. Real life is messy. Language models bring flexibility and common sense. They can handle edge cases and adapt to context. But the real value is removing interfaces, not adding them. We should always ask whether we even need to ask the user, or whether the system already knows enough to recommend the right action. Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.
Seth Earley: How are you handling models and architecture?
Forrest Zeisler: We use public models with fine-tuning and a sophisticated orchestration layer. A single request can involve many agents and multiple models. Different problems require different reasoning approaches. Some problems are better solved with fast, small models. Others need more compute and deeper reasoning. The orchestration layer decides which model to use for which task and how to combine the results. It's all about having the right tool for the right job.
Seth Earley: That aligns with what we see in document processing and entity extraction. Different models excel at different tasks.
Forrest Zeisler: Exactly. Our goal is to make that orchestration invisible so customers can focus on running their businesses. They don't need to know that fifteen different AI models just collaborated to handle their request. They just need to know that it worked.
Seth Earley: What lessons from your journey apply to larger organizations?
Forrest Zeisler: Start with trust. Don't try to automate everything at once. Prove value in small, controlled environments first. Build confidence gradually. We created an internal Slack channel called "Social AI" where teams can share experiments and learnings. It breaks down silos and accelerates knowledge sharing. Anyone can post what they're trying, what worked, what didn't. It creates a culture of experimentation and learning.
We also fast-tracked procurement for AI experimentation. Teams can experiment for three months with security approval before going through full budget review. If it works, then it moves into the standard process. If it doesn't, we learned something without a huge investment. That unlocks real experimentation and lets teams move quickly.
Seth Earley: That's a really practical approach to enabling innovation while maintaining governance.
Forrest Zeisler: It is. Our goal is simple. Jobber should be your most trusted employee. That's the bar. If we can achieve that level of trust and reliability, then we've succeeded. And the lessons we've learned scaling from 3 customers to 300,000 apply whether you're a startup or a large enterprise. Start with the customer. Prove value. Build trust. Scale gradually. And never forget that trust is earned through consistent, reliable performance over time.
Seth Earley: Those are fantastic insights. Thank you so much for sharing your journey and these lessons with our audience, Forrest. This has been incredibly valuable.
Forrest Zeisler: Thank you for having me. It's been a pleasure.
Seth Earley: And to our listeners, thank you for joining us on this episode of the Earley AI Podcast. We'll see you next time!