{"id":26240,"date":"2026-04-01T09:00:00","date_gmt":"2026-04-01T07:00:00","guid":{"rendered":"https:\/\/affidaty.io\/blog\/?p=26240"},"modified":"2026-03-25T16:06:16","modified_gmt":"2026-03-25T15:06:16","slug":"creativity-a-i-accelerates-human-direction-still-leads","status":"publish","type":"post","link":"https:\/\/affidaty.io\/blog\/en\/2026\/04\/creativity-a-i-accelerates-human-direction-still-leads\/","title":{"rendered":"Creativity: A.I. Accelerates, Human Direction Still Leads"},"content":{"rendered":"

Introduction: AI doesn\u2019t make us more creative – it forces us to clarify the goal<\/b><\/b><\/h2>\n

From my experience, A.I. didn\u2019t teach me what creativity is. Instead, it forced me to define more clearly what I\u2019m trying to achieve<\/b>.<\/p>\n

Because A.I. has a brutal trait: it\u2019s incredibly fast at producing possibilities<\/b>, but it\u2019s also extremely good at producing plausible things<\/b> that don\u2019t actually match what you had in mind.<\/p>\n

If you can\u2019t define the objective<\/b>, constraints, and quality criteria, it will take you \u201csomewhere\u201d in a few seconds. And that \u201csomewhere\u201d can be mediocre, incoherent, or even completely wrong.<\/p>\n

On the other hand, the very incoherence and aberrations of guided generation can open up unforeseen scenarios – and sometimes even more interesting ones. A mistake becomes creative only if you notice it and choose what to keep.<\/b><\/p>\n

In this article I\u2019ll show you how I apply this in practice<\/b>, with a focus on image generation. I\u2019ll walk you through a character-design case study<\/b> I had fun with a few months ago.<\/p>\n

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Two kinds of creativity: \u201cspark\u201d and process<\/b><\/h2>\n

Before talking about tools, it\u2019s worth clarifying what we mean when we say \u201ccreativity.\u201d<\/b> I see a major dividing line: there are at least two different ways creativity can show up<\/b> – the spark <\/b>(intuition) and the process<\/b>. Often, they alternate and blend. Artificial intelligence can interact with both. How? Let\u2019s see.<\/p>\n

1) Spark creativity: intuition and lateral thinking<\/b><\/h3>\n

There is an \u201cexplosive\u201d kind of creativity<\/b>: it arrives as an unexpected connection, a solution that flips the perspective – an idea that doesn\u2019t emerge by linear accumulation, but by a leap<\/b>. It\u2019s that moment when, facing the same problem, you don\u2019t add a detail: you change the question.<\/p>\n

It\u2019s hard not to cite Edward de Bono<\/b>: to break out of patterns, you need techniques and shifts in perspective that aren\u2019t just sequential logic<\/b>. In other words: you don\u2019t always reach something \u201cnew\u201d by doing more of the same thing; sometimes you must change plane, rule, or viewpoint.<\/b> As de Bono sums it up in Lateral Thinking: \u201cYou cannot dig a hole in a different place by digging the same hole deeper.\u201d<\/p>\n

If you want a different outcome, pushing harder isn\u2019t enough – you need to change direction.<\/b><\/p>\n

A.I. can become a powerful engine for this kind of stimulus: it generates associations, variations, and \u201ccontrolled breaks\u201d very quickly. But the real spark isn\u2019t in the output – it\u2019s in the moment you recognize <\/b>something worthwhile and decide to pursue it. A.I. shortens the distance between intuition and first draft.<\/b><\/p>\n

2) Process creativity: method, constraints, structure<\/b><\/h3>\n

Then there\u2019s the creativity that doesn\u2019t wait for inspiration<\/b>: the one that works with constraints, checks, and steps. It\u2019s the creativity that makes results replicable – and that lets you arrive at a good project even when the spark doesn\u2019t come immediately, or when you have many constraints to respect<\/b>.<\/p>\n

If I\u2019m developing a concept and I already know constraints, stylistic cues, and the expected output level, generative AI helps me treat those elements like ingredients<\/b>: it combines them, weighs them differently, and returns variations I can reason about<\/b>.<\/p>\n

In \u201cDa cosa nasce cosa\u201d<\/i>, Bruno Munari describes a way of designing where invention isn\u2019t lightning in a vacuum, but the outcome of an ordered sequence of choices, controls, and transformations.<\/p>\n

It\u2019s an accessible, transferable design method<\/b>: the same rigor can apply to a daily gesture or a complex project. And within that path there can still be space for the spark, but channeled inside a structure.<\/p>\n

A circular process like Design Thinking<\/b>, instead, works more like a loop: observe, define, ideate, prototype, test, and repeat.<\/p>\n

The practical difference today is that A.I. makes it \u201ccheap\u201d (in time terms) to run more cycles of that loop<\/b>: evaluate more options, calibrate the prompt at each iteration, and progressively move closer to the desired result.<\/p>\n

A generic example: instead of stopping at the first \u201calmost right\u201d image, I run micro-cycles – generate -> compare -> correct <\/b>– changing only one parameter at a time. Sometimes A.I. drifts and forces me to decide<\/b>: follow the new path or return to the more coherent one. In short: A.I. accelerates the process, but increases the value of evaluation criteria. Creative work is still choosing well.<\/b><\/p>\n

And that\u2019s where the next theme opens up: when technique becomes more accessible<\/b>, the real competitive advantage is the taste <\/b>behind your decisions.<\/p>\n

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AI democratizes technique (but it doesn\u2019t automatically democratize taste)<\/b><\/h2>\n

We\u2019ve seen how this technology reduces the distance between an idea and its communication.<\/b><\/p>\n

