From Certificates to Capability: How to Measure if Your Workforce is AI- Ready?

Across the GCC, the adoption of AI has evolved rapidly with organizations investing in programs, certifications, while national agendas such as- Saudi Vision 2030 and the UAE AI strategy have set the bar very high. The next logical step is to measure what these investments have actually built: capability that can be demonstrated and measured.

The latest SHRM research offers a way to do just this. In The Case for AI Aptitude 2026, SHRM talks about how it tested 4,065 US-based workers who use AI at work, using a short assessment of both conceptual knowledge and practical application. Its findings offer the MENA region a useful reference point for thinking about how AI aptitude can be measured.

Usage is Not Readiness

Workers who scored above and below the median use AI for almost the same share of their tasks: 46% and 47%. Self-assessment is not a reliable guide as well. Among below-median scorers, 27% describe their AI experience as advanced or expert. SHRM describes this as a “notable disconnect between perceived and demonstrated aptitude”. Usage data and self-assessment alone will not show you who is using AI well.

What Aptitude Delivers

After accounting for time spent reviewing AI output, high-aptitude works save 2.9 hours a week against 1.3 hours for low-aptitude workers. They also apply more judgement. When an AI recommendation conflicts with what they believe is the ethical choice or course of action, 74% follow their own judgement vs compared with 54% of low-aptitude workers. Compliance is stronger as well: 23% of above median scorers say they have breached their organization's AI policy against 38% of those below the median. They also place more value on critical thinking: 51% of high-aptitude workers agree it is important when using AI, against 31% of low-aptitude workers.

How to Measure and Build It

The best course of action is to start with a baseline. SHRM’s test uses eight questions across two sections: conceptual understanding and practical application, while each section contributes half of the overall score.  The assessment can complement existing L&D metrics, starting with roles where AI usage is highest.

The next step is to invest in the infrastructure and support that enable higher aptitude. Among above-median scorers, 47% say their organizations provide workshops or training on using AI in day-to-day tasks, against 28% of below-median scorers. Mentoring or coaching support was available for 38% of above-median scorers, compared with 25% of below-median scorers. SHRM puts it this way: “Workers are more likely to build confidence and effectively use AI when communication is paired with tangible support including dedicated time, training and tools.”

What does this mean for MENA?

For organizations in the region, measured aptitude turns training investment into evidence. It shows where your national talent is ready to lead, where frontline and service teams need applied learning, and how far your organization has progressed against its AI goals. 

SHRM is careful about how these findings should be interpreted, and that caution matters. The study notes that the link between higher aptitude, exposure to organizational change and reskilling intentions “should be interpreted with caution”, as the data does not establish direct causality.  Some findings are also based on a sample of 2,030 workers.  The value of the research, therefore, is not in treating the US findings as a direct MENA benchmark, but in using the methodology as a starting point. Establish a baseline, assess the roles where AI adoption is highest, invest in targeted learning and support, and measure how aptitude changes over time.