Artificial intelligence is changing jobs faster than many HR organizations can redefine what good performance looks like. New research from talent-management company Talogy suggests that while most organizations already use talent assessments, HR leaders are struggling to measure the combination of AI fluency, critical thinking, adaptability and data literacy that increasingly determines whether employees can work effectively with AI.
The enterprise AI conversation has largely focused on technology: which models to deploy, which workflows to automate and how quickly employees can adopt AI tools.
But a less visible problem is emerging inside HR departments: how do companies measure whether their people are actually prepared to work effectively with AI?
Research from Talogy, based on responses from 207 senior HR leaders, talent acquisition managers and learning and development professionals across the United States and United Kingdom, suggests that workforce assessment practices are struggling to keep pace.
The study covered seven sectors, including manufacturing, retail and commerce, financial services and fintech, healthcare, technology, and government and the public sector.
Its central finding is straightforward: AI adoption is moving faster than AI skills assessment.
Seventy-eight percent of respondents said they face significant challenges assessing AI skills, while only 38% said they felt “very prepared” to adapt traditional job descriptions and career paths for an AI-enabled workplace.
That gap could become consequential as companies move from experimenting with generative AI to redesigning jobs around it.
AI literacy is becoming more than tool proficiency
One of the most important findings is that HR leaders do not appear to view AI readiness as simply knowing how to operate ChatGPT, Microsoft Copilot, Google Gemini or another AI application.
Seventy-eight percent of respondents said future assessments need to move beyond technical tool proficiency. Instead, they want evaluations that consider AI readiness alongside capabilities such as critical judgment and data literacy.
That represents a meaningful shift in how organizations may define digital skills.
An employee who knows how to generate a prompt is not necessarily capable of determining whether an AI-generated answer is reliable. Someone who can automate a workflow may not understand what data should never be exposed to an AI system. And an employee who uses AI frequently may still lack the judgment to recognize when human intervention is required.
The more AI becomes embedded in everyday work, the more those distinctions matter.
Traditional talent frameworks have a blind spot
The research also reveals a paradox.
Talent assessment frameworks are already widespread, with 87% of surveyed organizations using them for most or all roles. Yet respondents identified significant shortcomings in how those frameworks work.
The most common complaint was that assessments are too generic and fail to provide enough role-specific insight. Respondents also pointed to insufficient attention to transferable skills.
That matters because AI is changing the relationship between technical and human capabilities.
A marketing employee may need less time producing routine content but more ability to evaluate AI-generated material. A financial analyst may automate portions of data analysis but spend more time questioning assumptions and interpreting outputs. A software engineer may increasingly supervise AI-generated code rather than write every line manually.
In each case, the underlying job changes without necessarily becoming an “AI job.”
That makes conventional job descriptions and competency frameworks increasingly difficult to maintain.
The skills HR leaders value are mostly human
The capabilities identified as most important by respondents reinforce this point.
AI tool proficiency ranked first at 48%, followed by data literacy at 43%, adaptability at 42%, problem-solving at 40%, and judgment and critical thinking at 36%.
The ordering is revealing.
AI capability matters, but the skills surrounding AI use may be just as important. Employees need to interpret outputs, recognize errors, adapt workflows and decide when an AI recommendation should—or should not—be trusted.
This is consistent with a broader shift in enterprise workforce strategy. AI is increasingly being treated not simply as a software implementation but as a redesign of how work gets performed.
Microsoft’s Work Trend Index, for example, has highlighted the emergence of human-agent collaboration as organizations experiment with AI agents and redesigned workflows. The World Economic Forum’s Future of Jobs Report 2025 similarly identifies analytical thinking, resilience, flexibility and agility among the most important workforce capabilities as technology changes jobs.
The implication for HR teams is that skills frameworks may need to evolve from static lists of competencies into more dynamic models of how people combine technical and transferable capabilities.
AI agents make assessment harder
The challenge becomes even more complicated as organizations move toward agentic AI.
A conventional AI assistant typically responds to a human request. An AI agent can potentially plan a sequence of tasks, interact with enterprise systems and execute actions with varying degrees of autonomy.
That changes what employers need from workers.
Employees may increasingly become supervisors of AI-driven processes rather than operators completing every task themselves. They will need to recognize anomalous outputs, define objectives, monitor automated work and intervene when an agent behaves unexpectedly.
This creates a new category of workforce capability: AI oversight.
It is not necessarily a technical skill. A finance manager overseeing an AI agent does not need to build the underlying model, but may need enough data literacy and critical reasoning to understand whether the agent’s recommendation makes sense.
That is why role-specific assessment could become more important as AI spreads across departments.
Integration may determine whether assessment platforms succeed
Talogy’s research also points toward the requirements HR leaders have for future assessment technology.
Respondents identified three priorities: scientifically validated methodology, evidence of business impact and integration with existing HR technology stacks.
The last requirement could prove particularly important.
Modern HR organizations already operate across applicant tracking systems, human capital management platforms, learning systems, performance-management tools and workforce analytics platforms. Adding another assessment layer without integrating it into those systems could create more administrative work rather than solving the underlying problem.
The competitive opportunity is therefore moving beyond testing. HR technology vendors increasingly need to connect assessment data with recruitment, internal mobility, learning and career development.
For enterprises, the objective should not be to identify who is “good at AI” in the abstract. It should be to determine which capabilities are required for specific roles, how those capabilities interact and where training or job redesign can close gaps.
The workforce measurement problem is just beginning
Talogy’s research captures an early stage of a much larger transformation.
Organizations are still figuring out which tasks AI should perform, which jobs will change and which capabilities employees will need. That makes it difficult for HR teams to create permanent competency models today.
The solution may be more adaptive talent systems that can update assessments as roles evolve.
For HR leaders, that means the AI skills conversation is becoming less about certifications and tool familiarity and more about judgment, adaptability, data literacy and the ability to work productively with automated systems.
AI may change the technology used to perform a job. The harder challenge is measuring the human capabilities that determine whether that technology actually creates value.
Market Landscape
The HR technology market is moving toward several connected categories:
- AI-powered talent assessment: Platforms are increasingly using data and AI to evaluate capabilities beyond conventional resumes and qualifications.
- Skills-based workforce management: Employers are moving toward skills taxonomies that can support recruitment, mobility, workforce planning and learning.
- AI-enabled learning and development: L&D platforms are using AI to personalize training and identify capability gaps.
- Workforce analytics: HR teams are increasingly using analytics to understand productivity, skills and workforce demand.
- AI governance for employees: Organizations need policies and training covering acceptable AI use, data handling, validation of outputs and human oversight.
- HR technology integration: Assessment platforms increasingly need to connect with HCM, ATS, learning and workforce-management systems.
The competitive advantage will increasingly belong to platforms that can translate assessment results into actionable workforce decisions rather than simply producing scores.
Top Insights
- Talogy’s research finds 78% of HR leaders struggle to assess AI skills, exposing a workforce-readiness gap as enterprises accelerate generative AI adoption.
- Only 38% of respondents feel very prepared to adapt job descriptions and career paths, complicating workforce planning as AI changes established roles.
- AI tool proficiency ranks highly, but data literacy, adaptability, problem-solving and critical thinking remain essential capabilities for effective human-AI collaboration.
- Most organizations already use talent assessments, yet respondents say generic frameworks fail to capture role-specific and transferable capabilities required in AI-enabled workplaces.
- HR leaders increasingly want scientifically validated, business-proven assessment platforms that integrate with existing HR technology rather than operate as standalone tools.
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