The AI Hit Machine: Lessons Learned from Exploring AI Music

My Hands-On Education in EDM

For months, I’ve been making Pop-Dance EDM tracks with AI. This has been a learning process, and more involved than I anticipated. Keeping in mind that an expert would have finished this project sooner. However, it is an interesting test and a learning experience that I enjoy. Working on something I like meant I would stay engaged through the inevitable challenges. I also wanted to test my marketing experience in a new area. This hands-on method set my project apart from theoretical discussions. I managed every step, from the initial idea to the final visuals.

When building a song, AI doesn’t automatically grasp the differences within the genre. It requires ongoing guidance to steer the output toward what my target audience would like to hear.

Each version of the song was a test. The back-and-forth process helped me understand faster than any course or tutorial ever could. My business background helped here. I could direct AI with commercial goals from the start, not just artistic ones.

Speed vs. Control

AI is great at generating ideas, processing market data, and creating rough tracks. All happened in minutes instead of weeks. The time from concept to near-finished product shrunk dramatically.

But speed came with a trade-off. I became the “prompt artist”, the strategist guiding each iteration. The AI handled the heavy lifting, but I provided direction. This changed my role from traditional music skills to clear communication with the machine.

Vocal quality was excellent. Suno.ai produced consistent results that worked well in the pop-dance context. They weren’t Grammy-level performances, but they met the genre’s needs. Starting with market analysis instead of pure creativity was a good choice. AI identified specific niches that informed every creative decision. The song concept was stronger from the beginning because it had a commercial foundation.

The Friction Problem

AI’s data-driven suggestions were often technically perfect, but they lacked the unexpected hooks I thought were key for “hit potential”. The machine would create flawless rhymes and rhythms based on training data. But I consistently modified the outputs. I would introduce an unexpected word or rhythm that added impact. These moments revealed that creativity is not just about optimization. It’s about surprising others and introducing purposeful friction.

A little note about “technically perfect”. Yes, the lyrics had strong structure, and coordinated perfectly with the beat. However, the actual lyrics, needed some work. I was getting lyrics that sounded good but I struggled to make sense of them. For example one lyric went (translating from Spanish): My people, My people that always feel them, Happy to be alive, to be conscious, that always raises the grade for the people, that’s my people.

Great beat though.

I admit this could be the way I am translating it, speaks to the regionalization of language. When I put the lyrics in Google Translate. It looked better, but still not great. Like “Happy to be alive, To be conscious?” it needs some work.

Nevertheless, I became the curator, looking through AI output to find what was compelling and original. The machine focused on technical accuracy; I focused on what would catch listeners’ attention.

Industry Implications

This isn’t just about individual creators. AI is changing how the music industry operates. The barrier to entry has dropped significantly. With solid ideas and good prompt crafting, anyone can “create” professional-sounding music without expensive studios or years of traditional training. AI also helps with creative blocks by generating melodies or lyrics when you’re stuck (Stack AI, 2025). Ai helps from mixing and mastering to generating marketing assets.

Labels are using AI to analyze streaming and social data. They identify talent and trends with a precision that wasn’t possible before (Tyron, 2024). We’re moving toward real-time music generation that matches listeners’ moods or activities (Soundful, 2025).

The cultural question remains; will listeners like AI-generated tracks in the same way they do with human-created music? Authenticity might matter more to fans, creating a demand for transparency about AI’s role.

My Role

I’m the editor who provides direction. But more than that, I bring motivation that machines can’t replicate. A machine doesn’t choose EDM out of enjoyment. It doesn’t enter a new field to test its abilities. My motivations, professional curiosity, love for the genre, and the desire to combine marketing skills with AI, drive this whole project.