Measuring What Matters: The Future of Certifications

Change, innovation, and growth are constant in the certification community. The latest developments center on employers’ shifts toward skills-based hiring and the responsible integration of AI into certification organizations’ operations.

Over the years, Workcred has heard from many leaders of certification organizations about how they are responding to the evolving landscape. While strategies vary, it’s clear that organizations must navigate these changes thoughtfully, ensuring that quality certifications measure job competence and/or ensure public safety.

Skills-Based Hiring: From Foundational Standards to Emerging Frameworks

For some certification bodies, this shift to skills-based hiring — rather than education- or experience-based hiring — helps validate their existing practices. In some occupations like financial planning, skills-based frameworks have long been used to determine the education, training, and ethical standards required for professionals to serve their clients’ best interests.

Other organizations have evolved their messaging and positioning to emphasize a skills-based approach to certification development in response to market shifts. Some are partnering with AI-powered skills intelligence platforms to explore the categorization and management of large skills datasets. As CompTIA’s senior vice president of exam services, Carl Bowman noted, “Certification has always been designed to measure competence for a job. Many jobs have not only knowledge requirements, but skills requirements.” Certification adaptations reflect this direction — highly skills-focused offerings that adhere to ISO/IEC 17024:2026, Conformity Assessment – General Requirements For Bodies Operating Certification Of Persons, but span multiple disciplines rather than focusing on a single job role.

The AI Implementation Challenge: Evaluating What Works

AI’s potential to support certification organizations is increasing, from the development of job task analyses to the creation of exam questions. Experts have weighed in on the changing roles for AI and humans in certification, sharing different philosophies about where AI adds value and where human judgment remains essential.

Some organizations have not yet incorporated AI into their exam development workflows, finding that human judgment outperforms current AI tools for high-stakes content creation. Others have been piloting AI use with mixed results — including stepping back from using AI to develop exam items and instead using it to generate ideas and surface research for subject matter experts to refine.

Across the board, final decisions must remain in human hands. Subject matter experts and qualified psychometricians should continue to drive all critical content and development decisions. Organizations are most focused on using AI to perform repeatable, lower-stakes tasks to improve efficiency, but AI has yet to take over any significant aspect of most certification development workflows.

What’s Next for AI in Certification?

What’s clear across the field is that there’s no established playbook yet for AI in certification. Measured pilot programs and deliberate wait-and-see approaches alike reflect a shared recognition that AI can improve efficiency, but there are still concerns that these gains should not come at the expense of validity.

As certification organizations navigate employers’ transition to skills-based hiring and emerging technologies, a shared priority remains clear: certifications must continue to reliably assess competence to perform a job, no matter what tools help build them.