Organizations that are pulling ahead on AI capability are the ones that measured first, according to new benchmark data. Skills platform Workera’s 2026 AI Skills Enterprise Benchmark Report, based on 88,753 assessments, found that verified AI skills aren’t the same thing as self-reported proficiency. This gap represents a growing risk for HR leaders who rely on self-attestation or course completion data to gauge workforce readiness.
What do employees already know?
Skills with a low technical barrier show the strongest scores, with Data Storytelling Essentials, AI and Data Communication and Responsible AI Essentials leading the enterprise benchmarks. On the other hand, the weakest scores appear where technical depth is required. On Workera’s 300-point scale, a score above 200 indicates an employee can design and build AI solutions, not just recognize concepts, yet deep Learning Fundamentals averaged 142 across enterprise employees.
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Agentic AI Fluency and Engineering averaged 179, placing both in the developing range, which means most employees can talk about these skills but are not using them effectively. As enterprises speed up automation and AI-assisted workflows, the data suggests that employees are not ready for agentic AI systems that can plan and carry out multi-step tasks with limited human input.
The report also illustrates a point of interest for HR leaders that many organizations aren’t tracking. There is a risk that if only a small number of employees have advanced AI skills, they could become project-stalling bottlenecks.
Where does training work best?
The good news is that targeted training works, even dramatically, in some cases:
- Employees who upskilled in Data Visualization and Storytelling improved by 77% on average
- Generative AI Essentials improved by 51%
- Responsible AI jumped from 25% accomplished to 94%
But the report also shows that improvement rates vary significantly by capability. Some skills respond quickly to training, while others require sustained effort. Machine Learning Fundamentals, for example, requires a longer development runway than Agentic AI Fluency.
ServiceNow’s experience, which is mentioned in the report, illustrates what measurement-first looks like in practice. Chief People and AI Enablement Officer Jacqui Canney (also an HR Executive Top 100 HR Tech Influencer) described the approach at the Wall Street Journal Leadership Institute’s CPO Council Summit.
The company assessed all 30,000 employees by job and level, set percentile targets for each capability, then gave employees transparent access to their scores and personalized development paths. “We didn’t make it a stick,” Canney said. “It was more like an incentive.”
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