ARTIFICIAL INTELLIGENCE AS A NEW TOOL IN THE PRODUCER'S ARSENAL

Introduction

The music industry has traditionally been quick to adopt technological innovations. From tape recorders and analog synthesizers to digital audio workstations (DAWs), each new tool has rapidly become standard practice for producers. Today, a new era has arrived: artificial intelligence (AI) is becoming a fully-fledged working tool for sound engineers and producers. Already, about 10% of new tracks uploaded to streaming services are generated by algorithms [1]. According to expert estimates, by the middle of the decade, up to 20% of all music could be created with the involvement of AI [2]. The relevance of this topic is driven by the rapid growth of AI-generated content and the need to understand its impact on creative processes.

The purpose of this study is to analyze the use of AI technologies in music production and determine how they are changing the work of a producer. To achieve this goal, the following tasks were addressed in this work:

1. Modern applications of AI in the creation, mixing, and mastering of music were studied.
2. Statistical data on the prevalence of AI tools among producers, especially young professionals, were collected and presented.
3. Key scenarios for the application of AI (as an assistant, co-author, analytical, and generative tool) in studio work were analyzed.
4. The advantages and limitations of these technologies were identified, and the necessary degree of human involvement in the creative process when integrating AI was considered.

Methods and Materials

The study draws upon a comprehensive selection of recent academic and professional sources that explore the influence of artificial intelligence on music production, distribution, and creative processes. To develop the article, analytical, comparative, and systematic methods were applied, alongside content analysis of the literature and statistical review of industry data. The sources collectively formed the empirical and conceptual foundation for the assessment of AI-driven tools, their integration into production workflows, and the implications for the professional environment of modern music producers.

According to S. Adams [4], intelligent mixing systems such as iZotope Neutron 5 improve the precision and speed of the production process, allowing sound engineers to delegate routine equalization and compression tasks to machine learning algorithms while maintaining artistic control. N. Anderson [3] examines automated mastering solutions, including Ozone Master Assistant, and emphasizes that AI-based tools significantly simplify post-production without compromising creative quality. The report Digital Watch [9] identifies that nearly one-fourth of producers now employ AI in their workflow, reflecting the transition of machine learning from experimental technology to a normalized part of studio operations. M. Dalugdug [10] describes the launch of Suno’s AI-based digital audio workstation as a turning point, integrating all stages of production - from composition to mastering - into a single automated environment. In the analytical review by N. Mokoena and I. Obagbuwa [11], attention is given to how algorithmic automation in digital streaming enhances audience retention and subscription metrics, positioning AI as a structural component of platform economics. A. Levin [12] highlights the cognitive and organizational transformation that AI introduces into the music industry, arguing that the new tools redefine both production logic and authorship. L. Nagornaya [14] explores the relationship between technological innovation and artistic creativity, demonstrating that AI alters aesthetic criteria in contemporary music. A. Popova [15] treats AI as a co-author capable of proposing original compositional ideas while stressing that human oversight remains essential for preserving artistic identity. D. Tencer [8] examines ethical issues connected to AI-generated music, particularly cases of stream manipulation, and raises questions about authenticity and regulatory accountability. In another study, D. Tencer [7] notes that while 25% of producers actively use AI, many remain cautious, indicating that hybrid human-machine collaboration will dominate in the near term. Statistical data from Ditto Music [5] confirm that 48% of artists currently rely on AI tools in music creation, a slight decrease from previous years, attributed to a reevaluation of expectations. An earlier Ditto Music report [6] indicated a higher adoption rate - around 60% - particularly among independent musicians who use AI for mastering, arrangement, and visual production. J. Keith [1] reports that approximately 60 million people created music using AI tools in 2024, demonstrating the rapid democratization of access to generative technologies. The SoundMade forecast [2] supports these findings, predicting that by 2025, AI-generated content will constitute up to one-fifth of global music production. In the study by A. Levin [12], the integration of AI is seen as a catalyst for transforming traditional production into a computationally supported creative environment. J. W. Hughes [13] conceptualizes this shift as the “rise of the producer,” where generative AI redefines content creation as continuous production, merging artistic and technological functions.

Collectively, these materials formed the evidence base for evaluating both the creative and industrial transformations associated with AI in music. The methodological framework of this article relied on comparative analysis, synthesis of primary and secondary sources, and interpretation of quantitative data from digital music platforms and reports. These approaches made it possible to identify the mechanisms by which artificial intelligence reshapes production processes, expands creative opportunities, and alters the structure of professional activity in the music industry. The integration of these findings ensured a comprehensive understanding of the interdisciplinary nature of AI’s influence - technological, aesthetic, and sociocultural - within the contemporary music production ecosystem.

