In the last few months, I’ve been in a lot of conversations with customer success leaders and practitioners. These are the folks who live closest to the customer, who deal with the churn risks, renewal prep, escalations, QBRs, and all the in-between.
And no matter the company or industry, I keep hearing the same things. The same blockers. The same hopes. The same frustrations.
Here’s a quick summary of what I’m observing, and what I think is worth unpacking.
The Real Friction
Let’s start with the pain. Everyone complains about the same problems:
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It’s hard to get the right customer data when you need it.
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Prioritization is guesswork. Who really needs attention today?
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So many things still require manual work. Preparing for a QBR takes hours. Context is scattered. Tasks are fragmented.
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And when teams finally have the data, it still takes a lot of time to make sense of it.
That’s just the operational pain. Then comes the conversation about AI.
How AI Is Being Used Today
Most CS teams are experimenting with AI at some level. But in most cases, it looks like this:
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People use it to write emails, create help center content, or summarize meeting notes.
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It’s a tool that lives in a tab. You copy-paste your prompt, get a response, and move on.
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It’s helpful, yes. But it’s mostly an individual productivity boost. It’s not embedded in the team's workflow.
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And while people are curious, there’s also fear. Quiet, but real. What if AI replaces parts of the CS role? What if it makes people feel less needed?
This is where the narrative needs a reset.
What’s Actually Worth Thinking About
Let’s zoom out for a second. Yes, customer success is full of operational friction. But that friction is not inevitable.
Some of it just feels normal because we’ve lived with it for too long. But we now have the tools to fix much of it.
AI is one of them. But we need to stop seeing it as a glorified writing tool.
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It can do much more when applied at the team level. Not just by individuals, but as part of how the CS org functions.
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It can connect to real-time customer data. That messy data across support tickets, CRMs, and product usage? With the right systems in place, it can actually become useful.
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AI can automate the repetitive work. Summarizing accounts. Finding red flags. Suggesting next steps. Prepping QBRs. Not replacing CS, but making space for more strategic work.
Imagine what becomes possible when your AI assistant knows your customers, understands their history, and can proactively suggest what to do next. That’s not science fiction. It’s the kind of future we’re building toward.
Why This Matters
CS is under pressure. Teams are leaner. Expectations are higher. Customers are more demanding.
But we are not stuck.
The shift will not come from one-off AI use cases. It will come from operationalizing intelligence into CS workflows.
That’s what we’re focused on at Actioner. Building the kind of intelligence layer that doesn’t just assist a CSM individually, but makes the whole org smarter. Because better customer outcomes come from better alignment, not more tabs.
There’s a long way to go. But we’re closer than ever.
