Should AI-assisted work be excluded?

The argument is to explain the designer’s problem framing and choices, rather than treat tool use as the sole assessment criterion.

How should an untested project describe outcomes?

Keep expected benefits as hypotheses and distinguish them from research or results that actually occurred.

Can a marketing team use this approach?

Yes, as a way to discuss why concepts were selected and what evidence informed a creative decision.

A designer portfolio needs evidence of decisions

A designer portfolio should explain the decisions behind the work as well as show the finished screens. Defining the problem, documenting rejected options and recording changes after feedback can reveal judgment that a collection of polished AI-assisted images does not communicate on its own.

What should a portfolio demonstrate when making gets faster?

It should make the selection process visible. A career-design opinion piece by Woody, republished by MobiInside on October 28, 2026, argues that junior designers need to show how they choose, not only what they can produce.

The author recalls routine assignments such as resizing banners, organizing icons and comparing screens with specifications. In that account, these tasks also helped newcomers learn product details and working relationships. The piece interprets faster AI-assisted production as a reason to rethink how juniors demonstrate that learning. This is an author's argument, not proof that AI alone explains changes in hiring across every company.

The examples in the contribution focus on publishing a service, collecting early feedback and deciding whether a particular feature belongs in the product. Their value is not a universal success formula. They illustrate how an applicant can explain why a version was released at a certain point, or why an alternative was omitted. The same discipline can help distinguish observed evidence from assumptions in a self-directed project.

What belongs beside the final screen?

Include enough context to connect the request, the choice and what happened afterward. More pages of output are less useful if the reader cannot identify the decision they demonstrate.

Evidence to include	Question it helps answer
Problem and audience	Whose need shaped the work?
Rejected options	Why did another direction lose?
Constraints and references	What limited the available choices?
Changes after release	What did actual feedback alter?

If user research was not conducted, it should not be presented as completed research. An expected improvement can be labeled as a hypothesis instead of being turned into an invented result. This preserves the distinction between a persuasive explanation and evidence the reviewer can actually assess.

Takeaways for marketers

Our application of this argument is to creative collaboration. When evaluating a designer or agency, ask how a proposed direction was selected under the brand brief and available audience evidence. A large set of attractive concepts alone does not show how the team will make the next difficult choice. This is an evaluation approach, not a promise that a portfolio format produces hiring or revenue outcomes.

Record the request and the reason it matters while work is underway.
Keep a brief explanation of the alternatives that were rejected.
Identify which facts informed the choice and which assumptions remain.
Return to the decision after feedback and document any revision.

These records can make a project discussion clearer without reconstructing the story from memory after delivery. Teams defining a creative collaboration can review Best Partner's Services alongside the process they expect a partner to explain.

Source: MobiInside, Woody contribution, October 28, 2026