Why the Best Interface in the AI Era Is No Interface

Yesterday, a friend who's been a product manager for 10 years came to me frustrated.

"I spent 3 months designing a 'perfect' AI workflow system. Over 50 nodes, hundreds of connection types, covering 90% of our company's business scenarios." He opened his laptop and showed me that densely packed flowchart.

"And the result?" I asked.

"After one month online, we have 12 active users. 10 of them are from our own tech team."

His experience isn't unique.

According to the latest data, over 3,000 enterprises have launched various AI workflow platforms in 2025. But 74% of users say they'd rather talk directly to AI than learn complex workflow systems.

Behind this lies a truth many overlook:

In the AI era, the best interface is no interface.

Two Eras, Two Logics

Let me show you two real scenarios.

Scenario One: The Traditional Workflow Era

Xiao Wang is an operations manager at an e-commerce company. Her boss asked her to analyze last month's sales data and find the best-selling products.

She opens the company's new "Smart Analytics Platform":

Step one, drag in the "Data Source" node. Step two, select Excel file, configure column mapping. Step three, add "Data Cleaning" node, set deduplication rules. Step four, connect "Statistical Analysis" node, choose analysis dimensions. Step five, configure "Visualization" node, select chart type. Step six, run. Error. Step seven, check each node, find date format is wrong. Step eight, go back to step three, add format conversion...

2 hours later, Xiao Wang gives up. She opens Excel and finishes in 10 minutes.

Scenario Two: The Intent Interaction Era

Xiao Li at the company next door faces the same task.

She opens a chat window and types: "Help me see which products sold best last month, and analyze why."

30 seconds later, AI replies: "Based on analysis, last month's TOP 3 bestsellers are:

Sunscreen (320% month-over-month sales growth) - mainly because you joined a platform promotion on March 15
Face masks (180% month-over-month growth) - new product launch combined with KOL promotion was effective
Serum (stable 45% growth) - repeat customer rate reached 68%

Recommend monitoring sunscreen inventory. At current growth rate, existing stock will only last until month-end."

Xiao Li follows up: "What's the user profile for sunscreen buyers?"

AI continues: "Mainly female users aged 25-35, concentrated in first and second-tier cities, 70% are new customers, indicating the promotion was effective for customer acquisition..."

See the difference?

In the first approach, users are learning software. In the second approach, software is understanding users.

This is the essence of intent interaction.

Why Is This Only Possible Now?

You might ask: hasn't this "conversational" interaction existed before? Isn't that what Siri does?

It's different. Fundamentally different.

Early voice assistants just turned "clicking buttons" into "speaking commands." You had to use specific phrases, specific commands. Change the wording slightly, and it wouldn't understand.

But starting in 2024, the rules changed.

1. AI Can Actually "Understand" Now

Models like Claude 3.5 have context windows of 200,000 tokens.

What does that mean? It can remember a 300-page book at once.

This means you can talk to it like you would a colleague. You can say "that thing," "what we mentioned earlier," "the plan we discussed last time," and it knows what you're referring to.

More importantly, it can understand your intent, not just your literal words.

When you say "help me look at the data," it knows you might want to:

Find anomalies
Discover patterns
Compare trends
Predict the future

Then it judges what you really need based on context.

2. AI Has Learned to "Use Tools"

This is the real breakthrough.

Before, AI was like a knowledgeable consultant—could answer questions but couldn't do things for you.

Now AI is like an all-capable assistant. You say "book me a meeting room," and it can:

Check calendar
Search available rooms
Send booking email
Add calendar reminder
Notify all attendees

One sentence, all done.

Data shows that companies adopting this model see average employee productivity increase of 40%.

3. Cost Has Reached a Tipping Point

In 2025, AI inference costs dropped 95% compared to 2023.

This means having AI understand a sentence and execute a task now costs less than the time cost of manually operating an interface.

In other words, instead of teaching users how to use software, just let AI operate for them.

This Isn't Just Technical Progress—It's a Paradigm Revolution

Many haven't realized how significant this change is.

Traditional Software Logic: "Feature Stacking"

Product managers' job was to design all possible features, then organize them with menus, buttons, and forms.

Users had to learn: Where is this feature? How do I operate it? How do I set parameters?

The more powerful the software, the more complex the interface. A paradox.

Intent Interaction Logic: "Need Response"

No menus, no buttons, not even an interface.

Just a chat box.

Users state their needs, AI understands intent, invokes capabilities, completes tasks.

Software complexity is hidden. Users only see results.

This is like going from DOS to Windows, from keyboard to touchscreen—a complete paradigm shift.

Who Will Be Eliminated?

The harsh reality: not everyone can adapt to this change.

Those Who Will Be Eliminated:

1. Product Managers Obsessed with Complexity

Those who pride themselves on designing complex systems, who measure expertise by flowchart count.

They're still adding parameter configurations to AI, still designing advanced options.

Users have already left.

2. Tool Collectors

Some people have 30 "productivity" tools on their computers.

Now, they just need one chat box.

3. Flowchart Artists

Those who complicate simple problems, using 100-page PPTs to explain "how to make a cup of coffee."

AI doesn't need flowcharts. It needs clear intent.

Those Who Will Rise:

1. Intent Expressers

People who can clearly articulate needs.

Not "help me make a spreadsheet," but "analyze this month's sales data, find the fastest-growing categories, predict next month's inventory needs."

2. Result Validators

AI can execute, but judging whether results are good still needs humans.

People who understand business, have judgment, and can spot problems will become more valuable.

3. Idea Proposers

AI can execute ideas, but ideas themselves are still the human domain.

Those who can propose "things no one has thought of" will become key.

A Future That's Already Happening

Here's an interesting statistic:

In Q1 2025, 73 of the top 100 software companies globally launched "conversational" features.

Not chatbots—conversationalizing core functionality.

Adobe: Edit photos by talking to Photoshop
Salesforce: Query customers and modify orders by speaking
Microsoft: Analyze data in Excel through conversation

Even more interesting, usage rates for these features are 3.2 times higher than traditional interfaces.

Users are voting with their feet.

Conclusion: The End of Interfaces, The Beginning of Interaction

Back to my friend's story.

Later, they launched version 2.0.

Removed all the node designers, left just one chat box.

User count grew 50 times.

"The ironic part," he said, "is that the backend logic is actually more complex now. But users can't see it—they just say what they want."

This is the magic of intent interaction.

It's not about humans learning machine language, but machines understanding human intent.

This isn't just a technology trend—it's a redefinition of human-machine relationships.

In this new world, the best interface is no interface.

The most natural interaction is conversation.

Welcome to the intent interaction era.

Are you ready?