Before, turning a concept into something shareable required specific technical skills<\/b>, time, and often handoffs across different roles<\/b>. Today many barriers <\/b>are lower: you can generate visual drafts in minutes, explore variations at near-zero cost, and communicate an idea even if you don\u2019t yet have the \u201chand\u201d or technique to render it exactly as you imagine.<\/p>\n

That\u2019s a democratic aspect<\/b>: many ideas die not because they\u2019re weak, but because they can\u2019t make themselves understood. AI can act as a translator between your head and the world.<\/p>\n

But here\u2019s the point that, for me, is crucial: A.I. democratizes technique; it does not automatically democratize taste.<\/b><\/p>\n

Taste can be an innate talent<\/b>, sure – but far more often it\u2019s a built skill:<\/b> years of study, practice, errors, visual culture, and awareness of what works (and why). If technique becomes increasingly democratic, value shifts even more toward thinking: defining criteria, taking a stance, deciding what to keep and what to discard.<\/p>\n

In other words, with A.I. the creative becomes more and more a decision-maker<\/b>: less a technical executor, more an author of direction.<\/p>\n

Errors and aberrations: when they become sparks<\/b><\/h2>\n

There\u2019s an element often seen mostly as a problem: A.I. makes mistakes.<\/b><\/p>\n

Sometimes obviously; other times in a plausible way – and that\u2019s more insidious. In the debate, people talk about \u201challucinations\u201d: invented or inaccurate outputs that still look credible.<\/p>\n

In creative work, though, some errors can become useful deviations<\/b>. My filter is simple: notice -> select.<\/b> First I notice something isn\u2019t \u201ccorrect\u201d compared to the brief; then I decide whether there\u2019s a detail or atmosphere worth saving.<\/p>\n

With the Metaphysical Yeti,<\/b> that\u2019s exactly what happened: A.I. misunderstood the creature – but inside that mistake there was a stronger direction than my initial idea, and I had to choose whether to follow it or step back.<\/p>\n

Two practical examples: the same project, two paths (spark vs process)<\/b><\/h2>\n

Here I\u2019ll make this more concrete with a personal character-design project: \u201cThe Metaphysical Yeti.\u201d<\/b> Ideally, there are two ways<\/b> to approach it: one born from a spark<\/b>, the other built as a process<\/b>. In this case it was born from a spark – from a question I asked myself: \u201cHow would De Chirico have depicted the Yeti?\u201d <\/b>But we can also imagine what a structured brief could look like to reach a similar result more methodically.<\/p>\n

In both cases I used (or could have used) a combination of tools: ChatGPT<\/b> to build and refine prompts, Leonardo.ai<\/b> to generate images, Kling<\/b> to create a final video.<\/p>\n

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Example 1 – When everything starts from a spark (and AI shortens the distance)<\/b><\/h3>\n

Starting point (spark)<\/b>: a visual idea arrives in my head like a single frame: \u201cDe Chirico\u2019s Yeti.\u201d In that moment I don\u2019t have a brief yet – I have an intuition.<\/p>\n

First attempt: I start generating by simply telling ChatGPT what I want: a Yeti in De Chirico\u2019s metaphysical style. It gives me a first prompt and decides 90% of the parameters on its own. I paste the prompt into Leonardo.ai using one of the available models. The result is \u201csomething\u201d coherent with the request, but not to my taste – not interesting.<\/p>\n

I try again by providing visual references: I look for 3 images of a Yeti (mostly illustrations) and 3 images of De Chirico paintings. I slightly refine the request: \u201cfor the prompt, use the Yeti illustrations as the base reference and De Chirico\u2019s images as the style reference.\u201d<\/p>\n

The surprise (useful mistake):<\/b> one generation outputs something unexpected<\/b>: what was supposed to look like a mythological creature looks like a person dressed as a Yeti.<\/b> A sort of helmet, a wooden mask, empty eyes like sunglasses; the fur becomes a coat. Gloves and boots. A disturbing, enigmatic figure. A solution I wouldn\u2019t have chosen rationally<\/b> – and yet it expresses <\/b>the idea better. That\u2019s where I recognized the metaphysical key: enigma and unease, beyond the surface<\/b>. What if the Yeti were just a man in disguise? Here AI doesn\u2019t create instead of me: it puts me in front of a deviation <\/b>worth following.<\/p>\n

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Selection and consolidation (the real human work)<\/b>: I select 2-3 images that contain the right DNA and develop them into poses and scenarios.<\/p>\n

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From frame to movement (Leonardo.ai -> Kling): I go back to ChatGPT and ask it to craft the prompt for Kling, to make my character walk through a forest during a snowstorm. In seconds I have a 5-second clip.<\/p>\n

VIDEO<\/h3>\n

What this taught me<\/b>: the spark is still mine, but AI makes the \u201chead -> first draft\u201d step cheap, and errors can become sparks only if I recognize and steer them.<\/p>\n

Example 2 – If I had approached it as a process (Method\/Design Thinking + fast iteration)<\/b><\/h3>\n

Same starting idea, but here I change mindset: I don\u2019t start from the frame; I start from a brief.<\/b><\/p>\n

A realistic design brief<\/b>: the goal is to develop the concept of a mythological creature<\/b> for short content (images + video), keeping a recognizable visual language consistent with early 1900s aesthetics<\/b> (traditional textures, clean geometry, symbolic atmosphere; no overly digital look). The project requires 2-3 key images<\/b> of the character (full body and a variant) and a 5-8 second clip with minimal movement. The creature must feel enigmatic rather than monstrous<\/b>, with a clear silhouette and controlled details; backgrounds should be essential or stylized sets to ensure coherence in the video.<\/p>\n

Here constraints are clear, but a creative choice remains<\/b>: which creature and which style<\/b>. Instead of going by feel, I could set up a mini procedural scheme:<\/p>\n