Are you feeling beaten over the head every day with AI talk? 

The pressure to "do AI" is intense, and the sheer volume of information is just overwhelming. 

But here's the thing you need to hear: for those of us leading customer success (CS) and customer experience (CX) – the people who actually own the customer relationship and the revenue – AI isn't a threat; it's the single biggest chance you’ll get for a transformational win in your business and your career.

The catch? Getting it right is a lot harder than most people sign up for. If you don't have a solid game plan, you might wake up a year from now and realize you just went through an expensive, drawn-out process and didn't achieve much.

Let's take a breath, step back, and map out a practical, near-term AI roadmap that focuses on what matters – creating a bigger pie for your customers.

In this article, we’re going to cover the urgent competitive shift, a clear strategy for using AI to create value, and the nuts and bolts of the infrastructure you need to actually pull it off.

10 practical ways to use AI in customer success
In this guide, we break down 10 practical, real-world ways CSMs can use AI right now to deepen customer understanding, scale proactive support, boost productivity, and level up their strategic value.
Customer Success CollectiveGrace Gupta

The AI wake-up call

Why the panic? Why the urgency? Because your biggest challenges aren't getting any better. The ability to differentiate your product is getting harder, and new market entrants are popping up everywhere.

Here’s the hard truth: in the world of AI, your technological moat is eroding fast. 

Technology costs are dropping like a rock, and it’s becoming shockingly easy for a few smart engineers to launch a compelling product with an intelligent system. If you’re built on a legacy codebase, AI-native platforms already have a huge head start.

Winning on experience, not tech

More competition means more price pressure. So how do you win long-term? You win on experience and brand. You win on how you deliver value.

But let's look at B2B SaaS today: our ability to deliver a great customer experience is, frankly, broken. And we broke it! Over the last decade, we’ve hyper-specialized everything. Sales is in one system, support is in another, and engineering is in a third. It’s a total hodgepodge.

To fix it, we have to look past the funnels and stages and find the hundreds of small touchpoints happening daily – things like getting documentation, handing off a case, or following up on a feature request. These are the points of friction you can, and must, automate. That’s where your AI strategy begins.

Where the money is going (and why AI fails in business)

Let's quickly check the market pulse. Spending is way up in the AI world, but it’s still maturing. 

We’re moving from the first phase of "what is this?" experimentation to a more programmatic approach.

Most of the money is still going to the foundation models (like buying an OpenAI model) for developers to tinker with. However, departmental spend is up six to nine times in horizontal use cases. That tells me the budget is shifting from "innovation" to actual, measurable use cases.

The big three (and why we need more)

Right now, only three enterprise use cases have really caught fire:

  1. Code generation (the easiest one, technically)
  2. Support automation (chatbots, reducing case load)
  3. Enterprise search (making sense of all your docs)

Beyond these? It drops off. We simply haven't built out our AI playbooks yet.

Executive priorities are also smarter now. Six months ago, it was, "We must buy AI!" Now, the big question is ROI. And here's the deal: these tools have to be highly customized. 

We've all gotten a terrible Zoom summary, right? It's shocking! If a simple summary fails, it's because my definition of a "good summary" or "positive sentiment" for my business is totally different from yours. Generic AI is useless; custom-tuned AI is everything.