![[Un]Churned by Gainsight](https://substackcdn.com/image/fetch/$s_!7AoO!,w_120,h_120,c_fill,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffe2167ac-0bcf-4575-9712-8d5ef3588851_300x300.png)
![[Un]Churned by Gainsight](https://substackcdn.com/image/fetch/$s_!hKlf!,e_trim:10:white/e_trim:10:transparent/h_72,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe14b36dd-52b9-48a3-9f93-3f6a459d55ff_1344x256.png)
The Performance Gap Your AI Rollout Is Quietly Creating
[Un]Churned Chapter 018
![[Un]Churned's avatar](https://substackcdn.com/image/fetch/$s_!vkJ0!,w_108,h_108,c_fill,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0464ad30-26c2-4f32-b429-ae4283dd5586_200x200.png)
Your newest CSMs are racing to adopt AI. Your best ones are holding back. The gap between them is about to get a lot more complicated.
🎯 The [Un]Churned Take: AI Adoption Plays Out Differently In Real Life
Something counterintuitive happened when Google Cloud Security rolled out AI to their CS team. Their best CSMs with years of institutional knowledge, refined playbooks, and proven results were the last to adopt. Their newest hires moved the fastest because they had nothing to lose.
This is one of the most important dynamics in CS right now, and one that Diane Wu, Global Head of Customer Success & Experience at Google Cloud Security, and Brady Bluhm, Sr. Product Manager (Staircase AI) at Gainsight, covered on the most recent [Un]Churned episode, “Inside Google’s AI-First Post-Sales Playbook.”
The Resistance Makes Sense
When Diane rolled out new AI tools, her top performers pushed back hardest. But why? While the high-performing CSMs had years of results and best practices to maintain, her newest hires were outpacing them in AI adoption, simply because they had nothing to lose.
The Plot Twist
As a former CSM turned product manager, Brady has lived on both sides of the CS tool conversation. An avid and early adopter of AI, Brady’s skills have advanced so rapidly, he’s doing more, faster, and accepting more work because he can. Instead of his productivity gain returning him hours, it’s raised the ceiling on what he’s expected to produce.
The Research Backs This Up
Researchers at Texas A&M and Peking University recently published a study identifying the AI-specific Matthew Effect. The Matthew Effect is the phenomenon where initial advantages compound over time, while those with fewer advantages fall behind. Essentially, “The rich get richer, the poor get poorer.”
For roles like a Customer Success Manager, where your value comes from expertise and judgment, the pattern goes like this:
Star performers who adopt extract disproportionate gains. Their existing workflows, domain knowledge, and judgment become force multipliers.
Average performers who produce excellent AI-assisted work often have their gains attributed to the tool, not to them.
Stars get the opposite treatment. Their AI-augmented output gets credited to innate talent.
The gap widens on both ends simultaneously. The newer CSMs are adopting AI early, but are missing out on the ability to grow through mistakes and also gain the credit for that growth. The high-performing CSMs may be lagging on adoption, but once they do, it’ll make their years of institutional knowledge even more powerful. The catch: they’ll likely take on even more work because of it.
So Diane’s newest hires aren’t winning the long game by adopting first. They’re logging early reps on a tool their senior colleagues will eventually use to pull further ahead.
What This Means for CS Leaders
If the last to adopt wins the most, how are you making sure your whole team eventually gets there?
For the high performing CSMs, Diane recommends rolling out adoption in two phases. Phase one focuses on getting the team comfortable with LLMs. Phase two is where you expand skillsets to include MCP, integrations, and workflow automation.
For the newer CSMs, AI tools can accelerate output but they can’t replace the judgment that comes from years of customer conversations, failed QBRs, and hard renewals.
The goal is to make sure AI is closing the skills gap, not widening it.
Here’s the Full Episode:
More insights await in the full episode. Watch it right here or on your favorite podcast app, and listen for these three things:
How Diane’s team uses NotebookLM as a living customer notebook, and why it’s changed how she thinks about institutional knowledge at scale.
Brady’s two-question habit that keeps him ahead every week and how he turns a good AI output into a reusable system.
How both speakers think about the future of a CSMs tech stack, and why the application UI as we know it may not survive.
☕ Off Topic: What Else We’re Reading
The Cassandra Complex: Why the Boardroom Punishes Safety Prophets by The QHSE Standard
The people in your org who can already see the performance gap widening due to AI may not be the ones getting airtime. Worth a read on why institutions are wired to discount early warnings through toxic positivity, “bring me solutions” culture, and deliberate marginalization of the exact people trying to save them.
Read the article that explains every industrial disaster ever →
📅 Coming Up
Pulse 2026 — Retention is being rewritten in real time. AI, scale, and digital are changing how customers stay. Pulse 2026 is the #1 conference where CS, education, community, and revenue leaders go to figure it out together. Join us May 27-28 in Las Vegas and use code UNCHURNED for a special rate.
Register at gainsightpulse.com →
The New Rules of CS — Gainsight’s ongoing webinar series is tackling the questions CS leaders are wrestling with right now. Three sessions are already on demand, and one more is coming up: Turning Risk Signals Into Action.
Catch up on the full series here →
Wrapping Up
Your best CSM built their mental library over years and AI just handed a version of that to everyone on your team. How are you maintaining that edge across all your CSMs?
See you next Tuesday 🧠