For a while now, my thoughts have been around on what the real challenge is when it comes to working with AI. What I can see, is that we’re witnessing a kind of shift in content creation process, but more on a cognitive level. It’s a fundamental change in how we approach the creative process, and it’s not merely the new tool or technique by them self.

Generative AI is both powerful and disruptive, and it challenges traditional workflows that often require mastery across multiple domains — be it graphic design, musicianship, audio engineering, scriptwriting, or UX design. With help of GenAI, you keep your focus sharp—on vision and intent—so that the creative energy stays alive, without the burden of executing every technical task yourself. To get there, you need shift you mindset a little bit.

Lot of the GenAI tools today supports parts of the creation process. But lately, some of these tools tries to go for a homerun, and take the whole content creation process from the initial start. Doing so by addressing the wanted end-result without diving into details. This means that instead of needing deep knowledge in every production step, generative AI demands that we condense and articulate the core intent up front. Imagine having to strip a complex idea down to its bare essentials, like reducing a movie to just a logline that tells you everything you need to know. It’s a lot like first principles thinking—breaking down a concept to its most fundamental components and then building it back up.

For example, in a recent project, I had to describe a tool in just three or four sentences—a task that forced me to:

• • • Clarify the intent: What is this tool about? What’s its core purpose?
    • Eliminate ambiguity – Cut the fluff: With limited words, there’s no room for vagueness.
    • Focus on utility: What are the key benefits and processes users need to understand?

The AI then took that core insight and turned it into a tangible workflow, visualized and ready to iterate on. With this example I want to show that how I forced myself to have a different approach to get to the very essence. It’s a different mental discipline compared to the traditional approach, where creators may have a risk to get lost in the details for every production step.

Having a focus on the core narrative

One of the biggest challenges in traditional creative processes—especially when working on complex projects like music, video production or UX design—is maintaining the core narrative, the guiding vision that ties everything together. It’s easy to lose sight of your original idea when you’re switching hats constantly—jumping from producing to mixing, or from designing to coding, each requiring different mental skillsets.

Generative AI helps maintain this objectivity. It keeps your core vision intact while handling some of the execution details. It’s like having a creative assistant who handles the repetitive bits, allowing you to keep your focus on the big picture and on how each piece fits within that larger vision. You can iterate quickly, test variations, and see a version of your outcome much sooner—without getting bogged down in every small decision. AI keeps the core narrative visible, allowing you to refine without losing your place in the creative journey.

Synthesis: The creative superpower

In this new landscape, synthesis is emerging as one of the most crucial creative skills. It’s not enough to just know how to use tools; you need to connect the dots—to see patterns, to create meaning, and to bring everything together cohesively. Generative AI can generate components, but it’s the human ability to synthesize that adds depth and significance.

In my own work, synthesis often begins with a blank canvas—gathering insights, grouping them, trying out different arrangements until something clicks. Whether it’s visual styles, interview findings, or symbolic concepts, it’s about constantly arranging and rearranging elements until the story makes sense. Generative AI can offer initial ideas, but the synthesis is where the real magic happens.

Adapting to a new Way-of-Working

The biggest challenge with generative AI lies at the cognitive level—changing how we think and how we approach creativity. This shift can feel unsettling, especially for those of us used to traditional workflows that are more linear and detail-heavy. Instead of taking a step-by-step journey, AI encourages an outcome-first approach:

• • • Articulate goals clearly: Generative tools require specific, clear prompts. If you can’t explain your desired outcome concisely, the AI won’t deliver anything useful. This forces us to refine our intent and be really clear about what we want.
    • Embrace iteration: Traditional workflows tend to make changes difficult once decisions are locked in at each phase. With AI, it’s all about creating rough versions, evaluating, and refining. It’s about embracing iteration—failing fast and learning fast.
    • Be open to unexpected results: AI can surprise you, sometimes generating outputs you hadn’t anticipated. This requires a willingness to adapt, to explore unexpected directions, and to view the AI as a creative partner, not just a tool.

180-degree shift in mindset

And it’s indeed a 180-degree shift from traditional approaches:

• • • From process to outcome: Traditional methods emphasize process—each stage must be perfect before moving on.
      With AI, you start with an outcome and work backward, refining and improving as you go.
    • From mastery of steps to mastery of intent: Instead of mastering every step of the process, your focus shifts to mastering the clarity of your intent. You must know what you want at a fundamental level, and then guide the AI to help bring that vision to life.
    • From deep expertise to broad creativity: Traditional creative work often demands deep specialization—mastering specific software or techniques. Generative AI, however, democratizes creativity by allowing you to focus on the broader vision. You don’t need to master every tool, but you need to guide the AI effectively—which requires a different kind of creative skill.

Tarot cards and iterative exploration

Take my tarot card project, for example. I wanted to create a visually consistent yet individually unique deck. DALL-E 3 had the exact visual style I was after, so even though it was slower compared to Midjourney, I chose it because it matched my vision. Each card had its own unique composition and symbolism, and to achieve this, every text prompt had to be meticulously prepared, tested, and iterated upon. This often meant generating 60-160 images to get each card just right. This project was a classic case of balancing speed versus artistry. Yes, Midjourney would have been faster, but DALL-E 3 provided the specific quality I was looking for. Sometimes, it’s not about how quickly you can finish a project; it’s about staying true to the vision and putting in the time to get it right.

Workflows and tool mastery

Using generative AI isn’t about just learning a new tool; it’s about creating a workflow that maximizes each tool’s unique strengths. For example, I use Midjourney and Ideogram for initial image generation, and then Krea and Magnific AI for refining and upscaling. Each tool serves a distinct purpose, and being able to curate a toolkit—knowing when to use which tool and why—is essential for achieving high-quality outcomes. The tools are evolving quickly, and so must we. The real power of AI isn’t in the tools themselves; it’s in your ability to guide them—shaping, refining, and combining different parts into a cohesive, impactful whole.

To better illustrate the differences between traditional and generative AI-driven content creation workflows, consider the following visual representation:

• • • Traditional Workflow: A step-by-step, linear process where each phase (research, setup, creation, refinement) happens in sequence, often leading to slow progression and potential loss of creative momentum.
    • Generative AI Workflow: A dynamic, iterative approach where initial drafts are quickly generated, followed by continuous

Some final thoughts

As AI grows more sophisticated, we need to grow as well—not just in skill but in our perspective on creativity. This work isn’t about getting every technical detail right; it’s about setting a clear direction and being able to collaborate with AI to iteratively bring that vision to life. The real magic happens when we embrace AI as a collaborator—using it to generate, refine, and iterate—while we bring the human touch that synthesizes meaning and coherence. It’s not a traditional, linear workflow, but it’s a journey that keeps creativity alive, adaptable, and constantly evolving.

Generative AI doesn’t just accelerate creativity; it redefines what creative work looks like. It blurs the lines between ideation, iteration, and production. This shift is challenging, but for those willing to adapt their thinking, it holds immense potential. The creative process becomes a dynamic partnership—one that values clarity, adaptability, and constant iteration.