The story of technology innovation over the past three years has become synonymous with artificial intelligence. It is now almost impossible to discuss the future of media and entertainment without invoking AI in some form. And yet, despite the scale of investment, the speed of technical progress, and the intensity of public debate, there remains a profound uncertainty about how AI will actually deliver for media organisations in the near term. Very few doubt AI will prove important. But there is a syncing issue. Move too soon, waste money and lose faith. Go too late, miss the opportunity and lose face.
The syncing challenge can be found at two levels: the global economy and the media economy.
In the global economy, there is a widening gap between consumer adoption and business economics. Consumers are embracing generative AI tools at extraordinary speed. AI assistants, image generators, and content creation apps are among the fastest-growing categories in app stores worldwide. But the financial contribution made by each individual consumer to the huge cost of delivering these services is tiny. Explosive demand paired with fragile sustainability has caused some to fear we’re in an AI bubble—the bursting of which could have repercussions far beyond the timing of AI adoption.
Within the media economy, there is a similar conflict between the consumer and business realms. Audiences are already using AI to search, create, and consume. This costs them little or nothing—but has the potential to profoundly impact the world of content discovery. Media companies, meanwhile, are attempting to deploy AI to make themselves more productive, so that they can deliver to consumer demand. These two trajectories are related, but they are not the same. And they threaten to throw the media industry wildly out of sync with the evolution of AI elsewhere.
Here’s why.
AI is often presented as an urgent necessity: a tool that must be adopted quickly because it is the only solution to the problem of an ever-expanding media economy. That sense of urgency is real. Media companies are under constant pressure to handle more content, in more formats, at greater speed, and with tighter margins. The traditional media factory—with its complex workflows, specialist roles, and long time-to-revenue—no longer seems fit for purpose.
And AI does indeed appear, at least on the surface, to offer a solution. The productivity potential of AI is now undeniable. The trouble is that the evidence of consistent, transformative and measurable returns on investment is still limited. Even among the most optimistic forecasts, meaningful impact is often projected two years into the future. This creates a tension between board-level expectations and operational reality.
This tension is already visible in adoption data. Surveys suggest that AI use within businesses in general is widespread but plateauing. Initial experimentation has given way to a more cautious phase, as organisations discover the limits of what current tools can deliver without significant redesign of processes, skills, and culture. In the specific case of the media industry, AI seems to work best either at the very start of the content supply chain—where it can assist creative exploration—or at the very end, where it can personalise, recommend, and monetise content for consumers. But the industrial middle that is the media factory remains stubbornly resistant to transformation.
This resistance is not because of a reluctance to change, but because the constraints are large. The inputs are from a production world that is highly specialised and deeply human. The outputs are to a consumer world that is unpredictable and capricious. Meanwhile, the systems that underpin media management and distribution are complex, sometimes fragmented, and often poorly suited to rapid change. The result is that AI adds value in pockets, rather than reshaping the whole.
At the same time, the most dramatic gains from AI are often occurring in environments with very few people. The potential for a ‘one-person unicorn’, and the celebration of minimal staffing among AI startups, underline a difficult truth: AI thrives where there is little organisational inertia. Large media companies, by contrast, are defined by legacy structures, workflows, and cultures. The very scale that gives them reach also slows their ability to adapt.
The result of all these conflicting forces? The boards of media companies look set to lose patience with the cost of investments in AI-driven productivity, and to return their attention to what they know best: driving as much revenue as possible from content IP.
And this is where the syncing issue returns. Established media organisations will try to remain competitive in a rapidly reshaping content ecosystem without fundamentally transforming their technology or their operating models. That means that by the time AI has evolved to provide the reliability and productivity they crave, the transformational challenge – and the urgency to undertake it – will be still greater. Meanwhile, challenger entities will have had another couple of years to spot their opportunity and make their mark.
This prediction—that media organisations will lose patience with AI-led productivity—is just one of five predictions in the newly released DPP Tech Trends 2026 report. Four other predictions explore how other developments in business and consumer technology will impact media and entertainment – and why it’s never been more important to understand the complex web of dependencies now shaping media businesses.
2026 will be the year in which successful media businesses equip themselves for the future by letting go of their wishful thinking about the potential of AI and getting themselves in sync with reality, frustrating and painful though it may be.
- This article first appeared in the March issue of TVBEurope, available to download here.
