5 Predictions for AI in Music Over the Next 5 Years

You can feel it in every session. What used to be a curiosity—a novelty plug-in, a fun “what-if”—now sits at the center of the session itself. Generative tools sketch chord progressions, draft melodies, split stems in seconds, and try on a dozen vocal styles before lunch. AI isn’t a vague dream anymore in the music industry. It’s part of how modern records get made, released, and found.

That puts platforms like Lalals right where creators need them: at the convergence of voice models, text-to-speech, music generation, stem splitting, cleanup (de-noise, de-reverb, de-echo), mastering, and more. In other words, the toolkit that turns an idea into a finished asset. And fast.

Below are five predictions for how AI will shape music over the next five years based on the latest research. Whether you’re a starting producer, a label PM, or a touring act looking to level up your live set, these predictions are a must to consider if you’re interested in growing as a professional while leveraging the tools available.

We’re already seeing this happen, but soon, we’ll move from “assistive” tasks—auto-mastering, artifact cleanup, instrument isolation—into genuine co-writing: prompts that yield melodies, harmonies, lyrics, instrumentation, and demo-ready vocals. The shift is already in motion as services move beyond template-bound outputs into from-scratch composition. For teams using Lalals’ Music Generator, Vocal Generator, Lyrics Generator, and 1,000+ AI voices, that looks like a roomful of virtual collaborators who never tire.

Why it matters. Barriers fall. Non-musicians can sketch ideas at a near-studio level. Producers become curators and directors of taste: shaping, editing, and refining what models propose. On the business side, expect growth in prompt-native workflows and voice-driven assets that differentiate catalogs quickly. This levels the playing field in a major way.

What the research says. Global projections show the generative AI content economy surging; in music and audiovisual, CISAC estimates the GenAI market could scale from €3B to €64B by 2028, while warning creators’ income could fall without new frameworks. Adoption and workflow automation are already visible in creator surveys and market roundups.

As AI output scales, the money flows will change with it: new licensing structures for voice models, micro-licensing of stems and loops, tiered access to model capabilities, and more granular metadata for attribution/royalties. Platforms like Lalals that host voices, covers, stems, and generators will sit inside these flows, and can help creators formalize how their assets get used. It is only a matter of years before we have to adapt our monetization models to account for what the latest technology is offering us all.

Why it matters. Traditional revenue lines (streaming, downloads) are already thin; AI introduces both risk and opportunity. Creators need clarity about where their money comes from when AI touches a track. Tool providers need rights frameworks that are simple, fair, and enforceable.

What the research says. A CISAC economic study forecasts that, without adaptation, music creators could see ~24% of income at risk by 2028 as GenAI scales; the same work projects GenAI content markets rising from €3B to €64B in that window. Commentary and reporting across the trade press highlight pressure on licensing, provenance, and attribution that will force new watermarking/fingerprinting and consent-first pipelines.

AI won’t stay in the DAW. It will ride on stage. Expect sets that adapt to crowd energy in real time, with models that listen to the room (through sentiment, macro tempo trends, or other signals) and suggest shifts in style, arrangement, or key/BPM. Think reactive backing vocals. On-the-fly loop generation. Real-time voice transformation for call-and-response moments. The up-and-coming developments in AI tech are going to make it easier than ever to create more immersive live shows for audiences.

Why it matters. Live is still the beating heart of music economics. Layering AI into the show creates a reason to buy a ticket again. Because last night’s set can’t be perfectly repeated. It’s also a fresh merch story (sell the unique mixdown or stem pack after the show) and a fan-club unlock (download your city’s custom version).

What the research says. Industry observers have pointed toward more interactive gigs and festivals as early as 2025, where artists adjust in response to real-time data; the same production gains we see in the studio—faster generation, more flexible delivery—spill into performance workflows.

The next five years will test the industry’s maturity and how its current leaders are able to adapt and make changes to keep up with the times. Some of the major areas of concern in the future include training on copyrighted catalogs without permission, reproducing a distinctive voice without consent, biased datasets that under-represent non-Western traditions, and muddy attribution when multiple systems touch a track.

Why it matters. Labels and artists will push back (and sue) when their rights are blurred; regulators will adapt; audiences will expect disclosure and consent. If models learn mostly from the Global North, we risk flattening the world’s music into a single accent. That’s why even now, we have to take steps as an industry to make this a focal point, not just background noise.

What the research says. A large-scale analysis of 1M+ hours of music-AI datasets and 200+ papers finds ~86% of dataset hours and ~93% of papers focus on the Global North; genres from the Global South account for only ~14.6% of hours, posing a clear diversity risk. Platforms are also tightening enforcement; for example, Deezer now tags AI music and is actively filtering fraud, citing large volumes of AI uploads and botting schemes.

AI has made it possible for anyone to create. It’s only going to become more and more accessible. And with accessible tools, we’ll see a surge in AI-assisted tracks from hobbyists, influencers, and small studios. The challenge won’t be “Can I make something?” It’ll be “Can I make something that moves people and gets heard?” We’ll focus less on completing a track and more on the quality of the songs and how they connect with audiences.

Why it matters. More supply means more noise. Human edge—storytelling, live presence, authenticity, the way a voice cracks on a line and means it—becomes the differentiator. Tools can draft. Only people connect.

What the research says. A new Ipsos for Deezer survey finds 97% of listeners couldn’t reliably tell AI-generated from human-made music in blind tests, yet majorities want clear labeling. Broader stats compilations also show rising creator adoption, with many producers automating parts of their workflow.

Across the next five years, AI won’t replace musicians. It will rewire how creativity gets expressed, monetized, and experienced. In the studio, it’s a co-writer. On stage, it’s a reactive bandmate. In the business office, it’s a new set of rails for licensing, tracking, and paying people.

The question is not whether AI will be in your process. It’s how you’ll use it, and how you’ll credit it. To reject the budding technology would be to shun an opportunity for greater efficiency, automation, and more time to spend on other areas, such as promotion and learning more about your audience. The research is blunt about the risks of ignoring consent and compensation; it’s equally clear about the scale of opportunity if we get the frameworks right.

So lean in with care. Find the right tools to help you scale and grow—even today. Use Lalals to move faster: generate ideas, try voices safely, split stems, clean audio, master demos, and prep show-ready assets. Keep consent and disclosure non-negotiable. And double down on the one thing models can’t fake: the story only you can tell.

Because in the future, it’s going to matter more that you can connect with people rather than that you have the skills to produce and release a track (or even a whole album).

Open a session. Draft a hook with Music Generator. Test two vocal styles with AI Voices. Split stems for live. Print a mix. Then share what you made: clearly labeled, proudly yours. The tools are ready. The audience is listening.