As founders plan for an increasingly AI-centric future, Gusto co-founder and head of technology Edward Kim said that cutting existing teams and hiring a bunch of specially trained AI engineers is “the wrong way to go.”
Instead, he argued that non-technical team members can “actually have a much deeper understanding than an average engineer on what situations the customer can get themselves into, what they’re confused about,” putting them in a better position to guide the features that should be built into AI tools.
In an interview with TechCrunch, Kim — whose payroll startup generated more than $500 million in annual revenue in the fiscal year that ended in April 2023 — outlined Gusto’s approach to AI, with non-technical members of its customer experience team writing “recipes” that guide the way its AI assistant Gus (announced last month) interacts with customers.
Kim also said that the company is seeing that “people who are not software engineers, but a little technically minded, are able to build really powerful and game-changing AI applications,” such as CoPilot — a customer experience tool that was rolled out to the Gusto CX team in June and is already seeing between 2,000 and 3,000 interactions per day.
“We can actually upskill a lot of our people here at Gusto to help them build AI applications,” Kim said.
This interview has been edited for length and clarity.
Is Gus the first big AI product that you’ve released to your customers?
Gus is the big AI functionality that we launched to our customers, and in many ways ties together a lot of the point functionality that we’ve built. Because what you start to see happen in apps is they get littered with AI buttons that are, like, “Press this button to do something with AI.” Ours was, “Press this button so we can generate a job description for you.”
But Gus allows you to remove all of that, and when we feel Gus can do something that is of value to you, Gus can in an unobtrusive way pop up and say, “Hey, I can help you write a job description?” It’s a much cleaner way to interface with AI.
There are some companies that say they’ve been doing AI for a million years but didn’t get attention until now, and others that say they only realized the opportunity in the last couple years. Does Gusto fall in one camp or the other?
The big change for me is, when you talk about software programming, for most people, it’s not accessible. You have to learn how to code, go to school for many years. Machine learning was even more inaccessible. Because you have to be a very special type of software engineer and have this data science skill set and know how to create artificial neural networks and things like that.
The main thing that changed recently is that the interface to create ML and AI applications [has become] much more accessible to anybody. Whereas in the past, we’ve had to learn the language of computers and go to school for that, now computers are learning to understand humans more. And that seems like not that big of a deal, but if you think about it, it just makes building software applications so much more accessible.
That’s exactly what we’ve seen at Gusto: People who are not software engineers, but a little technically minded, are able to build really powerful and game-changing AI applications. We’re actually using a lot of our support team to extend the capabilities of Gus, and they don’t know how to program at all. It’s just that the interface that they use now allows them to do the same thing that software engineers have always done, without needing to learn how to code. If you want, I could talk through one example of each of those.
That’d be great.
There’s this one individual who’s been at the company for about five years. His name is Eric Rodriguez, and he actually joined the customer support team [and then] transferred into our IT team. While he was on that team, he started to get pretty interested in AI, and his boss came up to me and was like, “Hey, he built this thing. I want you to see it.” My first time meeting him in-person, he showed me what he had built, which was essentially a CoPilot tool for our [customer experience] team, where you could ask it a question, and it will just give you the answer in natural language. Just like ChatGPT might, except it has access to our internal knowledge base of how to do things in our app.
At this point, we show this to our support team, and they loved it. It completely changed their workflows and how efficient they are. Basically, anytime they get a support ticket, instead of going through this knowledge base that we’ve built, they actually ask this CoPilot tool, and the CoPilot tool actually answers the question for them. There’s still a human in between the CoPilot and the customer, but a lot of times they’re able to just get the response from the CoPilot tool and then copy paste it to the customer. They verify that it’s accurate, which most of the time it is.
We immediately transferred [Eric] to the software engineering team. He actually reports directly to me, believe it or not, and he’s one of our best engineers now. Because he was one of the early adopters of just playing around with AI and now he’s on the forefront of building AI applications at Gusto.
Not everyone is technically minded like Eric, but we have found a way at Gusto to leverage the domain knowledge expertise of non-technical folks in the company, especially in our customer support team, to help us build more powerful AI applications, and in particular, enable Gus to do more and more things.
Anytime the customer support team gets a support ticket — in other words, one of our customers reaches out to us because they want our support team’s help on something — and if it comes up repeatedly, we actually have the customer support team write a recipe for Gus, meaning that they can actually teach Gus without any technical ability. They can teach Gus to walk that customer through that problem, and sometimes even take action.
We’ve built an internal interface, an internal facing tool, where you can write instructions in natural language to Gus on how to handle a case like that. And there’s actually a no-code way for our support team to be able to tell Gus to call a certain API to accomplish a task.
There’s a lot of conversation out there right now that’s like, “We are going to eliminate all these jobs in this one area and we’re hiring these AI specialists that we’re paying millions of dollars because they have this unique skill set.” And I just think that’s the wrong way to go about doing it. Because the people who are going to be able to progress your AI applications are actually the ones that have the domain expertise of that area, even though they may not have the technical expertise. We can actually upskill a lot of our people here at Gusto to help them build AI applications.
The scary AI scenario is this top-down thing where executives are saying, “We need to use AI” and it’s disconnected from the reality of how people work. It sounds like this is more bottoms up, where you’ve built tools to allow teams to tell you what AI can do for them.
Exactly. In fact, the non-technical folks that are closer to the customers, they talk to them every single day, they actually have a much deeper understanding than an average engineer on what situations the customer can get themselves into, what they’re confused about. So they are actually in a better position than engineers or AI scientists to write the instructions to Gus to solve that problem.
I think other people I’ve talked to have noticed the same thing. The best AI engineers are actually the people that are the domain experts that have learned how to write good prompts.
As you think about how this plays out over the next few years, do you think the company’s headcount across different teams is going to look pretty similar, or do you think that’ll change over time as AI is deployed across the company?
I think the role does evolve a little bit. I think you’ll see a lot of our CX folks not directly answering questions, but actually writing recipes and doing things like prompt tuning to improve the AI. Everyone’s going to just move up the abstraction layer, and then obviously it will bring more efficiencies to the company and also better customer experience, because they’ll get their questions answered immediately.
And that unlocks Gusto to do more things for our customers. There’s a huge roadmap of things that we want to be doing, but we can’t, because we’re constrained in resources.