In the past few weeks, we're fine-tuning how an AI-native CSM can deliver a timely, relevant QBR - on time.
These have been the most valuable signals:
1ļøā£ Goals & Objectives Agent
Before: sifting through old emails, a patchy sales handover and a wall of call notes to try and remember what they actually wanted to achieve
After: clear view of measurable business impact + goal progress for every QBR
How:
ā Agent gathers clarity on customer-stated objectives
ā Analyst agents understand impact of usage x sentiment x relationships...
ā Another agent analyzes how customer is trending towards original goals
2ļøā£ Product Adoption Analyser
Before: digging through dashboards and Amplitude to understand if usage is good or concerning
After: adoption intelligence that predicts growth potential and prevents churn
How:
ā Agent monitors adoption patterns over time
ā Compares to analyst agent with context of successful benchmarks
ā Identifies expansion opportunities and adoption risks with coaching recommendations
3ļøā£ Stakeholder Sentiment Intelligence
Before: guessing which stakeholders are engaged and who is ghosting you
After: relationship strategy that prevents champion exodus and identifies expansion advocates
How:
ā Agent analyses communication sentiment across calls, e-mails, meetings + on the web
ā Another maps power users and decision-makers
ā Then tracks relationship health with specific next steps for each contact
4ļøā£ Support Resolution Analysis
Before: reactive ticket reviewing, chasing support and hoping nothing major is broken
After: proactive support strategy that turns issues into trust-building opportunities
How:
ā Agent processes support interactions + satisfaction trends
ā Another understands resolution effectiveness and recurring issues
ā Flags escalation patterns that indicate relationship risk
5ļøā£ Strategic Next Steps
Before: cookie-cutter meeting agendas and generic follow-up tasks
After: strategic meeting outcomes that drive measurable business impact
How:
ā GTM Analyst Agents understand churn signals, expansion indicators, and value delivery patterns
ā Generate prioritised recommendations based on similar account success ā Create specific action plans tailored to account + contact context
Critically - all these agents work together in tandem.
And they draw from their GTM Memory (Revenue Labs) that learns from every customer interaction, every successful expansion, every prevented churn.
And once Claude has this context, it can route it anywhere:
ā Generate executive-ready QBR decks (Gamma)
ā Draft prep emails with specific action items
ā Create detailed meeting agendas with strategic talking points
ā Enrich context inside Salesforce
ā Turn into action plan (Notion)
ā Push context to all internal stakeholders (Slack)
How does this compare to your existing QBR process?
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