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Yes—often, but not universally. Companies can retrain many employees for AI-augmented or growing roles when they map the work that is changing, provide paid learning time, redesign jobs and guarantee a realistic path into an internal vacancy. Reskilling cannot save every position: jobs dominated by automatable tasks, or roles with no nearby opening, may still be reduced.
The practical question is therefore not whether AI will replace a whole occupation. It is which tasks will be automated, which will be enhanced, what skills the remaining work requires and whether the employer will fund a move into a real job.
What current evidence says about reskilling and AI
Employer and worker surveys point to reskilling as the main way organizations expect to adapt. The World Economic Forum’s 2025 employer survey says 77% of employers plan to implement reskilling or upskilling between 2025 and 2030. It identifies this as the most anticipated workforce strategy in 45 of 55 economies. That is a plan reported by employers, not evidence that 77% of jobs will be preserved.
The same survey finds that 55% of employers view funding for reskilling and upskilling as a top policy intervention, while 52% identify providing reskilling and upskilling as a priority. Its 2023 forecast estimated that 16 out of every 100 employees would be reskilled and redeployed into growing roles by 2027. Forecasts can miss economic or technological changes, so these figures should be read as intended direction rather than guaranteed outcomes.
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OECD analysis reaches a similar conclusion about the skills most workers need. Fewer than 1% of workers require advanced AI skills. Most need stronger digital skills and the ability to use, analyze and interpret data, alongside managerial, problem-solving, creativity and communication skills. In jobs with high AI exposure, AI often complements employees rather than eliminating them, increasing the value of higher-level judgment and coordination.
In an OECD 2024 workplace paper, four in five workers said AI improved their performance and three in five said it increased their enjoyment of work. The paper also estimated that occupations at high risk of automation represented about 27% of employment in OECD countries. That is an exposure estimate for occupations, not a prediction that 27% of people will lose their jobs.
Skills remain a major adoption constraint: 40% of employers in manufacturing and finance identify skills as the main barrier to adopting AI. Training is therefore not only a retention program; it can determine whether a new system delivers any value. More than half of workers using AI report receiving employer-funded training, and trained workers are more likely to report better performance and working conditions. Among more than 5,000 surveyed small and medium-sized enterprises in Austria, Canada, Germany, Ireland, Japan, Korea and the United Kingdom, 65% of those using generative AI reported improved employee performance (OECD, 2025).
Reskilling changes tasks, not just job titles
A credible plan starts with the work performed, not a list of fashionable courses. Break each role into tasks and place them in three groups:
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- Automate: repetitive, rules-based work with stable inputs and outputs, such as first-pass data classification or routine document extraction.
- Augment: work where software can draft, search, summarize or predict, but a person must check accuracy, apply context and accept accountability.
- Human-led: work requiring physical presence, negotiation, empathy, ethical judgment, relationship management, complex exceptions or legal responsibility.
An employee whose time is freed from the first group may be redeployed toward quality assurance, customer conversations, process improvement, data stewardship, supervision or domain-specialist decisions. The target role must still exist in the company; learning alone is not redeployment.
Roles with a shorter learning distance
Moves are more feasible when the current and target roles share tools, customers, processes or domain knowledge. Examples include a support agent moving into AI-assisted quality monitoring, a payroll specialist becoming a data-quality analyst, or a technician supervising an automated workflow. The person may need data literacy, prompt and output evaluation, privacy procedures and basic process redesign rather than advanced software engineering.
Roles with a longer learning distance
A move from a highly routine role into machine-learning engineering, for example, requires substantial mathematics, programming and production experience. It may be possible through a paid apprenticeship, but a short course cannot make it realistic. Employers should publish the prerequisite skills, expected time to proficiency and available entry-level positions instead of labeling every course a pathway.
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Map tasks and adjacent skills by role
Document the tasks being changed, the tools involved, the risk of error and the human decisions that remain. Create a skills inventory for each employee, including demonstrated ability, not just job-title assumptions. Identify adjacent roles with open or forecast vacancies and list the gap between current and target capabilities.
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Choose specific workflows and outcomes
Train people on the workflow they will actually use: preparing clean data, checking an AI-generated summary, escalating an uncertain result, documenting a decision or measuring quality. Set a practical standard, such as passing a controlled task with an agreed accuracy and review time, rather than counting video hours.
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Provide paid time and access
Protected work time, tuition assistance, apprenticeships, coaching and accessible tools determine who can finish. Training offered only after hours tends to exclude people with caring responsibilities, disabilities or second jobs. Make equipment, data access and accommodations part of the plan.
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Connect learning to internal hiring
Reserve or advertise roles for qualified participants, let managers nominate candidates, and use skills-based assessments where possible. Set redeployment targets and a timetable for interviews. A completion certificate without a hiring mechanism is professional development, not a reskilling guarantee.
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Govern AI before deployment
Teach privacy, security, copyright, bias, records retention, human review and incident reporting alongside tool operation. AT&T, for example, requires responsible-AI training before employees can access its internal generative-AI tools. The requirement illustrates that safe adoption includes policy and accountability, not only prompting technique.
