When AI reinvents the media business model

The media industry, when viewed through the lens of Artificial Intelligence, is both highly diverse and highly specific. It encompasses a wide range of categories : mainstream media, entertainment media, specialist publications, investigative journalism and opinion driven outlets. Each player fulfils a unique role, representing on average around 10 % of total employment, although this proportion varies significantly from one organisation to another.

An industry under pressure

The sector has already experienced several waves of rapid and profound transformation, particularly with the digitalisation of advertising formats. Overall, this shift has resulted in an average annual decline of approximately 2 % in net advertising revenues over the past decade, placing the industry under significant pressure : advertising revenue capture by digital players, the emergence of new programmatic intermediaries, shifting audience behaviours and more.

In this context, Artificial Intelligence may represent an additional layer of intensified competition, particularly through two major dynamics :

1. The commoditisation of competition, driven by the collapse in content production costs for virtually anyone, including both high quality and low quality content, as well as the proliferation of fake news
2. The emergence of new intermediaries further accelerating value cannibalisation, such as fully personalised daily media experiences generated by tools like OpenAI’s ChatGPT or equivalent platforms

At the same time, and based on our experience, Artificial Intelligence also creates significant opportunities to strengthen the position of media organisations, although unlocking this potential requires particularly careful strategic attention.

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Three key themes are emerging :

1. Augmented production

Already widely implemented, through granular content production during elections or major sporting events, dubbing technologies including facial synchronisation, writing and headline suggestions, infographic generation and illustration creation, AI already delivers significant productivity gains and still offers substantial value creation opportunities, particularly across three additional areas :

• Investigative journalism : through large scale analysis models applied to public content, such as social media trend analysis and massive multimodal content processing. One example is the The Wall Street Journal investigation into Google Street View imagery in New Jersey, used to identify poor environmental practices by telecom operators related to abandoned copper infrastructure
• Fact checking and investigation mapping : now achievable at limited cost using public data or journalistic case files, by combining technologies capable of identifying quantitative anomalies, factual inconsistencies or suspicious information cross references requiring deeper investigation
• Production assistance : for written, audio or visual media. While still relatively limited today, for example through headline suggestions in print media, adoption is accelerating across areas such as editing recommendations, written summaries and multimedia synthesis generation

2. New forms of interaction with readers

Media organisations now possess both audience knowledge and technological capabilities enabling them to establish more personalised and higher value relationships with readers and viewers. Three major developments stand out :

• Homepage and interface personalisation : already widespread across video on demand platforms and largely adopted by major US newspapers across their websites and applications. Adoption remains more limited in Europe. When properly implemented, the benefits are substantial, particularly through improved newsletter and notification opt in rates, stronger engagement conversion and ultimately higher subscription levels
• LLM powered chatbots : progressively enabling media companies, such as BILD with “Hey_” or The Washington Post with “Climate”, to provide structured and sourced answers based on editorial content while creating new forms of qualitative interaction with audiences
• Audience expansion through extended formats : AI generated adjacent productions such as translations, audio versions and subtitled content are now increasingly adopted, significantly expanding audiences and usage at dramatically reduced costs. For less mature technologies such as video teaser generation and automated summaries, human supervised models are already delivering major productivity gains

3. Advertising enhancement

AI also introduces innovative opportunities in advertising, such as :

• The revaluation of certain advertising inventories : current “brand safety” rules often penalise journalism, as blacklisting systems are primarily keyword based and fail to distinguish the actual context in which terms are used. For example, does an article mentioning “pure tech” create a positive or negative environment for an automotive advertisement, or even for a specific brand ? Similar issues also apply to video content
• The creation of multi local or personalised audio and video advertising campaigns at near zero marginal cost

Without aiming to be exhaustive, several major challenges emerge for media organisations seeking to embrace this transformation :

• Collaboration first : while these technologies and tools are now accessible at relatively low cost, relying exclusively on third party market solutions may require organisations to “give away their data”, whereas building fully dedicated systems independently may not always be economically viable. Multi stakeholder collaboration therefore becomes highly relevant. A few initiatives already exist, although they remain rare. One major project could, for example, involve the creation of a shared “ChatGPT for readers and audiences” jointly developed by French or European media players. In the field of audience measurement, media organisations previously succeeded in creating recognised collective structures such as Médiamétrie, demonstrating that this type of collaborative initiative is both possible and sustainable
• Quality and ethics : perhaps more than any other industry, media organisations operate under extremely high legal, professional and ethical standards. The response quality of current public LLM technologies still raises concerns for large scale media usage. However, these challenges are progressively being addressed through the development of “agents” and controlled intermediary layers capable of :
  • Moving closer to fully hallucination free responses
  • Preventing attempts to hack generative AI engines
  • Providing mechanisms for fact and data cross verification
• Long term vision and collective dynamics : major AI engines require media content to train their models. Unlike aggregators such as Google News, exhaustiveness is neither necessary nor even desirable for these technologies. In extreme cases, access to only a few archives and news streams may be sufficient, for example a right wing opinion newspaper, a left wing publication and a multi regional media source. To avoid a “prisoner’s dilemma” scenario in which the entire industry ultimately suffers, media organisations must adopt a collective, medium term and industrially coherent approach

There are also collective opportunities at the industry level, particularly regarding new measurement frameworks and the monitoring of best practices.

For example, through the work conducted by Eleven to develop the Observatoire des Médias sur l’Écologie, within a consortium bringing together Data for Good, Expertises Climat, Mediatree, Climat Médias and QuotaClimat, we provided audiovisual industry stakeholders with tools to quantify how they address the ecological crisis within their content.

To learn more about these topics and identify the impacts and opportunities relevant to your organisation, contact Jean-Charles Ferreri.

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