How Singapore can protect mid level jobs as AI reshapes work

A weak middle is the fault line in a fast changing labor market

At age 48, bank professional Rei Teo discovered how quickly a stable role can change. After her job was reshaped in 2024 during a restructuring, she left and struggled to secure a permanent position in a market where hiring had cooled and roles were being reworked with new technology. She eventually accepted a contract role. Her experience mirrors a pattern researchers have flagged in Singapore: mid level roles are too thin to absorb displaced professionals, so people are pushed down into temporary or lower paid positions.

The Institute for Adult Learning (IAL) calls this a push down effect. When firms automate routine tasks and streamline teams with artificial intelligence tools, fewer stable middle roles remain to anchor the workforce. Experienced staff who leave or are restructured out often find fewer seats at their former level. They step into contract assignments or move to lower tier roles, while junior hiring stalls. This strains the career ladder from both ends.

Generative AI accelerated these shifts. Since late 2022, banks, consultancies, and technology firms have been testing copilots and chatbots for customer service, code generation, compliance checks, marketing copy, and internal knowledge search. These tools change workflows and team shapes. Some tasks disappear. Others expand and demand new oversight. The change is real, and it is happening across white collar jobs that once seemed sheltered.

Singapore is a lead partner in an international research program that tracks how AI is adopted in ten global digital hubs, from the Republic to Silicon Valley. Results so far point to a clear risk for economies like Singapore with a high share of professional roles and a lean middle tier. Without deliberate action, the middle of the labor market can weaken further just when it is needed to catch displaced workers and mentor the next generation.

Why mid level jobs matter to Singapore’s social compact

Mid level roles include supervisors, team leads, business analysts, financial controllers, product managers, clinical coordinators, and senior technicians. They translate strategy into execution. They hold tacit knowledge about processes, clients, and systems. They coach juniors and carry continuity when teams change. In a high productivity economy, this layer creates stability and builds capacity.

If the middle thins out, companies flatten and become more brittle. Senior leaders juggle too much, while entry level staff see fewer chances to learn by doing. The result is a broken ladder. People struggle to climb from school to professional competence, then into leadership. That erodes the social compact that links effort to reward and weakens trust in institutions and employers.

Singapore has long guarded against a barbell economy with many high wage jobs at the top and many lower wage service roles at the bottom. The goal has been growth in well paid, skilled roles in the middle. AI adoption can support that goal if it augments teams and creates new specialist tracks. It can undermine it if it replaces learning roles and narrows the path to experience.

AI is removing the rungs on the ladder for young workers

Concerns about the entry point are rising. In early 2025, one large bank said AI could trim up to 4,000 contract and temporary roles over the next few years. Years earlier, the Monetary Authority of Singapore projected that one in three finance jobs in the Republic could be transformed or eliminated by automation. Across marketing, software, and operations, the work that used to train juniors is increasingly handled by tools that draft copy, design graphics, debug code, or summarize documents in seconds.

Fresh evidence suggests who feels the brunt. A Stanford University analysis of millions of US payroll records found a 13 percent relative decline in employment for early career workers in the most AI exposed jobs since the wave of generative AI adoption in late 2022. Older workers in the same occupations were stable or grew. The adjustment has come more through fewer hires than through lower pay. It is a strong signal that automation is taking out the first rung.

Surveys in Singapore echo that anxiety. Roughly half of Gen Z respondents expect major disruption to their roles from AI, and six in ten say they are already seeking extra training. Only about a third of companies offer structured AI learning. Some young professionals are pivoting toward skilled trades that are less likely to be automated, trading a white collar track for perceived stability and clear progression.

There is a training trap in all this. When AI drafts the first version of the work, junior staff lose hands on practice. Their output may look polished, but it may lack depth. Seniors spend more time revising, while juniors struggle to build judgment. If entry level jobs become performative, focused on prompting tools instead of mastering craft, the pipeline to confident mid level professionals gets weaker.

Banks and white collar services are reconfiguring roles

Financial services show the change in sharp relief. Automated checks and document review are spreading across risk and compliance. Operations teams are using copilots to prepare reports and reconcile data. Client facing teams test AI assistants for research and service. New roles are also appearing around model monitoring, data governance, and AI risk management. Hiring patterns are shifting in step.

What disappears first are not entire jobs but sets of tasks. When those tasks are entry level, the middle loses its farm system. When those tasks are mid level, displaced professionals crowd into a smaller pool of roles. Both effects are visible in banking, consulting, technology, and corporate services. Singapore’s research partners call this a push down effect because pressure from the top and the bottom squeezes the same middle band.

Age and experience cut both ways. Tacit knowledge and networks shelter some older workers, and the Stanford evidence shows stability for experienced staff in AI exposed roles. At the same time, professionals in their 40s and 50s who exit during restructuring often struggle to re enter. Permanent roles tighten, and contract assignments expand. A resilient middle needs both a steady inflow of trained juniors and structured pathways back for mid career workers.

Leadership choices decide if AI augments or replaces

Technology alone does not decide outcomes. Leadership does. Organizations that empower mid level managers to redesign workflows, retrain teams, and build trust in AI see different results from those that chase quick cost cuts. Mid level leaders translate strategy into practice. When they have time, resources, and air cover, they shape how AI helps people do higher value work.

There is a workable playbook. Build basic AI literacy across the company so teams understand data, model limits, and privacy. Encourage low risk experiments and share early wins so people see real value. Choose augment first use cases that pair AI with human oversight in customer service, finance, HR, and operations. Create cross functional squads to scale pilots and tackle process bottlenecks. Invest in new skills for model monitoring, prompt engineering, workflow design, and change management. Align incentives so managers are rewarded for training juniors and redesigning roles, not just for cutting headcount.

