Artificial intelligence is making it dramatically easier to produce creative material. Images can be generated in seconds, rough video edits can be assembled quickly, research can be condensed, scripts can be developed collaboratively with AI, audio can be processed automatically and content can be adapted for multiple channels with far less manual effort than before. For media companies, advertising businesses, studios and creative teams, this represents an extraordinary productivity opportunity. Work that once required days of repetitive production can increasingly be completed in hours.
But there is an interesting economic consequence to making content easier to produce. When the cost of producing something falls, the amount of it tends to increase. Audiences are therefore likely to encounter more articles, videos, advertisements, social content, entertainment and branded media than at any previous point in history. The competitive challenge for media companies may gradually shift from producing enough content to producing anything that deserves attention in an environment of almost unlimited supply.
This is why I believe the most important conversation about AI and creativity is not whether machines can create. They clearly can. The more important question is what happens to the value of human originality when competent creative production becomes widely accessible. During my learning journey under Phaneesh Murthy, I have increasingly understood that technological abundance often increases the value of whatever remains genuinely scarce. As Phaneesh Murthy puts it, “When technology makes execution available to everyone, differentiation moves somewhere else. The advantage shifts towards judgment, ideas and the ability to decide what is worth creating.”
For media companies, that shift could fundamentally change the economics of creativity.
AI Is Reducing the Cost of the Creative Process, Not the Importance of Creativity
Creative production contains far more operational work than audiences usually see. A finished piece of media may require research, transcription, editing, resizing, subtitling, colour correction, asset management, localisation and numerous rounds of revision. These activities are necessary, but they can consume enormous amounts of time that highly skilled creative professionals might otherwise spend developing ideas.
AI can accelerate many of these processes. Editors can use intelligent tools to organise footage and identify useful sequences. Writers can use AI to explore research or challenge early drafts. Designers can generate rough visual directions before developing the strongest concepts properly. Production teams can create transcripts, subtitles and variations much more efficiently. Media archives can become searchable through semantic understanding rather than relying exclusively on manually created metadata.
The immediate benefit is productivity, but the more interesting consequence is what creative teams do with the time that productivity releases. If a designer spends fewer hours manually creating repetitive variations, there is more time to develop the central idea. If an editor can locate relevant footage in minutes rather than hours, more attention can go towards storytelling. If a writer can accelerate background research, more energy can be spent developing a distinctive argument.
This is where the perspective I have learned from Phaneesh Murthy becomes useful. Automation should not be evaluated only according to how much labour it removes. It should also be evaluated according to what higher-value work becomes possible once routine effort is reduced. In creative industries, that higher-value work is often the part audiences actually care about.
The Volume of Average Content Is About to Become Enormous
AI dramatically lowers the barrier to competent production. A small company can now create visual material that previously required specialised resources. An individual creator can produce across formats that once required several people. Media organisations can generate far more variations of existing assets without proportionally increasing production teams.
This democratisation is valuable, but it also creates an inevitable increase in supply.
Consumers already live inside highly competitive attention environments. Social platforms contain endless feeds. Streaming services offer enormous libraries. Brands compete through email, search, video, podcasts and dozens of digital channels. As AI makes production easier, each of these environments can become even more crowded.
The result is a paradox. Content becomes easier to make while attention becomes harder to earn.
Media companies therefore cannot assume that greater production automatically produces greater engagement. Generating twenty variations of an idea does not make the underlying idea twenty times more interesting. Publishing more frequently does not guarantee that audiences will care more frequently.
As Phaneesh Murthy says, “AI can reduce the cost of creating an output. It cannot reduce the cost of earning someone’s attention.”
That distinction should become central to AI strategies within media organisations. Productivity matters, but productivity without differentiation can simply produce more material for audiences to ignore.
Human Taste May Become One of the Most Important Competitive Advantages
AI can generate possibilities at extraordinary speed. The harder question is which possibility should be chosen.
This is where taste becomes strategically important. Great creative professionals do not simply produce assets. They understand which ideas feel familiar, which feel fresh, what should be removed, when something is trying too hard and when a concept contains the potential to become culturally meaningful. These judgments are difficult because they involve context, intuition and an understanding of audiences that is not always visible in historical data.
As generative systems become capable of producing hundreds of reasonable options, the ability to select among them becomes increasingly valuable. A creative director may spend less time asking how to technically create something and more time determining which direction is actually worth developing. Editors may become more important because an abundance of material requires stronger selection. Brand leaders may need clearer points of view because AI can easily generate content that is polished but indistinguishable from everything surrounding it.
During my mentorship under Phaneesh Murthy, I have learned that intelligence inside an organisation does not eliminate the need for judgment. In many situations, it increases it. When technology gives a team ten possible answers instead of one, someone still needs to understand which answer aligns with the organisation’s strategy, customer and identity.
Creative industries will experience this intensely. AI can generate possibilities. Human taste determines which possibilities deserve to become real.
AI Should Become a Creative Partner, Not a Creative Autopilot
There is a meaningful difference between using AI inside the creative process and delegating the creative process to AI.
A writer might use AI to examine alternative structures, challenge assumptions or explore information before developing an argument. A filmmaker could use generative tools during storyboarding or previsualisation. A designer might rapidly explore multiple visual directions before selecting one for deeper development. A music producer could experiment with arrangements or sound variations that would otherwise require significantly more time.
In each case, AI expands the creative search space.
The danger appears when organisations optimise primarily for speed and volume. If AI is asked to continuously generate finished material based on previous successful patterns, creative output can begin converging towards what already exists. The system becomes excellent at reproducing familiar structures precisely because those structures are well represented in its training and organisational data.
