Developing staff for AI at work: three priorities backed by survey data

Generative AI is changing how companies train their people. This guide is for managers and HR staff, including foreigners working in or with Korean companies, who plan training for the AI era. It summarizes what recent international surveys show and three priorities the author recommends. The figures below come from global or US-based surveys published in 2024~2025, not from Korean statistics.

What recent surveys show

Together these point in one direction: buying AI tools is the easy part. Results depend on how people are trained, how projects are chosen and whether managers support the change.

Priority 1: introduce generative AI with a plan

New tools do not raise productivity on their own. Gartner named four reasons why many generative AI projects stall after the proof-of-concept stage: poor data quality, weak risk controls, rising costs and unclear business value.

The author's advice:

• Map your current work processes first and find where AI adds the most value.
• Start with repetitive work. For example, let AI answer routine customer questions so that staff can handle complex cases.
• Train people on their real tasks as well as on the tool itself. Practice, feedback and repetition matter more than a one-off lecture.
• Treat AI adoption as long-term change management. In Gartner's 2025 survey, high-maturity organizations chose projects by business value and technical feasibility and set up governance and engineering practices, and their projects lasted longer.

Priority 2: build development around skills

Job titles change slowly, while skills change fast. The WEF report found that analytical thinking is the core skill employers want most, named as essential by seven in ten companies, followed by resilience, flexibility and agility, and leadership and social influence. AI and big data top the list of fastest-growing skills, followed by networks and cybersecurity and technological literacy. Creative thinking, curiosity and lifelong learning are also expected to grow in importance.

A skills-based approach means:

• Record which skills each employee has.
• Compare them with the skills the organization will need in the next few years.
• Offer each person a learning path that closes the gap.
• Balance technical training with human skills such as creative and critical thinking.
• Open an internal talent marketplace: a platform that matches employees' skills with projects and new roles, so people can grow without leaving.

Priority 3: motivation, trust and culture

Technology and process changes are not enough if staff resist them. Many employees worry that AI will replace their jobs. Clear communication and a clear picture of new roles reduce that fear. The author recommends presenting AI as a tool that extends people's abilities, and involving employees early in choosing and testing it.

Trust grows with maturity. In Gartner's 2025 survey, business units trusted and were ready to use new AI solutions in 57% of high-maturity organizations, against 14% of low-maturity ones.

Managers carry the culture. In a Gartner survey reported in October 2024 for HR priorities in 2025, 57% of HR leaders agreed that managers fail to enforce the culture their organizations want, and HR leaders also said leaders do not feel accountable for showing it. Leadership development and culture change therefore need to move together. Leaders should model experimentation, accept small failures and favor steady improvement over perfection. Agile ways of working and cross-functional teams help.

Diversity and knowledge transfer

The WEF report notes that aging and shrinking working-age populations are reshaping labor markets, mainly in higher-income economies. When experienced staff retire, their knowledge can leave with them. Pair senior and junior employees, write down know-how, and include age as well as gender, cultural background and education in your diversity plans. The author also argues that people with different backgrounds help spot bias in AI outputs and lead to more inclusive solutions.

A practical roadmap

1. Pilot: choose one team or process, apply AI there and measure the results.
2. Spread what works: share lessons and good practices with other teams step by step.
3. Build data skills: train staff in data literacy and set up systems to collect and analyze data.
4. Use partners: work with outside experts or technology partners when adopting new methods such as virtual reality training or employee experience platforms.
5. Measure and adjust: set clear indicators, review progress regularly and change the plan when needed.

The author's closing point: keep people at the center. AI adoption and culture change take years, and organizations that keep learning from small failures are the ones that grow.