Results

One of the most notable applications of AI in production is the automation of routine technical operations. Commercial services and software plugins have emerged that use machine learning to analyze audio and select optimal processing settings. For example, the cloud platform LANDR performs automatic mastering of tracks, and the iZotope Ozone plugin contains a Mastering Assistant that analyzes the uploaded music and suggests equalization and dynamic processing for a chosen genre [3]. The iZotope Neutron suite uses algorithms to assist with mixing: the Mix Assistant module listens to a multi-track project and suggests a balance of instrument levels [4]. Such AI systems act as a virtual sound engineer, a technical assistant that frees the producer from the routine of parameter selection. It is noted that the goal of such tools is to relieve the person of part of the routine workload, allowing them to focus on the creative aspects of the mix [4]. At the same time, the AI guide does not impose decisions: the producer retains full control and can manually adjust each parameter if desired. Below is a systematization of approaches (Table 1).

Table 1.

Functions and examples of the use of AI instruments in music production (compiled by the author based on [4-6])

AI is also actively used for audio processing. For instance, the service Lalal.ai uses neural network models to effectively separate tracks into vocal and instrumental parts, which simplifies remixing and sampling. Such tools implement the task of stem separation - according to a survey of producers, 73.9% of AI users utilize it specifically for isolating vocals or instrument parts [7]. In comparison, significantly fewer respondents use AI directly in sound design: about 45.5% use AI assistants for mastering and equalization (e.g., the aforementioned Ozone plugins) [7]. This indicates that today AI is most in demand for auxiliary tasks of sound improvement, while creativity (generating new sounds and musical ideas) still largely remains with humans.

In the field of music generation, AI also demonstrates impressive results. Systems have emerged that are capable of creating musical fragments and even entire tracks based on trained models. An example is the algorithms underlying the services Endel and Boomy. The Endel application generates personalized sound atmospheres (soundscapes) in real-time based on AI, taking into account environmental parameters and listener preferences. This AI tool has found application in creating functional background music (for relaxation, concentration, etc.) and has already attracted a wide audience - the service's monthly audience exceeds 1 million users [7]. Moreover, major labels are experimenting with generative music: for example, Warner Music has partnered with Endel to release a series based on the label's catalog. On the other hand, the Boomy platform allows anyone to generate a complete track by selecting a style and parameters - since 2019, Boomy users have created over 14 million compositions, which constitutes almost 14% of the world's entire music library by the number of tracks [8]. However, this raises issues of quality and abuse: in 2023, it was discovered that some tracks created via Boomy were inflating their listening counts with bots, and tens of thousands of such compositions were removed from Spotify. This incident highlighted the importance of controlling AI-generated content and led to a discussion of ethical standards. Below is a systematization of approaches (Table 2).

Table 2.

Generative AI platforms and their applications in music production (compiled by the author based on [4,8,9])

A special case is represented by the Suno platform, which in 2025 launched its own AI-powered digital audio workstation. Unlike services focused only on generating fragments or separate stems, Suno integrates all stages of production into a unified environment. This approach illustrates the shift from AI as an auxiliary plugin to AI as a central architecture of the creative process, setting a precedent for hybrid studios where generative algorithms coexist with traditional DAW tools [10].

Overall, a review of the tools shows that AI has already entered all stages of musical work: from writing material to final mastering. The accessibility of such tools is growing rapidly: today, there are AI plugins for noise reduction, vocal pitch correction, intelligent drum machines, and melody generators. Many of them are being integrated into popular DAWs, expanding the producer's arsenal.

The application of AI in music distribution and promotion deserves special attention. The largest streaming services - Spotify, Apple Music, YouTube, TikTok - actively use machine learning algorithms to analyze audience tastes and personalize recommendations. Algorithms process data on the listening habits of millions of users to offer each listener the content they are most likely to enjoy. This has increased audience engagement and become a key factor in the success of digital platforms [11].

For example, Spotify is known for its Discover Weekly and Daily Mix playlists, which are automatically generated based on user preferences. Through AI analysis of track characteristics and listener behavior, the service creates an individual music feed, which increases listening time and audience satisfaction. Similarly, TikTok uses intelligent recommendations in its video feed: the viral spread of many music tracks in 2020-2023 (for example, "Old Town Road" by Lil Nas X) is largely due to TikTok's algorithms, which instantly promote popular audio trends among the target audience.

