The Trust Crisis in Remote Tech Teams
Remote work solved the talent problem. You can now hire the best AI engineers from anywhere in the world. But it created a different problem: trust.
In a 2024 Microsoft Work Trends survey, 85% of managers said that remote work makes it harder to trust that employees are being productive. And 73% of remote employees said they feel less connected to their teams compared to when they worked in an office.
This trust deficit is not just a feelings problem. It is a performance problem.
Google's Project Aristotle — a two-year study of 180+ teams — found that psychological safety (which is built on trust) was the single most important factor in team performance. Not technical skill, not experience, not resources. Trust.
When trust is low in remote AI teams, you see predictable symptoms:
- Engineers do not share early ideas because they fear judgment
- People over-document to "prove" they are working
- Decisions take forever because everyone wants consensus (to avoid blame)
- Knowledge hoarding becomes a survival strategy
- Code reviews become adversarial rather than collaborative
- Team members work in silos instead of collaborating
What Is the BRAVING Framework?
Brené Brown, a research professor who has spent 20+ years studying vulnerability, courage, and trust, developed the BRAVING Inventory — a framework that breaks trust down into seven specific, measurable behaviors.
BRAVING is an acronym:
- B — Boundaries
- R — Reliability
- A — Accountability
- V — Vault
- I — Integrity
- N — Non-judgment
- G — Generosity
The power of this framework is that it makes trust concrete. Instead of saying "I don't trust that team," you can say "I don't trust their reliability" or "I don't feel their non-judgment." This specificity makes trust buildable and repairable.
Applying BRAVING to Remote AI Teams
B — Boundaries
The principle: I trust you when you are clear about your boundaries and you respect mine.
In remote AI teams, boundary violations look like:
- Slack messages at 11pm expecting immediate responses
- "Quick calls" that interrupt deep work without warning
- Scope creep on projects because nobody said no
- Managers who say "take time off" but email during vacations
How to build it:
- Define core hours: Agree on 4-5 hours of overlap when everyone is available. Outside that window, asynchronous communication is the default.
- Create "focus blocks": Protect 2-3 hour blocks for deep work. Make them visible on shared calendars. No meetings, no Slack interruptions.
- Normalize saying no: When someone asks for something outside your capacity, "I can't take that on this sprint, but I can help next sprint" should be a normal response, not a career risk.
- Lead by example: If the CTO sends Slack messages at midnight, the team will too — regardless of what the policy says.
R — Reliability
The principle: I trust you when you do what you say you will do.
In remote teams, reliability is harder because:
- You cannot see people working, so you rely on outputs
- Timezone differences create handoff gaps
- Asynchronous communication means delayed accountability loops
How to build it:
- Small commitments, kept consistently: It is better to commit to less and deliver every time than to over-promise and occasionally miss. Trust is built in small deposits.
- Make work visible: Use project boards (Linear, Jira, GitHub Projects) that show what each person is working on, what is blocked, and what is done. Visibility replaces surveillance.
- Daily async standups: A short written update — "Yesterday I did X, today I will do Y, I am blocked on Z" — creates a lightweight accountability rhythm.
- AWARE check-in: Before committing, ask: Am I being realistic about my capacity? Will I actually prioritize this? Am I saying yes to avoid disappointing someone?
A — Accountability
The principle: I trust you when you own your mistakes, apologize, and make amends.
In remote AI teams, accountability failures look like:
- Blaming the model, the data, or the infrastructure — never taking personal ownership
- "I thought someone else was handling that"
- Hiding bad results from experiments instead of sharing learnings
- Silent failures that only surface in production
How to build it:
- Blameless post-mortems: When something goes wrong (a model fails, a deployment breaks, a deadline slips), focus on "what happened and what do we change?" not "whose fault is this?"
- Model accountability from the top: When a leader says "I made the wrong call on that architecture decision. Here is what I learned and what I would do differently" — it creates permission for everyone to do the same.
- Celebrate "fast failures": When someone discovers their approach will not work and pivots quickly, that is a win. Recognize it publicly.
- Accountability partners: Pair people up for weekly 15-minute check-ins on their commitments. Peer accountability is often more effective than manager accountability.
V — Vault
The principle: I trust you when you keep confidences and do not share information that is not yours to share.
