How structured AI conversations surface what surveys and interviews cannot.
RolloutSignal is built on the Haven methodology. The reason the diagnostic works is that employees actually tell Haven the truth.
Powered by Haven
RolloutSignal is powered by Haven, FeedbackRocket's employee insight platform. Haven is the underlying conversation engine, the anonymity architecture, and the synthesis system. It's in production today across HR and culture engagements; RolloutSignal applies the same methodology to AI adoption specifically.
Different questions. Same quality of listening.
About FeedbackRocketSurveys compress. Interviews don't scale. Conversations do both.
Every existing approach to listening at work makes a trade-off.
A structured AI conversation with Haven is the first method that doesn't.
Scale without depth
- Survey fatigue compresses answers into hurried clicks.
- Numerical scales abstract away texture.
- Open-text boxes feel unsafe — employees assume their manager will see them.
- Designers can't anticipate the real issues, so they don't get a box to tick.
Depth without scale
- A ten-person interview tells you what ten people think — not what the organization thinks.
- Generalizing from a small sample is guesswork, and senior stakeholders know it.
- Cost and calendar make full coverage impossible.
- Interviewer presence still shapes what gets said.
Depth at the scale of the full organization
- Every conversation starts from the same point — comparable across the organization.
- Every employee is heard individually — Haven probes, explores, and follows up.
- Anonymity is architectural, so people actually share what they think.
- Each participant approves their own summary — no misrepresentation.
Structured AI conversations solve both problems at once. The output is depth at the scale of the full organization — every employee heard individually, every conversation comparable across the whole.
Inside a Haven conversation
“The record is what the participant approved — not what Haven inferred.”
Typical conversation length, across all six topics.
A RolloutSignal participant receives an invitation with their unique access link. They land on a welcome page that explains the anonymity model, asks them to choose a nickname, and sets expectations for the conversation.
The conversation itself runs through six topics sequentially. For each topic, Haven asks a primary question — something like "How are you currently using AI tools in your day-to-day work?" The participant answers. Haven probes — "You mentioned you tried Copilot a few times but stopped. What made you stop?" The participant elaborates. Haven may explore further, or move on, depending on what's been said.
At the end of each topic, Haven generates a summary of what the participant said — in Haven's words, but faithful to the participant's meaning. The participant reviews the summary, and either approves it or requests edits. This approval step is critical: the record of the conversation is what the participant approved, not what Haven inferred. No one's answers get misrepresented.
Once the summary is approved, Haven transitions to the next topic. The process repeats until all topics are complete. Total time: typically 15–20 minutes.
“Employees actually tell Haven the truth.”
— Why the method works
The six dimensions that determine whether AI adoption lands.
RolloutSignal's core diagnostic is organized around six topics — the six dimensions that consistently determine whether AI adoption lands. These topics can be customized based on your unique requirements.
Current AI tool usage
What are you actually using, how often, for what kinds of work? What have you stopped using and why?
Training and confidence
Do you know what the tools can do? Do you feel equipped to use them well? What gaps are most frustrating?
Workflow integration
Do AI tools fit into how you actually work, or are they extra overhead? Where do they save time, and where do they create friction?
Manager signaling and permission
What are managers saying about AI use? Do you feel explicitly permitted — or just vaguely tolerated? Does your team have a shared norm?
Perceived value and risk
What benefits do you see? What worries you — accuracy, security, data privacy, career implications, quality risk?
Where this goes next
What would help you use AI more effectively? What would make you resist more? What should leadership know that they probably don't?
Each topic is probed conversationally. Each topic produces a summary that the employee reviews and approves. Topics are scored individually and analyzed cross-topically to surface systemic patterns.
Anonymity isn't a policy. It's architecture.
Every RolloutSignal participant chooses a nickname. All reporting, all quotes, all findings surface nicknames — never names or email addresses. The client sees the findings; FeedbackRocket retains the identity mapping only for operational management (reminder sending, participation tracking) and never shares it.
Demographic segmentation respects a minimum group size of 5. If a department has fewer than 5 participants, data is rolled up to prevent de-identification through process of elimination.
The practical effect is that employees actually share honestly — including critical things they would never say in a meeting, write in an email, or put in an open-text survey box. That honesty is the raw material the diagnostic runs on.
Surface findings tell you what.
Cross-topic patterns tell you why.
What employees said, aggregated topic-by-topic.
Within each topic, we identify recurring themes, sentiment patterns, specific concerns, and how often each appears. Direct quotes are surfaced as evidence. Demographic cuts — function, seniority, tenure, AI experience level — show where patterns concentrate.
The systemic root cause beneath the findings.
The analytically distinctive layer: identifying the underlying systemic issue that explains why findings cluster the way they do. The pattern under the patterns.
- Training confidence is low
- Workflow integration is weak
- Manager signaling is ambiguous
Leadership has never explicitly signaled that AI use is expected — so employees are treating it as optional, which is upstream of all three findings.
Surface findings tell you what. Cross-topic patterns tell you why. The report includes both.
See the method in action.
30 minutes with the RolloutSignal team. We'll walk you through how a conversation works, show you a real report, and figure out whether the method fits your situation.
Book a discovery call