The Spending Boom and the Training Gap
Across corporate boards, a high-stakes race is unfolding: accelerate AI adoption while squeezing back on people investments. In early 2026, a SHRM Foundation survey found that about three-quarters of knowledge workers now use AI at work, yet roughly six in ten say they have not received formal training to use these tools effectively. At the same time, industry forecasts show AI-related budgets rising sharply while training outlays stay flat or grow only modestly.
Industry watchers say the momentum is real. Firms are channeling money into software, data platforms, and automation, aiming to lift productivity and trim costs. Yet the people piece of the puzzle is not keeping pace. The data suggests a widening gap between what technology can do and how well workers can use it to its full potential.
As one HR researcher put it, the market has created a familiar paradox: technology can accelerate work, but sustainable advantage comes from judgment, adaptability, and trust—assets that only humans provide. In the current climate, the phrase ‘companies pouring billions into’ AI has become shorthand for a broader strategy, one that may overlook hidden costs tied to human capability and morale.
The Data Behind the Trend
Executives and investors are watching several indicators that point to a growing imbalance. AI spending is projected to climb about 44% in 2026 as companies rush to deploy chat systems, autonomous workflows, and predictive analytics. By contrast, training budgets are expected to increase only around 5% in the same period, and the average time employees spend on formal learning is slipping—from about 47 hours per year to roughly 40 hours.
Experts warn that skimping on training can blunt AI’s impact. When workers don’t learn how to critique, customize, and supervise AI outputs, the technology can deliver surface-level gains while creating new error risks and workflow friction.
- 73% of knowledge workers report using AI at work, up from last year
- 60% say they have not received formal AI training
- AI spending is forecast to grow ~44% in 2026
- Training budgets are projected to rise only ~5% in the same period
- Average learning time per employee is estimated to fall from 47 to 40 hours annually
- Gallup estimates disengaged and stressed workers cost the global economy nearly $9 trillion each year
- ADP’s Employee Motivation Index has fallen for a sixth straight month
“Technology accelerates work, but the real advantage comes from people who can guide, question, and adapt AI to real-world needs,” says Dr. Elena Park, president of the SHRM Foundation. “If training lags while AI capabilities soar, the entire program’s ROI can be at risk.”
We’re seeing a growing narrative in corporate leadership rooms: the expectation of faster margins from AI, paired with a quieter retreat from investing in the human elements that wield those tools.
What This Means for Workers
Employees face mounting pressures as AI tools reshape job tasks and performance expectations. Burnout has become a structural risk, not just a personal struggle, and many workers feel less secure about their roles as machines handle routine tasks. Analysts say disengagement and stress carry hidden costs—absenteeism, reduced collaboration, and higher turnover—that can erode even strong wage scales over time.
For workers balancing caregiving, commuting, and other responsibilities, the lack of structured training compounds stress. The result is a workforce that may respond by dialing back effort or seeking safer roles, ultimately undermining the productivity gains AI promises.
“If workers aren’t reskilled to work with AI, they won’t just miss out on new tasks—they’ll miss out on career advancement,” says Dr. Park. “That gap translates into higher turnover costs, slower adoption of new processes, and a slower path to profitability.”
Why the Mismatch Matters for Margins
Short-term financials might look healthier when AI is used to automate routine work and reduce headcount. But a growing body of evidence suggests that without parallel investment in training, those savings could be offset by costs tied to underutilization, errors, and lower quality customer interactions.
In practical terms, firms that invest in AI but deprioritize learning and development risk seeing slower real-world gains from automation. The HR and finance communities warn that the enterprise value of AI is highly sensitive to workforce readiness and engagement—two areas that training directly influences.
“The fastest way to erode AI ROI is to assume technology alone will carry the day,” notes Maria Chen, chief analyst at a corporate research outfit. “Learning programs are the connective tissue that turns theoretical capability into repeatable business impact.”
What Companies Should Do Now
The path forward isn’t about choosing between AI and people. It’s about synchronizing automation investments with a robust learning ecosystem and a plan to support workers through transition periods. Several actionable steps have gained traction among thoughtful boards and HR leaders:
- Integrate AI deployment with structured learning journeys that map to specific roles and workflows.
- Protect learning time as a non-negotiable part of the workweek, not a perforation on the quarterly calendar.
- Offer bite-sized, practical AI training that emphasizes decision-making, risk assessment, and ethics.
- Establish clear metrics linking training outcomes to AI performance, quality, and safety indicators.
- Promote internal mobility and reskilling programs to reduce turnover costs and preserve institutional knowledge.
Some firms are piloting ‘learning hours’ within AI rollout windows, ensuring staff can practice, ask questions, and correct missteps without penalties. Others are partnering with universities and industry groups to build curricula that stay current with fast-changing AI capabilities. The common thread is simple: technology and people must grow together, not in isolation.
The Market Backdrop and Risk Signals
Today’s market backdrop features aggressive AI investment balanced against persistent talent pressures. Inflation-adjusted salaries are rising slowly, but competition for skilled workers remains intense. If AI pilots fail to translate into tangible work outcomes, boards may face pressure to reallocate budgets, which could trigger a negative feedback loop: reduced training leads to slower AI diffusion, which undermines productivity gains, which then prompts further belt-tightening.
Investors are watching this dynamic closely. The short-term margin expansion that AI can deliver could give way to longer-term risks if a large portion of the workforce remains undertrained or disengaged. The best-performing firms, according to current market chatter, will be those that treat AI as an enabler of people—investing in training in lockstep with automation, not as a separate, optional expense.
In this environment, the phrase 'companies pouring billions into' AI signals a broader strategy that demands equal investment in human capability. If leaders neglect the learning and well-being of their teams, the AI lift may stall, and the promised gains won’t sustain over time.
Conclusion: A Balanced Path Forward
The debate is not whether AI should matter in business strategy; it certainly does. The question is how to balance the heat of AI investment with the essential work of training, supporting, and engaging employees. When done thoughtfully, AI and human capital reinforce each other, generating stronger margins today and a more resilient organization tomorrow.
As the data rolls in through 2026, executives who align AI initiatives with robust learning ecosystems—and who commit to keeping workers engaged and supported—will likely outperform rivals who chase automation at the expense of people. The path to durable value lies in a simple truth: technology amplifies capabilities that are learned, practiced, and trusted.
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