Investing in people is what makes technology investments work.
There is a version of the AI and technology conversation that most organizations are having right now, and it goes something like this: which tools should we adopt, how much will they cost, and when can we expect a return on investment? It is a reasonable set of questions. It is also the wrong starting point.
The more important question, the one that determines whether any of those tools actually deliver value, is this: do the people using them know how to use them well?
The evidence is striking. According to DataCamp’s 2026 State of Data and AI Literacy Report, organizations with mature, organization-wide AI training programs are nearly twice as likely to report strong returns from their technology investments. Yet only 35% of organizations have such programs in place. Most are deploying tools without building the human capability to match.
That gap between investment and return is not a technology problem. It is a training problem. And for any organization operating under budget pressure and time constraints, it is one of the most expensive gaps to leave unaddressed.
Technology Training Has Always Delivered Measurable Returns
The case for training employees on the technology they use is not new, and the data behind it has been consistent for years. According to research cited by PwC, 93% of business leaders who prioritize employee training report improvements in productivity, staff retention, and organizational resilience. Organizations with structured training programs have been shown to achieve 24% higher profit margins compared to those without.
What has changed is not the argument for training. What has changed is how much more consequential the training gap has become.
ManpowerGroup’s 2026 Global Talent Barometer captured a pattern that deserves attention: regular AI tool usage among workers jumped 13% to reach 45% of the global workforce, while confidence in using technology fell sharply by 18%. More people are using AI tools. Fewer people feel they are using them well. The result is adoption without understanding, which in practice means mistakes made faster, at greater scale, and with more confidence than before.
A 2026 study of enterprise technology adoption found that employees who use AI tools with skill and confidence save nearly 9 hours per week, 4.5 times more than colleagues who use the same tools minimally or reluctantly. The defining variable is not which tools an organization has purchased. It is whether the people using those tools have been properly trained on them.
Why Most AI and Technology Training Falls Short
In 2026, 82% of enterprise organizations report offering some form of technology training. Yet 59% of their leaders still report a significant skills gap, according to DataCamp’s 2026 survey of over 500 enterprise leaders. The training exists. The capability does not.
The reason, consistently identified across multiple studies, is how the training is designed. Generic online courses are the most common format, and they tend to produce awareness without practical ability. An employee who has completed a tutorial can often describe what a tool does without knowing how to use it effectively within the specific workflows, systems, and requirements of their actual job.
This distinction matters in any organization. A team member who has watched an online introduction to a new software tool is not the same as one who has practiced using that tool within the actual workflows, systems, and reporting structures of their job. The gap between those two employees is not a knowledge gap about the technology. It is a gap in applied, context-specific training.
What the research consistently shows is that training works best when it is built around the actual tasks people perform, delivered in a format that includes hands-on practice, and supported with follow-up. Organizations whose training reflects these principles are the ones seeing measurable results. Teams trained specifically on how to work effectively with AI tools, including how to frame requests clearly and verify the results they receive, have been shown to produce over 40% higher quality output than colleagues doing the same work without that preparation.
The Real Cost of Skipping Training
Delaying or skipping training can feel like a sensible budget decision. The evidence suggests it is a costly one.
IDC estimates that global skills shortages could cost organizations up to $5.5 trillion by 2026 in delayed projects, quality failures, and missed opportunities. That figure is large and aggregate, but the local version is familiar to any manager or team leader: the staff member who spends hours on a task that a trained colleague would handle in 20 minutes; the suspicious email that goes unopened but still causes disruption because staff were not sure what to do; the document produced with an AI tool that goes out with errors no one was prepared to catch.
There is also a retention dimension that is often overlooked in technology planning. Research consistently shows that learning and development opportunities are among the top drivers of employee retention. In 2024, providing those opportunities ranked as the number-one retention strategy among organizations focused on keeping staff. Replacing a staff member costs, on average, 33% of their annual salary, and for skilled or specialized roles that figure is considerably higher. Training that helps retain one experienced person typically costs a fraction of what it takes to recruit and onboard a replacement.
This calculation applies across every sector. A 2026 analysis of workforce pressures found that organizations are no longer simply competing for staff in a tight labor market. They are competing in an environment where AI and technical skills are increasingly what employers across every industry are seeking. Staff who feel confident and supported in their use of technology are more likely to stay. Those who feel left behind by tools they were never properly taught are among the first to look elsewhere.
AI Data Risks: Why Responsible Use Requires Trained Staff
There is a dimension of this conversation that general technology coverage often underplays: the responsible use of AI with sensitive data.
Any organization handling client health information, financial records, legal files, personal employee data, or confidential customer information operates under a different set of stakes than a team testing a new content tool in isolation. A staff member who connects a third-party AI application to a sensitive database, or shares confidential information with an AI tool without understanding the organization’s policies about where data is stored and who can access it, can create a privacy problem that is costly to resolve and damaging to the trust the organization has built over years.
This is not a risk confined to regulated industries, though the consequences in healthcare, finance, legal services, and education are particularly significant. It applies wherever people handle information that others have entrusted to them. Training is what closes the gap between a policy that exists on paper and a workforce that understands what responsible technology use looks like in daily practice.
In this context, training is not a compliance exercise. It is what makes technology adoption trustworthy rather than merely convenient.
The AI Confidence Gap Is the Real Problem
Perhaps the most human dimension of this issue is the one that appears least often in technology planning conversations: how people feel about the tools they are being asked to use.
The ManpowerGroup data cited earlier describes something that any manager will recognize immediately. Staff are being asked to use tools they were not trained on. They are adopting them out of necessity rather than confidence. The result, which researchers have begun calling a “job hugging” effect, is a tendency for workers to hold tightly to familiar tasks and resist new workflows. This is not stubbornness. It is a predictable response to being asked to do something new without adequate preparation.
That kind of quiet resistance has a direct cost. It slows the adoption of useful tools, increases the likelihood of errors, and contributes to the kind of disengagement that often precedes staff turnover.
The antidote is not more sophisticated tools. It is training that builds genuine confidence: the kind that comes from practicing with the actual systems an organization uses, in the context of real work, with support from people who understand the mission and the environment. According to the 2025 Training Industry Report, 84% of employees say that learning adds purpose to their work. That connection between training and a sense of purpose is significant in any organization where people care about what they do and want to feel capable of doing it well.
Well-designed training does not just build skills. It signals to staff that the organization values their development and that they are not expected to navigate a changing technological landscape on their own.
How to Start Building a Technology Training Program
The organizations seeing real returns from their technology investments are not necessarily the ones with the most advanced tools. They are the ones that treated technology investment and people investment as inseparable: structured, deliberate training built around the actual work people do, rather than generic courses designed for a generic workforce.
For any organization operating with lean staff and limited room for error, that approach is not an optional enhancement. It is risk management. Every investment in training that prevents one data incident, retains one experienced staff member, or helps one person on the team recover five hours a week from tasks that could be handled more efficiently, pays for itself in ways that are genuinely measurable.
The arrival of AI tools has not changed the fundamental argument for technology training. It has made that argument more urgent. The tools are more capable. The consequences of using them without preparation are more significant. And the benefit of using them well, for the staff who work with them every day and for the communities an organization serves, has never been greater.
For organizations wondering where to begin, a training needs assessment is typically the most practical first step. It removes the guesswork from prioritization and ensures that whatever training a team undertakes is built around how people actually work, not a standard curriculum designed for someone else’s organization. Varsity Technologies offers that kind of tailored, needs-driven training, from initial assessment through delivery and follow-up support, for organizations of every size and sector. Reach out to start the conversation.