Arab Media in the Age of AI

To provide a focused yet comprehensive view of the industry, the report examines four major segments of the media ecosystem:

Video, encompassing news, films, and scripted and non-scripted audiovisual content across traditional television and over-the-top (OTT) platforms, where visual content remains a major driver of audience engagement, cultural influence and economic value.

Gaming, one of the industry’s fastest-growing segments, combining interactive content, technology and digital experiences to create increasingly immersive forms of entertainment.

Communications & PR, spanning public relations, government communications, and related strategic communication activities that shape how public organisations engage with audiences and stakeholders.

Audio, encompassing music, radio and podcasts across traditional broadcasting and digital streaming platforms.

While each segment has distinct characteristics, business models and levels of AI maturity, they increasingly share common technologies, platforms and capabilities. AI should therefore be understood not as a standalone media segment, but as a horizontal capability increasingly embedded across the wider media ecosystem.

To assess AI’s impact consistently across these segments, the report considers the media value chain through three broad stages:

Content Creation covers the development and production of media content from ideation, research and writing to production, editing, generation and packaging.

Content Distribution encompasses how content is organised, packaged, curated, delivered, discovered and surfaced to audiences across channels and platforms.

Content Monetisation captures how media organisations convert content and audience engagement into commercial value, including advertising, subscriptions, licensing and other emerging revenue models.

Together, these three stages provide a common analytical framework for understanding where AI is creating the greatest impact, how that impact differs across media segments, and where the most significant opportunities and challenges are likely to emerge next.

The remainder of this report applies this lens to examine the evolution of AI across Arab media—from today’s most tangible use cases to the more disruptive capabilities beginning to redefine how media is produced, experienced and valued.

AI is not simply changing individual sectors; it is emerging as a general-purpose technology reshaping business, industries and, increasingly, the way economies and societies function. Across sectors, organisations are rethinking how work is performed, how decisions are made, how products and services are created, and how value is delivered to customers.

What began largely as experimentation with discrete use cases is rapidly evolving into a broader transformation of the enterprise. AI is increasingly being embedded across business functions to augment human decision-making, automate routine activity, accelerate knowledge-intensive work and enable new sources of growth and value creation.

The pace of adoption illustrates the scale of this shift. 88% of organisations now report using AI in at least one business function, compared to 78% in 2024. Yet adoption remains relatively immature: only around one third of organisations have begun scaling AI initiatives across their operations, suggesting that much of the economic and organisational impact of AI still lies ahead.

The next stage of this evolution is likely to be driven by Agentic AI. Whereas earlier applications largely assisted people with individual tasks, AI agents are beginning to plan, coordinate and execute multi-step workflows across systems and functions.

This transition is still nascent, with only 23% of organisations reporting scaling Agentic AI in at least one business function, while a further 39% are experimenting with it, and scaled implementations remain concentrated in a small number of functions.

Nevertheless, the direction of travel is clear. AI is moving from a collection of tools deployed around the enterprise toward an increasingly horizontal intelligence layer spanning workflows, functions and decision-making.

While AI is transforming virtually every industry, the pace and nature of adoption are not uniform. Technology companies were natural early movers, but media and telecommunications organisations have rapidly closed the gap and are now among the sectors reporting the highest levels of AI adoption, alongside technology and insurance.

This places media in a distinctive position. The industry is not insulated from the wider AI transformation; rather, it is becoming one of its proving grounds.

Few industries combine such a broad range of AI-exposed activities: creativity and content production, information discovery, intellectual property, distribution, audience engagement, advertising, personalisation and monetisation. As AI capabilities advance, they increasingly touch not only the operational backbone of media organisations but also the product itself — the content audiences see, hear and experience.

As a result, media provides an early view of how AI can progress from an efficiency tool to a more fundamental force reshaping an industry’s value chain. AI is already supporting information capture and processing, content drafting and ideation, marketing, customer engagement and service operations. Increasingly, it is moving further upstream into creative production, editorial workflows, personalisation and entirely new forms of AI-generated and synthetic media.

