What does a complete AI music production workflow look like in 2026?

Here's the actual workflow I run for commercially viable AI music tracks.

Phase 1: Ideation (15 min)

• Pick a reference (an existing track or artist you want to triangulate)
• Write a one-sentence brief: "[Genre], [tempo feel], [emotional arc], [use case]"
• Example: "Dark progressive house, 128 BPM feel, builds from tension to release, late-night workout"

Phase 2: Prompt development (30 min)

Generate 3 variant prompts from different angles:

• Instrumentation-led: focus on what's playing
• Mood-led: focus on emotional content and arc
• Reference-led: point at a latent pattern the model recognises

Run 4–6 generations per variant. Budget ~24 clips total.

Phase 3: Selection and extension (1 hr)

• Score each clip on: hook strength (0–3), production quality (0–3), originality (0–3)
• Keep top 2–3 clips per variant
• Extend the best clips to full length (Suno's continuation feature)
• Merge takes where intro from clip A + chorus from clip B produces a stronger track

Phase 4: Post-production (1–2 hr)

Even AI-generated tracks need post:

• Vocal EQ — AI vocals often need 2–4kHz boost and de-essing
• Master-bus limiting — Suno renders hot, not loud-loud
• Stem separation if needed (Suno's download option, or Moises/Lalal.ai)
• Add subtle room verb to weld layers together

Phase 5: Release

• Upload to DistroKid or similar for streaming distribution
• Tag metadata accurately: genre, mood, instrumentation
• Create short-form video with the track for social promotion

Tools in the stack

• Suno v4 — primary generation
• Adobe Audition / Logic — post-production
• Moises — stem separation
• DistroKid — distribution
• CapCut — social video creation