SafePorter lets organizations collect protected feedback about AI use and its effects on work. Ask employees and other participants where tools help, where they create rework, and what support is missing. Receive group results and insights without individual responses.
A person can use an AI tool frequently and still spend substantial time checking it. A completed task can conceal correction work. A team can follow the approved workflow while developing informal workarounds to make it usable.
Usage figures alone cannot tell you which explanation applies. Ask people about the work they do before and after the tool produces its output.
Before a rollout: Which parts of the task could benefit from assistance, and which require knowledge the tool may not have?
During adoption: Can people tell when an output needs further checking? Do they have enough time and authority to reject it?
After deployment: What work became easier? What new work appeared? Where do people work around the system, and what does that reveal about the process?
Before expansion: Does the reported benefit hold when verification, correction and escalation are included?
These are example questions for a feedback program, not claims that SafePorter independently measures productivity or evaluates a model's technical performance.
A survey about AI use can become a record of which employee tried which tool, encountered which problem or departed from an approved process. That record serves a different purpose from understanding how the organization should improve its approach.
SafePorter provides protected group reporting. Employees can contribute what they know without giving leadership access to their individual answers or written comments. Small-group controls and suppression of revealing details apply to the results.
A recurring issue about unreliable output may require changes to tool selection or review requirements. A finding about time pressure may require management to change how work is assigned. A finding about unclear rules may require an explanation people can actually use.
The response should match the issue. Training cannot fix every process problem, and a new policy cannot establish that a tool performs well.
Repeat relevant questions after a change to understand what participants report next. Use that evidence alongside technical testing, incident information and other sources.
SafePorter uses institutional sight to describe the organization's ability to understand what people are experiencing as AI changes its work. The idea informs founder Shoshana Rosenberg's book, Practical AI Governance.
SafePorter supplies one source of information. Leadership remains responsible for interpreting findings, investigating concerns and deciding what to change. It is not a replacement for model testing, an incident-reporting channel or the organization's governance program.
See examples of protected results or read about SafePorter.
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