For years, product teams have treated onboarding as a fixed journey.

Sign up.

Complete your profile.

Watch a product tour.

Try a feature.

Hopefully, reach the “aha” moment.

It works reasonably well.

But there is a problem.

Not every user starts with the same goal, the same level of experience, or the same amount of patience.

I’ve seen users abandon onboarding not because the product was difficult, but because the onboarding experience was explaining things they simply didn’t care about.

This is where AI can change the way we think about onboarding.

Instead of asking every user to follow the same path, AI can help create an experience that adapts to the individual.

What Makes AI-Powered Onboarding Different?

Traditional onboarding usually follows predefined rules.

If a user selects “Manager,” show these screens.

If they select “Developer,” show those screens.

AI can go further.

It can combine information such as user intent, behaviour, role, product usage, and previous interactions to determine what guidance might be useful next.

The goal isn’t simply personalization.

It’s contextual assistance.

A new user shouldn’t have to learn the entire product.

They should learn what they need to accomplish their immediate goal.

Start With Intent

One of the most useful applications of AI is understanding what the user is actually trying to accomplish.

Imagine a user says:

“I want to create my first assessment and send it to candidates.”

Instead of presenting a generic onboarding checklist, the product could guide them through exactly that workflow.

Create the assessment.

Add candidates.

Configure the settings.

Send it.

Once the user completes the workflow, the product can introduce other capabilities based on what they actually did.

The onboarding journey becomes connected to the user’s goal rather than the product’s feature hierarchy.

AI Can Adapt to Behaviour

Another interesting possibility is adapting onboarding based on what users actually do.

Suppose a user repeatedly visits a particular feature but doesn’t complete the workflow.

The system might recognize the pattern and offer contextual guidance.

Another user might move quickly through the product and clearly understand what they’re doing.

They probably don’t need the same level of assistance.

This creates an important shift:

The product doesn’t simply ask users to learn. It learns how users are learning.

AI Assistants Can Reduce Friction

AI-powered assistants can also provide support without forcing users to leave their workflow.

Instead of searching a help center, a user could ask:

“How do I create a report?”

The assistant can explain the process or, where appropriate, guide the user directly to the relevant part of the product.

This can be particularly valuable for complex B2B products where users may have dozens of workflows to learn.

The key is making assistance available when needed rather than constantly pushing it toward users.

Don’t Let AI Create More Complexity

This is where product teams need to be careful.

AI personalization can sound impressive while actually making onboarding harder.

If the product changes too much between users, people may struggle to understand the underlying experience.

If the AI gives too many recommendations, users may feel overwhelmed.

If the system makes incorrect assumptions about someone’s goals, the experience can quickly become frustrating.

Personalization should reduce cognitive load, not increase it.

AI Should Know When to Stay Quiet

One of the most important design principles for AI onboarding is knowing when not to intervene.

If a user is successfully completing a workflow, there’s no reason to interrupt them with suggestions.

If someone is struggling, that’s when assistance becomes valuable.

The best AI onboarding may therefore be almost invisible.

It appears when needed and disappears when it isn’t.

Measure Outcomes, Not AI Activity

It’s tempting to measure how many users interacted with the AI assistant.

But that’s not necessarily success.

Better questions are:

• Did users reach activation faster?
• Did time to first value decrease?
• Did onboarding abandonment decline?
• Did feature adoption improve?
• Did users require less support?
• Did week-one retention improve?

The AI is simply the mechanism.

The outcome is what matters.

Final Thought

I don’t think AI will eliminate onboarding.

It will change what onboarding looks like.

Instead of forcing every user through the same predefined journey, products can increasingly adapt to what users are trying to accomplish and how they behave.

But the goal shouldn’t be to make onboarding feel more intelligent.

It should make onboarding feel less necessary.

The best AI-powered onboarding experience might be the one where users barely realize they’re being onboarded at all.

They simply tell the product what they want to accomplish, get the right guidance at the right moment, and start experiencing value.

And that’s probably where onboarding is heading: from teaching users how the product works to helping them accomplish what they came for.