We’ve all told little white lies. They’re the glue that keeps relationships intact and avoids unnecessary friction over things that don’t matter in the long run. But like a color chart with hues ranging from optic white to cream, there are gradations of denial that separate a harmless bid for harmony from systemic blind spots.
In the enterprise, we’ve started painting our AI strategies in these same shades, telling ourselves little untruths to bridge the gap between the policies we theoretically adhere to and the messy reality of how teams use the technology.
Indeed, relying on policy alone can be a blind spot. While many leaders feel their AI path is clear, 2026 data shows a divide. A small group of high performers is capturing the lion’s share of AI’s value partly because they’ve aligned their AI rules and processes with how teams work.
Recognizing the hues is an important first step in strengthening governance and realizing the value of AI. That said, here are some specific AI lies that many have allowed to settle into the workday.
Despite years of well-documented evidence of AI’s propensity to make up information, most people don’t bother to validate its outputs. A 2026 Resume Now survey found that 35% of AI users rarely or only occasionally review outputs before using them. Meanwhile, 18% said they typically trust AI outputs as-is, and another 17% review them only when something seems off.
The result is a growing volume of low-quality, error-prone work—and a heightened risk of brand and trust erosion when mistakes slip through. Recent incidents underscore the stakes, including a top law firm filing a brief that contained inaccurate case citations.
Encryption matters, but it doesn’t make an AI system secure on its own. Research shows it’s just one piece of a much bigger security picture.
The key issue is that encryption protects data while it’s being stored or sent. Once an AI system runs and responds to prompts, that protection doesn’t really apply anymore.
Large language models (LLMs) can reveal some of their training data if prompted the right way. OWASP, a nonprofit cybersecurity organization, highlights the risks of prompt injection, where carefully crafted inputs can trick models into ignoring safeguards and spilling sensitive info.
Even if everything is encrypted, an AI system can still be misused or manipulated based on how users interact with it.
It’s tempting to think that strong prompting skills make you an expert in everything. They don’t. Academic research shows that non-experts are significantly more likely to accept “semantically plausible” hallucinations, precisely because they lack the domain knowledge to spot errors.
This suggests that expertise is shifting from prompting well to having the critical discernment to question the validity and soundness of the outputs generated by those prompts.
Writing in the Wall Street Journal, theoretical neuroscientist Vivienne Ming says the best results happen when people use AI as a sparring partner, pushing back, demanding evidence, and interrogating assumptions. “The goal of working with AI isn’t to get the answer faster,” she writes. “It’s to find out what you’re missing.”1
AI is very good at producing answers that sound right. And the better the output sounds, the easier it is to overestimate your own understanding of the topic. The main risk, then, isn’t that AI is wrong, although that is a significant risk; it’s that you won’t even notice when it is.
We like to think we can always spot AI. From a six-fingered hand to copious use of em dashes, we’re confident we’ve caught it. But that confidence is misplaced because we’re noticing only the obvious failures.
It’s easy to spot the slop. But, of course, AI is getting more sophisticated, with high-quality outputs that mirror human nuance and cadence. In fact, studies show that when AI is prompted to adopt a specific persona, humans struggle to identify it about half the time.
It’s no wonder that 54% of people preferred AI-generated writing in a blind New York Times quiz. Quality is no longer a reliable indicator of origin.
This puts us in a post-detection era. The question isn’t if you can tell it’s AI; it’s what happens when you can’t. In an enterprise setting, this could turn into a control problem. If people believe they can rely on their intuition, they may be less likely to verify sources or escalate suspicious content.
The most convincing AI won’t look like a machine. It will look like a colleague, a vendor, or a customer. And it will feel routine enough not to trigger a second look.
Many organizations have established AI guardrails to govern its use, but that doesn’t mean they have a handle on what’s really happening.
Fifty-nine percent of workers in a 2025 Cybernews survey admitted to using shadow AI tools not formally approved by their company. And three-quarters of those said they shared possibly sensitive info, such as employee and customer data and internal documents with public AI tools.
Once that information is in an unsecured AI tool, you lose control, as it can be stored, reused, and exposed in other AI outputs.
The misuse could stem from willful disregard or a lack of training. In the Resume Now study, half of the respondents said their company’s AI guidelines were unclear, and 47% reported they lacked the training to use AI tools in approved ways.
The obvious risk is in believing that your AI policy is synonymous with risk management. In reality, policies that lack training, oversight, and safe alternatives are just suggestions that many employees will probably ignore to get their work done faster.
What do we do with all this? Occasional white lies likely won’t doom an organization. What might is the accumulation of them into a culture where no one’s sure what’s real, what’s verified, or what guardrails are quietly being ignored.
That means enterprises need to upshift from policy to practice with:
- Training that reflects actual workflows
- Guardrails that are easier to follow than to bypass
- Norms where verifying AI outputs is an expectation, maybe even a requirement
Perhaps most importantly, you need to cultivate an environment in which questioning AI is seen as an indication of competence and fluency.
Find out how ServiceNow can help you put responsible AI to work for people.
1 Vivienne Ming, The Wall Street Journal, AI is cannibalizing human intelligence. Here’s how to stop it, April 24, 2026
