Cities racing to 2030: AI soft skills become the deciding factor

A new peer-reviewed study published in Sustainability asserts that non-technical “AI soft skills” are now mission-critical for smart city teams working toward the UN Sustainable Development Goals. The researchers develop and validate a rigorous instrument to measure these abilities and offer a blueprint for hiring, training, and policy design in technology-driven urban programs.

Titled “Smart Skills for Smart Cities: Developing and Validating an AI Soft Skills Scale in the Framework of the SDGs,” the paper introduces and validates the AI Soft Skills (AISS) scale, a practical assessment tool aligned to smart city needs and sustainability outcomes.

Why AI soft skills matter for smart, sustainable cities

The study positions AI soft skills as the human counterpart to fast-moving automation, skills that help people collaborate with AI systems, adapt to novel contexts, and make sound, human-centered decisions in critical public services. The authors argue these capabilities are a prerequisite for realizing smart-city goals, because they improve how multidisciplinary teams use AI for decision support, citizen services, and cross-agency coordination.

To anchor this claim in evidence, the paper defines AI soft skills not as generic “soft” traits, but as a targeted competency set calibrated for AI-rich environments. This focus connects workforce development to the SDGs by emphasizing inclusive, effective, and ethical AI use in city operations, from mobility and energy to health and public safety. The AISS scale, the authors contend, lets organizations move beyond intuition by measuring the specific human capabilities that determine whether AI augments or impedes outcomes.

How the AISS scale was built and statistically validated

The authors adopt a sequential, exploratory mixed-methods approach and field a survey to 685 professionals who work with AI (all with at least a bachelor’s degree and one year of AI-related experience). The sample provides a realistic view of practitioner needs while enabling robust psychometric testing across development and validation phases.

Exploratory factor analysis identifies a five-dimension structure, persuasion, collaboration, adaptability, emotional intelligence, and creativity, that collectively explains 67.37% of total variance. The final instrument comprises 24 items, and the reported factor loadings (0.621–0.893) and communalities (0.587–0.875) indicate strong item-construct relationships and shared variance. These results demonstrate that the AISS scale captures distinct, interpretable facets of AI-relevant soft skills without redundancy.

Confirmatory factor analysis then tests and affirms the structure with strong model-fit indices: GFI 0.940; AGFI 0.947; NFI 0.949; PNFI 0.833; PGFI 0.823; TLI 0.972; IFI 0.975; CFI 0.975; RMSEA 0.052; SRMR 0.035. Reliability is high both within dimensions (Cronbach’s alpha 0.804–0.875) and overall (0.921), while convergent and discriminant validity checks support construct robustness. Taken together, these metrics indicate the instrument is statistically sound and ready for operational use.

What employers, educators, and city leaders can do next

Because the AISS scale ties measurement directly to smart-city and SDG objectives, it provides a practical foundation for workforce action. For employers, the instrument can guide job design, hiring, and performance development by pinpointing the AI-adjacent human strengths like adaptability and collaboration that correlate with safer, more effective deployments.

For educators and training providers, it offers a map for curriculum emphasis and individualized learning pathways, ensuring technical AI literacy is complemented by the human capabilities that determine real-world impact. For city leaders, it creates a common language for procurement, vendor readiness, and cross-department capacity building around responsible AI.

The study’s framing also helps align investments with measurable outcomes. Persuasion and collaboration matter where service delivery depends on multi-stakeholder buy-in; adaptability and creativity support innovation under uncertainty; emotional intelligence anchors citizen-centric design and equitable access. By diagnosing strengths and gaps across these five dimensions, public agencies and partners can target interventions that tangibly improve AI-enabled services and accelerate SDG-linked results.