Nearly half of salaried workers received no AI training. The gap creates a demand signal for corporate learning vendors. Here is the sector-by-sector breakdown.
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Nearly half of all salaried workers in the United States received no on-the-job training on AI tools or automated processes in the last 12 months. That finding, from an April PYMNTS Intelligence study, signals a structural gap between corporate AI ambition and workforce readiness. For investors, the gap itself – not the AI tools – creates a demand signal for vendors that close it.
The report, titled “Wage to Wallet™ Index – The Resilience Deficit,” surveyed workers in roles typically requiring a four-year degree: engineers, lawyers, product managers, doctors, and similar positions. 48% of these educated professionals face AI tools they cannot use effectively. At the same time, 53% of those same workers said they are confident they could find comparable-paying work if technology eliminated their roles. That leaves nearly half of a multi-million-person pool who feel they could not.
On confidence, 74% of non-Labor Economy workers believe their skills will remain valuable as AI evolves. Belief and employer behavior are diverging. The Federal Reserve Bank of Atlanta noted in a March working paper that “AI’s near-term labor market effects are characterized less by aggregate job losses and more by shifts in tasks and occupational exposure across workers.” It also pointed to a “lack of workforce training” as a concrete risk.
Schmidt’s confidence skipped over the training mechanics. If workers do not get the tools to shape AI, the technology’s diffusion into the economy slows – and so does the revenue growth of companies selling AI products.
A separate May PYMNTS report, “Financial Services Pulls Ahead in the Enterprise AI Race,” quantified how uneven the AI rollout is across sectors. Financial services firms have scaled AI across 27 distinct tasks, nearly three times as many as healthcare, which has deployed AI across 10 tasks. Media and advertising companies sit in the middle with 16 tasks.
| Sector | Number of AI-Deployed Tasks |
|---|---|
| Financial Services | 27 |
| Media and Advertising | 16 |
| Healthcare | 10 |
The tasks span marketing, sales, supply chains, data, product, risk, compliance, HR, and payments. Financial firms are concentrating on back-office functions such as revenue recognition, credit risk assessment, and sales forecasting. Healthcare is using AI mainly through customer service chatbots. Media and advertising companies apply AI to content quality assurance, executive briefing preparation, and logistics.
That deployment asymmetry means financial services employees face the widest gap between the tools being introduced and the training to use them. If a bank rolls out AI for credit risk assessment but does not teach analysts how to interpret the output, the investment in AI produces less return. For investors, the sector with the largest deployment-to-training gap may also be the sector most likely to increase spending on corporate learning.
Block (SQ) CEO Jack Dorsey recently blamed AI for the company’s decision to cut 4,000 jobs – nearly 40% of its workforce. Chitra Nawbatt, an advisor to venture capital- and private equity-backed companies and a former Deutsche Bank executive, called that a misleading narrative.
Nawbatt argued that AI as the reason for job cuts is a “quick, popular narrative” that obscures the real question: what new jobs will emerge across industries, products, and services? Her framework shifts the focus from job replacement to job creation – and that creation depends on training.
For traders watching Block or other companies announcing AI-driven downsizing, the distinction matters. If the job cuts are actually about over-hiring or strategic refocusing, the AI narrative may be masking execution risk. The real test is whether those companies subsequently invest in training for the remaining workforce.
The PYMNTS data, combined with the Fed’s warning about a “lack of workforce training,” point to a specific market gap. Companies that provide corporate learning platforms, AI upskilling programs, and workforce readiness software are positioned to capture budget allocations that are currently underfunded.
Bottom line for traders: The companies that close the training gap may see the next wave of AI-related revenue growth, not the AI tool vendors themselves. The logic is simple: if 48% of salaried workers are untrained, the potential customer base for training solutions is large. As Stanford University’s 2026 Artificial Intelligence Index Report noted, “AI experts and the public have very different perspectives on the technology’s future.” That disconnect creates a buying opportunity in the businesses that bridge it.
Investors should watch for:
If the training gap narrows, the productivity gains from AI deployment will follow. If it persists, the AI investment thesis across sectors may take longer to realize.
Schmidt’s commencement speech at the University of Arizona drew boos every time he mentioned AI. The Pew Research Center found that 35% of workers with a bachelor’s degree think AI will lead to fewer job opportunities for them in the future, and 24% of post-graduates feel the same. That skepticism is rational when nearly half of workers have been given no training.
“If you don’t care about science, that’s OK because AI is going to touch everything else as well,” Schmidt told the graduates. Training produces the knowledge needed to shape AI in the workforce. Companies still have a lot of work to do. For investors, that work is an opportunity.
Companies that treat AI training as a one-time workshop rather than a continuous capability will struggle to retain talent and realize returns on their AI spend. The firms that build systematic training programs – and the vendors that sell them – are the ones most likely to compound value over the next cycle.
The rocket ship is here. The question is whether the workforce gets a seat, a manual, or both. For related analysis on AI reshaping revenue models, see Hexaware Targets AI and Cloud to Reshape Revenue Mix. For broader stock market analysis, AlphaScala tracks sector-level training spend as a forward indicator.
Prepared with AlphaScala editorial tooling from the source reporting linked above. Indexable analysis may include a cited Alpha Score value. Publishing checks screen each story before release. Educational coverage, not personalized advice.