In remote teams, vault violations are common because:
- Slack channels blur the line between private and public communication
- Screen-sharing can expose private conversations
- "Venting" in DMs about colleagues often gets forwarded
- Salary, performance, and personal information spreads easily
How to build it:
- Clear confidentiality norms: Define what is shareable and what is not. "1:1 conversations stay in the 1:1 unless we agree otherwise."
- No triangulation: If person A has a problem with person B, A talks to B — not to person C. Triangulation destroys trust faster than almost anything else.
- Protect vulnerability: When someone shares a personal struggle, a mistake, or an uncertainty in a team meeting, that information does not leave the room.
- Be explicit: "I'm sharing this in confidence" should be a normal phrase in your team vocabulary.
I — Integrity
The principle: I trust you when you choose courage over comfort, practice your values, and do what is right — not what is easy or expedient.
In remote AI teams, integrity challenges include:
- Shipping models you know have bias because the deadline is tight
- Overpromising AI capabilities to clients or leadership
- Cutting corners on testing because "it works in dev"
- Not disclosing limitations of your AI system to stakeholders
How to build it:
- Define team values and reference them in decisions: "We said we value transparency. Shipping this model without disclosing its 15% error rate on edge cases violates that value."
- Create safe channels for raising concerns: An anonymous feedback mechanism, a designated "ethics check" in code reviews, or a standing agenda item for concerns.
- Reward integrity, not just results: When someone delays a launch to fix a data quality issue, celebrate the decision — not just the final launch.
N — Non-judgment
The principle: I trust you when I can ask for help without being judged.
This is arguably the most important element for AI teams because:
- AI is a rapidly evolving field. Nobody knows everything.
- Admitting "I don't understand this paper" or "I can't figure out this bug" feels risky.
- Imposter syndrome is rampant in AI/ML — especially in remote settings where you cannot see that everyone else is also Googling basic things.
How to build it:
- Normalize not-knowing: Leaders should regularly say "I don't know" and "Can someone explain this to me?" If the CTO does it, junior engineers feel safe doing it.
- Pair programming / pair debugging: Working together on hard problems normalizes struggle and makes asking for help a natural part of the workflow.
- "Stupid question" channels: Create a Slack channel explicitly for questions that people feel are "too basic." Rename it something like #learning or #ask-anything.
- Respond to questions with gratitude: "Great question" and "I was wondering the same thing" are trust-building phrases. Sighing, eye-rolling, or "I already explained this" are trust-destroying behaviors.
G — Generosity
The principle: I trust you when you extend the most generous interpretation possible to my words, actions, and intentions.
In remote teams, generosity is critical because:
- Text communication lacks tone. "OK" in a Slack message can be read as agreement, indifference, or passive aggression.
- Cultural differences across global teams mean the same words carry different meanings.
- When you cannot see someone's face, you fill in the gaps with your own insecurities.
How to build it:
- Assume positive intent by default: Before reacting to a terse message, ask: "What is the most generous interpretation of this?"
- Ask before assuming: "I want to make sure I understand — did you mean X or Y?" is always better than assuming the worst.
- Use video for difficult conversations: Text is for information. Video is for nuance. If a conversation is getting tense on Slack, switch to a call.
- Share context: "I'm giving short responses today because I'm juggling a deadline, not because I'm upset" prevents misinterpretation.
Building a BRAVING Practice for Your Team
Here is a practical plan to implement the BRAVING framework:
Month 1: Assess
- Share the BRAVING framework with your team
- Have each person privately rate the team on each dimension (1-10)
- Aggregate the results anonymously and discuss the patterns
Month 2: Focus
- Pick the 2 lowest-rated dimensions
- Create specific, actionable commitments for each
- Assign a "trust champion" to model and monitor progress
Month 3: Embed
- Add a BRAVING check-in to your quarterly retrospectives
- Celebrate specific trust-building behaviors you observe
- Address trust violations quickly and directly
How Hyperion Consulting Builds High-Trust Teams
At Hyperion Consulting, we help tech companies build the trust infrastructure that high performance requires. Our team coaching and leadership development programs use frameworks like BRAVING to create concrete, measurable improvements in team dynamics.
Trust is not soft. It is the hardest, most important thing you will build.
Ready to build a high-trust remote team? Book a free consultation to discuss how we can help your leadership team.
