Workforce Agility
Intelligence

The PVQ is the behavioural backbone of the AQ framework. It is an extended version of the 15FQ+, combining personality scales from the 15FQ+ and OPPro with values scales from the Values and Motives Inventory (VMI). This produces a single comprehensive assessment covering 35 trait and value scales, 12 derailer scales, leadership and subordinate styles, team roles, influencing approaches, culture fit, Grit, and Emotional Intelligence.

The Adapt-g is a Computerised Adaptive Test (CAT) measuring general mental ability through three subtests: Verbal Reasoning, Numerical Reasoning, and Abstract Reasoning. Built on Item Response Theory (3-Parameter Logistic Model), it adapts in real time to the test-taker's ability level, providing a more precise measurement in fewer items than traditional fixed-length tests. Grounded in the Cattell-Horn-Carroll (CHC) model of intelligence.

The CPP is fundamentally different from traditional cognitive assessments. Rather than measuring what someone knows or how fast they can solve structured problems, it tracks how they process unfamiliar, ambiguous information in real time. It is a simulation-based assessment developed by Dr Maritie Prinsloo (Cognadev), grounded in Stratified Systems Theory (Jaques, 1989) and holonic cognitive processing models.

The 15FQ+ is a well-established broad personality assessment based on the fifteen-factor personality model. It is the personality foundation from which the PVQ was extended. At the Essential tier, the 15FQ+ provides the personality data that maps to the AQ nine-facet framework, making it suitable for volume assessment scenarios where the full PVQ depth is not required.

De Meuse, K.P. (2022). Learning Agility: Could it Become the G-Factor of Leadership? Consulting Psychology Journal: Practice and Research.

This meta-analytic review is the theoretical backbone of the AQ. De Meuse establishes that learning agility, the willingness and ability to learn from experience and apply those lessons to new situations, is a distinct construct from cognitive ability (meta-analytic r = .091). This near-zero correlation means that being intelligent does not predict being agile, and vice versa. Both need to be measured independently.

De Meuse proposes a nine-facet framework organised in a 3x3 matrix: three components (Ability, Motivation, Application) across three domains (Cognitive, Social, Self). This is the exact architecture the AQ operationalises. He explicitly notes that the question of differential facet importance remains unanswered empirically, which is why the AQ weights facets equally within each domain.

Schmidt, F.L. & Hunter, J.E. (1998). The Validity and Utility of Selection Methods in Personnel Psychology. Psychological Bulletin, 124, 262-274. Updated by Sackett, P.R. et al. (2022).

The most cited paper in personnel psychology. Schmidt & Hunter's meta-analysis established that general mental ability (GMA) has an operational validity of .51 for predicting overall job performance, making it the single strongest predictor ever identified. When combined with structured interviews (.51 + .51 = .63 combined validity) or integrity tests (.51 + .41 = .65), predictive power increases substantially. Sackett et al. (2022) updated these findings with modern statistical corrections and confirmed GMA's pre-eminence.

This underpins the AQ's weighting of cognitive capacity: GMA carries 15% in development contexts and 25% in selection contexts at the Professional tier. At the Executive tier, the CPP replaces GMA entirely and carries 30-48% of the composite because it provides a richer measure of cognitive complexity than a single stanine score.

Jaques, E. (1989). Requisite Organization. Operationalised through the CPP by Dr Maritie Prinsloo (Cognadev).

SST proposes that organisational effectiveness depends on matching the cognitive complexity of role-holders to the complexity demands of their roles. Jaques identified distinct strata of cognitive processing, each characterised by qualitatively different information handling capabilities. The CPP measures this directly through simulation, mapping individuals to one of five work environment levels from Pure Operational to Pure Strategy.

The practical implication is profound: a person operating at Tactical Strategy (Level 3) will struggle in a role that demands Parallel Processing (Level 4), regardless of their experience, personality, or motivation. This is the single most consequential data point in executive assessment because it answers the question "can this person handle the information processing demands of this role?" before any personality or behavioural data is considered.

Lombardo, M.M. & Eichinger, R.W. (2000). High potentials as high learners. Human Resource Management, 39, 321-329. De Meuse, K.P. on dark-side trait activation under reduced oversight.

Research from the Center for Creative Leadership (CCL) and subsequent work by De Meuse established that leadership failure (derailment) is rarely caused by a lack of technical competence. Instead, it is driven by dark-side personality traits that emerge under pressure, fatigue, or reduced oversight: traits like rigidity, volatility, detachment, and arrogance. These traits become more consequential at senior levels where there is less structure, more autonomy, and greater organisational impact.

The AQ operationalises this through 12 derailer scales that directly penalise the Selection Readiness Score. The penalty is heavier at Executive tier (-2.0 cap) than Professional tier (-1.5 cap), reflecting the research finding that dark-side traits are more damaging in senior roles.

The World Economic Forum's Future of Jobs Report 2025 provides the macro context for why agility measurement matters now. Based on data from over 1,000 employers representing 14 million workers across 55 economies:

22% of all jobs will be structurally disrupted by 2030. 39% of core workforce skills will change. 63% of employers cite skills gaps as their primary barrier to business transformation. 59 out of every 100 workers globally will need reskilling by 2030. The top skills rising in importance are resilience, flexibility, and agility; analytical thinking; curiosity and lifelong learning; self-awareness; and leadership and social influence. Eight of the WEF's top ten skills map directly to the AQ's nine facets.

McKinsey's "Superagency in the Workplace" report (2025) identifies a state where AI amplifies human creativity and productivity, but only for those organisations that invest in supporting their people alongside their technology. 88% of organisations now use AI in at least one function, yet only 10% deploy AI agents per function, revealing a massive execution gap.

Gartner predicts that by 2026, 20% of organisations will use AI to flatten their structures, eliminating more than half of current middle management positions. PwC's 2025 Global AI Jobs Barometer found that workers with AI skills command wage premiums up to 56% higher, but crucially, human capabilities, judgment, creativity, leadership, and resilience, become more valuable, not less, as AI handles technical tasks.

The implication for the AQ: as AI automates routine cognitive work, the human capabilities that differentiate high performers shift toward exactly what the AQ measures: agility, self-awareness, social influence, resilience under complexity, and the capacity to learn and adapt. Organisations that can identify, develop, and retain people with these capabilities will outperform those that cannot. The dashboard makes this visible. The reports make it actionable.