AI now touches most jobs—about 60% in advanced economies
Hire for verified skills that complement AI, using portfolios, micro-credentials, and apprenticeships
Redesign schooling around agentic AI to widen mobility and prevent exclusion

One number should guide every education and workforce plan this year. In advanced economies, about 60% of jobs are at risk of automation. Roughly half of these jobs could see wage and hiring changes as AI takes over key tasks. The other half may benefit as AI enhances human work. Globally, the figure is close to 40%, but the pressure is most significant in areas focused on knowledge jobs. This isn't just an estimate from a vendor; it's a serious report from the International Monetary Fund. Given this situation, skills-first hiring isn't just a catchphrase. It's the new standard in the labor market. The stakes are clear. If we miss the mark on skills-first hiring, we risk limiting opportunities and sidelining many learners. If we succeed, agentic AI can improve access, accelerate skill development, and help people remain employed as job tasks change. The future hinges on what schools, colleges, and employers do next.

Redefining skills-first hiring for an agentic era

Skills-first hiring isn't a new concept. Employers have been gradually relaxing degree requirements for years. Before the pandemic, nearly half of middle-skill jobs and about a third of high-skill jobs experienced a "degree reset," as listings dropped the bachelor's requirement. However, the practice varies widely. Many companies updated their job ads but kept the old filters in hiring and promotions. In other words, they changed the job advertisement, but not the job structure. Today's agentic AI makes this gap risky. If AI can handle most observable tasks, simple degree checks will overlook what really counts: the ability to work with AI, turn goals into prompts and workflows, and verify results. Skills-first hiring must transform from "no degree necessary" to "proof of specific, valuable skills that support AI."

The demand signal is changing. Surveys show that three out of five workers fear AI will threaten their job security, and many expect wage pressures in their fields. Yet, significant productivity increases are possible. GenAI might generate between $2.6 and $4.4 trillion in value annually. Employers who adopt skills-first hiring can access broader talent pools and more diverse candidates, including women and candidates without degrees. However, the shift from intent to action remains limited. A recent international survey revealed that only about 13% of employers had edited job postings to drop degree and tenure requirements. The risk is evident. If skills-first hiring stalls while automation speeds up, displacement could exceed mobility. This is a design flaw, not an unchangeable rule.

Agentic AI raises the bar by altering what is considered "skilled." The most valuable employees in a skills-first hiring environment will not only code, analyze, or write. They will break down complex problems, manage AI agents, decide when to trust automation, and communicate results to teams and clients. Those skills can be taught, but they don't fit the old credential model. They are ongoing, observable performances. This is why reform is needed in K-12 schools, community colleges, and universities. They must prepare graduates who excel in working with AI, not just consuming it. Otherwise, skills-first hiring will favor those who are already privileged, and the promise of greater access will disappear.

What the evidence says about skills-first hiring and automation risk

The labor market feedback is mixed yet consistent. The World Economic Forum estimates that nearly a quarter of jobs will change by 2027, with 69 million new roles created and 83 million eliminated. The OECD finds that jobs most at risk of automation make up about 27% of employment across member states. Being exposed doesn't guarantee loss, but it does guarantee changes. When exposure is high, skills-first hiring must be faster, fairer, and based on objective evidence. It should require clear demonstrations of skill rather than proxies. This means using performance tasks, portfolios, and on-the-job projects that showcase problem-solving with AI. Hiring teams also need training to interpret this evidence. Without these elements, skills-first hiring will remain just a slogan.

Early employer data show the benefits when this approach succeeds. Skills-first hiring can enlarge candidate pools in ways that support equity and growth. Analyses of LinkedIn's Economic Graph reveal more qualified candidates without degrees when companies filter by skills, resulting in meaningful increases in women's representation. Yet, the practice often lags behind policy. Even as job postings remove some formal requirements, hiring decisions frequently revert to traditional credentials. This misalignment damages trust. Applicants realize the new rules are superficial. Faculty assume that industry won't recognize new learning models. The cycle continues. To break this pattern, schools and employers must collaborate to create skill signals that are hard to fake and easy to validate. Digital micro-credentials that include task evidence are one solution. Paid apprenticeships with public data on completion and wage increases are another.

Educational evidence is advancing, which is significant for hiring. Randomized trials now show that well-designed AI tutors can produce greater learning gains in less time than conventional in-class active learning. Other studies suggest that AI support for tutors enhances student mastery, particularly where tutor skills are lower. These findings are practical. They highlight a way to make skills-first hiring a reality: drastically reduce the time and cost learners incur to develop tangible skills. Suppose every student can access top-quality practice and feedback after school. In that case, the gap between well-resourced and under-resourced learners can narrow. This is a hiring issue, not just an education one. It expands the number of candidates prepared for real work.