For media organisations, the implications could be particularly profound. As an industry already at the forefront of AI adoption, media is likely to encounter many of the opportunities, tensions and structural changes associated with this next phase earlier than other sectors.

Media is therefore leading the global AI transformation and is one of the industries through which that transformation is becoming most visible.

The current AI era represents the latest stage in a broader evolution of the media industry. Successive waves of technological change from the Digital Foundation Era and Social Media Revolution to the Mobile and Personalisation Era progressively changed how content was created, distributed and consumed, while generating the digital infrastructure, data and audience behaviours that enabled today’s Generative & Agentic AI era.

During the Digital Foundation Era (1990s–2000s), the expansion of the internet, satellite television and digital technologies began shifting media away from predominantly analogue formats. Content became increasingly digitised and accessible through new channels, establishing the technological foundation for media to be created, stored and distributed at far greater scale.

The Social Media Revolution (2000s–2010s) fundamentally changed who could create and distribute content. Platforms such as YouTube, Facebook and Twitter reduced traditional barriers to publication, enabling audiences to become creators themselves and accelerating the volume, variety and speed of content circulating across the media ecosystem.

This evolution accelerated further during the Mobile and Personalisation Era (2010s–2020s). Widespread smartphone adoption made media increasingly mobile, on-demand and continuous, while platforms such as Instagram, Snapchat and TikTok introduced new formats and consumption behaviours. At the same time, growing volumes of audience data and advances in machine learning enabled media platforms to increasingly personalise how content was recommended and surfaced to individual users.

AI had already begun playing an important role during this period. Recommendation engines increasingly used machine learning to analyse viewing and listening behaviours, while AI was applied to areas such as visual effects, animation, gaming and audience analytics. Platforms such as Netflix and Spotify increasingly relied on algorithmic recommendations to tailor content discovery and user experiences. These early applications demonstrated AI’s ability to interpret large volumes of data, automate specific activities and personalise media experiences at scale.

The emergence of Generative & Agentic AI (Now) marks a more fundamental shift. Rather than primarily analysing data or optimising existing processes, the technology can now produce text, images, audio and video, expanding AI’s role directly into creative and production activities.

Hence, the AI revolution in media is the next stage of the industry’s digital transformation, building on the connectivity, content volumes, audience data and personalisation capabilities developed over the preceding decades.

Artificial intelligence is moving from a set of discrete tools towards a broader capability across the media value chain. Its role now extends beyond content generation to support how media is researched, produced, localised, distributed, monetised and measured.

The first wave of AI focused largely on automation and prediction, with applications such as audience analytics, recommendation engines, metadata tagging, transcription and advertising optimisation. These tools improved the efficiency of specific activities but generally operated within existing workflows.

Generative AI expanded the opportunity from automation to augmentation. AI can now support activities traditionally associated with creative and knowledge work, including research, concept development, scriptwriting, visual and audio generation, promotional content and adaptation across formats and languages.

For media organisations, this can reduce production time and cost, increase personalisation and make it easier to serve more audiences, languages and formats at scale. It also raises important questions around intellectual property, authenticity, editorial integrity and the appropriate role of human creativity.

The next shift is from AI that creates to AI that coordinates and acts.

Agentic AI can pursue an objective across multiple steps rather than respond to individual instructions. For example, an AI agent could retrieve archive material, prepare content, create platform and language variants, check predefined requirements and recommend distribution timing, while referring editorial or creative decisions to humans.

This changes the potential role of AI from a tool within a workflow to an orchestrator of the workflow itself.

As these capabilities become connected, specialised agents could increasingly work together across the same workflow. Research, rights, localisation, audience and distribution agents could coordinate around a piece of content, which can reduce manual hand-offs and accelerate the journey from idea to audience.

