AI without explainability creates a trust issue: If a human colleague produces a questionable result, teams can ask what information they used and how they reached their conclusion. Together, they can figure out what happened and what went wrong. This is less straightforward with AI. Employees can ask the AI follow-up questions, but that doesn't necessarily provide a reliable explanation of how the system reached its original answer.
As organizations push employees to use AI and give the technology greater autonomy, these trust issues become increasingly relevant. If employees cannot understand why an AI tool made a mistake, they may lose confidence not only in that output, but also in the tool itself.
When employees don’t trust the AI tools their organization provides, adoption can stall. Some employees may need to spend extra time checking its work, while others may turn to personal or unapproved AI tools they trust.
What starts as an explainability problem becomes a productivity, security and compliance problem for the organization.
AI errors go beyond bad output
Research published in Harvard Business Review describes a phenomenon in which people are expected to trust AI but lack confidence that the trust is warranted. When an AI system confidently produces an incorrect recommendation, employees may start questioning its other outputs and even their own ability to judge when the system is right.
The lack of explainability with AI interrupts the normal learning cycle that happens when a human mistake occurs. Teams instead struggle to understand what went wrong, prevent repeat errors and know when to trust future outputs.
“It's very hard to interrogate AI models reliably,” says Andrew Hewitt, Vice President of Strategic Technology at TeamViewer. Providers are increasingly trying to show users how AI reached its decisions, but Hewitt says that capability is still far from reliable.
Without visibility, employees may be reluctant to rely on a given AI tool even as organizations ask them to use it more. That lack of trust undermines the productivity gains AI is supposed to deliver.
Autonomous AI raises the stakes
The problem becomes even more consequential as AI autonomy expands from simply generating answers to generating answers and then taking action.
If employees already struggle to understand why or how an AI system reached a recommendation, they'll be even less willing to trust it to act autonomously. Organizations could deploy increasingly powerful AI only to have employees spend extra time checking, second-guessing, or correcting its output. That extra oversight can erode AI productivity gains and shift unnecessary work back onto employees.
Human review is an essential part of the operating model, not an optional safety net. Employees should know they are expected to validate AI-generated work before passing it along, just as they would review their own work before sending it out to a colleague or customer. For leaders, however, the challenge is finding AI tools that give employees the explainability needed to vet outputs reliably and efficiently.
When trust breaks, employees look for alternatives
When poor explainability undermines adoption of a particular AI solution, employees do not simply stop using AI. They look for alternative AI tools. This contributes to another growing problem: shadow AI.
Employees have become accustomed to consumer AI tools that are fast, easy to use and increasingly capable. Corporate alternatives sometimes feel slower or less capable — for example, if they run older models or struggle to carry context across a conversation. That gap can push employees toward the tools they already use outside of work.
Additionally, a worker using a corporate AI tool might worry about who can see their prompts or whether the company is monitoring their conversations. If the approved option feels less useful or less trustworthy than a personal AI account, some employees will opt for the external tool.
Hewitt sees that as a continuation of IT's consumerization. Employees expect workplace technology to perform like the tools they use in their personal lives. But with AI, a workaround can quickly become a data problem: employees may move sensitive prompts, company data, or organizational knowledge into tools IT cannot see or control.
Shadow AI is not merely evidence that employees are breaking policy. It can be a signal that the approved experience doesn't meet their needs.
Treat adoption patterns as a signal
Blocking unapproved AI tools can reduce immediate risk, but it doesn't explain why employees felt the need to use them in the first place. Understanding that requires an experience-centered approach to IT.
Organizations need visibility into which AI tools employees use and why. Answers to key questions can turn shadow AI into useful information:
- Is the approved platform too slow?
- Does it lack a capability employees rely on?
- Are workers worried about how their prompts are handled?
- Do they understand the organization’s AI policies?
The same principle applies when AI gets something wrong. Leaders need mechanisms for employees to flag questionable outputs, discuss failures openly and understand what changes as a result.
Seen this way, explainability is not only about helping employees understand AI. It can be the deciding factor in whether the organization realizes AI's productivity benefits without introducing new security and compliance risks.
For MSPs and other technology partners, that creates an opportunity to move beyond policing AI usage. Partners can help the companies their solutions serve identify where unofficial tools are appearing and control genuinely risky behavior. That insight can guide recommendations for safer, more effective alternatives that employees will trust and use.
Learn how TeamViewer ONE can help your organization detect issues sooner, automate faster and fix recurring problems to build employee trust.
