What executives can learn from the music industry

The signal inside the noise

Three waves of disruption and what they reveal about expectations, platform power, and AI

The music industry is often dismissed as a creative outlier, interesting, but not instructive. Executives should treat it differently. Over the past quarter-century, music has been one of the most reliable early indicators of how digitisation resets expectations, reallocates power, and re-bundles work. Its value chain is unusually exposed: creation, distribution, discovery and monetisation have been digitised end to end, with fewer buffers than most sectors enjoy. When change arrives, it hits music early, then moves outward into other industries on a slower clock and with more formal governance.

Seen this way, music functions as a live laboratory for three recurring dynamics that now define most transformation agendas: expectation shifts, platform intermediation, and synthetic production. Across three waves of disruption, the pattern has been remarkably consistent. New capabilities become normal. Power migrates to new intermediaries. And jobs are not simply eliminated, they are reconfigured around the bottlenecks that remain.

Over the past 25 years, the industry has moved through three waves that map cleanly to enterprise change: digitisation (early 2000s downloads), platformisation (streaming), and synthetic production (the generative AI wave accelerating since 2023). Each wave looks different on the surface. The underlying behaviour is familiar. Organisations that fare best are rarely those that predict the future perfectly. They are the ones that distinguish signal from noise early, then redesign how value is created and governed.

Digitisation resets expectations before it resets business models

Digitisation is often remembered as a commercial pivot from CDs to downloads. What mattered more was the shift in what audiences came to expect. iTunes normalised instantaneous access, searchability, portability and individual selection. It did not merely change how people bought music. It changed what “normal” felt like.

Once expectations move, they do not revert. Organisations can argue about features and timelines, but customers and employees quietly recalibrate. This matters for leaders watching AI unfold today because the early impact is not only automation. It is expectation. When a new capability becomes accessible, even in rough form, stakeholders begin to ask why it is not standard everywhere.

That dynamic is not limited to consumer markets. Expectations shift inside enterprises as much as outside them. When teams see high-quality drafts produced in minutes, analyses generated on demand, or first-pass designs created instantly, the perceived pace of work changes. Leaders who treat those moments as novelty risk a slow erosion of credibility. The question becomes less “Should we adopt?” and more “Why are we still operating as if we cannot?”

Streaming did not just distribute music, it reallocated power

Streaming is often described as a channel change. In practice it was a governance change. It reorganised value around three chokepoints: discovery, distribution and data. When discovery becomes algorithmic, the platform’s logic becomes the market’s logic. Those who control recommendation systems, playlists and user interfaces gain disproportionate influence over what succeeds.

This is where music’s story becomes a direct analogy for other industries. Platformisation changes where competition occurs. It is no longer only product versus product. It is positioning inside someone else’s system. That is now true in procurement platforms, app ecosystems, marketplaces, and increasingly in AI-mediated search and assistants. If a customer’s first encounter with your brand is through a platform’s ranking, summary, or recommendation, your commercial outcome depends partly on rules you do not control and may not fully understand.

Many leaders underestimate how quickly this becomes structural. Once customers adopt the convenience of platform discovery, they rarely revert to older habits. The strategic question is not whether platforms are good or bad. It is how you maintain differentiation when the interface to your customer is owned by someone else.

Generative AI dissolves boundaries rather than simply speeding tasks

The third wave is qualitatively different. Generative AI is not only accelerating production. It is dissolving the boundaries that used to separate roles and stages of work. In music, the traditional chain of songwriter, producer, vocalist and engineer is being compressed into hybrid workflows where elements can be generated, transformed and recombined with minimal friction.

What was once difficult is now cheap, and what is cheap becomes abundant. A creator can draft a song, generate a voice in the style of a known singer, align vocals, add beats and instrumentation, and assemble a track that is “good enough” to test with an audience. The point is not whether the outcome rivals elite human creators. It is that the barrier to producing something viable has collapsed.

This is how AI will reshape many industries. The most important shift is not that tasks can be automated. It is that the cost of iteration drops so sharply that the volume of output explodes. When output becomes abundant, the constraint moves elsewhere. In music it moves towards taste, trust and distribution leverage. In business it moves towards decision rights, governance, integration and adoption. When everyone can generate reports, the scarce capability becomes deciding what matters, aligning action, and ensuring accountability.

The moment disruption becomes real: when experts cannot tell

The inflection point is not a polished demo or a conference claim. It is the moment a new capability clears an expert threshold while blending into existing workflows.

A single anecdote captures this shift. A producer asks a mix engineer what they thought of background vocals. The engineer comments on the tuning, then learns the vocals were not sung by the artist at all. They were performed by someone else and transformed using an AI model trained on the artist’s vocal tracks. The engineer did not notice.

