Keeping credentials credible in the age of AI

Traditional credentials—such as college degrees—have long signaled workforce readiness. However, the rapid rise of artificial intelligence (AI) is now challenging the relevance of these time-honored qualifications. Research by Microsoft and LinkedIn   shows that 75% of knowledge workers now use generative AI, and 66% of business leaders wouldn’t hire someone without AI skills. Many academic institutions are struggling to keep pace with these technological advances.

Moreover, AI’s presence in professional life is expanding: the McKinsey Global Institute estimates that existing AI technology could automate tasks accounting for 60–70% of employees’ time. As automation grows, workers and organizations face growing pressure to adapt their skills and workflows to new ways of working. The credibility of a credential today now depends on how well it verifies real-world skills—especially those that AI cannot easily replicate. Navigating this disruption will require a realignment of credentialing practices, one grounded in technology, psychometrics, and human oversight. To achieve that realignment, we must first understand how AI is already affecting credentials.

How Has AI Impacted Credentials?

AI’s emergence is influencing credentials on multiple fronts, from academic integrity challenges to shifts in the skills that credentialing programs must assess. One alarming consequence of AI’s rise is its impact on academic integrity: the misuse of AI tools to cheat on exams has become a global problem for schools and universities. This trend not only undermines learning but also carries financial cost; research from the Higher Education Policy Institute estimated that such academic cheating costs the UK economy about £12 million and the U.S. economy about $200 million each year.

At the same time, employers are shifting focus from academic pedigree toward practical skills and competencies. AI’s growing capabilities have diminished the relative value of knowledge-heavy credentials. Today, there is greater pressure for credentials to certify skills that machines can’t easily replicate—critical thinking, creative problem-solving, adaptability, social influence, and the ability to use or oversee AI systems effectively. Much of this shift is driven by AI’s ability to automate routine tasks—everything from basic coding to bookkeeping—that once required specialized knowledge. An article from Microsoft suggests that jobs heavily involving “information work” are especially susceptible to AI automation, whereas roles relying on physical or manual skills (like water treatment operators or phlebotomists) are far less likely to be disrupted in the near term. This reality is pushing employers to emphasize the uniquely human abilities that algorithms can’t replicate.

Given these factors, attitudes toward the traditional university degree are evolving. It’s also important to remember that not all credentials serve the same purpose. The major types of credentials include:

Degrees – Broad academic programs (usually at colleges or universities) that provide technical and theoretical foundations.

Certificates – Focused training programs in specific subjects, aimed at targeted skill development.

Licenses – Mandatory credentials required to work in regulated professions, primarily to ensure public safety.

Certifications – Voluntary, industry-recognized credentials that validate a person’s competency and expertise in a given field.

So far, AI’s encroachment has put disproportionate pressure on the value of general degrees, while certifications and licenses have retained their credibility through rigorous assessment practices. This disparity creates pressure for recent graduates and the current workforce, since degrees alone struggle to keep pace with industry changes and often don’t emphasize the durable skills that employees now need. Public confidence in traditional degrees is also waning: a recent Gallup poll found that only 35% of Americans consider a college education “very important,” down from 75% in 2010.

Employers Look to Certifications

Degrees still hold value, but employers today often favor candidates with job-specific certifications. For instance, 94% of senior-level finance professionals said they would choose to hire a candidate with a relevant certification over an equally qualified candidate without one. In an AI-accelerated job market—where workplace needs evolve rapidly—companies are prioritizing validated skills and competencies over general credentials. This pattern is widespread: LinkedIn’s Workplace Learning Report notes that certifications appear with increasing frequency in job postings, reflecting a broad shift toward skills-based hiring.

Critically, the skills that matter most are changing. Employers now prize candidates who excel at tasks that are not easily automated by AI. As AI handles more routine work, it is heightening the demand for so-called durable skills—traits like creative problem solving, analytical thinking, adaptability, and social influence. Indeed, the World Economic Forum’s Future of Jobs report predicts that by 2030 the top skills will include not only AI and data literacy but also these human-centric abilities.

Ultimately, organizations seek professionals who can thrive in human–AI partnerships. In addition to technical skills like AI and data literacy, essential human competencies include strong communication, leadership skills, self-initiative, and the capacity to manage and collaborate with AI systems. These attributes help organizations maintain adaptability and resilience amid rapid technological change.

Certification programs also offer an agility advantage. They can be updated far more quickly than traditional degree programs, making them well-suited to teaching and validating new skills in step with technology’s evolution. As certifications gain prominence, ensuring their quality and credibility becomes paramount.

What to Look for in Quality Certification Programs

High-quality certification programs design their exams to measure the skills truly needed on the job. To stay relevant, credentialing organizations regularly conduct job analyses to pinpoint the critical tasks and knowledge required for each role. They also involve subject-matter experts in exam development to keep content aligned with up-to-date technologies, tools, and industry best practices. In short, the exam development process is built to mirror real workplace requirements.

Top-tier programs also protect exam integrity and uphold the credential’s reputation. Exams are delivered in secure, proctored settings. Human proctors—often aided by AI monitoring tools—guard against cheating or other irregularities to ensure scores reflect each candidate’s own knowledge.

Furthermore, quality certification programs require ongoing maintenance through periodic recertification or continuing education. This ensures that professionals keep their skills up to date as their field evolves. That continuous renewal is a key distinction between certifications (and licenses) and one-time credentials like degrees or course completion certificates.

For now, these measures keep certification programs credible and aligned with industry needs. However, it’s also critical to consider how credentialing must continue to adapt as AI and workforce demands evolve.

Looking Ahead: The Future of Credentialing

The AI-driven workplace will demand that credentialing become even more adaptable, precise, and relevant. Modular and micro-credentials are likely to take on a greater role, offering an agile way to validate skills in sync with rapid technological change. These smaller, stackable credentials zero in on specific competencies, allowing professionals to build and demonstrate expertise incrementally and in real time.

AI is also transforming how we assess skills. Automated tools can accelerate exam development, data analytics can personalize assessments at scale, and content can be refreshed more frequently to match shifting industry needs. Renewal and recertification cycles may likewise become more dynamic, helping professionals stay current in fast-moving fields.

However, greater reliance on AI in assessments makes human oversight more vital than ever. Assessment experts and subject-matter experts must remain closely involved to ensure that AI-driven testing is fair, accurate, and truly reflective of job requirements. Their guidance safeguards the integrity of credentialing programs and ensures that new assessment tools—such as simulations and other performance-based exams—measure what actually matters on the job.

Far from eliminating the need for credentials, AI is accelerating it. In a world where skills evolve faster than traditional degree programs can adapt, agile credentials and rigorous certifications have become indispensable. They provide employers with credible, up-to-date evidence of a professional’s ability to perform in today’s AI-augmented workplace, effectively bridging the gap between education and employability in the age of intelligent technology.