What I’d Approach Differently If I Were Creating an AI Tool Today

If I were starting fresh today—new ideas, new markets, new AI products—I wouldn’t copy the common playbook many founders are still following.

Not because it’s completely wrong, but because the way AI products are built has already shifted, and a lot of decisions are still based on outdated assumptions.

After observing countless AI tools, launches, failures, and pivots, here’s how I’d rethink building an AI product in 2025. This isn’t theory—it’s practical thinking shaped by what actually works.

1. I’d Focus on Owning a Workflow, Not Shipping Features

Most products start with ideas like:

“Let’s add a smart feature”

“Let’s use the latest model”

“Let’s rebuild X with AI”

Instead, I’d begin with a simpler question:

Which real-world workflow should this product fully handle?

Not just one step—
but the complete journey from intent → output → decision → action.

Features are easy to copy. End-to-end workflows are much harder to replace.

1. I’d Design for Behavior Change, Not Just Speed

Many AI tools promise to make users:

faster

more efficient

more productive

That helps—but it’s not enough.

I’d design for:

habit shifts

reduced mental effort

simpler decision-making

replacing old workflows entirely

The strongest tools don’t just speed work up. They change how work is done.

1. I’d Treat Prompting as Core Infrastructure

Prompting is often treated as:

an afterthought

a quick experiment

something temporary

That’s risky.

I’d invest early in:

structured prompt design

role-based instructions

validation layers

consistent system behavior

Prompting isn’t just UX. It’s how intelligence is controlled. Strong prompting makes products more reliable and harder to break.

1. I’d Optimize Costs Before Scaling Users

A common pattern:

get users first

worry about costs later

hope margins improve

In AI, this approach fails fast.

I’d design from day one for:

token efficiency

smart model selection

caching and reuse

retrieval instead of regeneration

structured outputs

Poor cost structure doesn’t fail early—it fails at scale, when fixing it is hardest.

1. I’d Avoid “Everything-in-One” Tools

Large dashboards may look impressive, but they rarely last.

I’d go:

narrow

deep

specific

One problem.
One user type.
One painful task.

Generic tools compete on price. Focused tools compete on value—and value builds loyalty.

1. I’d Treat Trust as the Main Metric

Most teams track:

active users

retention

engagement

usage time

I’d prioritize one thing above all: trust.

Because if users trust the output, they keep coming back. And that’s what makes products durable.