MacPaw’s 2026 survey of 100 senior IT leaders found that AI adoption is running ahead of documentation readiness, formal measurement, and confidence in outputs. Its report points to a practical problem for IT teams: AI may answer questions or trigger actions, but people still need reliable sources, appropriate permissions, and a way to keep control. MacPaw’s Leebry is designed to address some of that operational gap, though the company’s survey and product descriptions are not independent proof of performance.
What MacPaw’s survey says about AI at work
The AI at Work 2026 report says Leebry and MacPaw surveyed 100 director-level-or-higher IT leaders through Wynter in March 2026. Respondents represented 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.
These are vendor-reported survey findings, not universal adoption rates. The published report page gives the sample size, survey channel, date, respondent seniority, and industry categories, but does not establish a full questionnaire or detailed methodology beyond those disclosures.
Documentation was often used before it was fully audited
MacPaw’s report says 90% of surveyed organizations had not extensively audited internal documentation before deploying AI. The breakdown shows the different states of readiness:
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| Documentation status before AI deployment | Share reported by MacPaw |
|---|---|
| Deployed on existing documentation as-is | 45% |
| Partially cleaned some areas | 38% |
| Audited extensively | 10% |
| Started an audit but did not finish | 7% |
That matters because an AI system can make inconsistent or obsolete internal guidance easier to retrieve without making the underlying guidance more accurate. Documentation quality is therefore an operational dependency, not a detail that a model or chatbot can reliably fix by itself.
Expectations and measurable results remain out of step
In the same survey, 61% of IT leaders reported a gap between leadership expectations and what teams could realistically deliver—about six in ten respondents. MacPaw also says 73% lacked formal outcome-based ROI metrics, while only 17% said most of their AI initiatives had delivered measurable results. Those figures suggest many organizations are pursuing AI without a consistent way to demonstrate what it has improved.
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Human review remains the norm
MacPaw reports that 98% of respondents verify AI outputs before acting, while 2% trust AI without human review. This is a finding about the surveyed IT leaders, not a measure of how every employee or organization uses AI. It does, however, underline that the trust question is not simply whether a system can generate an answer: teams also need to know when an answer is grounded and when a consequential action should require human oversight.
What Leebry is, and how MacPaw says it began
Leebry is MacPaw’s enterprise AI knowledge and workflow product. According to 9to5Mac’s October 2026 account, it began as an internal MacPaw hackathon project in 2025. The initial Slack bot surfaced answers to employees’ internal questions and linked to source material. 9to5Mac reports that within a few months it resolved 30% of MacPaw’s own level 1 IT tickets.
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That 30% figure describes MacPaw’s internal experience as reported by 9to5Mac; it is not evidence that another organization should expect the same result. The available sources do not provide independent validation of Leebry’s performance.
How Leebry is intended to address the trust problem
Leebry is described as automating level 1 support, employee questions, and access provisioning or removal. Its product site says it connects with tools including Confluence, Slack, Jira, Okta, and HRIS, and describes cited answers, role-based permissions, and human-in-the-loop rules for workflows where mistakes are costly. The 9to5Mac article also describes a mechanism for users to flag stale information and an MCP server that exposes device-management actions with administrator-set guardrails.
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These capabilities, as described by Leebry and 9to5Mac, map to several distinct safeguards: providing source links can help employees check an answer; permission-aware access can limit what information a person sees; stale-content reporting can give teams a way to surface outdated material; and approval rules or administrator-set guardrails can constrain actions with greater consequences. They do not by themselves establish that an organization’s knowledge base is accurate, that every access rule is configured correctly, or that a workflow will deliver a particular level of savings.
MacPaw’s B2B Product Development director Dan Jaenicke summed up the product’s stated goal: “Businesses don’t need another tool to check, they need work taken off their plate, without sacrificing oversight or control,” according to 9to5Mac.
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Questions IT buyers should ask about AI knowledge and workflows
The survey does not compare Leebry with competing products. An IT team evaluating any knowledge or workflow platform can use the following questions to test whether its controls fit the work it will handle:
- Can users verify answers? Check whether answers cite the underlying documents and whether users can open those sources.
- Are permissions enforced per user? Confirm that search and answers respect the access rights of each employee, rather than exposing everything the connected systems contain.
- How does it surface knowledge problems? Ask how stale, duplicate, or conflicting information is identified, and how employees or administrators can flag it.
- What controls apply to consequential actions? Determine which workflows require approval, what administrators can restrict, and how actions such as provisioning or removing access are logged and governed.
- Does it connect to tools already in use? Validate the specific integrations and permissions needed for your environment rather than assuming a listed connection covers every workflow.
How to read the findings
The report offers a useful snapshot of how a group of senior IT leaders describe AI readiness and oversight, but its publisher is also the company selling the featured product. Treat the percentages as MacPaw’s survey results and the product capabilities as vendor or article descriptions—not independent evaluations. The results do not establish that weak documentation caused low trust, that Leebry resolves the wider measurement problem, or that its reported internal ticket outcome will transfer to other organizations.
The strongest practical implication is narrower: deploying AI on top of internal knowledge is not the same as making that knowledge trustworthy, and automating work requires controls that match the risk of the action. Teams considering Leebry or another platform should evaluate the source quality, permissions, review rules, and integrations against their own workflows.
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