GenAI is transforming customer success. Teams that embrace it are gaining leverage across the board—faster responses, sharper insights, more personalized onboarding—without scaling headcount. Meanwhile CS teams that aren’t using it are already falling behind.
But customer experience is high stakes. The same tools that surface insights from customer calls or auto-suggest help content can also hallucinate fixes or misread sentiment. GenAI needs human oversight not just to catch errors, but to guide the model with the right data, context, and tone.
The most effective CS orgs aren’t handing off responsibility to AI. They’re redesigning their workflows around it. They’re learning when to automate, when to keep a human in the loop, and how to continuously sharpen both the system and the people inside it. To see how this plays out in practice, we sat down with two RSCM founders who are putting GenAI to work in their CS orgs today—for onboarding, knowledge management, and day-to-day support.
Founder Bios
Ablorde Ashigbi is the CEO and Founder of 4Degrees, a relationship intelligence platform and industry-specific CRM designed for professionals in financial and professional services. Ablorde leads a 15-person distributed team that supports hundreds of firms across these network-driven industries.
Dakota Younger is the CEO and Founder of Boon, a referral hiring optimization platform that helps companies build and scale high-performing referral programs with minimal effort. Boon's AI pinpoints referral-ready candidates within a user’s network and integrates with existing hiring workflows to automate the referral pipeline end to end.
Though not technical themselves, Ablorde and Dakota have personally built out their core GenAI capabilities for customer success. Their approach shows how non-technical founders can use GenAI to significantly scale support with minimal headcount.
Build vs. Buy: Choosing GenAI Tools
When it comes to building a GenAI stack, both Ablorde and Dakota prioritize control, flexibility, and early validation over prepackaged solutions.
Ablorde considered existing platforms like Front or Help Scout’s built-in AI but ultimately opted to build with Zapier Agents and OpenAI APIs. “Part of it was that we were moving faster than those systems were,” he said. More importantly, he wanted a user experience that felt unique to 4Degrees. “I want my customers to have a bespoke experience with us. A custom-built system gives us a greater level of control, and increases the level of precision we can give in terms of answers and high quality responses to our customers.”
Dakota takes a pragmatic, test-first approach to building his GenAI workflow. “I want to plug in a tool, get a sense of the value that it can provide, do some iteration, and get a clear picture of how we can use it,” he says. “I can’t make a decision without any data. I can’t even assess the cost of a tool unless I have a sense of how much we’re going to be using it.”
Automating Support at Scale
Ablorde uses his custom customer support workflow to automate responses for inbound customer messages. “The specific way we manage CS inbounds is through a platform within Zapier,” he explains. While traditional Zaps are deterministic, Zapier Agents allow for more agentic workflows, enabling Ablorde to configure a system that responds intelligently to support queries. At the core is a custom bot that uses their Help Scout knowledge base as a data source.
When a new conversation is created in Help Scout, the Zapier Agent is activated and guided by specific instructions defining both its role and tone. “We’ve told the agent, ‘You are a customer representative. It’s important that you have great explanatory skills,” says Ablorde. The agent also knows when to engage and when to skip a message. Early on, responses were generated as drafts, but Ablorde notes, “if you eventually get confidence that it’s doing everything correctly, you can have it push answers straight out through Help Scout.”
Turning Customer Calls into Product Feedback
Customer calls can be a great source of feedback about the product, but only if the CS team can translate that feedback into a clean, digestible format for other teams. “Getting usable bug reports to product and QA teams used to be a major bottleneck,” Dakota explains. “Customer support was speaking a completely different language than engineering.”
Now, instead of trying to teach CS reps how to structure technical tickets, they use Loom’s AI to convert support calls into structured outputs. “You can record a call and tell Loom to turn it into a bug report, and within seconds it will give you an overview of the bug and how it can be replicated.” These reports even include screenshots and step-by-step summaries, making it easier for QA to take action.
The clarity and consistency of this AI-generated output has enabled Boon to track patterns in support volume and address friction more proactively. “We’ve been able to catalog the tickets that have been created and organize them to better understand when something is a bug ticket or when it’s a request ticket,” Dakota says. “Before, it was easy to confuse missing features with broken ones. But now information is bulletpointed and can be segmented.” That segmentation reveals trends by feature, user type, or onboarding stage, giving the product team real signal on where to improve. “We’re not only improving the user experience, but reducing the amount of bandwidth we need,” says Dakota.
Building a Queryable Brain
Dakota and his team have transformed onboarding and cross-functional alignment by building a dynamic, queryable knowledge base powered by GenAI. “It used to be that you’d hire a new employee and then hand them an encyclopedia to read,” Dakota jokes. But instead of dumping static documentation on new hires, Boon now provides interactive, searchable systems. “It allows employees to start querying information as it makes sense to their logic flow, making onboarding faster and more relevant to each role,” says Dakota.
The GenAI layer doesn’t just make internal docs accessible; it allows sales and CS teams to hone their answers when talking to customers and prospects. Boon’s team uses Coda to create modular hubs that blend brand messaging and positioning with technical facts. “You don’t want your answers to be feature-focused. You want them to be value-focused,” says Dakota. “You spent all this time and effort developing your messaging. You should make sure your team is delivering responses that are on brand and on point.”
Who Do You Hire When AI Does Half the Job?
Both Ablorde and Dakota agree: GenAI is fundamentally reshaping hiring needs by shrinking team sizes while raising expectations for individual output. The open question is what kind of talent wins in this new landscape.
“I definitely lean towards hiring people that can bring a lot of value to the table,” he says. But he’s also seen how AI closes the gap between senior and junior roles, especially in functions like sales or CS. “A junior employee using AI can operate as a proxy for a more senior person,” he says.
Ablorde echoes that ambiguity. “On one hand, AI makes expertise and ability even more valuable. That would push you in the direction of only hiring senior people,” says Ablorde. “On the other hand, if GenAI matures to the point where it embeds senior-level expertise, it would allow a junior to operate like a senior. I’d probably lean towards hiring senior people over junior people, but I can’t be sure what the future holds.”
Whether you favor hiring senior employees or filling your startup with junior staff, the key is ensuring everyone on the team can use AI. Dakota puts it bluntly: “If somebody’s not able to use GenAI at this point, then I think that would be a concern.”
Ablorde agrees. “If you tell me that you are uncomfortable chatting with a ChatGPT agent, then I don’t know that 4Degrees is the right place for you,” he says. He adds that while GenAI competence is the baseline for new hires, his ideal candidates would be able to showcase their initiative. “If you’re someone who can build their own system using GenAI, then that would be a very attractive trait in a candidate.”
The New Playbook for Customer Success
GenAI is creating a new interface between teams, tools, and customers. It’s allowing customer success orgs to rethink workflows and systems from the inside out. As Ablorde and Dakota show, you don’t need a technical background to start. You just need the willingness to experiment and the discipline to systematize what works.
Whether you’re auto-generating help desk responses, cataloging support insights, or building an internal brain that speaks marketing and engineering at once, the goal is the same: faster answers, fewer tickets, and better alignment. The winners won’t be the ones with the biggest teams, but the ones who figure out how to make every team member—and every interaction—exponentially more effective.
If you’re not already testing these workflows, start now. The gap between “exploring” and “executing” is where most teams will get left behind.