Designing schools and colleges for a skills-first hiring world

The main change needed is in the curriculum. Programs should focus on solving problems rather than completing courses. Learners need regular practice that combines subject knowledge with working alongside AI. They should learn to define tasks, choose or develop workflows with agents, verify results, and communicate decisions. Assessments should reveal process evidence. Grading should emphasize clarity, judgment, and handling errors, not just final answers. In this model, skills-first hiring has a dependable dataset. Employers can observe how students use AI to achieve correct and valuable outcomes. Students can demonstrate their progress over time. Faculty can compare groups using shared, public standards—the concept of "job-ready" shifts from a promise to a proof.

Micro-credentials are vital. Europe has adopted a common framework for recognizing short, skill-based learning. This system allows universities and employers to exchange verified qualifications that can cross borders. When these micro-credentials include real work samples, the qualifications become more credible. A badge in "data cleaning and prompt engineering" that comprises code, prompts, logs, and error analysis is more impressive than just a line on a résumé. This is the currency needed for skills-first hiring. It also helps students build on their learning toward degrees without losing progress or support. With agentic AI in the mix, many learners will master job-related workflows more quickly than traditional courses allow. The credentialing system must keep pace.

Practical accessibility is as crucial as design. Evidence shows that AI tutors and AI-assisted tutoring can boost achievement and save time. Schools should implement these tools where time is limited: after school, in homework clubs, and in community centers. The aim is not to replace teachers but to bring practice and feedback closer to where students learn. This is how we raise skill standards. When more learners can complete problem sets with quality guidance, more can try out for jobs. This shifts the dynamics for skills-first hiring. It also changes what hiring managers notice when automated systems evaluate applicant skills.

A policy playbook to slow exclusion and boost mobility

K-12 systems should incorporate agentic AI into core subjects like literacy and numeracy rather than treating it as an add-on. Students should write with AI and then fine-tune it. They should solve math using AI and explain each step. The aim is structured collaboration, not passive dependency. School districts can publish clear guidelines aligned with UNESCO's standards and follow a simple principle: AI can assist your thinking, but you must show you understand. This approach maintains academic integrity while preparing students for environments where AI is common. It also keeps the door open for skills-first hiring, as it fosters habits of documentation and verification.

Community colleges should drive skills-first hiring efforts. They are closest to local employers and can quickly refresh programs. The model is straightforward. Assess local task demand. Work with employers to design AI workflows. Teach these workflows along with relevant theory. Evaluate using real work examples. Publish results. Each micro-credential should connect to a role with earning potential, and schools should track placement and earnings. Apprenticeships should cover not only trades but also fields such as data, healthcare, logistics, and green jobs. Statistics show this is achievable. Youth apprenticeships have increased in the United States, both in number and percentage of participants, with tens of thousands added since 2020. A skills-first hiring strategy is more effective when paid learning is common, not the exception.

Universities should restructure general education to focus on enduring skills for an AI-driven world. Skills like reasoning, modeling, evidence interpretation, and communication should be foundational. Every major should have an "AI + X" studio for students to create and defend workflows using AI in their fields. Capstone projects should be public, searchable, and linked in job applications. Career services should shift from résumé enhancement to evidence collection. Meanwhile, registrars should adopt micro-credentials that align with European recognition standards so international students can take verified qualifications home. To maintain their edge in a skills-first hiring market, universities need to produce graduates ready to lead AI-driven teams from day one.

Employers must also play their part. They need to move beyond just revising postings. Clearly define essential skills, publish them, and assess them. Use work samples and job trials early on. Train hiring teams to evaluate portfolios and accurately prompt records. Adjust AI-based screening to prevent old biases from reappearing in new systems. The potential benefits are substantial. Analyses show that skills-first hiring can broaden candidate pools and enhance diversity. It also promotes internal mobility when paired with clear skill frameworks. None of this is kindness. In a market where AI rapidly reshapes tasks, companies that hire for adaptability will outpace those that rely on outdated measures.

An obvious concern is that this vision may be overly optimistic about AI's role in learning and work. We shouldn't overlook the risks. While some randomized trials and field studies highlight significant benefits from AI tutoring, others caution that unsupervised use can hinder learning or widen disparities. The lesson isn't to slow down; it's to create safeguards that keep human judgment central. Clear guidelines on data privacy, model bias, and age-appropriate use are increasingly available. The practical solution in classrooms involves strict alignment with standards, transparent prompts, and visible reasoning. The sensible solution in hiring is to assess skills using real tasks and to publish success metrics by pathway. This ensures that skills-first hiring doesn't become just another empty promise.

