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AI is more likely to change many jobs than to eliminate them outright, but the effects will vary by task, occupation, access to technology and how employers redesign work. “Superworker” is The Josh Bersin Company’s term for an employee whose productivity, creativity or service is expanded with AI—not a formal job category or a guaranteed outcome.
What is a superworker?
The Josh Bersin Company uses “superworker” to describe an employee empowered by AI to contribute more through greater productivity, creativity or service. The idea puts the person, rather than the software, at the center: AI handles or assists with parts of work while people apply judgment, expertise and context.
It is a branded management framework, not an independently validated occupational classification. It does not mean every employee will become dramatically more productive, nor does it identify a new kind of worker with a standard set of duties. The practical question is whether AI helps a person or team achieve a better result—and under what conditions.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe company’s framework describes a conceptual progression from AI assistance and augmentation toward replacing routine work and, in some cases, autonomous processes. This is a way to think about possible organizational change, not a universal sequence that every employer will follow or an assurance that work will inevitably become autonomous.
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How will AI change the future of work?
AI changes work first by affecting tasks: a system may draft, classify, summarize, search, recommend or generate material within a role. Employers then decide whether to keep the task with a worker using AI, change the workflow, reassign responsibilities or automate more of the process. Those choices can alter a job without removing the occupation.
The International Labour Organization’s 2025 refined index estimates that 25% of global employment is in occupations with some generative-AI exposure; the estimate is 34% in high-income countries. These are modeled estimates of potential task effects, not forecasts that those shares of workers will lose their jobs. The ILO’s assessment is that most jobs are more likely to be transformed than made redundant, because many roles still involve tasks requiring human input.
Exposure is not the same as automation or job loss
Three ideas are easy to conflate:
- Task exposure: some duties within an occupation could be affected by generative AI.
- Automation: an employer uses technology to perform a task or process with less direct human effort.
- Job displacement: a position is eliminated or a worker loses employment.
An occupation can have substantial exposure while retaining a need for human work. Whether exposure leads to changed duties, fewer positions, new tasks or little immediate change depends on the technology’s limits, the work setting and employer decisions.
Some occupations and groups face greater exposure
The ILO identifies clerical occupations as having the highest exposure, while digitized professional and technical roles are also increasingly exposed. In its 2025 index, 3.3% of global employment falls in the highest exposure category. The modeled share is 4.7% of female employment and 2.4% of male employment globally. In high-income countries, the corresponding estimates are 9.6% for women and 3.5% for men.
These figures describe potential occupational exposure, not realized job displacement. They also show why a single prediction about “the worker” can be misleading: effects differ across occupations and demographic groups.
Will AI replace my job or help me do it?
There is no reliable yes-or-no answer for an individual job based on exposure alone. A better way to assess a role is to look at its tasks and the consequences of getting them wrong. Repetitive, digitized tasks may be easier to automate or accelerate. Work involving ambiguous goals, relationships, physical environments, accountability or consequential judgment may continue to need substantial human involvement, even when AI supports parts of it.
For a worker, useful questions include:
- Which tasks in my role are routine, digital and easy to check?
- Which tasks rely on context, trust, negotiation, care or accountability?
- Can I verify the system’s output, and do I know when not to use it?
- Will AI remove tedious work, increase expectations, or shift responsibility without giving me authority?
- What training and access will I receive if the workflow changes?
The answers depend not only on what a model can do, but on how the employer implements it. AI can augment a worker and still bring pressure to handle more cases, meet tighter deadlines or take responsibility for machine-generated errors. A productivity gain for an organization does not automatically mean a better job for the employee.
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Some tasks can be completed faster with AI, but a faster task is not automatically a measurable gain in total output, quality, earnings or employment. The International Labour Organization’s June 2026 review, drawing on experiments, firm-level data, platform studies, and worker and firm surveys across multiple countries, concludes that productivity gains are real but uneven and often unverified. Worker-reported time savings of a few percent of working hours have not consistently translated into measured gains in output, earnings or employment.
The Josh Bersin Company’s 2025 HR infographic describes examples involving manager preparation for compensation discussions, recruiter application review, HR skills-architecture creation, employee mobility satisfaction, skills matching for projects and feedback analysis. Its reported figures are specific to those examples; the infographic does not establish randomized causal estimates. They should not be treated as evidence that an AI system will deliver the same result across other teams, employers or occupations.
When evaluating a productivity claim, ask what task was measured, who performed it, what comparison was used and whether the reported outcome was speed, quality, cost, workload or another measure. A time reduction on one activity may not improve the full workflow if workers must spend more time checking errors or handling exceptions.
What skills will workers need as AI changes jobs?
The superworker idea depends on more than access to a chatbot or other AI tool. Workers need the ability to use systems appropriately, judge their outputs and connect them to the goals of their work. Employers need to provide training and redesign workflows so that people know where AI fits and who is responsible for the result.
Skills that matter in AI-enabled work
- AI and information literacy: understand what a tool can and cannot do, and protect sensitive information according to workplace rules.
- Verification: check factual claims, calculations, sources and recommendations before relying on generated output.
- Domain expertise: use knowledge of the customer, field or process to recognize missing context and unacceptable results.
- Problem framing: define the actual need and provide relevant context rather than treating a plausible answer as a correct one.
- Human skills: communicate, collaborate, exercise judgment and handle situations where empathy or trust matters.
- Adaptability: learn revised processes as tasks and responsibilities change.
These skills are useful only if workers have meaningful access to tools and time to learn. The ILO’s 2026 cross-country analysis highlights that opportunities to benefit from augmentation vary with occupational structure and digital infrastructure. Across the detailed countries it examined, it estimates about 441.8 million jobs in augmentation-oriented exposure gradients, with approximately 66.9 million lacking internet access. These are analytical estimates—not counts of workers already using AI or measured productivity gains—and underline that access is part of the future-of-work question.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should employers do to build a genuinely helpful AI-enabled workplace?
Providing AI access is only one step. The Josh Bersin Company’s 2026 material urges leaders to move beyond pilots and assistants while emphasizing data and architecture, employee support and leadership practices. The ILO’s evidence on uneven productivity reinforces the need to measure outcomes and consider how work changes for employees.
- Start with the work, not the tool. Identify a specific workflow and the task or bottleneck AI might improve.
- Define the human role. Decide who checks outputs, handles exceptions and remains accountable for consequential decisions.
- Train affected employees. Provide practical guidance, tool access and a way to raise problems or suggest improvements.
- Measure more than speed. Track output and quality alongside workload, errors, employee experience and job quality.
- Review who benefits and who bears the costs. Assess whether productivity improvements are shared and whether workers face new burdens or transition risks.
- Revise the workflow when evidence warrants it. Expand, change or stop an implementation based on observed results rather than assuming that a pilot’s promise will carry over to the whole organization.
A useful evaluation compares the system’s performance on the task with the existing process, examines the whole workflow rather than one isolated step and records where human review is needed. It should also ask whether workers can challenge an output and whether responsibility matches their authority to act.
What the superworker idea gets right—and what it cannot promise
The term is useful when it directs attention to how AI might expand human contribution instead of treating automation as the only possible outcome. It also reminds employers that results depend on workflow design, skills, support and oversight—not simply on purchasing software.
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