This does not mean that every media activity should become autonomous. The appropriate level of human oversight will depend on the activity and its risk. More standardised tasks such as metadata enrichment, asset retrieval, versioning and scheduling may support greater automation, while editorial decisions, creative direction, sensitive reporting and high-impact publishing decisions will continue to require stronger human judgement and accountability.

At the same time, synthetic media is changing the media product itself. AI-generated presenters, synthetic voices, virtual performers, digital replicas and generated environments are already entering real-world production. These capabilities can change the economics of talent, localisation and production, while creating new forms of content and intellectual property.

The emerging model combines human creativity and judgement with AI-enabled creation, coordination and adaptation at scale. The opportunity is therefore not only to produce existing media more efficiently, but to expand what media can become through more synthetic, adaptive and personalised experiences, while preserving human authorship, cultural relevance and trust.

Across the media value chain, six effects are becoming increasingly visible:

The significance of AI therefore lies less in any single application than in its ability to connect the value chain, increasing creative capacity upstream while improving audience relevance, distribution efficiency and commercial outcomes downstream.

AI is moving beyond accelerating individual creative tasks towards becoming part of the creative production environment itself. Models can increasingly generate and adapt text, imagery, audio, video and other creative assets, while organisations can configure these capabilities around their own intellectual property, visual language and content libraries.

For media organisations, the opportunity is to use AI not simply to produce existing content faster, but to expand creative capacity and develop new forms of content at scale. Proprietary content, data and creative assets can also be used to develop differentiated AI capabilities that become extensions of the organisation’s creative environment.

AI can reduce the time and resources required across production-intensive activities such as editing, adaptation, VFX, dubbing and localisation. More advanced applications can go further by replacing parts of the physical production process with digitally generated environments, imagery, voices and other assets.

For media organisations, this creates an opportunity to rethink the production cost base. Content that previously required large crews, physical locations or lengthy post-production cycles may increasingly be produced through smaller teams that combine human creative direction with AI-enabled production systems.

The first generation of personalisation focused primarily on selecting the most relevant content from a catalogue. Generative AI can increasingly adapt the experience itself, including recommendations, language, explanations, format and duration. Agentic AI could extend this further by continuously interpreting user preferences and coordinating how an individual experience is assembled.

For media organisations, the opportunity is to move from asking “Which content should we recommend?” towards “What experience is most relevant to this individual?” This is particularly relevant in Arab markets, where audiences span multiple languages, dialects, cultures and consumption preferences.

AI and synthetic media are creating new ways for audiences to interact with media brands. Synthetic presenters, virtual personalities and dynamically generated content can extend beyond fixed programming and create experiences that adapt to different audiences, languages and contexts.

For media organisations, the opportunity is to use these capabilities to create new audience touchpoints and deepen interaction, particularly where traditional production economics make highly targeted formats difficult to sustain. The value depends on maintaining clear attribution, editorial oversight and audience trust as AI becomes more visible within the experience itself.

AI is making content distribution more responsive by automating how content is prepared, packaged, localised and adapted for different channels and consumption contexts. Generative AI can create platform-specific versions, metadata, subtitles, translations and shorter formats, while more advanced agentic systems can increasingly coordinate when and where these different versions should be delivered.

For media organisations, the opportunity is to build distribution workflows in which one underlying piece of content can be transformed into multiple audience-ready formats and delivered more quickly across platforms, languages and markets. This can increase the reach and utilisation of existing content without requiring a proportional increase in manual distribution effort.

Traditional media monetisation is largely built around predefined units of value such as advertising inventory, subscriptions, content rights, sponsorships and digital purchases. AI can make the commercial proposition more dynamic by combining audience understanding with personalisation and optimisation to determine what to offer, when to offer it and through which format.

Agentic AI could extend this further by coordinating decisions across the commercial journey. Systems could increasingly interpret audience intent, select an appropriate monetisation mechanism, tailor the proposition and continuously learn from the response.

