How people navigate education and career pathways would significantly benefit from broad adoption of skills-first strategies — matching workers to opportunities based on their verified skills and certifications, and helping learners identify which training programs serve their goals best. AI-powered tools have the potential to organize and connect credential and skills data in a way that provides learners, workers, and employers with concrete action steps.
But the promise of AI and today’s reality are not aligned, yet. Anthropic research reveals a striking gap between what AI is theoretically capable of doing across different occupations and how it’s actually being used today. The gap they elevate goes beyond workplace task automation, and points to a much bigger opportunity: we’ve barely begun to explore AI’s full capabilities.
If AI can transform how we work, imagine what could happen if we used AI to map, organize, and connect the sprawling credential and skills landscape. The potential to accelerate skills-based hiring, connect learners to career pathways, and give employers better signals about talent is massive if we build it upon the right foundation.
Skills-First Hiring Gains Momentum
Recent research from SHRM indicates that there’s movement happening toward skills-first hiring, with 34% of organizations indicating they now use skills-first strategies often or almost always in their hiring processes. Educational background has dropped in importance for both HR professionals and supervisors, and 78% of organizations report that skilled credentials now factor into their hiring decisions.
This is encouraging progress, though many organizations that express interest in skills-first hiring still struggle to implement it effectively. They face challenges around ROI, lack standardized practices, and, perhaps most critically, don’t have the tools and infrastructure to assess, compare, and trust the growing universe of credentials and skills data.
Employers, learners, workers, and policymakers all need better ways to make sense of this complexity. Theoretically, AI has the ability to help. But right now, most AI tools can’t, because the data they need doesn’t exist in a format they can use.
Missing Infrastructure
AI is only as good as the data it can access. When credential or skills information is locked in PDFs, scattered across disconnected systems, or described inconsistently, even the most sophisticated AI tools struggle to make useful recommendations. This is an infrastructure problem that holds back both AI and skills-first hiring.
However, if data about skills and learning outcomes are published in a common format, AI tools can recognize patterns, make connections, and surface insights that would otherwise remain invisible. Structured, open, linked, and interoperable data (SOLID) — such as the Credential Transparency Description Language (CTDL) — is the answer to enable career navigation platforms to recommend pathways, hiring systems to match candidates to roles, and learners to understand what skills their credentials translate into.
Building the Foundation for AI — with AI
At Credential Engine, we’re addressing this challenge head-on by using AI to accelerate the creation of the very infrastructure that will make AI tools more effective.
We have developed AI-powered capabilities to dramatically speed up the process of publishing credential and skills data. Collecting and structuring this information about credentials, programs, courses, and competencies can be time-intensive and resource-heavy. Our solution, CTDL xTRA, uses AI to extract and publish credential and skills data from publicly available sources and documents that providers share, at a pace that wasn’t previously possible.
If resourced at scale, this approach could publish the full U.S. skills and credential landscape — 1.85 million credentials to date. That means every training program, every certificate, every competency framework could be described in a way that both humans and AI tools can understand, compare, and use to make better decisions.
From Ambition to Reality
This scenario is not in the distant future; rather, AI’s impact on career pathways and skills-based hiring is unfolding right now. Closing the gap between AI’s theoretical capability and practical application requires transparent, structured data. This is what will enable AI tools to provide clear guidance and let skills-first hiring scale beyond pilot programs.
The opportunity is genuine for everyone working to build a more connected, transparent, and effective workforce system. Credential Engine’s focus is to build a strong foundation to make the ambition a reality.
Learn more about Credential Engine’s AI-focused work and how CTDL creates the infrastructure for smarter career pathways. Connect with us at info@credentialengine.org to explore how structured credential data can power the AI tools your organization is building.