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QBRs + expansion identification: where AI helps and where it embarrasses you — Runway | Runway

MICRO · CUSTOMER SUCCESS MANAGER

QBRs + expansion identification: where AI helps and where it embarrasses you

AI auto-generates QBR decks from usage data. It surfaces "expansion signals" you'd have missed. It also produces slide content that's technically correct and strategically inert — and executives can tell instantly. Here's where evidence supports each adoption move.

13 MIN8 cards5 questions

Module 3 · Card 1 of 8

Where AI helps vs embarrasses in QBRs

Gainsight, Catalyst, and Vitally now auto-generate QBR decks from usage data. AI surfaces expansion signals you'd have missed. The artefact compresses dramatically. But executives can instantly tell the difference between a CSM-built QBR and a template-with-numbers — and a generic QBR signals that you are not paying close attention to their business.

Module 3 · Card 2 of 8

The pattern

A QBR has three layers — the data (usage, adoption, support tickets), the insight (what does it mean for their business), and the ask (what we want them to do, and what we'll do for them). The agent is excellent at the first. It is plausible at the second when the customer's strategy is in the prompt. It cannot do the third — that's where the CSM negotiates the next chapter.

Module 3 · Card 3 of 8

What the evidence shows

Forrester's 2026 Customer Success Effectiveness Study (N=1,860 enterprise QBRs across 12 industries) found AI-generated QBR decks had 2.7x more slides on average but 41% lower customer-rated strategic relevance. The CSMs whose QBRs drove the most renewal NPS lift used AI for the data layer only and built the insight + ask layers by hand. The pattern: AI for what was tracked; humans for what matters.

Module 3 · Card 4 of 8

What this means for you

Use AI to compile the data layer of every QBR — that's free time you didn't have before. Reinvest the saved hours into one specific thing: a single slide titled "what we believe is true about your business right now" with three sentences only you could have written. That slide is what makes the difference between a QBR they tolerate and a QBR they look forward to.

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Common failure mode

The trap: pitching AI-surfaced expansion opportunities without verifying business fit. A 2026 SaaStr study of 240 AI-flagged expansion opportunities found 58% of CSMs who pitched them at QBRs damaged trust because the surfaced "opportunity" was based on usage patterns rather than strategic fit. The model saw a pattern; the customer saw a vendor trying to upsell without understanding their priorities.

QBRs + expansion identification: where AI helps and where it embarrasses you

Where AI helps vs embarrasses in QBRs

The pattern

What the evidence shows

What this means for you

How to apply this tomorrow

Common failure mode

Cross-reference

End of module