Publication: Built In
Date: November 14, 2025
Your next AI hire probably doesn't look like what your job description says they should. That's the paradox every hiring manager faces right now, and it's costing companies the talent they actually need.
In a contributor article published on Built In, SoftSnow founder & co-CEO, Larry Fisher, draws on three decades of building companies through major technological shifts to reframe the AI hiring conversation entirely. When meaningful AI experience is still measured in months, screening for credentials is a losing strategy. The real question is: what character traits actually predict success in fast-moving, ambiguous environments?
This piece is essential reading for any leader building—or trying to build—an AI-capable team. It's a practical framework, battle-tested across three industry transformations, with tools you can use tomorrow.
Key Highlights:
- Why 74% of employers prioritizing AI talent can't find qualified candidates, and why that's a hiring strategy problem, not a talent shortage
- Six character traits that predict success in AI-first roles when credentials don't yet exist
- How SoftSnow built an AI agent to screen for behavioral patterns, not just keywords
- Practical steps to rewrite job descriptions, design better assessments, and interview for character over credentials
- Why coachability, adaptability, and collective-success mindset matter more than a resume full of AI certifications
The article introduces a hiring model directly aligned with SoftSnow's belief that sustainable AI transformation happens at the individual level. It also showcases a practical application of AI agents—built by SoftSnow—to systematize the identification of soft skills at scale, turning a historically subjective process into a data-informed one.
"You can't always rely on credentials and direct experience for successful hiring in a space that's changing daily." — Larry Fisher, Co-Founder, SoftSnow