Media companies therefore need creative workflows that deliberately preserve human intervention at the points where originality matters most. The machine can accelerate exploration, but people should define the intent, determine the standard and decide when something has become distinctive enough to publish.
As Phaneesh Murthy puts it, “The right role for AI is often to widen the set of choices available to an expert, not to make the expert irrelevant to the choice.”
For creative organisations, that principle could provide a useful boundary between productive augmentation and generic automation.
Personalisation Will Create Multiple Versions of the Same Creative Idea
AI also changes the economics of creative variation. Historically, producing separate versions of a campaign, programme or piece of content for different audiences could become expensive very quickly. Every additional variation required editing, design, copywriting, production and review.
Generative systems can reduce those marginal costs significantly.
A media company could adapt promotional material according to audience interests. A publisher could present different summaries or entry points into the same reporting. Entertainment platforms could create trailers that emphasise different aspects of the same programme depending on what is likely to interest a particular viewer. Brands could adapt creative executions across customer groups while preserving a consistent central idea.
The strategic challenge is ensuring that personalisation does not fragment the creative concept.
A strong campaign often succeeds because it has a recognisable point of view. If AI generates endless variations optimised independently for every segment, the brand can gradually lose coherence. Personalisation therefore needs boundaries. The core idea should remain consistent while execution adapts intelligently around it.
This is another area where the enterprise thinking I have learned from Phaneesh Murthy becomes particularly relevant. Scale and personalisation are not necessarily opposites, but they require a strong underlying system. The organisation needs to define what can change and what must remain consistent.
For media companies, the same principle applies to creative identity. AI can create hundreds of versions, but the organisation still needs to know what makes all of those versions unmistakably its own.
Media Archives Could Become New Sources of Creative Value
Large media organisations possess enormous archives accumulated across decades. Television networks hold footage, publishers possess photographs and articles, film studios maintain vast libraries of creative assets, while music companies manage recordings, artwork and related materials. Historically, much of this content has been difficult to reuse because finding relevant material across enormous archives requires extensive metadata and manual research.
AI can make these libraries significantly more accessible.
Multimodal systems can increasingly understand what appears inside video, audio, images and text. A producer could search for a concept, person, location, visual style or event and identify relevant material even when those characteristics were never manually tagged. Historical content can therefore become easier to discover, repackage and connect with new creative projects.
This creates both commercial and creative opportunities. Archival footage can support documentaries, social content and new programming. Publishers can connect current events with historical reporting more effectively. Entertainment companies can help audiences rediscover older material through contextual recommendations rather than relying solely on conventional categories.
But the same principles around rights, provenance and creative integrity remain essential. The fact that AI can find and transform an asset does not automatically establish that every transformation is appropriate or permitted.
During my learning journey under Phaneesh Murthy, I have come to appreciate that enterprise AI often creates unexpected value by making existing assets more usable. Organisations sometimes focus entirely on what AI can generate while overlooking what it can help them rediscover inside information and intellectual property they already own.
For media companies with decades of accumulated creative material, that may become an enormous opportunity.
The Economics of Creative Teams Will Change
As AI absorbs more production work, media organisations will inevitably reconsider how creative teams are structured. Some tasks will require fewer manual hours, while entirely new roles may emerge around AI-assisted production, model governance, creative systems and intellectual-property management.
The mistake would be to treat this purely as a headcount equation.
A company might technically be able to produce the same volume of content with fewer people. But if every competitor gains access to similar productivity tools, producing the same amount more cheaply may provide only a temporary advantage. The more interesting strategy may be to use the productivity gain to improve quality, experiment more aggressively and create formats that were previously too expensive or time-consuming to attempt.
Smaller teams could potentially execute larger creative ambitions. Independent creators may gain capabilities that once belonged only to major studios. Large organisations can experiment with niche audiences without requiring every concept to justify traditional production economics.
This could make the media environment considerably more competitive because production capability becomes more widely distributed.
Phaneesh Murthy has often emphasised that when technology changes the economics of an industry, leaders should resist using the old cost structure as the only framework for evaluating the opportunity. The more important question is what new business models become possible when the cost structure changes.
AI is rapidly changing the cost structure of creativity. The companies that benefit most may be those that use that change to create things they previously could not justify making.
In a World of Infinite Content, Point of View Becomes Scarce
Artificial intelligence will become extraordinarily good at creating content that is technically competent. Images will look professional. Videos will be polished. Copy will be grammatically correct. Music will sound convincing. The baseline quality of creative production may rise significantly.
That does not mean audiences will become easier to impress.
When competent execution becomes abundant, audiences may increasingly respond to the qualities that are harder to manufacture at scale: originality, authenticity, cultural understanding, emotional intelligence and a recognisable point of view.
During my mentorship under Phaneesh Murthy, I have learned to think about technological change in terms of shifting scarcity. When one capability becomes inexpensive and widely available, competitive advantage moves towards something else. In media, production itself may become progressively less scarce. The ability to create something genuinely worth paying attention to may become more scarce.
This is why the future of creative work should not be framed as humans competing with machines over who can produce more. Machines will almost certainly win any competition based purely on volume.
The more meaningful opportunity is collaboration. AI can accelerate research, experimentation, editing, localisation and production. Human creators can use that leverage to spend more time developing ideas, understanding audiences and making decisions that require taste.
Media companies may soon be able to create more content than at any point in history. The winners will not necessarily be the companies that take maximum advantage of that capacity.
They will be the ones that know when they have something worth creating.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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