A second concern is that skills-first hiring might be a distraction—lots of talk without meaningful change. This concern is valid. Even dedicated companies often revert to prestige screens when urgency arises. The countermeasure is public accountability. Regions can link incentives to concrete results: the number of apprenticeships created, retention rates of non-degree hires, wage increases for first-generation graduates, and the time taken to fill critical roles. Transparency is vital. When results improve, others are likely to follow. When they don't, policies should be adjusted. The same accountability should apply to schools and colleges. If programs claim to deliver job-relevant skills, they should provide evidence and placement data to substantiate those claims.

The final critique is darker. What if the need for human labor collapses in many fields, leaving only a small elite of adaptable workers? That risk is real. However, it is not the most likely outcome in the near future. Significant estimates suggest job changes rather than a full collapse. Tasks are shifting, and productivity is rising with substantial differences across sectors. Even in roles with high exposure, cooperation is possible when people effectively manage AI. Education can improve the odds for collaboration. Hiring based on skills can turn that cooperation into job mobility. Together, these approaches can reduce exclusion and spread the benefits. This is not an idealistic view. It is a practical way to prepare for the uncertainty ahead.

In conclusion, the labor market is entering a phase in which AI systems do more than predict. They start, coordinate, and learn. This change increases the importance of hiring based on skills. The IMF’s exposure figure—60% in advanced economies—should grab our attention. It shows that the risk is widespread and uneven. It also indicates significant opportunities. We can either allow exposure to lead to exclusion, or we can rethink how people learn and how companies hire. The direction is clear. Create curricula that focus on AI collaboration and reasoning. Expand micro-credentials that include evidence of skills. Increase paid apprenticeships. Require hiring teams to evaluate what candidates can actually do. Use AI tutors and AI-supported tutoring where time is limited and feedback is scarce. Share results. Make quick adjustments. If we take these steps, skills-first hiring can become the means through which capable AI enhances human work rather than diminishes it. The wave is already here. Our role is to mold it.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

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AI agents in education boost learning while cutting time
Build home-first workflows for practice, planning, and records
Scale with evidence and guardrails to protect equity and trust

One data point should change our thinking: a recent study shows that students using an AI tutor learned significantly more, and in less time, than those in an active learning class covering the same material. The AI tutor used evidence-based teaching methods and still performed well. This is not just a marketing claim; it is supported by peer-reviewed research. Suppose an AI tutor can save time and improve learning in controlled settings. What happens when these “agentic” systems are used in the family home, where most homework takes place? The effects are real. They point to the potential for families to adopt AI agents in education that assist children each night, coach parents, and allow schools to focus their limited human expertise where it is most needed. In short, AI agents in education are transitioning from novelty to necessity, and the question is how quickly we can adjust to that reality.

We can see the shift in usage data. In 2025, 34% of U.S. adults reported using ChatGPT, roughly double the 2023 figure. Among UK undergraduates, use of generative tools increased from 66% in 2024 to 92% in 2025. Teen adoption is also growing: 26% of U.S. teens reported using ChatGPT for schoolwork in 2024, up from 13% the previous year. None of this guarantees learning improvements, but it shows where attention and effort are being directed. The opportunity now is to turn basic tool use into accountable, agent-led workflows that prioritize families first, schools second, and vendors last. This means creating a dependable “home system” for tutoring, planning, and documentation that works just as well on a Tuesday night as it does on a weekend for test prep.

AI agents in education: from single tools to household systems

An AI agent is more than just a chat window. It is software that can plan tasks, use tools, and work towards a goal. Businesses are already using agents to answer complex questions, manage workflows, and connect different systems. The lesson for schools is clear: what works in business will also work at home. Families need agents that help children study, automatically log progress for teachers, draft emails about accommodations, and schedule therapy sessions without paperwork getting lost. The key points from consulting apply at home: it’s not just about how intelligent the agent is; it’s about redesigning the workflow around what learners and caregivers actually do. Focus on the weekly rhythm of assignments and supports rather than worrying about flashy demonstrations.

A clear example of where this is heading comes from a recent story about a parent who used “vibe coding” tools to build an AI tutoring platform for her dyslexic son. She did not wait for a perfect product. She combined research-based prompts, dashboards, and student intake forms to personalize the agent’s guidance, drawing on hundreds of studies about learning differences. The result was not a toy; it was a home setup that adjusted to the child’s motivation and needs. When a parent can create a functioning tutor, we have entered a new era. For context, specialized reading support in the U.S. averages about $108 per hour. An AI agent that can enhance or partially replace some of those human sessions changes the dynamics of time, cost, and access—all while keeping the human specialist for the more complex parts.

