What Eight Years in Customer Success Taught Me About AI Adoption

Aisha D

Aug 10

5 min read

Personal Brand Blog · Aisha Ariel Davis

What Eight Years in Customer Success Taught Me About AI Adoption

The playbook already exists. Organizations just aren't applying it to their own people.

Aisha Ariel Davis

Artificial Intelligence Workforce Organizational Transformation Strategist. Helping leaders solve the problems they think they have by uncovering the ones they actually do.

June 2026

I published a LinkedIn article this week on why organizations need to apply customer success discipline to their internal AI adoption. The framework. The health signals. The intervention model.

But I want to talk about where this actually comes from for me. Because it isn't theoretical.

I spent eight years building and executing customer success strategy before moving into AI sales and transformation. Eight years of watching what happens when organizations invest in a product and then do nothing intentional to make sure their customers actually get value from it.

The patterns I see in AI adoption today are identical to what I watched play out in customer success a decade ago. And the organizations that figured out CS early had a significant advantage over the ones that didn't.

The same thing is happening right now with AI. The organizations that apply CS discipline to their internal adoption will be ahead. The ones that don't will keep wondering why the licenses keep coming back.

What I Learned in Eight Years

Customer success taught me that the sale is never the finish line. It is the starting gun.

Everything that matters retention, expansion, advocacy, real ROI happens after the contract is signed. And the organizations that understood that invested accordingly. They built health score models. They tracked time to value. They created structured check-in cadences. They trained their teams to read disengagement signals before they became cancellation decisions.

The ones that didn't? They celebrated the close and then wondered six months later why churn was high.

"In CS we never waited for a customer to cancel. We intervened at the first sign of disengagement. AI adoption deserves the same discipline."

Now I sit in rooms where organizations have invested significantly in AI tools and are struggling to show ROI. And when I start asking questions, I hear the same things I used to hear in CS when a customer was about to churn.

They don't really understand how to use it for their specific role. They tried it a few times and it didn't immediately solve the right problem. Their manager hasn't changed anything about how work gets done so there's no natural moment for AI to fit in. They heard someone else had a bad experience and decided it wasn't worth the effort.

These are not AI problems. These are adoption problems. And customer success has been solving adoption problems for years.

What I've Seen

I worked with an organization that went big on AI early. Bought licenses for a large number of users because they were hearing genuine enthusiasm on the ground. Leadership was excited. The rollout happened.

Then usage plateaued. Then it dropped. Then licenses started coming back.

When I started asking questions the answers were familiar. Users didn't have role-specific use cases. Training happened once and never again. There was no feedback channel. Nobody was tracking whether employees had actually experienced value just whether they had logged in.

This is exactly what happens in customer success when you treat onboarding as a one-time event instead of a continuous practice. The customer gets access. They try it. If they don't immediately see value they move on. And by the time you notice the engagement has dropped it is very hard to recover.

The fix wasn't a new tool. It was a new approach to the human side of adoption. Persona-based use cases. A champions network. Regular touchpoints. A feedback loop that actually went somewhere. The same things a mature CS team would have built from day one.

The NPS Question Nobody Is Asking

Every customer success team tracks NPS. Net Promoter Score. How likely are you to recommend this to someone else?

It is one of the most powerful signals in CS because it measures sentiment not just behavior. Someone can be logging in regularly and still be a detractor. Someone can have low usage but be a strong promoter because the value they do get is significant.

Nobody is asking this question internally about AI.

How likely are your employees to recommend your AI tools to a colleague? Are they promoters actively evangelizing and bringing others along? Are they passives using it but not convinced? Are they detractors quietly telling their peers it isn't worth the effort?

Your detractors are more dangerous than your non-users. A non-user hasn't formed an opinion yet. A detractor is actively shaping the opinions of everyone around them.

The organizations that understand this track internal AI sentiment the way CS teams track customer NPS. They know which teams have promoters and which teams have detractors. And they act on that information proactively not after the licenses come back.

The Incentive Gap

One of the things customer success figured out early is that customers who feel valued stay longer and expand faster. Recognition matters. Value acknowledgment matters. Making the customer feel like a partner in the relationship rather than a transaction matters.

Most organizations are not doing this for their employees around AI.

There are no incentives for being an early adopter. No recognition for finding a powerful use case and sharing it with the team. No career development signal that says AI proficiency matters here. No moment where leadership publicly celebrates an employee who figured out something genuinely innovative with AI tools.

Without those signals the rational employee calculus is: this is extra effort with no visible reward. Why bother?

Change the incentive structure and you change the adoption curve. It is that direct.

What Your Employees Actually Need

The same three things every CS team knows their customers need:

A fast path to their first real win. Not a generic demo. A specific moment where AI solved a problem they actually have in their specific role. The faster you get them there the more likely they are to stay engaged.

A place to surface friction safely. Employees who hit a wall with AI need somewhere to put that without feeling like they're admitting failure or creating problems. Build that channel. Make it easy. And close the loop when it's used.

A reason to keep going. Recognition. Community. Career signal. Something that makes the investment of learning feel worth it. Customer success teams build loyalty programs and communities of practice for exactly this reason. Your AI adoption program needs the same thinking.

The Uncomfortable Parallel

In customer success, when churn is high the first instinct is often to blame the product. The product isn't good enough. The features aren't right. The competition is better.

Sometimes that's true. But more often the product is fine and the experience is broken. The onboarding was weak. The relationship was neglected. The value was never properly realized.

AI adoption is the same. When licenses come back the first instinct is to question the tool. But most of the time the tool is fine. The human infrastructure around the tool is what failed.

Build the infrastructure. Apply the CS playbook. Your employees deserve the same intentional engagement strategy your best customers get.

I spent eight years learning how to keep customers. Everything I learned applies directly to keeping employees engaged with AI.

The playbook exists. It just needs to be applied in a new direction.

If you're leading AI transformation and adoption is stalling the answer is probably not a new tool. It's a new approach to the people using the one you already have.

This is the companion piece to my LinkedIn article: "Your Employees Deserve the Same Treatment as Your Best Customers."