For media organisations, this creates an opportunity to move beyond optimising existing revenue models towards creating new monetisable experiences around audiences, content and intellectual property. These could include personalised access, premium experiences, digital goods, commerce, subscriptions and other propositions that adapt to audience behaviour.

Taken together, these six impacts demonstrate that AI is not reshaping isolated parts of the media lifecycle; it is increasingly connecting and transforming the entire value chain. From Generative AI accelerating creativity and production, to Agentic AI autonomously coordinating workflows, optimising decisions and acting across multiple stages of the value chain, AI is enabling a more adaptive, intelligent and continuously optimised media ecosystem. As capabilities evolve from assisting individual tasks to orchestrating end-to-end processes, the role of AI is shifting from a point solution to a horizontal intelligence layer influencing how media is created, distributed, experienced and monetised. While the pace and applications differ across segments, these six impacts provide a common lens for understanding this transformation—and how it is beginning to reshape the Arab media landscape.

The previous section examined how AI is reshaping activities across the media value chain—from content creation and production to audience engagement, distribution and monetisation. These impacts are already changing how media organisations operate and where AI can create value.

Yet the longer-term significance of AI extends beyond individual activities. As these capabilities become more widely available, they are beginning to alter the underlying structure and economics of the media market itself: what remains scarce, who can compete, which assets create differentiation, where organisations need to invest and how audiences determine what they trust.

For Arab media organisations, this creates a different strategic question. Competitive advantage will depend increasingly not simply on whether organisations adopt AI, but on how they respond to the structural shifts AI creates around them.

Five shifts are particularly important.

AI is reducing the effort required to create, adapt and reproduce media across formats, languages and markets. As production capacity expands, content itself becomes less scarce.

This changes the basis of competition. The challenge is increasingly not whether an organisation can produce enough content, but whether its content can attract attention and remain distinctive within an environment in which audiences have access to significantly more choice.

Greater abundance could also make differentiation more difficult. As organisations gain access to increasingly comparable AI models and production capabilities, purely technology-enabled output risks becoming easier for competitors to replicate—a challenge already emerging as synthetic content scales.

Media organisations will need to become more selective about where they deploy creative and financial resources.

Rather than using AI primarily to increase output, leaders should determine which content, franchises and formats warrant disproportionate investment because they can attract attention, sustain audience loyalty or generate value across multiple channels and markets.

This increases the importance of portfolio discipline: identifying which IP can be extended, which formats can scale across the Arab world, where human creative input creates disproportionate value and which lower-value activities can be produced or adapted more efficiently through AI.

For regional players, the advantage may therefore lie not in producing the greatest volume of content, but in building a smaller number of distinctive, recognisable and scalable media properties around which broader audience and commercial ecosystems can develop.

Core shift: From maximising content output to concentrating investment behind distinctive, scalable content.

AI is making capabilities that previously required substantial production infrastructure, specialist teams and capital increasingly accessible to smaller studios, creators and digital-native businesses.

Creating professional-quality imagery, video, audio, localisation and marketing assets can increasingly be achieved with smaller teams and more flexible technology stacks. This is lowering some of the traditional barriers separating established media organisations from emerging competitors.

At the same time, the boundaries between media companies, creators, technology platforms and AI-native businesses are becoming less distinct. The competitive set is therefore expanding beyond traditional broadcasters, publishers, studios and streaming platforms.

Scale and legacy infrastructure alone are likely to become less powerful sources of competitive advantage. Established organisations will increasingly need to compete on speed, agility and their ability to mobilise capabilities across an ecosystem.

This could require different approaches to innovation: smaller multidisciplinary teams, faster experimentation, greater collaboration with creators and technology companies, and selective partnerships or investments where external capabilities can be accessed faster than they can be built internally.

For major Arab media organisations, incumbency can still provide significant advantages—but those advantages increasingly need to be activated rather than protected. Strong brands, distribution reach, talent networks and commercial relationships can be combined with the speed and innovation of a wider ecosystem.

