The AI awareness problem is over. Leaders and teams know AI is changing how work gets done. In our new report, 61% of professionals place AI upskilling among their top strategic priorities. The tension is no longer if, but how.

Why urgency is rising

AI is not a side project. It touches core workflows in sales, service, operations, finance, and product. AI assistants draft, retrieve, summarize, and recommend. When grounded in trusted sources and used consistently, work is faster and more accurate. The companies pulling ahead treat AI as a capability-building tool, not as content distribution.

Where execution lags

Most organizations still lack an operating model for AI learning. Ownership is blurred across L&D, HR, IT, and line managers. Content proliferates without decision rights or standards, and dashboards track enrollments and hours rather than adoption and outcomes. Activity often has the appearance of positive momentum, but results in few actual significant changes in outcomes.

What fluency actually means

Fluency is practical, role-based, and measurable. Here’s what it can look like at different organizational levels:

• Foundation for all: safe use, privacy, prompt quality, and verification habits.
• Applied skills for practitioners: workflow design, model choice, error handling, and evaluation.
• Governance for leaders: risk appetite, decision rights, procurement guardrails, scenario planning.

This structure turns “AI for everyone” into something you can staff, schedule, and measure.

How to move from signal to system

Treat upskilling like product work: pick a real task, define the job to be done and the metric up front, and make the workflow observable so adoption and impact are visible. Ground assistants in vetted internal and licensed knowledge to protect trust at speed. Capture edge cases, turn them into playbooks, then scale what works and retire what doesn’t.

The strategic takeaway

Upskilling in 2025 is table stakes. What is missing from most plans is execution. Name accountable owners. Set role-based paths. Ground outputs in trusted knowledge, and measure behavior change in the work itself: time saved, error rates down, quality up, and safe use compliance.

Make AI fluency reliable, repeatable, and measurable. Read the full report from getAbstract: Urgency Without Ownership: AI Upskilling at Work 2025