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Recruiter

Source candidates for hard-to-fill roles

Enhances✓ Available Now

What You Do Today

Search LinkedIn, niche job boards, and your network for passive candidates, write personalized outreach, get responses from people who aren't looking

AI That Applies

AI identifies candidates matching complex criteria, generates personalized outreach messages, predicts response likelihood

Technologies

How It Works

For source candidates for hard-to-fill roles, the system identifies candidates matching complex criteria. The recommendation engine scores each option against the user's profile — behavioral history, stated preferences, and contextual signals — ranking them by predicted relevance. The output — personalized outreach messages — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Candidate identification is 10x faster. AI finds people you'd never have discovered through manual searching

What Stays

Writing the outreach message that makes someone stop scrolling, your network and reputation in the talent market

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for source candidates for hard-to-fill roles, understand your current state.

Map your current process: Document how source candidates for hard-to-fill roles works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Writing the outreach message that makes someone stop scrolling, your network and reputation in the talent market. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support AI sourcing platforms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long source candidates for hard-to-fill roles takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Talent or CHRO

What's our time-to-fill for the roles that are hardest to source, and where in the funnel do we lose candidates?

They set the AI adoption strategy for the recruiting function

your HRIS admin

How would we validate that an AI screening tool isn't introducing bias we can't see?

They manage the ATS and integration points that AI tools depend on

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.