The strategic advantage therefore shifts from owning every capability internally toward being able to orchestrate talent, technology, content and partnerships more effectively than competitors.

Core shift: From scale as the primary advantage to speed, agility and ecosystem orchestration.

As increasingly sophisticated AI models become accessible to organisations around the world, access to the technology itself is unlikely to remain a sustainable source of differentiation.

Competitive advantage can instead shift toward the assets organisations bring to those technologies: proprietary content, archives, audience data, metadata, intellectual property, cultural knowledge and specialist datasets.

This is particularly significant for Arab media. Arabic encompasses substantial linguistic and cultural diversity across countries, dialects and communities. Global models can produce fluent Arabic while still struggling with dialect, context, mixed-language communication and culturally specific references.

Arab media organisations possess assets that could become increasingly valuable in an AI-enabled market: decades of audiovisual archives, regional journalism, entertainment IP, audience relationships, Arabic-language content and deep understanding of local cultures and consumption patterns.

These assets should increasingly be viewed not only as content repositories, but as strategic AI assets.

Well-structured archives and proprietary data can support more accurate discovery, localisation and personalisation; regional language datasets can improve the performance of AI across Arabic dialects; and cultural intelligence can help organisations create experiences that are more relevant to Arab audiences than those generated through globally standardised models.

The opportunity for Arab media organisations is therefore not simply to consume AI capabilities developed elsewhere, but to combine those capabilities with regional assets that global competitors cannot easily replicate.

Core shift: From AI technology as the differentiator to proprietary data, content and cultural intelligence as the differentiator.

AI is frequently positioned as an efficiency lever—and in many parts of the media value chain it can reduce the time, labour and physical resources required to produce and distribute content.

However, AI does not simply remove costs. It also introduces new cost pools.

Traditional expenditure on production crews, locations, editing, localisation and repetitive workflow activities may decline in selected areas, while expenditure increases around model access, compute, data infrastructure, technology integration, repeated generation, specialist AI talent and intellectual-property rights.

The economics demonstrated by emerging AI-led production models are therefore more complex than a straightforward reduction in production cost. The existing impact analysis already highlights that the business case depends on whether savings in traditional production inputs outweigh the new technology costs introduced by AI.

Media organisations will need to develop a more sophisticated understanding of AI unit economics. The winners may not be those deploying AI across the greatest number of activities, but those that can identify where AI genuinely improves the economics of their business—and where the cost of technology, implementation and oversight outweighs the value created.

This could also reshape investment priorities. Rather than funding multiple isolated tools and pilots, organisations may increasingly need to invest in shared AI platforms, reusable models, connected data foundations and enterprise capabilities that can support multiple business areas.

For leadership, the question therefore moves from “How much cost can AI remove?” towards “What should the future media cost base look like, and where should capital be redeployed to create sustainable advantage?”

Core shift: From reducing production cost to redesigning the media cost base.

As synthetic and AI-generated media becomes more sophisticated and more abundant, audiences will find it increasingly difficult to determine whether content is authentic, altered or entirely generated.

This creates an important paradox for media. The same technologies that enable greater scale, personalisation and creativity also increase the potential for manipulated content, misinformation, misuse of likeness and uncertainty around provenance.

The current market dynamic is therefore not simply an increase in the need for governance. Authenticity itself becomes scarcer as synthetic content becomes more abundant. The existing insights already point to the increasing importance of provenance, verification and transparent labelling as audiences find authentic and manipulated media harder to distinguish.

Trust can become an increasingly important source of differentiation.

Established media organisations possess assets that AI cannot easily manufacture: editorial accountability, recognisable brands, established standards and long-term audience relationships.

For journalism and news organisations, this is particularly important, but the principle extends across entertainment, creator media and branded content as virtual personalities, synthetic voices and AI-generated formats become more common.