What policy can do now

First, rebuild the ladder into mid level work by making apprenticeships in knowledge jobs mainstream. Singapore used traineeships during the pandemic to give graduates a start. A permanent national apprenticeship track for digital and professional roles could go further. Design 12 to 24 month paid placements across finance, healthcare, public service, manufacturing, and media. Require structured mentorship, skill checklists, and regular feedback. Treat AI tools as part of the craft and teach judgment as well as technique. Countries with dual track systems keep youth unemployment low for a reason. The model works when it is large and long lived.

Second, support mid career transitions with targeted funding and time. Career Conversion Programmes, on the job training grants, and SkillsFuture credits can be tuned for AI era roles in data, operations, and service. Expand mid career subsidies for those aged 40 and above, and make it simpler for employers to take a chance on experienced candidates from adjacent sectors. Micro credentials that stack into diplomas or degrees help workers show progress and give firms a common language for capability.

Third, tie public support for AI adoption to commitments on people. Job redesign grants can require employers to create trainee seats, document process changes, and publish learning resources internally. Where automation affects roles, offer wage support to redeploy staff into new teams that need human judgment, such as AI risk, customer success, and product operations. Build an AI safety and governance workforce with clear standards so that new compliance demand offsets some automation risk.

Finally, let the public service lead by example. All public officers will be required to complete an AI literacy course, and leaders are being asked to adopt AI thoughtfully in daily work. One in three officers already use an in house assistant, with thousands of custom bots built to automate routine tasks. Deputy Prime Minister Gan Kim Yong has called on leaders to set the tone by embracing AI responsibly and by encouraging teams to apply these tools where they improve service and governance.

How companies can rebuild a stronger middle

Employers that want a resilient workforce can take concrete steps now. The focus is to widen the middle, keep experience in the firm, and make growth visible for juniors.

• Adopt an augment first policy. Use AI to remove drudgery, then reinvest the time in training, client care, and product quality.
• Institute structured apprenticeships for knowledge work. Pair every junior hire or trainee with a mentor, set a skills curriculum, and track progress to mid level responsibilities.
• Codify tacit knowledge. Build playbooks, checklists, and internal wikis so experience is not lost when teams turn over.
• Measure managers on people development. Tie bonuses to the number of juniors trained to independence and to successful internal promotions.
• Open internal mobility. Post roles transparently and run skills based hiring so displaced staff can move across functions.
• Create mid level specialist tracks in data, operations, design, and risk, with clear pay bands and prestige equal to managerial paths.
• Stand up AI governance. Assign owners for model risk, data quality, privacy, and bias, and train staff for these roles.
• Share early wins and failures. Build trust by being candid about what works and what falls short when teams first try AI tools.

What workers can do to stay marketable

Workers are not powerless in this shift. The goal is to become the person who can frame a problem, choose the right tools, and deliver outcomes that matter. That mix of domain knowledge, AI fluency, and human skills travels well across roles.

• Build AI literacy. Learn how copilots work, where they fail, and how to prompt for better results. Practice verification and debugging so you can trust your outputs.
• Deepen domain expertise. Pick a sector and learn its data, regulations, workflows, and customer pain points. AI skills are strongest when grounded in a field.
• Master process and communication. Map workflows, write clear handovers, and present findings. The ability to explain and persuade is a durable advantage.
• Develop data judgment. Learn to read dashboards, design simple experiments, and track metrics linked to revenue, risk, cost, or customer outcomes.
• Create a portfolio. Keep artifacts that show how you used AI to improve speed, quality, or service. Concrete results beat generic claims.
• Pursue stackable credentials. Short courses in data analytics, cloud tools, cybersecurity basics, or AI safety can open doors when combined with experience.
• Network with purpose. Join professional communities and volunteer for cross functional projects where AI is being tested. Proximity accelerates learning.

For mid career professionals, look for adjacent roles that value your judgment. Operations managers can move into AI enabled process improvement. Relationship managers can shift into customer success with analytics. Compliance officers can step into model risk oversight. Reframe your experience in terms of problems solved and outcomes delivered, then show how AI extends your reach.

Metrics that show whether the middle is recovering

Singapore can track the health of the middle with simple signals. Transparent metrics help policymakers, employers, and workers see if the ladder is being rebuilt.

• The ratio of permanent to contract roles in professional and technical occupations.
• Median duration of job search for professionals aged 40 to 55 after displacement.
• The share of entry level postings that include structured training or apprenticeships.
• Internal promotion rates from junior to mid level across major sectors.
• Number of mid career conversions completed each year through national programs.
• Participation and completion rates in AI literacy courses across the public and private sector.
• Adoption of AI governance roles, such as model risk and data stewardship, that create new mid level opportunities.

Key Points

• Mid level roles in Singapore are under strain as AI changes team structures, creating a push down effect that leaves displaced professionals with fewer stable options.
• Entry level tasks are increasingly automated, which weakens the pipeline of future mid level talent and reduces on the job learning opportunities.
• A Stanford study found a 13 percent relative decline in employment for early career workers in AI exposed jobs since late 2022, while older workers in those roles remained stable or grew.
• Surveys show roughly half of Gen Z in Singapore expect major disruption from AI, but only about a third of employers provide AI training.
• The public service is mandating AI literacy for all officers, signaling how large institutions can build baseline capability and share effective practices.
• Policies that scale apprenticeships, support mid career conversion, and tie AI grants to people outcomes can rebuild the middle.
• Companies can adopt augment first design, measure managers on training, open internal mobility, and create specialist tracks to strengthen mid level careers.
• Workers who blend domain depth, AI fluency, and human skills will be most resilient, with portfolios and stackable credentials that show real outcomes.