SHL, one of the pioneers in industrial and psychological assessment, has now built a new instrument to assess an individual’s readiness for AI.
Sara Gutierrez, Chief Science Officer at SHL, explains how SHL is addressing this challenge.
Drawing on SHL’s 45-plus years of talent intelligence and behavioral science and a large study of millions of data points, Sara introduces a skills model built on four factors – AI literacy, analytical thinking, continuous learning, and AI promotion – that together define who is ...
SHL, one of the pioneers in industrial and psychological assessment, has now built a new instrument to assess an individual’s readiness for AI.
Sara Gutierrez, Chief Science Officer at SHL, explains how SHL is addressing this challenge.
Drawing on SHL’s 45-plus years of talent intelligence and behavioral science and a large study of millions of data points, Sara introduces a skills model built on four factors – AI literacy, analytical thinking, continuous learning, and AI promotion – that together define who is truly ready to become a superworker – any employee who uses AI to elevate their work to create more impact and have more meaningful work.
The conversation is full of grounding data. Across more than one million assessments, SHL found that graduates and early career workers know how to use AI tools but not necessarily how to use them well, while seasoned professionals show strong contextual judgment but struggle to reimagine solutions from scratch. Neither generation arrives superworker-ready and each needs help to develop the superworker readiness. And it’s not technical skills that differentiate superworkers from the rest. Powerskills – those durable human skills, like adaptability, critical thinking, and learning orientation – predict who will thrive as AI reshapes every role, and every worker can develop them.
The proof is in a real case: a media and telecommunications company used SHL’s AI readiness model to de-risk an AI transformation. Enthusiasm for AI was high, but gaps in applying it responsibly – using guardrails, exercising judgment on outputs – were putting success at risk. The assessment gave leaders something they rarely have: a data-backed, actionable development plan to help all employees thrive.
Employees welcomed the personalized recommendations. The lesson, as Sara puts it, is that leaders must frame the assessment not as evaluative but as developmental and supportive – because with 70% of employees not trusting that their employer knows how to use AI responsibly, trust is the whole game.
Related Resources
Let’s Stop Talking About Soft Skills: They’re PowerSkills
Podcast: How To Become An AI Pacesetter: Superworker Explained – article
Galileo Learn: The Superworker Organization: AI Goes Enterprise (certificate course)
Research: The Superworker Organization: AI Goes Enterprise
Research: AI Pacesetters: Six Secrets of the Superworker Company
New book: Superpowered, coming this Fall
Chapters
- (00:00:03) - What Works: The Future of Talent and Work
- (00:00:54) - What is the role of the Chief Science Officer at SHL?
- (00:02:49) - WSJD Live: The Need for Reskilling
- (00:03:53) - Will AI Make Hiring and Promotion Decisions Easier?
- (00:05:05) - WSJDLive: The Need for AI Talent
- (00:08:56) - The Super Worker
- (00:16:09) - What gets in the way of a company applying this super worker profile
- (00:18:19) - WSJDLive: AI Workforce Assessment
- (00:22:21) - The Future of the Super Worker
- (00:23:45) - What's next for SHL in AI readiness and leadership?
- (00:26:22) - What Works: The AI Superworker
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