Lessons from Chinese business leaders about integrating AI in the workplace

China is trying to scale AI without letting its workforce fall behind, by pushing “AI+” (a policy for integrating AI into real-world sectors) deeper into the economy, while building the employment and skills systems needed to absorb the possible disruption.

As AI moves rapidly into business processes, many companies are still trying to work out how jobs and management systems should change around it. China offers an early test of whether AI adoption can translate into durable productivity, while helping workers adapt to the transition.

Early evidence suggests that the shift is less about replacing individual jobs than reorganizing the tasks, capabilities and systems around them.

Ian Lee, President of Geographic Regions, The Adecco Group, describes the transition in three stages: disruption, as AI compresses existing tasks and roles; augmentation, as people and AI work together within redesigned processes; and creation, as new roles and forms of economic activity emerge.

The first stage is already visible. Coding, customer service and other defined knowledge tasks are being compressed or automated. Yet Lee has also seen experienced workers being brought back into organizations to guide AI-enabled work, bringing context and business judgement that task-level automation cannot provide.

In China, the test will extend well beyond large technology companies. A business with 20 or 30 employees may have no dedicated transformation team, but it still needs to determine which processes are ready for AI. As Lee puts it: “You cannot have AI in a bad process.”

The sequence matters. Management first needs to understand how work moves across the business, where accountability sits and where human judgement remains essential. Only then can tasks be automated, augmented or reassigned.

Lee compares augmentation to conducting an orchestra. Individual AI agents may become increasingly capable, but coordination still requires someone who understands how the parts fit together, where trade-offs arise and when intervention is needed. The technology may perform individual tasks, but productivity gains depend on strong organization of the wider process.

This shift is also reshaping talent strategy. A former Huawei executive recruitment specialist observes that AI is lowering the technical threshold for applying technology within businesses in China. This increases the value of people who combine learning agility and AI fluency, with expertise in the specific domains where the technology may be integrated.

“AI talent” therefore extends beyond AI engineers. In one finance case she cited, business professionals built an AI-enabled workflow themselves because they understood the underlying process and business context better than a separate technical team.

The implications also reach workforce management. Installing an AI recruitment system or adding an AI course does not amount to workforce transformation. HR still needs to understand how the business operates, what employees are expected to do, and how changing tasks affect capability and accountability.

In fact, that principle predates generative AI. When Huawei launched its first campus recruitment programme for HR roles in 2012, half of the 12 hires were required to have science or engineering backgrounds. The aim was to develop HR professionals with the analytical and business understanding needed to operate beyond administrative functions.

AI makes that expectation more important. Workforce leaders increasingly need to participate in the redesign of work itself.

The same changes are altering how careers are built. Professor Wenxia Zhou of Renmin University describes the emerging environment as a “mapless career era”. For decades, careers followed a relatively predictable path: education, entry into a stable organization and gradual promotion. That model worked because jobs and industries changed slowly. AI is making that stability harder to assume.

Zhou contrasts the traditional career ladder with "cultivating a forest". Instead of depending on one employer or a fixed path, workers need to keep learning, move across roles and build experience that remains useful as work changes.

AI is also changing how that experience is acquired. As routine and junior tasks are automated or accelerated, some of the work that once helped employees learn a business may disappear before new development pathways are in place.

For organizations, this goes beyond individual career planning. Junior work has historically served as a training ground where employees learn how clients behave, how decisions are made, how mistakes are corrected and how professional judgement develops.

Exploratory research from the AIGW Policy Observatory identifies a related risk: Organizations may automate some of the tasks through which junior employees developed judgement, while leaving behind work with limited learning value. Strong AI-assisted output can also conceal whether an employee can independently explain, verify or challenge the result.

The implication for talent leaders is straightforward: Before compressing a junior task, the capability that task was building needs to be identified. Otherwise, short-term efficiency can quietly weaken the future leadership pipeline.

Creation, the final stage in Lee's framework, is harder to anticipate. Earlier technologies displaced existing work before producing industries and occupations that were difficult to foresee at the start of the transition.

That makes any fixed list of “future skills” fragile. A more durable response is a system that can detect changing demand, update training and help workers move towards new opportunities.

China's 15th Five-Year Plan (2026–2030) addresses AI alongside its employment implications, calling for measures to respond to the effects of emerging technologies on work, while strengthening their employment-creation potential.

The subsequent Employment Priority Strategy for the 15th Five-Year Plan period makes this more concrete. It calls for new forms of human-AI collaborative work, a large-scale youth employment skills initiative aiming to help 1 million young people, and around 1 million internship positions, including technology, technical and management roles.

The direction is notable. Skills policy is moving towards model based on a feedback loop between labour-market demand, training and employment, rather than treating education as something completed before work begins.

China's AI workforce transition is still at an early stage. But one lesson is already emerging: Workforce infrastructure is becoming part of AI infrastructure. For enterprises, process redesign, capability development and career mobility increasingly belong inside AI strategy from the start. Organizations that build those systems early will be better placed to turn technological adoption into sustainable productivity.