How top AI companies build and lead AI-first recruiting teams

Recruiting at an AI-native company comes with a unique pressure: your hiring managers, engineers, and leadership live and breathe AI every day. They expect recruiting to keep pace. They expect the TA function to be just as AI-forward as the products they're building.

So what does that actually look like in practice? How are recruiting teams changing when everyone around them is pushing the boundaries of what AI can do?

We brought together three TA leaders from Glean, ClickUp, and Luma AI to discuss how they're building AI-first recruiting teams. They shared how the recruiter role is shifting, where AI agents fit into their orgs, and how they hire for brand-new role categories. Here's what we learned. learned. (You can also watch the highlights here.)

Recruiters must become talent partners

And the shift isn't subtle. When AI handles the mechanical work of recruiting, the role itself transforms.

AI now handles sourcing, scheduling, and outreach automatically: Recruiters spend far less time on manual searches, scheduling coordination, and administrative follow-up. AI handles candidate sourcing from massive databases, coordinates interview schedules across time zones, and automates routine outreach.

Strategic work replaces admin: Recruiters are spending more time on strategic work — building relationships with passive candidates, advising hiring managers on market conditions, and thinking critically about talent strategy. 

The skill set is evolving: AI-first recruiting teams are hiring for different capabilities. Prompt engineering matters now. So does the ability to evaluate AI outputs critically and know when automation helps versus when human judgment is essential. Some teams are hiring people with non-traditional backgrounds — former analysts, operations specialists, or even product managers — who bring structured thinking and technical fluency.

Do you still need sourcers, recruiters, and coordinators as separate roles?

Not always. AI is enabling generalists to operate with capabilities that previously required specialists.

When AI can surface qualified candidates, personalize outreach at scale, and handle scheduling, a single recruiter can manage the full lifecycle for standard requisitions — sourcing, engaging, coordinating, and closing.

This doesn't mean specialist roles disappear entirely. Complex executive searches, employer brand strategy, and recruiting operations still benefit from dedicated focus. But the traditional rigid role splits are blurring.

Assessing for AI fluency is now part of the hiring process

At AI companies, recruiting teams look for candidates who are naturally curious about AI and comfortable working alongside it.

What AI fluency looks like in practice:

• Candidates who already use AI tools in their current role without being told to

• People who ask thoughtful questions about how AI is used in the recruiting process

• A mindset of experimentation: trying new tools, testing hypotheses, iterating based on results

• The ability to evaluate AI recommendations critically rather than accepting them blindly

How AI-native teams hire for brand-new role categories

AI is creating entirely new categories of work, and recruiting teams are figuring out how to hire for roles that barely existed two years ago.

Examples of new or rapidly evolving roles:

• AI safety researchers and red team specialists

• Prompt engineers and LLM evaluators

• AI product managers who bridge technical and business needs

• Machine learning operations engineers

• AI ethics and policy specialists

For cutting-edge AI roles, the qualified candidate pool is extremely small. Teams need to get creative — looking at adjacent disciplines, focusing on learning ability over specific experience, and building talent pipelines years in advance of actual hiring needs.

AI is embedded throughout the hiring process

These teams aren't just talking about AI. They're using it across every stage of recruiting.

What's in production:

• AI-powered application review that surfaces best-fit candidates based on complete interaction history

• Intelligent sourcing that searches across hundreds of millions of profiles and flags candidates you've already contacted

• Interview scheduling that handles time zones, preferences, and load balancing

What teams won't go back on: Automated scheduling is universally praised. No one wants to return to the days of endless email chains trying to find a time that works for everyone. 

What hasn't worked: AI-generated job descriptions often miss the nuances that make a role compelling. Fully automated candidate communication can feel impersonal and damage candidate experience. AI excels at augmenting human decision-making but struggles when deployed without human oversight on high-stakes, relationship-driven tasks.

What recruiting looks like in five years

Looking ahead, panelists expect recruiting to become even more strategic and consultative.

The role shifts further: Recruiters become talent advisors who spend most of their time on market intelligence, relationship building, and strategic planning. AI handles execution — sourcing, screening, scheduling, and administrative work.

Fewer recruiters, but different recruiters: Teams will likely be smaller but more technically sophisticated. The bar for what recruiters need to know — about AI, data, and technology — will continue to rise.

Critical skills to build now:

• Comfort with AI tools and willingness to experiment

• Data literacy and the ability to interpret analytics

• Strategic thinking and business acumen

• Strong relationship-building and consultation skills

• Technical fluency to work effectively with engineering and product teams

Where to start if you're not at an AI-native company

The panelists' advice for TA leaders who want to become more AI-forward:

Start experimenting tomorrow. Pick one workflow that's repetitive and time-consuming: scheduling, sourcing, application review, and testing AI tools that can help. Learn what works and what doesn't in your specific context.

Build AI fluency across your team. Don't wait for perfect solutions. Get your recruiters comfortable using AI tools, even imperfect ones. The learning curve matters more than immediate ROI.

Focus on augmentation, not replacement. The goal isn't to eliminate recruiters. It's to free them up for higher-value work. Frame AI as a tool that makes recruiters more effective, not as a threat to their roles.

Measure what matters. Track how AI impacts recruiter productivity, time-to-fill, candidate experience, and quality of hire. Use data to refine your approach and demonstrate value to leadership.

Stay curious. AI capabilities are evolving rapidly. The tools available six months from now will be meaningfully better than those that exist today. Build a culture of continuous learning and adaptation.

The teams that lead will decide what comes next

The recruiting teams at leading AI companies have an advantage: they're surrounded by people who understand AI's potential and expect innovation. But the strategies they're implementing — augmenting recruiters with AI, hiring for new capabilities, rethinking team structures — are relevant for any TA organization.

The question isn't whether AI will change recruiting. It already has. The question is whether your team will lead that change or react to it.

Want to hear the full conversation? Watch the complete AI Showcase panel → https://www.youtube.com/watch?v=jceEW4JKIDM