For SkillCouncils.Com

Artificial intelligence could help workers overcome one of the labour market’s most persistent barriers: the tendency to judge people by their educational qualifications rather than by what they have learned and demonstrated on the job.

Consider a warehouse supervisor who struggled academically in school, joined the army at 18, earned a General Educational Development (GED) credential and entered the workforce in a role requiring few formal qualifications. After 15 years with the same company, his educational record may still appear unremarkable. Yet his employer has seen him solve problems, manage teams, handle difficult situations, exercise sound judgement and learn from mistakes.

His school record describes the person he was at 18. His work performance offers a far richer picture of the professional he has become.

This distinction lies at the heart of a growing opportunity for artificial intelligence (AI) to reshape workforce development. By helping employees perform tasks that previously required more specialised knowledge, AI could enable capable workers to move into new responsibilities without always having to follow traditional educational pathways.

For employers, the implication is significant: experience, reliability, judgement and the capacity to learn may deserve greater weight in decisions about recruitment, promotion and reskilling.

Why qualifications do not tell the whole story

Jobs are not simply collections of technical skills. They involve multiple tasks, decisions and responsibilities that evolve with changing business needs.

An accountant, for example, must do more than calculate figures. The role also requires recognising inconsistencies, questioning assumptions and deciding which information deserves scrutiny. A programmer must understand not only how to write code but also which errors could affect a system in production.

Formal education provides valuable evidence of a person's knowledge and preparation, particularly when employers are assessing candidates with limited work experience. But once employees have spent years in the workplace, their demonstrated performance can offer a more direct indication of their capabilities.

The article cites a 2016 study using personnel records from two large companies. The researchers estimated that errors in expectations about employees' abilities fell by half within three to six years of observing their work. The finding suggests that employers learn considerably more about workers as they accumulate practical experience.

Yet recruitment and promotion systems can continue to place substantial weight on qualifications earned early in life. Employees who develop valuable skills on the job may consequently find themselves excluded from roles for which they could perform many of the essential tasks.

The overlooked potential of the existing workforce

The idea of giving workers another opportunity is not new. Companies have long supported employees who want to complete their education or prepare for better-paid occupations.

According to the source article, Amazon's Career Choice programme has helped more than 8,900 employees earn GEDs and more than 2,000 obtain high school diplomas. Starbucks' Pathway to Admission programme has enabled more than 5,500 employees who initially did not qualify for admission to Arizona State University to enter through an alternative pathway.

These examples illustrate how workers' prospects can change when employers invest in their development rather than treating their initial qualifications as a permanent measure of their potential.

AI could offer another route. Instead of requiring every employee to acquire all the knowledge traditionally associated with a job before attempting it, employers may be able to use AI tools to support workers as they take on selected tasks, build competence and prepare for broader responsibilities.

This does not mean that formal education is unnecessary. It means that learning, assessment and career progression could become more flexible, with opportunities to demonstrate capability through work as well as through conventional credentials.

AI could narrow performance gaps

Evidence cited in the source article suggests that generative AI can improve the performance of workers who have less experience or lower initial skill levels.

A 2024 study involving more than 5,000 customer-service workers found that productivity increased on average after a generative AI assistant was introduced, with the largest gains among less experienced and lower-skilled employees. One possible explanation is that AI helped these workers adopt practices used by more experienced colleagues.

The article also describes a randomised controlled experiment involving 1,174 adults completing a workplace-style problem-solving task. Without AI, participants with more education outperformed those with less education by 0.55 standard deviations. With AI access, the gap fell to 0.14 standard deviations—a reported reduction of approximately 75%.

These results point to AI's potential to make certain tasks more accessible by supplying information, explaining unfamiliar concepts, assisting with calculations and helping users work through problems.

However, the findings should not be interpreted as proof that AI eliminates skill gaps across all occupations. The benefits depend on the task, the quality of the AI system, the worker's existing knowledge and the ability to evaluate the output. Evidence from particular experiments also cannot automatically be generalised to every workplace or industry.

The more measured conclusion is that AI may help some workers perform beyond the limits imposed by their current knowledge, especially when they have relevant experience and can recognise whether the system's recommendations make sense.

From credential-based hiring to skills-based progression

For employers, the opportunity extends beyond making existing employees more productive. AI could prompt organisations to reconsider who is capable of doing particular work.

Rather than asking only whether an employee meets every conventional requirement for a role, managers could examine three questions:

1. Which tasks can the employee already perform competently?
2. Which additional tasks could the employee undertake with appropriate AI support?
3. Which responsibilities still require formal training, supervised practice, professional judgement or recognised qualifications?

This approach could help businesses identify internal candidates who are overlooked by conventional recruitment filters. It could also make career progression more practical by breaking complex jobs into task groups, identifying specific skill gaps and providing targeted learning.

