By Kristen Hayer

If you went to any CS industry conference in the last year, you heard a lot about AI. The tools are evolving fast, the use cases are multiplying, and the expectation that CS professionals should be fluent in at least the basics is growing. What gets talked about less is the reality inside many CS teams: adoption is uneven, and some of your best people have never opened a single AI tool. Not because they're behind, but because no one has created a real on-ramp for them.

The leader's job isn't to mandate adoption or bury the team in new software. It's to create enough safe entry points that everyone can find their footing, at their own pace, without feeling left behind.

Why some CSMs are still on the sidelines

Resistance to AI is rarely about stubbornness. It's usually about uncertainty. Will this replace my job? Will I look foolish if I get it wrong? Is this actually relevant to what I do every day? These are reasonable questions, and CSMs who are asking them deserve a thoughtful response, not a rollout plan.

Leaders who skip past that uncertainty and jump straight to implementation create anxiety, not adoption. Before you introduce any tool or program, acknowledge that the hesitation is real and legitimate. The CS professionals on your team who are nervous about AI are not outliers. They are the majority of the workforce right now, and meeting them where they are is the first practical step toward moving forward together.

Start with personal, low-stakes experimentation

The lowest barrier to entry is personal use. Encourage your team to try AI tools in their everyday lives first: planning a trip, drafting a message to a landlord, researching a hobby, getting a recipe suggestion based on what's in the refrigerator. When there are no professional stakes attached, people can build intuition and comfort without worrying about doing it wrong

To make this genuinely accessible, get budget approval for the paid versions of the major tools. Claude, ChatGPT, and Gemini each run around $20 per month, and the paid versions are meaningfully better than the free tiers. If you're asking your team to experiment, remove the financial barrier so no one has to decide whether it's worth spending their own money. That's a small investment with a significant signal attached: this matters, and I'm making it easy for you.

Model the behavior yourself. Share what you've been experimenting with personally, not just professional wins. If you used AI to help plan a vacation or draft a difficult personal email, say so. It normalizes the learning curve and makes it easier for others to admit they're still figuring it out too.

Create structured practice opportunities at work

Once personal experimentation is underway, bring it into the workplace with low-stakes practice projects. Ask CSMs to use AI to draft an internal process document, summarize a long meeting recording, build a template for a QBR agenda, or research a customer's industry before a renewal call. The goal is to create situations where people can try the tools without the output carrying too much weight.

Frame mistakes as part of the process, because they are. AI tools produce unreliable output sometimes, and learning to recognize that is itself a valuable skill. A CSM who tries an AI-drafted customer summary and catches an error has learned something important. Create space for those moments rather than expecting polished results from the start.

Run an AI hackathon

A hackathon gives the whole team a shared experience and surfaces ideas that leaders wouldn't come up with on their own. It doesn't need to be elaborate. A half-day works well: give teams a real CS problem to tackle using AI tools, then have each group present what they built or learned. It could be a better onboarding workflow, a faster way to prep for QBRs, a draft of a customer health scoring framework, or anything else that would actually make their work easier.

The collaborative and slightly competitive format helps draw in people who might not have engaged with a solo assignment. It also levels the playing field in an interesting way: the CSM who has never used AI before sometimes comes up with the most creative application, precisely because they're not anchored to how they think it's supposed to work.

The leader's role throughout

Adoption doesn't happen by announcement. It happens through sustained, visible modeling from the top. Share your own learning openly, including the experiments that didn't work. Create psychological safety around trying things and getting them wrong. Celebrate curiosity and creative application rather than just measurable output.

Pay attention to how you talk about AI in team settings. If it only comes up in the context of efficiency metrics or cost reduction, you're telling your team that AI is a business imperative to comply with. If it comes up in the context of making their work more interesting and their careers more resilient, you're telling them something quite different. The framing matters as much as the program.

The goal isn't uniformity

The aim isn't a team where everyone uses AI the same way or hits the same adoption benchmarks. It's a team where everyone has enough familiarity to make their own informed choices about where these tools genuinely help them. Some CSMs will run with it and find applications you never anticipated. Others will land on a handful of specific uses that make their work a little easier. Both outcomes are valid.

Leaders who invest in gradual, supportive adoption end up with more confident and creative teams, regardless of where any individual lands. The entry point is just making it safe enough to start.

Is your team struggling with change? The Success League is a Customer Success training and consulting firm. We offer a stand-alone class on change management, called Leading Change, that is a part of our CS Leadership Certification program. Find out if individual classes or full program training is best for you or your team by visiting TheSuccessLeague.io.

Kristen Hayer - Kristen is the Founder & CEO of The Success League, a global, customer-focused consulting and training firm. Kristen’s background includes leading award-winning sales, marketing, and customer success teams in early and growth-stage tech companies. She is the host of several podcasts on CS and leadership, and has written over 100 articles on the field of customer success. The book she recently co-authored with 5 other CS thought leaders - The Customer Success Talent Playbook - recently hit #1 on Amazon in 5 categories. Kristen received her MBA from the University of Washington and splits her time between San Francisco and San Felipe, Mexico.