How AI can turn the art of hiring into a science
Hiring the right people is near the top of every leader’s agenda – and for good reason. The cost of a bad hire can reach $240,000 when you factor in recruitment, training, lost productivity, and morale. Yet despite the stakes, hiring today is still often inconsistent and time-consuming:
- Teams aren’t aligned on what “great” looks like: Without a shared definition of success, every interviewer brings their own assumptions about the role. This leads to mixed signals for candidates and inconsistent decisions internally.
- Interviewers show up underprepared: In fast-moving companies, interviewers often walk into meetings without clear questions or evaluation criteria. This creates disjointed conversations and poor candidate experience.
- Notes are scattered and critical signals are missed: Feedback is captured in email threads, spreadsheets, or informal chats. Important details slip through the cracks, making it harder to compare candidates objectively.
- Decisions drag on: With unclear criteria and fragmented feedback, hiring managers struggle to reach consensus. Time-to-hire increases and top candidates drop out.
- Candidate assessments become subjective: Without structured rubrics, evaluations are influenced by gut feel, recency bias, or who spoke last in the debrief. This undermines fairness and predictive accuracy.
- Duplicate work across systems wastes hours: Recruiters and hiring managers re-enter the same data in multiple tools, copy-paste interview notes, and chase colleagues for updates. Administrative overhead drains resources.
- Instincts often replace evidence: In the absence of consistent data, teams rely on “fit” or “feel” rather than measurable competencies. This raises the risk of mis-hires and bias creeping in.
Research from Korn Ferry shows structured, standardized interviews are far more accurate than free-flowing conversations – but few organizations apply that discipline consistently.
How Competency-Based Approaches and AI Tools Can Help
Competency-based approaches and modern AI tools can transform hiring from a slow, error-prone process into one that’s structured, faster, and fairer – improving both quality of hire and the candidate experience. By combining clear criteria with automation, they:
- Create a shared definition of success so everyone knows exactly what “great” looks like for each role.
- Guide interviewers to ask consistent, comparable questions and score against the same criteria, reducing bias and inconsistency.
- Provide real-time prompts during interviews to keep conversations on track and ensure nothing important is missed.
- Capture and synthesize feedback into structured debriefs, making it easier to compare candidates objectively and reach decisions quickly.
Competency Frameworks for People-First Companies
Over the years, well-known competency frameworks have helped organizations articulate the skills and behaviors that drive high performance and fairer hiring. While AI tools can significantly improve consistency, speed, and decision quality, people-focused companies may still find these frameworks valuable as a foundation for their hiring practices:
1. Lominger Competency Model
Developed in 1991, this model defines 67 competencies across strategic, operating, personal, and interpersonal categories.
Why it matters: It’s comprehensive – covering everything from strategic agility to emotional intelligence. Leaders can build balanced skill sets and create targeted development plans.
2. SHL Universal Competency Framework (UCF)
SHL’s UCF is built on decades of occupational psychology and defines workplace behavior in a 3-tier structure: 8 factors, 20 dimensions, and 96 skills.
Why it matters: It links behaviors directly to performance outcomes and underpins SHL’s assessment tools.
3. Schroder High-Performance Behaviours
This model groups behaviors into four clusters: Think, Involve, Inspire, Achieve.
Why it matters: These are observable, coachable behaviors that balance strategy, empathy, motivation, and execution.
4. Spencer & Spencer’s Iceberg Model
Introduced in Competence at Work (1993), this model shows how most critical traits sit below the waterline: motives, values, self-image, and thinking styles.
Why it matters: It reminds hiring teams to probe deeper than résumés and surface skills.
5. Korn Ferry Leadership Architect™ (KFLA)
The KFLA is a global competency framework with 4 factors, 12 clusters, and 38 competencies.
Why it matters: It’s research-based, widely adopted, and ties leadership competencies directly to business outcomes.
Takeaway
By combining proven hiring practices with modern technology, startups and enterprises alike can move beyond gut feel to make smarter, faster, more equitable hiring decisions. Tools powered by AI are emerging to help teams embed consistency and fairness into every interview and every decision.
If your team wants to make competencies more than a buzzword – and actually embed them into every interview, every decision – modern tools are emerging to help. Explore how Godric is approaching this challenge with our proprietary, AI-age competency model.