In a March 2026 survey of 100 director-level-or-higher IT leaders, 98% said they verify AI outputs before acting, while 90% said their organizations had not extensively audited internal documentation before deploying AI. The results, published by MacPaw in its AI at Work 2026 report, suggest that workplace AI is being introduced faster than many companies are preparing the knowledge and oversight it depends on. MacPaw’s Leebry product is one attempt to address that gap, but the report is vendor-published and does not independently demonstrate that Leebry solves it.
What the survey says about workplace AI
MacPaw says it surveyed 100 IT leaders at director level or above through Wynter in March 2026. Respondents came from SaaS, professional services, IT consulting, manufacturing, financial services, healthcare, cybersecurity, government and retail. The report cautions that company-size subgroup samples are limited, so differences between those groups are indicative rather than statistically conclusive.
Three findings point to a gap between adopting AI and being ready to rely on it:
- Documentation preparation: 90% of organizations had not extensively audited internal documentation before deploying AI. The detailed breakdown was 45% deploying AI on existing documentation as-is, 38% cleaning some areas, 10% auditing extensively and 7% starting but not finishing an audit.
- Expectations and delivery: 61% of IT leaders reported a gap between leadership expectations and what teams could realistically deliver—about six in ten respondents.
- Measuring results: 73% said they lacked formal, outcome-based ROI metrics, while 17% said most of their AI initiatives had delivered measurable results.
On trust specifically, 98% said they verify AI outputs before acting; 2% said they trust AI without human review. That is evidence about the surveyed leaders’ reported practices, not a universal rate or a direct measure of AI accuracy. Verification can be sensible oversight, but it also means organizations cannot assume that deploying an AI tool removes the need for human work.
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These are findings from a company report published by MacPaw, which also makes Leebry, the product featured in the discussion. The report page gives the survey size, channel, timing, respondent seniority and industry categories, but not a full questionnaire or detailed methodology. The figures are best read as vendor-reported survey results, not independent proof of how all organizations use AI.
Why documentation and oversight matter
Workplace assistants answer from material such as policies, support guides and internal knowledge bases. If that material is outdated, incomplete or inconsistent, an assistant may produce a confident answer that reflects the source rather than current practice. An extensive documentation audit is one way to find those problems; partial cleanup or using documents as-is leaves more uncertainty about what the system can safely answer.
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The survey’s measurement findings add a separate concern. Without outcome-based metrics, leaders may struggle to distinguish a useful workflow improvement from activity that merely looks like adoption. And where employees or IT teams still verify answers before acting, the practical value depends on what work the system actually removes while preserving necessary checks.
What Leebry is designed to do
According to a 9to5Mac report dated October 3, 2026, Leebry began as an internal MacPaw hackathon project in 2025. Its Slack bot surfaced answers to internal questions and linked back to source material. The article reports that it resolved 30% of MacPaw’s own level 1 IT tickets within a few months. That is a reported result from MacPaw’s internal use; it should not be treated as a forecast for other companies or as independently verified performance data.
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The 9to5Mac article describes Leebry as handling level 1 support, employee questions and access provisioning or removal. It says answers link to sources, users can flag stale information, access is permission-aware, and its MCP server can expose device-management actions subject to administrator-set guardrails. Leebry’s product site also describes role-based permissions, cited answers and human-in-the-loop rules for workflows where mistakes are costly. These are product and article descriptions, not independent validation of how well the features work in practice.
MacPaw’s Director of B2B Product Development, Dan Jaenicke, told 9to5Mac: “Businesses don’t need another tool to check, they need work taken off their plate, without sacrificing oversight or control,” The statement captures the product’s proposition: automate routine work without removing the controls needed for more consequential actions.
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Questions IT teams should ask before adopting an AI workflow tool
The survey does not compare products, and it is not evidence that Leebry outperforms alternatives. IT buyers can use the issues it raises to assess any proposed platform:
- Can users trace answers to their sources? Check whether a response links to the underlying policy or record, and whether the cited material is current.
- Are permissions enforced for each user? Confirm that the assistant respects existing access boundaries rather than surfacing restricted content in a response.
- How does the system reveal knowledge problems? Ask how stale, duplicate or conflicting documents are identified, who can flag them and what happens after a flag is raised.
- Which actions require approval? Map human approvals, administrator-set guardrails and recovery paths for consequential workflows such as changing access or managing devices.
- Does it connect to tools already in use? Leebry says it connects with services including Confluence, Slack, Jira, Okta and HRIS tools. Buyers should verify that the required integrations and permissions fit their own environment.
- How will value be measured? Define an outcome before rollout—such as fewer support tickets requiring staff intervention—and decide how to measure it alongside accuracy, escalations and time spent reviewing answers.
Leebry’s product site invites organizations to request a demo, and the report page offers an assessment and a way to request the full report. The reviewed material does not establish pricing or independently tested results.
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What the numbers do—and do not—show
MacPaw’s survey paints a consistent picture: leaders report cautious verification, limited documentation audits and difficulty measuring outcomes, alongside a gap between leadership ambitions and teams’ delivery capacity. It does not prove that inadequate documentation caused the reported delivery or ROI problems, nor that one product will fix them.
Leebry offers a concrete approach—answers tied to sources and selected routine workflows with permission controls and human oversight. Whether that reduces work without weakening control depends on the quality of a company’s knowledge, its access rules, the actions it permits and the results it measures. The reported 30% ticket figure describes MacPaw’s own experience, not a general benchmark.
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