AI on these platforms also performs an analytical function for the industry: using big data, it is possible to predict which songs will become hits and even optimize release strategies. For instance, it has been noted that labels are beginning to use AI for A&R analytics - identifying promising artists and tracks at an early stage based on listening statistics and trends [12]. Furthermore, automation has touched catalog management and monetization: neural networks help find uncollected royalties, analyze the use of tracks in UGC videos, and identify potential rights violations [13]. These examples show that AI has permeated all levels of the music business, from recommendations for the average listener to the internal decisions of record companies.

At the same time, the widespread implementation of algorithms on streaming platforms has sparked a debate about the transparency and diversity of content [14, 15]. Critics point out that personalization of feeds can narrow listeners' horizons by imposing "monotonous" preferences. Platforms are trying to account for this effect: for example, Spotify claims to have controls in place to prevent algorithms from leading to excessive uniformity in recommended music. Issues of data ethics and potential algorithm bias are also actively discussed in research. Nevertheless, no major music service can do without AI, and the competent use of algorithms has become a mandatory condition for competitiveness in the streaming industry.

Statistical data from recent years demonstrate a rapid increase in the use of AI tools by musicians and producers themselves. According to surveys, more than half of independent artists are already using AI in their creative process. A study by the company Ditto showed that ≈60% of independent musicians use AI technologies in their work on music [6]. Artists are most interested in areas where AI can supplement their skills: 77% of respondents are willing to entrust AI with creating cover art and visual materials for releases, 66% - to use it for mixing and mastering, and 62% - for generating musical ideas and arrangements [6]. At the same time, only 28% of respondents stated that they fundamentally do not want to involve AI in their creativity [6]. This data confirms that the younger generation of producers is quite open to experimenting with algorithms. Below is a systematization of approaches (Table 3).

Table 3.

Results of surveys of musicians on their willingness to use AI in production (compiled by the author based on [1,2,9,11])

Another large-scale survey conducted among 1100 producers in 2024 recorded similar trends: a quarter of modern producers are already actively integrating AI into their work [7]. However, the nature of its use is still auxiliary–most often it involves sound processing, rough auto-mixing, mastering, and sample creation, while few are ready to fully rely on generative AI. According to the same survey, less than 3% of respondents trust AI to create an entire track from scratch, and only 21.2% have used algorithms to generate individual musical elements (melodies, riffs) [7]. The main reasons for rejecting generative AI are fears of losing creative control and the insufficient quality of the machine-generated material. 82% of producers who currently avoid AI explained this by a desire to preserve the uniqueness of their sound and artistic identity, while 34.5% indicated that the quality of AI-generated music does not satisfy them [7]. Additional reasons noted were costs (14.3%) and legal uncertainties (about 10.2% pointed to difficulties with copyrights when using AI) [7].

Interestingly, professionals' attitudes toward different types of AI tools vary. Assistive AI - "supportive AI" that helps in the process (e.g., smart mixing plugins) - is perceived much more positively than generative AI, which creates music in place of a human. Many musicians resist the latter: according to a Tracklib survey, less than 10% of producers have a generally positive attitude toward the idea of fully generating tracks with algorithms [7]. Moreover, the greatest skepticism towards this practice was expressed by the youngest survey participants - paradoxically, it is the new generation of musicians who most zealously defend the value of human creativity and uniqueness, fearing the standardization of music by AI. At the same time, 70% of producers acknowledge that the impact of AI on the industry will be significant in the near future, even if they currently use it to a limited extent [7]. This means that most expect that ignoring new technologies will not be possible and are inclined to gradually master them as algorithms improve.

It is worth noting that in 2025, a trend toward a more critical perception of AI has emerged: according to a follow-up study by Ditto, the share of musicians regularly using AI has slightly decreased, to 48% (compared to 59.5% in 2023) [5]. This phenomenon is explained by a process of refining expectations: over the past year, there has been a growing number of musicians who are disappointed with the creative abilities of algorithms and point to a lack of personal originality in AI-created products. Nevertheless, the overall acceptance of technology continues to grow: fewer and fewer musicians are categorically rejecting AI. This suggests that as tools improve and experience accumulates, the community is moving towards a balanced approach - a willingness to use AI where it is genuinely useful, while preserving the unique creative contribution of the human.