For example, an experienced warehouse supervisor might not be ready to move directly into an accounting position. However, with appropriate guidance and controls, AI could help the supervisor prepare a budget memo, organise spreadsheet data, carry out a basic reconciliation or flag unusual figures.

The employee's existing strengths—understanding operations, recognising problems, coordinating teams and making decisions under pressure—could provide a foundation for learning new responsibilities.

The crucial distinction is that AI can assist with a task without automatically making the user competent to perform it independently. Employers would still need to assess the employee's work, provide training and determine which responsibilities can safely be delegated.

Why human judgement becomes more important

AI can produce drafts, explain unfamiliar concepts, summarise information and suggest solutions. But it can also generate inaccurate or misleading answers with considerable confidence.

Workers therefore need more than access to an AI tool. They must learn how to frame questions, provide relevant context, check the quality of responses, recognise uncertainty and understand when a task requires expert intervention.

This makes foundational skills more important, not less. Reading comprehension, numeracy, communication, problem-solving and digital literacy help workers use AI effectively and identify errors. Occupational knowledge provides the context needed to judge whether an answer is appropriate.

Some professions also have non-negotiable entry requirements. Surgeons need extensive medical education and supervised clinical training. Engineers must understand the principles needed to assess structural safety. Licensing and regulatory requirements cannot simply be bypassed because an AI system can generate a plausible response.

AI's potential is therefore greatest when it complements a worker's existing strengths and supports a structured pathway to additional competence—not when it is treated as a substitute for all training or professional expertise.

What this means for India's skilling ecosystem

For India, the argument has particular relevance to efforts to improve employability, support career mobility and make better use of the skills workers acquire outside formal education.

Many employees develop practical expertise through years of work, apprenticeships, informal learning and repeated exposure to real operational challenges. Yet these capabilities may not always be visible to recruiters or adequately reflected in conventional qualifications.

AI-assisted learning could help such workers build specific capabilities while remaining employed. A logistics worker might receive support in inventory analysis; a frontline supervisor could learn to prepare operational reports; and a customer-service employee could practise more complex case handling with AI assistance.

These are potential applications, not guaranteed outcomes. Their success would depend on access to suitable tools, language support, digital infrastructure, relevant training and reliable assessments.

Skill development institutions, employers and training providers could respond by designing programmes around demonstrable competencies rather than course completion alone. Employees could be assessed on their ability to perform defined tasks, use AI responsibly and explain or validate the decisions they make.

Recognition of prior learning could also become more useful when combined with workplace assessments and targeted training. Instead of asking experienced workers to repeat learning they have already mastered, institutions could identify what they can demonstrate and focus additional instruction on genuine gaps.

At the same time, AI-assisted training must not become a reason to lower occupational standards. Competency assessments should establish what a worker can do independently, what they can do with technological support and where supervision or formal certification remains necessary.

A practical agenda for employers

Organisations seeking to turn AI into a tool for career mobility can begin by reviewing their internal talent and promotion systems.

First, identify transferable skills. Evaluate employees' demonstrated performance, reliability, problem-solving and experience instead of relying exclusively on educational history or job titles.

Second, break roles into tasks. Determine which responsibilities require specialist expertise and which could be learned more quickly with structured AI support.

Third, provide guided practice. Give employees opportunities to use AI on realistic work assignments, with appropriate supervision and feedback.

Fourth, assess actual competence. Measure the quality, accuracy and consistency of completed work rather than assuming that access to AI proves readiness for a new role.

Finally, create transparent progression pathways. Explain what employees must demonstrate to move into higher-responsibility positions, including any qualifications, safety requirements or professional credentials that remain essential.

Success should be measured through meaningful outcomes: improved work quality, successful internal promotions, reduced skill gaps, stronger retention and better access to higher-value roles. Participation in training or access to AI tools, by themselves, are not evidence that workers have become more capable.

A second chance built on demonstrated ability

AI will not erase the importance of education, nor will it make every job accessible to every worker. But it could help employers distinguish between qualifications that are genuinely necessary for a role and requirements that have become barriers to recognising capable people.

The warehouse supervisor in the opening example may not be ready to become an accountant overnight. Yet his years of experience could provide a valuable foundation for learning new responsibilities, with AI offering support along the way.

The larger opportunity is to build workplaces where a person's early academic record does not permanently define their career prospects. When employers combine practical experience, targeted training, responsible AI use and rigorous assessment, they can create more routes for workers to demonstrate what they know—and develop what they do not yet know.

For workforce development, the central question is no longer simply what qualifications a person already holds. It is what that person can do today, what they can learn next and how employers can help them progress without compromising the standards that good work demands.