Building AI Experiences That Customers Actually Use..
One of the biggest problems in AI right now has nothing to do with model quality.
It's adoption.
Every week, new AI products launch.
Many of them are technically impressive.
Most of them struggle to gain meaningful usage.
Why?
Because customers don't adopt products simply because they're powered by AI.
They adopt products because they solve a problem.
The AI Trap
A common mistake product teams make is starting with technology instead of user needs.
The conversation often sounds like:
"We have AI."
"Let's find something to do with it."
This approach usually leads to feature-heavy products that users rarely return to.
The opposite approach works better.
Start with a specific problem.
Then determine whether AI can help solve it.
Simplicity Wins
The most successful software products often do one thing extremely well.
Users don't care about model architecture.
They care about outcomes.
Can the product save time?
Can it remove friction?
Can it help them achieve a goal?
Those questions matter far more than technical complexity.
AI Needs Context
Another challenge is context.
Many AI products are generic.
They can answer questions.
Generate text.
Summarize content.
But generic capabilities rarely create long-term value.
The strongest AI experiences are usually tied to a specific workflow.
For example:
Customer support
Recruitment
Sales
E-commerce
The more contextual the experience becomes, the more useful it feels.
A Good Example
An interesting category that has emerged recently is AI shopping advisors.
Unlike traditional chatbots, these systems focus on helping customers discover products and make purchase decisions.
What makes the category compelling is its narrow focus.
Rather than trying to solve every problem, it concentrates on a specific customer journey.
Platforms such as Steps AI are building around this idea by helping online stores guide visitors through product discovery and purchasing decisions.
That level of focus often leads to better adoption than broader AI products.
Final Thoughts
The future of AI won't belong to products with the most features.
It will belong to products that fit naturally into existing workflows.
Technology attracts attention.
Utility creates adoption.
And in most cases, utility wins.