‘As roles evolve, success depends less on whether employees use AI and more on how they combine the skills that matter the most to help them work with the tools effectively’
HR leaders say AI skills assessment tools must be built on scientific rigour and proven business impact before they will trust them, according to new research.
HR leaders want assessment frameworks offering rigorous AI fluency evaluation that can still be tailored to role-specific and transferable skills, reports Talogy.
The report identifies three "non-negotiable priorities":
- a deeply scientific and validated methodology
- solid proof of business impact through case studies
- and integration into existing HR technology stacks
Organizations are ready to move beyond a first phase centred on adoption toward one that "focuses on performance," requiring consistent ways to identify, assess, and develop capabilities that drive better outcomes, the report states.
Anthropic, Meta and Bell Canada have all announced data centre development projects in Canada. A federal document shows that proposed data centre projects nationwide could total more than 20 gigawatts.
The assessment gap
Talogy surveyed 207 senior HR, talent acquisition, and learning and development leaders across the United States and the United Kingdom, spanning seven sectors. While 95% of organizations say AI is automating work at least moderately, 78% report difficulty assessing AI-related skills, and just 38% feel "very prepared" to adapt job descriptions and career paths for an AI-enabled workforce.
Top barriers to assessing AI skills, per Talogy, include:
- a lack of a formal framework (51%)
- skills evolving too quickly (46%)
- and unclear definitions of AI competencies (42%).
Talogy found 87% of organizations already use talent assessment frameworks, but users say the tools are "too generic and provide no role-specific insight."
"AI is reshaping talent management faster than most HR teams can design frameworks," said Dawn Curless, VP, Global Marketing at Talogy. "The challenge organizations now face is defining, assessing, and developing the relevant transferable skills needed to drive better outcomes alongside AI."
Skills and role changes
When it comes to assessing applicants, the capabilities HR leaders value most are largely transferable, softer skills rather than purely technical ones.
"As roles evolve, success depends less on whether employees use AI and more on how they combine the skills that matter the most to help them work with the tools effectively," the report states. Leaders ranked AI tool proficiency (48%), data literacy (43%), adaptability (42%), problem solving (40%), and judgment and critical thinking (36%) highest.
Talogy found 99% of organizations have observed shifts in role design due to AI, with analytics and reporting (69%) and administrative functions (53%) most affected.
More than 9 in 10 (93%) said AI is moderately or significantly reducing entry-level work, and 83% expressed concern AI-driven role changes will (43%) or might (40%) create future leadership-pipeline gaps.
Adoption versus performance
However, AI adoption remains uneven, per Talogy: 46% of organizations have fully deployed AI for content, just 25% have deployed multimodal AI, and use cases such as career pathing (48% in pilot) remain largely experimental.
Many organizations remain focused on adoption rather than measuring performance; 63% describe their AI upskilling approach as "somewhat effective" rather than "very effective," according to the report.
Talogy's data suggests these gaps in measurement, not the pace of AI deployment itself, are what leave HR teams unable to demonstrate the business impact leaders say they now require.
Employers now have an obligation to build AI skills – even when workers leave, according to a previous report.