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Measure employment and business results
Track course completion, assessed skill attainment, internal applications, moves, time to proficiency, productivity, quality, error rates, workload and employee experience. Compare outcomes for workers by location, contract type, age, gender and disability status to detect unequal access. Enrollment by itself is not evidence of reskilling.
What to teach employees who will work with generative AI
A broadly useful curriculum can be layered so that workers learn only what their workflow requires:
- Digital and data foundations: file management, structured data, data quality, basic statistics, dashboards and how to interpret uncertainty.
- AI use and evaluation: writing clear instructions, supplying approved context, checking sources and calculations, recognizing hallucinations and documenting human review.
- Domain judgment: applying company policy, handling exceptions, explaining decisions to customers and escalating cases that the system should not decide.
- Process and managerial skills: mapping a workflow, setting service levels, redesigning handoffs, coaching colleagues and managing change.
- Risk and responsibility: privacy controls, confidential-data handling, security, accessibility, bias testing, copyright and regulatory obligations.
Only a small minority of workers need to build models or maintain production systems. The larger opportunity is enabling domain experts to use AI safely and to perform the higher-value tasks created when routine work is automated.
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Examples of large-scale approaches
AT&T’s multi-level learning model
AT&T reported investing more than $89 million in training during 2025. More than 147,000 employees completed 5.8 million training hours. Its Ask AT&T Academy offered foundational, intermediate and advanced pathways, while 20,000 employees received Microsoft Copilot licenses paired with learning tracks. The company states that its goal is to enhance rather than replace its workforce. These figures describe the company’s own activities; they do not independently prove that every participant moved into a new role.
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An industry consortium for affected ICT roles
In April 2024, Cisco, Accenture, Eightfold, Google, IBM, Indeed, Intel, Microsoft and SAP announced the AI-Enabled ICT Workforce Consortium. Its purpose is to identify roles affected by AI and define reskilling pathways. A consortium can make skills descriptions and training routes more portable across employers, which helps workers who cannot find an opening in their current company. It still cannot guarantee a job for every learner.
As Hannah Calhoon of Indeed said in the announcement, companies that help employees learn new skills and gain on-the-job experience with evolving AI tools can deepen their expert bench, improve retention and expand the pool of qualified candidates. The value depends on providing that on-the-job experience, not merely publishing course catalogs.
Is reskilling cheaper than layoffs and rehiring?
There is no universal answer. Compare the full cost of both options for a specific role and time horizon:
| Decision factor | Questions for a reskilling plan | Questions for replacement or reduction |
|---|---|---|
| Task exposure | Which tasks are automated, augmented or still human-led? | Would most of the whole job disappear, or only part of it? |
| Skill distance | Can the employee reach the target standard with a realistic program? | Would external hires already have the scarce skills? |
| Time and cost | What are paid learning time, instruction, tools, coaching and temporary backfill costs? | What are severance, recruiting, onboarding, vacancy and institutional-knowledge costs? |
| Available work | Which funded internal roles will participants enter, and when? | Are there enough external candidates and can they be productive quickly? |
| Risk and regulation | Can the new workflow meet privacy, safety, quality and accountability requirements? | Would turnover remove critical expertise or create compliance risk? |
| Results | What productivity, quality and workload changes will count as success? | What evidence shows replacement will deliver better results after transition costs? |
| Fairness | Who gets access, and are selection criteria transparent? | Who bears the impact if roles are eliminated, and is there a redeployment route? |
Reskilling is more economically credible when domain knowledge is valuable, the target role is nearby, hiring is difficult and the employer can keep the worker productive during training. Replacement may be more practical when the target skills are far away, the business has no internal vacancies, safety requirements demand extensive certification or nearly all tasks in the role are automated. A company can use both approaches at once: redeploy some employees while reducing roles for which no viable pathway exists.
How employees can judge whether a promise is real
Ask for concrete answers before enrolling:
- Which tasks in my current role will change, and which new tasks will I perform?
- What exact skills and assessment standard are required?
- How many paid hours, months and attempts are funded?
- Which internal roles are in scope, and who makes the hiring decision?
- Will I receive access to the approved tools and relevant data?
- What privacy, security and human-review rules apply?
- What happens if I complete the training but no opening exists?
A credible employer publishes the roles covered, learning support, selection rules, internal-mobility process and fallback options. Vague promises to “teach everyone AI” without these details shift the risk to employees.
What reskilling cannot solve
Training cannot create demand for a role that the business no longer needs. It also cannot remove legal obligations, guarantee that an AI system is accurate, or make a large skill gap disappear in a few weeks. Workers may face unequal access because of scheduling, language, disability, location or manager discretion. Employers need transparent criteria, accommodations and appeal channels, and they should report both successful redeployments and cases where no pathway existed.
The strongest evidence supports a task-by-task strategy: use AI to remove routine work, invest in the digital and human skills that complement it, and connect learning to an actual job. Where that chain breaks, reskilling becomes a promise without employment protection.
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