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A recent Writer and Workplace Intelligence survey found that some employees are actively resisting workplace AI initiatives by avoiding training, ignoring internal guidelines, refusing approved tools, or using unauthorized alternatives. In more serious cases, employees reported entering company information into public AI tools or manipulating performance results to make AI systems appear ineffective. The findings show that poorly communicated AI rollouts and overly restrictive governance can encourage shadow AI, sensitive-data exposure, and inconsistent adoption rather than responsible AI use.

Source: ITPro

What to know:

• The survey covered 1,200 employees and 1,200 C-suite executives across the US, UK, and Europe.
• Twenty-nine percent of employees admitted to engaging in behavior that undermined their organization’s AI strategy. This increased to 44% among Gen Z employees.
• Reported actions included avoiding AI training, ignoring usage guidelines, refusing approved tools, entering company information into public platforms, and tampering with performance metrics.
• Thirty percent of employees who resisted AI adoption were concerned that AI would replace their jobs.
• Twenty-six percent believed AI reduced their value or creativity, while 20% said the technology added to their workload.
• Seventy-six percent of executives considered employee resistance a serious threat to their company’s future.
• Sixty-seven percent of executives believed their organization had already experienced a data leak or security breach because an employee used an unapproved AI tool.

Why it matters:

For mid-sized businesses, employee resistance can create risks beyond low AI adoption. When workers ignore internal guidelines, bypass approved platforms, or enter company information into public AI tools, policies may exist on paper without being followed during actual AI interactions.

Clear communication, practical training, and useful approved tools can reduce employee workarounds. However, organizations also need AI policy enforcement that applies governance rules where employees use GenAI. Policy-aligned controls can help prevent sensitive data from being shared, restrict unsafe interactions, and guide employees toward approved behavior without relying solely on blanket bans. This allows businesses to support productive AI adoption while reducing data-protection, security, and compliance risks.

A recent PagerDuty survey found that employees are widely using unauthorized AI tools at work and sharing business information through public platforms such as ChatGPT, Claude, and Gemini. Customer data, emails, meeting notes, financial information, and confidential documents are being entered into AI systems without complete organizational oversight. The findings show that restrictive policies alone may not prevent shadow AI when employees believe approved tools do not meet their needs.

Source: TechRadar

What to know:

• PagerDuty surveyed 1,250 office professionals across the US, UK, Australia, and Southeast Asia.
• Two-thirds said they had used AI tools at work despite believing those tools were not permitted by company policy.
• Eighty-eight percent had shared some form of work-related information with public AI platforms.
• Forty-three percent had entered work emails, while 40% had shared meeting notes or summaries.
• Thirty-four percent had shared customer information, and 31% had entered financial data, confidential documents, or business strategies.
• At companies with fewer than 1,500 employees, 40% had shared customer data with public AI tools, compared with 27% at larger organizations.
• Seventy-seven percent believed workplace AI restrictions were limiting their professional development, suggesting that bans alone may encourage employees to bypass approved processes.

Why it matters:

For mid-sized businesses adopting GenAI, shadow AI can make it difficult to know which tools employees are using, what information is being shared, and whether internal policies are being followed. Personal accounts and browser-based AI tools may also remain invisible to traditional software inventories.

This reinforces the need for continuous AI usage visibility, prompt-level monitoring, and sensitive-data protection. Proactive AI risk monitoring can help organizations identify shadow AI activity, understand where sensitive information may be exposed, and enforce safer AI usage without blocking productive adoption.