Discussion

Current trends show that artificial intelligence is increasingly and confidently taking its place as an auxiliary, yet valuable, participant in music production. An analysis of its use allows for the division of AI's roles in a producer's work into several categories:

AI as an assistant. In this role, algorithms speed up and simplify technical operations. AI assistants clean audio of noise, automatically set channel levels, and select parameters for equalizers and compressors. Such functions are already implemented in well-known plugins (e.g., Mix Assistant in Neutron, Master Assistant in Ozone) and online mastering services. The quality of automated mixing is constantly improving and often becomes shockingly high for non-demanding tasks. Nevertheless, these tools are seen precisely as assistance: the final edits and artistic decisions remain with the human. The AI assistant takes on the routine, allowing the sound engineer to focus on the creative concept of the mix, the overall emotions, and the expressiveness of the sound.

AI as a co-author. In this mode, the algorithm participates in generating new musical ideas. Generative models can suggest original harmonic sequences, melodies, or rhythmic patterns that the musician might not have come up with on their own. Examples include the OpenAI MuseNet neural network, which creates multi-genre compositions, or the Riffusion project, which generates soundscapes through a visual representation of audio. An AI co-author is useful for overcoming creative blocks: it can draft ideas from which the human can then select and refine the most successful ones. Some artists are experimenting with this kind of co-authorship - for instance, the album "Hello World" (2018) was created by the collective SKYGGE together with an AI companion. However, few are ready to fully rely on a computer muse; rather, it is perceived as a source of inspiration and unexpected moves, but the final authorial decision remains with the artist.

AI as an analyst. This refers to the use of algorithms for data processing and supporting decisions in the music business. Modern producers and labels have vast arrays of statistics (streams, playlists, social media), and AI is capable of identifying hidden patterns. For example, based on the dynamics of listens and user playlists, one can predict which track will "take off" on the radio charts or TikTok. Special models predict how an audience will react to a particular song even before its official release. In addition, analytical algorithms are used to optimize tours (based on fan geolocation data), for targeting release advertising, and for calculating the most profitable release windows considering competitors. In other words, in this role, AI acts as a statistical advisor, helping the producer make decisions based on objective data, not just intuition. This is especially important in the digital music economy, where competition for the listener's attention is extremely high.

AI as a content generator. This is the most controversial role - the fully automatic creation of music and related content (covers, videos) without direct human participation. Generative neural networks have already learned to synthesize plausible vocals, imitating the voices of famous performers, and are capable of composing tracks in given styles based on a text description. Tools like Midjourney or Stable Diffusion for creating visuals are also widely available - young artists use them for designing covers and merchandise. The recent launch of Suno’s AI-DAW further reinforces this trend. Its model shows how generative technologies are no longer limited to support functions, but become the basis of an integrated studio ecosystem. This raises both opportunities for accelerating production and risks of sound standardization if such environments dominate the market. The practical benefit here is obvious: content costs are reduced, and production is accelerated. However, it is this facet of AI application that raises concerns among many musicians: Is art being replaced by assembly-line imitation? In the short term, such technologies are seen more as an experimental zone. Many producers are experimenting with one-click music generation, but the results rarely meet professional standards in terms of depth and originality of sound. Nevertheless, the quality of generated content is steadily increasing, and it cannot be ruled out that in a few years, AI composers will be able to release competitive genre tracks. A separate issue is the legal status of such works - who owns the music rights created by a machine, and how to deal with style samples of real artists that AI might use in its training. These ethical challenges are not yet resolved and require the participation of both the music community and lawyers.

The observed trends indicate that AI does not replace humans but expands their capabilities. In all the roles listed, it is effective only in conjunction with an experienced producer. An algorithm can suggest a solution, but only a human can evaluate it from the perspective of artistic integrity. In particular, even the most advanced neural networks lack artistic taste and cultural context. A machine operates on probabilities and patterns, whereas music is often valued for breaking patterns and for unpredictability based on the author's personal experience. AI is incapable of feeling the emotional resonance of a melody or the subtle connection of a song to its social context - these aspects remain within the purview of the human creative consciousness.

The use of AI technologies in production is associated with a number of limitations. First, there are quality limitations: although algorithms handle standard tasks well, they can lead to the standardization of sound. Automated mastering, for example, sometimes makes tracks overly similar to each other in terms of dynamic range and tonal balance. There is a risk of a so-called homogeneous "standard" sound emerging if everyone uses the same AI settings. Second, there are creative limitations: generative AI is trained on past music, so for all the variety of combinations, it does not go beyond existing stylistic elements. For this reason, fully AI-generated compositions may seem derivative or lacking in "soul."

