Introduction
The problem with 'best practices' in the age of artificial intelligence
When AI executes the playbook better than you... critical thinking becomes the only remaining advantage
Years ago, 'best practices' were the standard of professionalism.
Today, they have become the standard of mediocrity.
AI did not come to break the rules.
It came to apply them with greater precision than you.
Tools like Claude and Figma Make are capable of:
Writing a comprehensive product brief
Generating a professional landing page
Applying sound visual hierarchy
Adding social proof
Setting a clear CTA
Committing to standard user experience engineering
The result?
A 'correct' design.
Professional.
Compliant with every SaaS playbook.
... and completely forgettable.
First: how did best practices turn from a floor into a ceiling?
Best practices were not created to kill creativity.
But to raise the minimum quality.
Methodologies like Design Thinking that originated from IDEO and Stanford d.school were originally a mental framework for tackling complex problems.
But over time:
They turned into a checklist
Reduced to 5 steps
Made teachable in a weekend
Then became automatable
Natasha Jen from Pentagram clearly criticized this shift, considering that Design Thinking has become a primitive framework that pretends to be scientific when used uncritically.
The problem is not with the methodology.
The problem is with our relationship to it.
When best practices turn into a mechanical recipe, they raise the floor... but lower the ceiling.
Secondly: artificial intelligence amplifies similarity on a large scale.
Before artificial intelligence, websites resembled each other due to:
Bootstrap
Design Systems
Dribbble Trends
Today, AI did not invent the problem.
It made it faster and broader.
Language models do not look for the "best".
They look for the "most common".
What is repeated often becomes the norm.
This is why design teams at Figma acknowledge that AI always produces the traditional approach. Because it is trained on the past.
And the real danger here is:
If your value as a designer lies in following the process and producing the expected outcome...
then you are now competing with a machine that excels at this role more than you.
Thirdly: the difference between implementing the solution... and redefining the problem.
AI is excellent at solving the problem you give it.
But it does not ask:
Is this even the right problem?
The best strategic example is what happened with Airbnb.
The apparent problem was slow growth.
The traditional solution: better marketing.
But reframing revealed that poor listing images were the real barrier.
The advertisement was not improved.
The essence of the offer was changed.
This kind of thinking does not rely on a Playbook.
But on questioning it.
Read also:10 principles of design critique that actually change the work.
Fourthly: what is critical thinking in an AI-supported design environment?
Many articles end with the sentence: "Be more creative."
This is impractical.
In an institutional context, critical thinking means four layers:
1️⃣ Editorial Judgment
AI generates options.
The designer chooses.
But the choice is not a random intuition.
It is a decision based on:
Understanding the brand
Understanding the market context
Understanding actual user behaviour
Understanding competitive differentiation
AI may place the correct CTA.
But you know its language is impersonal.
The difference between "AI-powered in seconds" and
"Turn your product link into an ad that actually sells"
is not just linguistic... but a stance.
2️⃣ Problem Framing
A playbook drives you to the solution.
Critical thinking drives you to question.
Sometimes the problem is not in the Conversion Rate.
But in Trust.
Or in Positioning.
Or in unrealistic expectations.
AI improves the path.
Humans change the direction.
3️⃣ Contextual Synthesis
Data does not come with meaning.
Analytics shows drop-off.
But interpreting it requires:
Understanding conversations with the sales team
Knowing support complaints
Perceiving team dynamics
Reading competitor movements
AI sees the point.
The designer sees the system.
4️⃣ Technical knowledge as a design advantage
The current crisis is not a creativity crisis.
But a superficial crisis.
The designer who does not understand technical constraints, and cannot discuss performance or architecture, will lose their impact.
In contrast, design teams at companies like GitHub write actual components within the design system.
The result?
More realistic design.
More influential decisions.
AI will consume design systems as an API.
If you do not understand your system from the inside, you will not know when AI produces something that seems correct... but breaks in production.
Fifth: Where is value going now?
The landscape can be simplified into four layers of work:
Taskwork: Repetitive execution
Brainwork: Applying frameworks and methodologies
Heartwork: Managing relationships and dynamics
Soulwork: Creating new frameworks and questioning assumptions
Best practices live in brainwork.
AI excels here.
Future value shifts to:
Who can read the organisation
Who changes the definition of the problem
Who makes an uncommon but strategic decision
Who knows when to break the rule
The question is no longer:
Do you know best practices?
But:
Do you know when to go beyond them?
🚀 What does this mean for companies today?
Organisations that rely on ready playbooks will turn into similar copies.
The competitive advantage will not be in:
A better CTA
Or a stronger hero
Or a faster landing page
The advantage will be in:
Clarity of position
The power of framing
Depth of understanding
Measured boldness
AI will make the average cheaper.
But excellence will remain a human decision.
How does Ecomedia deal with this transformation?
At Ecomedia, we do not use artificial intelligence to reproduce templates.
But to rethink the problem.
We do not start with the question:
“How do we improve the page?”
But with the question:
“Is this the page that should even exist?”
Our methodology is based on:
Analysing the institutional context before the solution
Reframing the problem if necessary
Integrating AI as a tool for execution, not as a thought reference
Building real differentiation, not just compliance with best practices
If you want to use artificial intelligence without becoming a copy of the market —