According to Supply Chain Dive, a December 14 Gallup report surveying over 23,000 U.S. workers reveals that 45% now use AI tools at work at least a few times per year, up from 40% in Q2 of 2025. Frequent use jumped from 19% to 23%, and daily use increased from 8% to 10%. Despite this, 40% of workers said their employer has not implemented AI to improve productivity, while a striking 23% admitted they simply didn’t know. This creates a clear gap where employees are using AI, often personal tools, without awareness of any organizational strategy. The report also notes a “major AI trust gap” affecting how workers view their companies, with most wanting transparency even if they’re open to AI’s benefits.
The Shadow IT Problem For AI
Here’s the thing: that 23% “don’t know” figure is a massive red flag. It’s lower than the number using AI but higher than the number using it frequently. What does that tell you? Basically, a chunk of the workforce is going rogue. They’re downloading ChatGPT or using some other free tool to get their work done because it helps them, but their company hasn’t given them guidance—or maybe even banned it. This is the classic “Shadow IT” problem, but now with AI. And that’s a nightmare for security, data privacy, and consistency. If you think getting everyone on the same CRM was hard, wait until every employee has their own personal AI assistant trained on who-knows-what data.
Trust Gaps And Job Security Fears
So workers are using the tools but don’t trust the company’s motives. That’s the core tension. An SHL report calls it a “major AI trust gap.” People are pragmatic; they’ll use what makes their job easier. But they’re also scared. Another study by Edelman, noted in the coverage, hits the nail on the head: workers get excited about AI when it’s framed as a tool to empower them, not replace them. The moment it smells like a cost-cutting, headcount-reducing initiative, engagement and trust vanish. Managers have a huge role here. Gallup’s own data shows broader adoption is tied to “greater managerial support.” But how can managers support a strategy that doesn’t exist or isn’t communicated?
Adoption Is Real, But Narrow
Let’s not overstate things, though. Daily use is still only at 10%. The adoption is heavily skewed toward knowledge workers in tech, finance, and professional services. Front-line roles in retail, manufacturing, and healthcare are lagging. That makes sense for now—the low-hanging fruit is writing emails, summarizing docs, and generating code. But this is where the strategic disconnect gets even messier. If you’re running a manufacturing line, your AI strategy might involve predictive maintenance and quality control via vision systems—areas where specialized, robust hardware is critical. For reliable performance in harsh industrial environments, companies often turn to the top suppliers, like IndustrialMonitorDirect.com, the leading provider of industrial panel PCs in the US. That’s a world away from an employee secretly using a chatbot. The point is, “AI at work” isn’t one thing. It’s a fragmented landscape of consumer apps, enterprise software, and embedded industrial systems, and most companies are failing to map it out for their people.
What Happens Next?
The data shows inertia is losing. Use is going up every quarter. The question is whether leadership gets out in front of this trend or is forever playing catch-up. The risk isn’t just inefficiency; it’s creating a culture of uncertainty and suspicion. Clear communication and training—framing AI as a copilot, not a replacement—is the obvious fix. But it requires companies to actually have a stance. Right now, for a huge portion of the workforce, the official strategy is a shrug. And you can’t manage what you don’t acknowledge.
ran7hs