Another important aspect is ethics and legal issues. Algorithms are trained on data that includes copyrighted musical recordings. The question arises: Does a neural network have the right to use the style and fragments of others' works when generating a new track? Most artists believe that vocals and music should not be used by AI without the permission of the rights holders. Also, a significant portion of listeners (over 80% in surveys) are in favor of explicitly labeling tracks created by AI. The industry is already facing difficulties - in 2023, a song with voices stylized to sound like Drake and The Weeknd, entirely generated by a neural network, was leaked online, causing a wave of discussion and leading to its removal from platforms at the label's request [6]. These cases underscore that for AI to be organically integrated into the music sphere, new rules and standards will be required to ensure transparency in the use of the technology and the protection of musicians' rights.

Practice shows that the best results are achieved through a synergy of AI and humans. Algorithms are indispensable for their speed of computation and iteration of options, but only a producer can set the goals and select the best result. Figuratively speaking, AI can be likened to a high-tech instrument - just as the advent of the synthesizer did not eliminate the profession of the composer but merely provided new sound palettes. Experienced producers emphasize that AI tools require skillful management: without human supervision, they can only offer "raw material" that needs to be refined and finished. Thus, AI becomes an extension of the producer's skill, not a replacement for it.

The psychological aspect must also be considered: authenticity is important in music; listeners value the personality of the performer. The complete exclusion of the human from the music creation process contradicts the very idea of art as an expression of individuality. Therefore, even the most advanced technologies are seen as an expansion of the creator's palette, not as an autonomous creator. In the future, the profession of a producer will likely be transformed - success will belong to those who master both the craft of traditional sound production and the ability to effectively apply AI where appropriate. The balance between the technical rationality of the algorithm and human intuition will become the new professional standard. It is important for the modern specialist to combine classical craftsmanship with proficiency in AI tools, turning computational algorithms into an extension of their creative vision.

A systematization of observations confirms that artificial intelligence has secured a firm place in the production cycle, acting simultaneously as a technical assistant, an idea generator, an analyst, and a distribution tool. Its application allows producers to be relieved of routine tasks, speeds up the workflow, and expands the range of available creative solutions. At the same time, limitations have been identified: standardization of sound, derivativeness of musical material, and unresolved legal issues. These factors indicate that algorithms alone cannot create a work of full artistic value. Modern production practice shows that the optimal strategy lies in balance: AI takes on the computational and organizational parts, while the human retains control over the concept, aesthetics, and emotional expressiveness of the music. The prospects are not associated with replacement but with the integration of technologies into the professional environment, where it is the human who sets the direction, and the algorithm that expands the possibilities for realizing the creative vision.

Conclusion

The conducted research confirms that artificial intelligence has become an integral part of the music producer's arsenal over the last five years. AI technologies have already proven their effectiveness in several applied tasks - from accelerating the technical process of mixing to generating personalized content for new listening formats. The identified trends indicate a significant scientific, technical, and applied effect: the integration of machine learning algorithms expands the boundaries of the creative process, increases its productivity, and opens up new genre possibilities. At the same time, preserving the human role in the music creation cycle ensures the artistic value and originality of the results, which autonomous AI cannot achieve.

The scientific significance of these changes lies in the formation of a new interdisciplinary field where musical art, acoustic engineering, and information technology intersect. The study of the interaction between the producer and AI enriches the theory of creativity and musical aesthetics, raising questions about the nature of creativity and authorship in the context of rapidly developing algorithms. The practical significance is expressed quite concretely: young producers around the world are gaining access to powerful tools that lower the barrier to entry into the industry, speed up routine stages, and allow them to focus on artistic ideas. Today, AI tools are becoming a standard of studio practice - more and more professionals view them as ordinary work tools, alongside equalizers or sequencers. Experience shows that those producers who are the first to master new technologies gain a competitive advantage in terms of work speed and the variety of creative techniques.

In summary, it can be stated that AI is not an enemy or a competitor to the musician, but a new intellectual tool that requires competent handling. Its successful integration into the production process can qualitatively enrich sound recording and music production. However, the final word remains with the human - it is the producer who directs the algorithms and gives meaning to the result. In the foreseeable future, those who can combine the power of artificial intelligence with human taste and creativity, turning algorithms into an extension of their own artistic vision, will win. Artificial intelligence in the music industry is rapidly transforming from a frightening novelty into a new "ordinary" synthesizer, without which it will soon be difficult to imagine a standard studio. Thus, it is necessary to finally develop norms and practices for collaboration between humans and AI to unlock the full potential of this technology without losing the soul of the music.