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Role: Copy and creative direction via Speedwork Agency · Client: Findem (AI recruiting, talent CRM) · Audience: recruiting and Talent Ops leaders · Format: three LinkedIn post types · Tools: Canva · Result: Work sample
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LinkedIn content for Findem (AI recruiting + talent CRM platform), produced via Speedwork Agency. Audience: recruiting and talent leaders at mid-sized companies. Three post types — product feature, blog/nurture, and report insight. Each written to match brand voice, carry a clear POV, and pair with matching design.
Warm candidates respond 80% more often, so why are recruiters still chasing cold leads?
Most teams already have qualified talent. They just can't surface them. Past applicants, previous pipeline talent, and people who have engaged with the brand before.
The problem: legacy CRMs make it nearly impossible to find, refresh, and prioritize that warm talent at scale.
With Findem's Talent CRM, every search turns into an always-on, dynamic pool of warm, qualified candidates. No manual tagging. No stale records. No spreadsheets.
Warm outreach isn't just faster. It builds hiring momentum across the entire funnel.
Why this angle: I led with the stat "warm candidates respond 80% more often" because it anchors a benefit already on the Talent CRM page, then framed the product against the specific pain (legacy CRMs can't surface warm talent) rather than listing features.
AI is supposed to make recruiting easier, so why is it making your job harder?
Most AI projects don't fail because of the technology. They fail because of the mess of data underneath.
Years of ATS history, siloed systems, and incomplete profiles mean the AI models don't have enough clean, connected information to learn from. So instead of better candidates or a faster time to hire, AI ends up making the process harder. More systems, more noise, and more manual cleanup for teams already short on time.
Deloitte found that 77% of employees say AI has actually increased their workload, not reduced it.
AI doesn't fail because it's bad technology. It fails because it's placed on top of bad data. The teams that see value take a different approach: