The AI feature race
Products everywhere are adding:
AI assistants
AI recommendations
AI summaries
AI chat
AI-generated content
But impressive AI features don't automatically create successful products.
Some are barely used.
The problem isn't always the model.
It's often the product thinking around it.
An AI feature is a capability. An AI product is an experience built around a meaningful user outcome.
Start with the problem, not the model
Teams often begin with:
"What can AI do?"
Then they search for places to insert it.
A stronger question is:
"What problem becomes meaningfully easier because AI exists?"
That shift changes the design process.
Instead of adding AI because it's available, teams identify where AI can:
reduce effort
improve decisions
accelerate workflows
personalize experiences
help users achieve better outcomes
Technology becomes the means, not the product strategy.
Redesign the workflow
Users rarely care about model architecture or benchmark scores.
They care about what they can accomplish.
Can the product help them:
write faster?
research better?
make a decision?
complete a task with less effort?
The strongest AI products don't simply automate an existing step.
They often rethink the workflow around what AI makes possible.
That's a much bigger opportunity.
AI changes the user relationship
Traditional software generally follows predictable rules.
AI introduces uncertainty.
Users need to learn:
when to trust an output
when to verify it
when to correct it
when to take control
This creates a new design responsibility.
The product isn't only designing an interface.
It's designing the relationship between human judgment and machine intelligence.
Trust is part of the product
An AI system can be technically impressive and still feel unreliable.
Users need to understand:
what the system knows
where it may be uncertain
what it has done
what they can change
when human judgment is needed
This connects directly to the idea of calibrated trust.
The goal isn't to make users trust AI more.
It's to help them trust it appropriately.
Design for collaboration
The strongest AI products often don't replace humans completely.
They create collaboration.
Examples include:
coding copilots
writing assistants
research tools
decision-support systems
The AI handles parts of the process while the human provides:
judgment → direction → correction → approval
This can create a more useful relationship than simply asking AI to do everything.
Measure outcomes, not prompts
AI teams can easily become obsessed with activity metrics:
prompts submitted
AI sessions
generations
feature usage
These tell you that people interacted with the system.
They don't tell you whether the interaction mattered.
Better questions are:
Did users save time?
Did task completion improve?
Did decision quality improve?
Did errors decrease?
Did users accomplish something they couldn't do before?
AI product success should be measured by outcomes, not AI interactions.
AI capability is becoming a commodity
As access to powerful models becomes easier, the model itself becomes less of a differentiator.
The competitive advantage increasingly shifts toward:
Workflow design
User experience
Trust
Data
System integration
Behavioral understanding
Two products can use similar AI technology and deliver completely different levels of value.
The difference is the product surrounding the model.
Takeaway
AI features are relatively easy to add.
AI products are harder to design.
The difference lies in understanding:
Problem → Workflow → AI capability → Human interaction → Outcome
The winners won't necessarily be the products with the most AI.
They'll be the products that make AI genuinely useful, understandable, and integrated into how people already work.
Technology powers the experience. Product thinking determines whether the experience matters.
