Why Field Service Can't Be One-Size-Fits-All

I sat down with Andrew Wolf, Co-founder and CPO of Zinier, who knows that field service is inherently complex and that every organization does it differently. That's why, he argues, no single FSM solution has won the market. We discussed this complexity challenge and how AI agents are opening up new approaches to field service automation.

The Build vs. Buy Dilemma in Field Service Management

Q: Why do you think no single FSM solution has dominated the market?

Andrew: You know, I usually start these types of conversations with one simple observation - field service is very complex, and every field service organization is unique. You connect to different systems than your competitors, you organize your field teams differently. Your field workflows, your processes differ.

It's this unique complexity to each organization that has led to the fact that there isn't a single dominant FSM solution in the market today. No one has emerged victorious, and that's simply because every field service solution is unique. So what choices do service organizations typically have? You're faced with this age-old dilemma of build versus buy. Do I purchase an existing FSM product or do I try and build something myself?

The key drawback to trying to build something yourself is that this is a time-consuming and expensive process, and it may not be your main competency. Now if you decide to go off the shelf, there is typically a lack of flexibility that forces you to adopt the processes that are built into a solution and adapt your own processes to fit the product. This is not ideal. Service companies really need to be innovative, you need to be able to continue to evolve and iterate, and when you put yourself in a position where you have to match your processes to the software, it's not an ideal situation.

Field service is very complex, and every field service organization is unique. No one has emerged victorious because every field service solution requires different approaches.

How AI Agents Transform Field Service Operations

Q: There's a lot of AI hype in enterprise software. What's different about AI in field service?

Andrew: I really believe that AI agents and generative AI is going to be as transformative to SaaS software as SaaS software was to on-premise software. There are so many opportunities, especially with generative AI, to create value in the field service arena.

But here's the thing - most of the service providers out there, most of the field service solutions, aren't really capable of leveraging the full power of AI and agents, especially on a customized solution. You will see people talking about agents and AI and being able to do this. But when you actually customize your solution, what does that mean for AI?

At Zinier, we've taken a different approach. We have a method of being able to configure and deploy AI agents for any use case in any solution, and it doesn't matter how customized that is. We do this through Studio Z, through a product that we call our AI agent builder. This gets back to that earlier theme I mentioned - our platform and Studio Z are there to be able to build and modify the solution to meet your specific needs. We've really taken that same approach with AI and agents, giving you the ability to deploy agents for any use case specific to your solution.

AI agents and generative AI will be as transformative to SaaS software as SaaS was to on-premise software, especially for customized field service solutions.

AI's Role in Workforce Transition

‍Q: We’ve talked about the aging workforce problem before in field service. How could AI agents help in these industries?

Andrew: This is actually one of the most critical use cases we're seeing. You might have two different documentation agents - one that's trained on your client-specific documentation such as operations manuals or technical documents related to your equipment and other assets. I might be able to answer questions like "what does this red flashing light mean for asset A" or "what is the next step in this process?" If you have operations manuals that have that information, then technicians can get access to it.

The beauty of this approach is that when an experienced technician retires, their knowledge doesn't walk out the door with them. We can capture that institutional knowledge in documentation that AI agents can then access and share with newer technicians in the field. It's about democratizing that expertise across your entire workforce, not just having it locked up in the heads of your most senior people.

When an experienced technician retires, their knowledge doesn't walk out the door. AI agents can capture institutional knowledge and democratize expertise across your workforce.

The Path to Touchless Operations with Field Service Scheduling

Q: You’ve also mentioned “touchless operations” before. Touchless operations sounds risky for critical infrastructure. How do you balance automation with control?

Andrew: You know, we like to say that there is a vision in front of us to move towards this idea of a touchless future. This is a world where field service delivery is almost entirely automated and only managed by exception. But I think it's important to understand that we're not talking about removing humans from the equation entirely.

In an ideal world, we want dispatch teams to go from a single dispatcher maybe managing eight to ten technicians to a single dispatcher being able to manage a hundred technicians. You always need a human to be there and to manage certain things. But the idea behind our scheduler and recommendations in the context of scheduling is to automate as much of that process as possible so those dispatchers can focus on more value-added work.

We approach this through two types of automation: rule-based automation through our recommendation center, and AI-based automation. With recommendations, you can basically come up with any scenario to codify logic that might happen in the head of an experienced dispatcher. These are triggered by events, and you can accept or reject them. When you accept it, it triggers a workflow in our system.

We want dispatch teams to go from managing eight to ten technicians to managing a hundred technicians, with humans focusing on exceptions and value-added work.

The Next Paradigm Shift: Beyond Traditional SaaS

Q: Where is field service technology headed?

Andrew: Honestly, the software industry is on the brink of another paradigm shift. Just as the rise of SaaS redefined enterprise software by making it more accessible, scalable, and continuously improving, the next wave of transformation is being driven by AI agents powered by large language models.

But here's what I think executives need to understand - this isn't about adopting AI just to check a box. The basic concept of adopting a new technology which is game-changing means you cannot force-fit that technology into the standard way of doing business. There has to be a new paradigm. There has to be an understanding of what problems need to be solved and how they have to be solved differently.

That's where we come in - not treating it as SaaS where you just sell the product and run away, but treating it as a problem to be solved, the job to be done. We're looking beyond the product to understand what it would take in this phase of evolution to get the job done. Your job doesn't stop after buying the product. It only starts. Then what steps do we need to take to help our customers solve the problem in the way that we're advocating?

The next wave of transformation is driven by AI agents powered by large language models. This isn't about adopting AI to check a box—it requires a new paradigm for solving problems.