TL;DR
- CEO analyzed hundreds of hours of meeting transcripts to identify his conflict avoidance patterns
- Claude found specific behaviors: redirecting topics, premature agreement, excessive softening, postponement
- Three months of practice showed measurable improvement in directness and conflict resolution
- Best for: Leaders who suspect blind spots but cannot observe themselves in real-time
- Key lesson: AI provides pattern visibility without judgment; emotional readiness determines whether findings become improvement
A CEO discovered his invisible conflict avoidance patterns by having Claude Code analyze months of meeting transcripts, identifying specific behaviors like topic redirects and premature agreements that he could not see in real-time.
Dan had a suspicion about himself.
He was good at many CEO things. Vision. Strategy. Building product. Talking to customers.
But he had a pattern he couldn’t quite see: avoiding conflict.
Tough conversations got delayed. Difficult feedback got softened. When disagreement arose, he smoothed things over rather than digging into the tension.
“I knew I did it. But I couldn’t catch myself in the moment. It happened below conscious awareness. By the time I realized I’d dodged a conflict, the moment had passed.”
His therapist suggested self-reflection. His coach suggested journaling. Both helped, but neither solved the core problem: he couldn’t observe himself in real-time.
The Hypothesis
Dan recorded most of his meetings. Partly for note-taking. Partly so his team could reference decisions.
One day he wondered: What if Claude could analyze those recordings?
“I had months of meeting recordings. Hundreds of hours of me talking to people. If my conflict avoidance pattern was real, it should be visible in that data.”
He set up a simple experiment. Downloaded all his meeting recordings. Fed them to Claude Code. Asked a question that felt vulnerable to type:
“Tell me all the times I subtly avoided conflict.”
The Technical Setup
The workflow required several steps:
- Meeting recordings → Transcripts (using transcription tools)
- Transcripts → Claude Code’s context
- Claude analyzes patterns across many conversations
- Output: specific instances with timestamps and observations
Dan created a folder of transcripts and gave Claude a prompt:
“You’re analyzing meeting transcripts from a CEO. Identify moments where the speaker appears to avoid, deflect, or soften conflict. Note: the subject knows this is a blind spot and wants honest feedback. Don’t hold back.”
The Initial Results
The first analysis made Dan uncomfortable.
Claude identified dozens of instances across the first month of transcripts. Patterns he hadn’t consciously noticed:
The redirect: When someone raised an issue, Dan would acknowledge it briefly then change subjects before resolution. “That’s a good point, let’s also talk about…”
The premature agreement: When disagreement emerged, Dan would agree with both sides without resolving the tension. “You’re both right in different ways…”
The softening buffer: When delivering feedback, Dan would sandwich criticism in so much praise that the message got lost. “You’re doing amazing work. One tiny thing. But really, amazing.”
The postponement: When tough topics arose, Dan would suggest discussing “offline” or “later” — and later often never came.
“Reading the analysis felt like being caught on camera doing something embarrassing. These were MY words. I couldn’t deny them.”
The Emotional Reaction
Dan’s first instinct was to dismiss the findings.
“Part of me wanted to argue. ‘That wasn’t conflict avoidance, that was diplomacy.’ ‘That wasn’t softening, that was being kind.’”
But he’d asked for honest feedback. Claude provided it. The question was whether to accept it.
He sat with the analysis for a week. Discussed it with his executive coach. Recognized that the discomfort was evidence — if the analysis was wrong, why did it sting so much?
“The patterns Claude found matched what people had gently suggested to me for years. My coach. My co-founder. Even my wife. I just hadn’t been able to see it in myself.”
The Ongoing Practice
Dan made the analysis a regular practice.
Once a month, he’d add new meeting transcripts to the folder and ask Claude to identify conflict avoidance instances. He’d review them, trying to understand what triggered each one.
Patterns emerged:
- Avoidance increased when he was tired or stressed
- Certain topics (compensation, performance, strategy disagreements) triggered avoidance more than others
- Specific people triggered more avoidance — those he most wanted to please or feared disappointing
“The data turned an abstract weakness into concrete examples. I could see exactly what I did, when, and with whom.”
The Real-Time Application
The retrospective analysis was valuable. But Dan wanted to catch himself in the moment.
He started a pre-meeting ritual. Before important conversations, he’d review his avoidance patterns and set an intention: “I will not redirect away from difficult topics. I will sit with disagreement until it resolves.”
After meetings, he’d sometimes ask Claude to analyze just that conversation. Real-time feedback on whether he’d fallen into old patterns.
“It was like having an accountability partner who’d watched every meeting and would tell me the truth.”
The Improvement Evidence
Three months into the practice, Dan asked Claude a different question: “Compare my conflict avoidance patterns from the first month of data to the most recent month. Have they changed?”
The analysis showed improvement.
Fewer redirects. Less premature agreement. More instances of sitting with tension until resolution. The softening buffers were shorter.
“I wasn’t perfect. The patterns were still there. But they were less frequent and less severe. The data showed I was learning.”
His co-founder noticed too. “You’ve been more direct lately. Meetings are more productive because we actually resolve things instead of tabling them.”
The Broader Self-Analysis
Emboldened by the conflict experiment, Dan expanded his self-analysis.
“I asked Claude to find patterns in how I made decisions. Were there biases? Did I favor certain people’s opinions? Did I rush decisions at certain times of day?”
Some findings were surprising. Dan gave more weight to recent information than historical context. He was more decisive in mornings than afternoons. He favored input from people who communicated confidently, even when quieter team members had better points.
“The AI became a mirror. It showed me things about myself that I couldn’t see directly.”
The Privacy Consideration
Dan thought carefully about sharing meeting transcripts with AI.
“These were conversations about people, strategy, challenges. Sensitive stuff.”
He made several choices:
- Only analyzed meetings he hosted or led
- Didn’t share recordings where others expected confidentiality
- Used the analysis for self-improvement, not to evaluate others
- Kept the practice private initially
“I wasn’t comfortable analyzing other people without their consent. But analyzing my own patterns, using conversations I’d participated in? That felt ethically okay.”
The Limitation Recognition
The analysis had limits.
Claude could identify speech patterns. It couldn’t fully understand context. Sometimes what looked like conflict avoidance was actually appropriate diplomacy — choosing when to fight matters strategically.
“I had to add judgment. The AI said ‘here’s what it looks like you’re doing.’ I had to decide ‘was that actually a problem in this specific situation?’”
Not every identified instance was a mistake. But enough were that the practice remained valuable.
The Unexpected Side Effect
Dan discovered an unexpected benefit: reviewing transcripts made him a better listener.
“I’d re-read conversations and notice things I’d missed in the moment. Good points team members had made that I’d rushed past. Questions I should have asked. Context I should have absorbed.”
The self-analysis practice became a reflection practice. Beyond finding blind spots, it helped Dan understand his conversations more deeply.
The Recommendation
Dan shared his approach with other founders (with caution).
“It requires emotional readiness. If you ask an AI to find your weaknesses and then get defensive about every finding, you’ve wasted your time.”
For those ready to try it, he suggested starting small. Pick one suspected blind spot. Ask Claude to look for it. See what emerges.
“The AI doesn’t judge you. It just observes patterns. What you do with that information is up to you.”
The Ongoing Journey
A year into the practice, Dan still regularly analyzed his conversations.
The conflict avoidance had improved. New patterns emerged to work on. The feedback loop continued.
“I’m not sure I’ll ever be done. Growth is ongoing. But now I have a tool that helps me see myself more clearly. That’s invaluable for anyone trying to improve.”