What AI agents can do for families now?

We should identify specific use cases because they relate to real challenges families face. Start with support for reading and writing. Recent evidence shows that AI tutors can deliver greater gains in less time when designed around effective teaching methods. This makes agents ideal for structured, paced, and responsive nightly practice. Add school logistics: an agent can extract deadlines from learning platforms, generate study plans, and remind both children and caregivers. It can summarize teacher feedback into two specific actions per subject. It can maintain a private, ongoing learning record that parents can share at the next meeting. Because these are agents, not static programs, they can access external tools to retrieve school calendars, assemble forms for accommodations, or draft that email you've been putting off.

Families also need help connecting limited, costly expert support with critical moments. Reading intervention tutors are essential, but capacity and cost are issues. With agents enabling focused practice between sessions, children can arrive ready for human instruction. This does not mean total replacement; it means better use of resources. Additionally, broader tool usage suggests families are becoming comfortable asking AI for help with information and planning. Surveys show that many adults rely on AI to search and summarize, and students report routine use. Suppose we can direct that comfort toward a household agent focused on learning goals. In that case, we can reduce back-and-forth communication, minimize wasted time, and make the hours spent with a human expert more effective.

Risks, equity, and the danger of “agent washing”

There is a strong temptation to label every scripted workflow an “agent.” Analysts caution that many agent projects may fail within 2 years because teams pursue trends rather than results. This warning is essential in education, where trust is the most valuable asset. The safeguard against soaring expectations is precise planning and measurable outcomes: time on task, growth on validated assessments, and teacher-observed application. This also means avoiding unsafe autonomy. Household agents should have limited default functions, operate under strict guidelines, and provide logs that parents and teachers can quickly review. The standard must focus on verifiable improvements, not flashy showcases.

We also need strong protections for equity. Global policy organizations warn that AI's potential will vanish if we ignore access, bias, and data handling. This is not just hand-wringing; it is about practical design. AI agents in education must prioritize human-centered guidance, protect student data, and be implemented with teacher training and clear rules. We should account for low-resource settings—for instance, offline options, simple devices, and multilingual prompts. Remember the staffing challenge: the world needs millions more teachers this decade to achieve universal education goals. Agent systems should help by taking on routine tasks and structured practice, not by replacing irreplaceable specialized roles.

A playbook to make AI agents in education work—at home and at school

Start with small, manageable successes. Pick one subject and one grade level. Use an agent to assign tasks, coach, and check practice aligned with the existing curriculum. Treat the agent as a redesign of the workflow, not an add-on tool. The most effective implementations in industry focus on the process rather than the tool itself; schools should do the same. As usage increases, integrate the agent with gradebooks and learning platforms to automatically populate progress logs and reduce administrative burdens. Monitor fidelity: if the agent’s prompts stray from the curriculum, correct them. The goal is to free up teacher time for valuable feedback and meetings while providing families with a reliable nightly routine.

Then scale based on evidence. Use validated measurements and compare agent-supported practice against standard methods. If the results favor the agent, make it official. If they do not, adapt or stop. Build trust within the community by showing not only that students used an AI tool but that they learned more in less time. That is the standard set by recent research on AI tutoring.

Meanwhile, monitor usage trends. Both adult and student use indicates that agents are becoming part of daily life; institutions should meet families where they are by offering approved agent options, clear data policies, and easy-to-follow guides. Finally, align purchasing strategies to avoid “agent washing” by using straightforward criteria for limits on autonomy, logging, and outcomes. This reduces vendor turnover and keeps the focus on learning rather than features.

An AI tutor can lead to much more learning in less time than an active-learning class covering the duplicate content. This single insight serves as a guiding principle for policy. It suggests that the right kind of AI—integrated into a workflow, limited for safety, and measured by outcomes—can help families gain hours back and help schools focus on their valuable human expertise. The consumer trend is moving in the same direction, as adults and students incorporate AI into their everyday tasks. The task ahead is to direct that momentum into accountable agents designed for home use and connected to schools. If accomplished, “AI agents in education” will evolve from a buzzword to a dependable part of every household’s learning resources. The key question is simple: do students learn more, faster, without compromising trust or equity? If the answer is yes, we should scale and start now.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

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Pew Research Center. (2025, June 25). 34% of U.S. adults have used ChatGPT.
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Reuters. (2025, June 25). Over 40% of agentic AI projects will be scrapped by 2027, Gartner says.
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Stanford/Harvard team via Scientific Reports. (2025). AI tutoring outperforms active learning. Scientific Reports.
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