THE SHORT ANSWER
Current evidence points to combinations of AI and data literacy, analytical judgment, communication, collaboration and learning capacity. The right emphasis depends on your role, sector and goals; advanced AI development is necessary for relatively few workers compared with effective and responsible use.
Build a combination, not a top-ten list
| Group | Examples | Value at work |
|---|---|---|
| Digital | AI literacy, data literacy, security awareness | Use systems safely and interpret outputs |
| Thinking | Analysis, problem solving, judgment | Frame problems and test conclusions |
| Human | Communication, collaboration, leadership | Coordinate people and build shared understanding |
| Adaptive | Curiosity, experimentation, learning agility | Update practice as tasks and tools change |
Research describes patterns, not your personal ranking
The OECD's 2026 synthesis reports broad demand for digital and data interpretation skills alongside problem solving, creativity and management. It also notes that advanced AI-development skills remain relevant to a much smaller share of workers than applied digital capability.
The World Economic Forum's 2025 employer survey reports expectations from more than 1,000 employers representing over 14 million workers. Those expectations are useful signals, not guaranteed outcomes through 2030.
Evidence & context: OECD · World Economic Forum
Translate a trend into role evidence
A marketer may combine customer knowledge, experimentation and AI-assisted analysis. An operations professional may add workflow mapping, data quality and exception handling. A manager may need enough AI literacy to set boundaries and enough communication skill to redesign responsibilities.
South Asian labour markets include formal, informal, digital and place-dependent work. Online job postings illuminate only part of that landscape, so use local conversations and actual role descriptions alongside regional research.
Evidence & context: World Bank
Choose the next skill from a real task
- Select an outcome you need to improve.
- Identify the skill that currently constrains it.
- Define a small piece of observable work.
- Practise with feedback and record what changed.
- Reassess after applying the skill in context.
Sources & further reading
- Skills in the AI Age
OECD. A 2026 synthesis of cross-country evidence on AI adoption, task change and complementary skills. Country, sector and firm differences mean its findings do not produce one universal skills ranking.
- Future of Jobs Report 2025
World Economic Forum. A 2025 employer survey covering more than 1,000 employers representing over 14 million workers across 55 economies. It reports employer expectations through 2030, not certain labour-market outcomes or one prescription for every worker.
- South Asia Development Update: Jobs, AI, and Trade
World Bank. A 2025 regional report using labour data and job postings to distinguish AI exposure from human complementarity in South Asia. Online listings underrepresent informal and some local labour markets, so the findings are directional rather than a complete picture of India or the region.
Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.
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