The Humanly Guide to Category Creation in HR Tech: Lessons from Building a Conversational AI Category

Creating a new category in HR tech isn’t just about building innovative technology – it’s about fundamentally reshaping how the market thinks about solving problems. In a recent episode of Category Visionaries, Humanly founder Prem Kumar revealed their playbook for category creation, drawing inspiration from successful predecessors while charting their own path.

Learning from Drift’s Category Creation

Prem’s approach to category creation was heavily influenced by Drift’s success in creating the conversational marketing category. “I’m definitely a fan of David Cancell, who founded Drift and really built a category around something that hadn’t existed before,” he shares.

This inspiration shaped how Humanly approached their own category definition. Just as Drift transformed how businesses think about website engagement, Humanly aims to revolutionize recruiting conversations.

Redefining Conversational AI

Rather than accepting the conventional definition of conversational AI in recruiting, Humanly is deliberately expanding it. “When people think conversational AI, they’re thinking chatbot,” Prem explains. “But to me, conversational AI for recruiting is about any conversations you’re having with job candidates, not just the automated ones… how do you make that more efficient and more equitable?”

This broader definition allows Humanly to address problems that traditional chatbot solutions miss. “I oftentimes look at what’s kind of happening with buyers on the marketing and sales automation tools and then not just from like a buyer standpoint, but an innovation standpoint. And then you eventually see that coming to HR tech and recruiting tech and talent tech.”

Building Differentiation in a Crowded Market

In HR tech, standing out isn’t just about features – it’s about solving problems in fundamentally different ways. Prem outlines their approach: “There’s kind of two strategies I’ve seen founders take… One is just getting very specific in who you’re solving the problem for… In our case, it’s a little bit of a combination of both.”

Their differentiation strategy focuses on high-volume recruiting scenarios: “We’re not helping you hire for engineers… We’re very much focused on these high volume, entry level roles where there’s pain points around having to do seven phone screens a day and write seven sets of follow up emails.”

The Data Advantage

Unlike many players in the space, Humanly views itself primarily as a data company. “I consider ourselves and I wouldn’t say we’re not the only one in the world doing this, but we’re definitely consider ourselves a data company. We’re using data to help solve problems in the recruiting conversation space.”

This focus on data extends to how they measure success. With over a million candidates engaged and an average experience rating of 4.8 out of 5, they’re building evidence that their approach works.

Navigating the AI Revolution

The emergence of tools like GPT-3 hasn’t derailed their category creation efforts – instead, it’s reinforced their approach. “We’re using our own generative AI built on top of GPT-3 to automatically generate follow up emails for recruiters,” Prem notes. “Using the generative AI as well as other technology in ways that isn’t just throwing technology to human problem, but kind of helping things happen faster or more efficiently.”

Future of the Category

Looking ahead, Humanly aims to own the conversational AI for recruiting space. As Prem explains, “We definitely want to win this conversational AI for recruiting space… any company that is hiring for high volume roles, we want to be the engine that’s helping them have more efficient and equitable direct conversations with their job candidates.”

For founders looking to create new categories in crowded markets, Humanly’s approach offers valuable lessons: draw inspiration from successful category creators in adjacent spaces, redefine conventional terms to match your vision, focus on specific high-value problems, and build differentiation through data and measurable impact.