4 principles for building a scalable, human-centered tech stack

These days, there’s literally a tool for everything. Sourcing, screening, assessments — and even AI-driven interviews. And when there’s a new launch every week, teams can often find themselves getting FOMO and solving challenges that they didn’t know they had.

This is why the first question you should be asking isn’t “What’s the best AI recruiting tool?” — it’s “Do we even really need a tech solution?”

“We often start with the assumption that all teams need to be tech-enabled,” says Jessie Schofer, founder of HR tech database Stakkd. “They think, ‘Oh, we need AI’, or ‘We need a specific tool.’ But the step they’re missing upfront is prioritizing what the business needs. Many TA teams are siloed — they start from the perspective of what they need and how that impacts their outcomes, not what impacts the broader needs of the people function and the business.”

In other words, you’re not just looking for tools — you’re looking for the right problems to solve. To get a sense of what yours are, you need to take a systems-focused view of how your processes contribute to (or block) business outcomes.

Start by analyzing key TA metrics, including time-to-fill, quality of hire, cost-per-hire, candidate experience, and drop-out rates. Identify where your biggest challenges are that map to broader business goals — including loss of talent, lengthy hiring cycles, or talent scarcity. 

Despite the fearmongering on robot recruiters, hiring is still (and will forever be), a human-first job. That means your tech stack needs to work for the humans using it — including TA, your candidates, and your broader organization.

One of the biggest mistakes teams make when building their TA tech stack is zooming all the way in on operational efficiency without considering how tools feel to use. Because a tool can have all the bells and whistles, but if it’s hard to apply for a role, sort through resumes, or export key data on hiring outcomes, then it impacts every single stakeholder in your hiring process.

“Every process has two sides: what works operationally for your business, and how it impacts the people who actually use it,” Schofer said. “When designing HR and TA processes — your onboarding flows, contracts, candidate experience — you need to consider both perspectives.”

Beyond UX, there’s also the question of which parts of talent acquisition should stay human, versus which can be outsourced to a tool. And in the age of AI, the question is less “Can we use tech here?”, and more, “Should we?”

The goal isn’t to implement tech to the point that you’re a lean, mean TA machine, ruthlessly programmed for efficiency. Instead, it’s about removing friction points for all users while supporting better moments of human connection.

The most important thing when building your stack is assessing what your organization’s needs will be down the road — and how tricky it’ll be to retire an entire tool if it’s not scaling at the same pace as your company.

“This is a challenge for every team,” Schofer says. “You can outgrow your tech — and that’s okay. But you need to take a medium- to long-term vision depending on the tech itself,” Schofer notes. “How hard it is to implement, the cost of implementation, and the impact on the business for the change process… That’s where you need to start.

“We have to go through that understanding and really know how your operating model works within your business,” she adds. “There’s a lot of tooling that just affects TA, for example — it doesn’t affect anyone else. The change process is smaller. If you swap out an AI notetaker, for example, that’s going to have less impact than core tooling. But your core infrastructure — your HRIS or ATS — that’s not going to be something you swap out regularly. The heavier the change required, the longer you want your tool to last.”

Defining what’s foundational and what’s an add-on depends on your organization’s goals – and how your processes are set up to support them. Because copy and pasting the exact TA stack from a similar organization in your sector won’t account for how your data flows, who makes decisions, or what goals you’re working towards. 

And this is where building in flexibility is key. Your core infrastructure — such as your ATS, HRIS, and analytics tooling — needs to add structure and stability to centralize data flow. But the tooling around it needs to adapt, scale, or be swapped out without breaking the whole hiring machine.

Once you’ve uncovered your biggest bottlenecks, you probably want to solve them as quickly as possible — that’s normal. But here’s where Schofer says teams need to cool their jets and slow down.

“I always recommend having much more time put into the planning and assessment phase than people give it,” she says. “If it’s going to take you six months to do something, spend three months just preparing yourself to get the right tool, even if that means you have to delay things.”

Once you have your shortlist, you’re ready to vet tools. But that process needs just as much rigor — and it’s one of the biggest places teams are leaving money on the table. This, says Schofer, is where teams need to get really specific about exactly what they’re looking to achieve.

“I find that people jump into demos unprepared,” Schofer says. “But this is your interview with a potential tooling company. You’d never go into a candidate interview unprepared. Once you’ve decided which problems you need to fix, you need to go into any sales calls knowing what you want to achieve with this tool. It’s not just saying ‘I need an AI notetaker’ — it’s saying, “I need to reduce the amount of admin my team is doing because everyone is burned out, and we need to achieve x hires per month.”

To vet your shortlist, Schofer says you need to prep for sales demos like a candidate interview. Get your scorecard and assessment criteria set up to help you evaluate tools consistently across different vendors.