Disruption accelerates when it becomes invisible …

This is what leaders should watch for in their own sectors: the moment the output arrives in familiar formats and passes routine scrutiny. Disruption accelerates when it becomes invisible. Adoption no longer requires a cultural leap. It becomes a default. Organisations are caught off guard not because they ignored technology, but because they monitored it as a separate domain rather than noticing when it quietly entered standard operating practice.

A counterintuitive signal: automation arrived, and human work persisted

The most useful part of the music story is not the fear of replacement. It is the evidence that a comparable disruption already occurred without producing the predicted outcome.

AI mastering services have been available for years. They offered exactly the promise many professionals now fear: upload a track, choose a profile, preview options, export instantly at a fraction of the cost. When those tools emerged, the industry predicted collapse for mastering engineers. Yet many continued working, and human mastering remained in demand.

The explanation is not that the tools were ineffective. It is that human behaviour shapes adoption as much as capability does.

Two forces are particularly instructive.

First, people do not only buy outputs. They buy agency. They want to be involved, to revise, to feel ownership. In music that shows up as requests for minute changes that are barely audible. In organisations it shows up as late-stage edits to board decks, repeated steering committees, and “one more iteration” that seems irrational until you recognise it as a bid for legitimacy. AI can generate an answer, but it does not automatically create the social conditions for acceptance.

Second, in high-stakes environments, someone must own the decision trail. Even where AI is technically capable, leaders and stakeholders ask, often implicitly: who is accountable if this goes wrong? That question slows full automation and elevates roles that combine judgement with governance.

Together these forces point to a more realistic view of the future. Many jobs will not disappear in a clean sweep. They will be re-bundled. Routine elements compress, while value migrates to orchestration, quality control, integration and the ability to secure buy-in.

What music reveals about change in any industry

Music’s repeated reinventions illustrate a broader principle: technological change is rarely experienced as a single event. It arrives as a sequence of expectation resets and power shifts, each of which forces organisations to renegotiate how value is created and captured.

Three lessons follow.

The first is that “good enough” is the real tipping point. Most organisations wait for perfection before they move. Markets do not. Once a capability is good enough for meaningful use cases, adoption begins and improvement follows adoption. The same pattern will play out across knowledge work, analytics, design, software, customer service and internal communications.

When production becomes abundant, the bottleneck moves

The second is that when production becomes cheap, attention becomes expensive. Abundance creates noise. The differentiator becomes the ability to filter, prioritise and align action. That requires sharper decision rights, stronger governance, and operating models designed for speed without losing accountability.

The third is that platforms and intermediaries tend to win when they control discovery, interfaces and data. Streaming proved this in music. AI will prove it again as assistants, copilots and aggregators become the new front doors to work and information.

A leadership response that avoids both denial and hype

For executives, the right response to inevitable change is not panic investment or cautious waiting. It is disciplined redesign. Start with the operating system of the organisation, not the toolset.

Begin by naming the new expectations. Identify the moments where teams and customers have already recalibrated. If good drafts can be produced in minutes, what does “normal pace” become in your context?

Then separate judgement from pattern. Where can AI accelerate repeatable work, and where is accountable judgement still mandatory?

Clarify decision rights. When output becomes abundant, deciding what matters becomes the constraint. Make it explicit who decides priorities, quality thresholds and acceptable risk.

Redesign one workflow end to end. Choose a single high-friction process and rebuild it so AI reduces friction while governance protects quality and accountability.

Finally, measure value and adoption, not activity. Pilots need success metrics, feedback loops and clear sponsorship. Otherwise they become theatre.

Design for agency rather than fighting it. If stakeholders need involvement, build controlled review points and transparent alternatives into the process instead of allowing ungoverned shadow processes to grow.

Treat provenance as a strategic capability. As synthetic production spreads, questions of authorisation, data use, auditability and defensibility will become part of quality. In music this is visible in debates about voice rights and authenticity. In business it will appear as compliance requirements, contractual assurances, and governance standards.

Adoption, in the end, must be treated like a product, not a memo. Behaviour changes when friction is removed and value is visible in real workflows.

The signal inside the noise

Music’s journey from iTunes to Spotify to AI-generated voices is not merely a story about a creative industry. It is a reminder that change becomes inevitable long before it becomes comfortable. The deeper disruption is not the technology itself. It is the shift in what people expect, what they trust, and who gains leverage when the bottleneck moves.

In that sense, music remains an early-warning system. It tells leaders what is coming: expectation resets, platform power, and synthetic abundance. The organisations that respond best will be the ones that build the capacity to adapt, redesign and govern, rather than clinging to old bundles of work or chasing every new tool.