AI is everywhere. Companies are taking every opportunity on press releases or Linkedin to proudly boast about how they are using it to improve their efficiency and deliver for their customers.
Behind these claims, however, sits a contradiction. Despite its ubiquity, AI rollouts are reaching a bottleneck. But it isn’t the technology that’s stalling, it’s the training to use it.
The enterprise AI rollout has followed a predictable but flawed pattern: purchase licenses, showcase a proof-of-concept, then wait for employees to figure it out. What’s missing is the unglamorous middle layer: teaching people not just how to prompt AI, but how to verify its work, handle sensitive information responsibly, and recognize when automation should hand off to human judgment.
Without this foundation, organizations are discovering that their AI investments aren’t delivering the returns they expected, and the problem starts with a fundamental mismatch between deployment speed and workforce readiness.
From Pilots to Production: The Implementation Gap No One Is Talking About
If you read the headlines, enterprise AI adoption looks like a success story. The 2026 Lenovo CIO Playbook shows that businesses have moved past the experimental phase, with close to half of their AI pilots now running in production environments. That sounds like momentum.
But there’s a disconnect. An EY report released in the tail-end of 2025 showed that while nearly 88% of employees use AI in their daily work, their usage is mostly limited to basic applications, such as search and summarizing documents. Only 5% were found to be using it in advanced ways to transform the way they work. That’s because the study found only 12% of employees reported receiving sufficient AI training to unlock the full productivity benefits.
Brady Lewis, Senior Director of AI Innovation at Marketri, sees this gap play out constantly:
“In practical terms, AI fluency in the workplace is more than the ability to generate clever prompts and experiment with new tools,”
Lewis explains. “It requires the ability to understand how to incorporate AI into actual workflows, for example, where our judgment still belongs.”
Companies are racing to scale AI deployments while their teams are still figuring out the basics. The result isn’t just underutilized software, it’s wasted investment in technology that never becomes part of how work actually gets done.
The fix isn’t buying smarter tools. It’s building smarter users through training that keeps pace with both the evolving technology and how your organization needs to use it.
The Rework Trap: Where Time Savings Disappear
On paper, AI should free up employee time by automating routine tasks, speeding up content creation, and accelerating analysis. In reality, many companies are discovering a different pattern.
Workday’s data reveals that employees do report time savings from AI, but close to 40% of those gains evaporate in cleanup work: fixing mistakes, rewriting unclear sections, and double-checking outputs that turned out to be wrong.
This isn’t a software problem. It’s a skill gap. When people don’t know how to use AI tools effectively, the tools generate more work than they eliminate. Every output needs extensive editing. Content requires a complete rewrite. Analysis demands line-by-line fact-checking.
Lewis sees this pattern repeatedly: “The largest gap that we are seeing however, is in the area of training. Organizations place their faith in the mistaken belief that the introduction of AI will produce significant time savings for their employees, when, in fact, the opposite can be true. Rather than producing time savings, poorly trained employees tend to create a rework cycle as a result of working with AI-generated outputs that are inaccurate, and will take longer to correct than it will to accomplish the original task without using AI and create an increased level of risk in the organization as a result of incorrect usage of AI-generated outputs. When AI is represented as a process shortcut, as opposed to a method with its own specific support and processes, there will be a reduction in productivity and trust will deteriorate quickly.”
Companies that invest in proper training get better results and fewer incidents. Those that skip training face longer project timelines, more errors, increased escalations, and teams that lose faith in the tools. The variable isn’t which AI assistant you bought—it’s whether your people know how to use it.
Training bridges that gap. Not a single onboarding session, but ongoing development that adapts as the tools improve and your organization’s needs evolve.
What AI Fluency Actually Means at Work
Being “good at AI” in the workplace means more than knowing how to ask ChatGPT for a summary. It’s about building habits around checking AI’s work, knowing which company data stays internal, distinguishing between a one-off prompt and a reusable process, and understanding when a task needs human oversight.
Salesforce recently published a framework that breaks this down into three stages. First, employees need to be willing to try AI tools, that’s the engagement phase. Second, they need to weave those tools into their daily routines, not just use them for special projects. Finally, they combine their professional judgment and problem-solving skills with AI capabilities to work more effectively.
This framework works as the bricks in the wall, but companies have to ensure they have the right mortar to bind them together.
Ravi Teja Surampudi, Senior Manager GTM at Workday, describes what this looks like in practice:
“When we talk about AI fluency inside teams, I am not talking about prompt tricks or tool familiarity. I mean the everyday habits that make AI safe and useful at work,”
he explains. “Things like knowing how to verify outputs, understanding what data should never be shared, recognizing the difference between a clever prompt and a repeatable workflow, and being clear on where and when a human must stay in the loop.”
Understanding what AI fluency requires is one thing. Building it systematically across an organization is another. The question isn’t whether these capabilities matter, the rework data makes that clear. The question is how to develop them at scale, in a way that sticks.
The 90-Day Blueprint: Turning Copilots into Infrastructure
So what does effective AI enablement look like in practice? Ravi Teja Surampudi recommends a phased approach spread across three months.
“The first month should focus on shared standards. What good usage looks like, how outputs are verified, and what data is off limits.” This opening phase sets the ground rules that apply to everyone, establishing clear expectations around quality checks, information security, and acceptable use before teams branch into specialized applications.
“The second month should move into role specific workflows for engineers, product, marketing, and operations, with managers coached on how to review AI assisted work.” The second phase gets specific. Engineers explore code generation and review workflows. Product teams learn research acceleration techniques. Marketing experiments with content development. Operations identifies automation opportunities. Meanwhile, managers build the ability to evaluate AI-assisted work from their reports.
“The final month should be about measurement and reinforcement. We should ensure we are tracking cycle time, rework percentage, escalations, and team sentiment so leaders can see whether AI is actually improving outcomes or just shifting work around.” The final phase introduces accountability through metrics that show whether AI is genuinely making work better.
Visa demonstrates what this looks like at scale. Their enablement approach to AI embedded learning directly into workflows through team-specific training, created safe spaces for experimentation, and equipped managers with tools to identify skill gaps and support development.
The business impact was measurable. Sales teams reported 78% higher confidence in product discussions. Engineering saw 84% of developers using AI code generation, delivering nearly 20% efficiency improvements. Most telling: nearly 90% of Visa employees with AI access now use it weekly, with many incorporating it into their daily routines.
Making AI Fluency the Operating System
The next chapter of workplace AI won’t be written by whichever vendor ships the flashiest features. It will be determined by which organizations can actually implement it. Companies need to treat AI capability as a foundational competency—building it into how their teams think, work, and improve.
When companies get AI fluency right, it shows up everywhere: higher adoption, better output quality, fewer security incidents, and actual return on the software investment. It’s the difference between AI that genuinely changes how work gets done and AI that becomes another underused tool generating compliance headaches.
The organizations that pull ahead will stop treating AI training as a checkbox and start treating it as ongoing infrastructure, combining skill development, cultural norms, and performance measurement that evolves alongside the technology. They’ll define clear standards for verification and data governance. They’ll build role-specific training that connects AI capabilities to actual job functions. They’ll prepare managers to coach AI-assisted work. And they’ll track the metrics that matter: how long work takes, how often it needs redoing, how frequently issues escalate, and how employees feel about the tools.
The technology is already deployed. The capability gap is what stands between current state and actual productivity gains. The winning move isn’t buying better AI, it’s building better users.