Media organisations that can demonstrate where content originated, how it was created and who remains accountable for it may be able to strengthen audience confidence while competitors struggle with increasingly indistinguishable synthetic output.

Trust should therefore be considered not only as a regulatory or reputational safeguard, but increasingly as part of the media value proposition itself.

Core shift: From trust as a safeguard to trust as a competitive asset.

Taken together, these dynamics suggest that AI will not simply make the existing media industry faster or more efficient. It is beginning to change what creates advantage within it.

As content becomes more abundant, value shifts toward differentiation. As barriers to entry fall, speed and ecosystem orchestration become more important. As AI technologies become more accessible, proprietary regional assets become more strategic. As traditional and technology cost pools converge, organisations need to rethink the economics of media production. And as synthetic content proliferates, trust becomes increasingly valuable precisely because authenticity is becoming harder to establish.

For Arab media leaders, the implication is therefore broader than AI adoption. The organisations best positioned for the next phase will be those that use AI to reinforce what makes them distinctive while redesigning the capabilities, economics and operating choices required to compete in a fundamentally different media environment.

As AI moves from discrete use cases towards broader transformation of the media value chain, spending is following.

This section first positions the Middle East and North Africa (MENA) within the global AI-in-media market before examining how AI spend is developing across key Arab markets.

The global media and entertainment industry is estimated to generate approximately USD 2 trillion in annual revenue, spanning both traditional and digital media.

Industry benchmarks suggest that media organisations typically allocate around 5% of revenue to IT, implying a global media technology spend of approximately USD 95–100 billion. This encompasses a broad range of expenditure; however, estimates indicate that spending specifically associated with AI software and related implementation and managed services is in the range of USD 15–16 billion.

The boundaries of AI spending are increasingly difficult to isolate as AI capabilities become embedded across cloud, data platforms, enterprise software and technology modernisation programmes. As a result, the total economic commitment required to enable and operate AI across media organisations is likely higher than the AI software and services market captured here.

Available market estimates indicate that spending on AI software and services within media has nearly doubled since 2024, reflecting annual growth of close to 40% over the period.

Looking forward, the precise trajectory will depend on how AI spending is defined and classified. However, broader market estimates generally point to AI spending in media growing at least in line with the broader AI market and potentially faster in selected parts of the value chain.

This reflects media’s position as one of the industries at the forefront of AI adoption. AI can influence not only enterprise productivity, but the industry’s core product and economics—from content creation and localisation to personalisation, discovery, audience intelligence, monetisation and increasingly agentic workflows.

As adoption shifts from experimentation towards integration into core workflows, the intensity of AI investment in media should therefore remain structurally high relative to many other industries.

Building on the broader context of AI spending within media IT budgets, the geographic outlook shows that AI-in-media spending is expanding across all major regions, with MENA standing out for its pace of growth. While the region currently represents a relatively small share of global AI-in-media spending, its market is expected to grow at 3% above the global average CAGR, which places it among the fastest-growing regions worldwide.

This growth trajectory is being enabled by governments across the region prioritising AI through national strategies, digital economy initiatives and investment in enabling infrastructure, alongside media organisations and professionals expanding experimentation with AI and embedding new capabilities across the value chain. Together, these developments point to an accelerating transition towards higher levels of AI maturity across the media ecosystem, as organisations move from early experimentation towards more structured, integrated and scalable adoption.

Looking specifically at the MENA, AI-in-media spending remains concentrated in the region’s more mature Gulf markets. In 2026, Saudi Arabia, the UAE and Qatar are estimated to account for approximately half of regional spending, with Saudi Arabia representing around one quarter and the UAE close to one fifth.

At the same time, faster growth is emerging in markets that are earlier in the adoption curve. Countries such as Egypt and Morocco currently account for a smaller share of regional spending, but their stronger growth trajectories indicate that AI adoption is beginning to broaden beyond the Gulf as capabilities become more accessible and media organisations increase experimentation and deployment.

Overall, the outlook remains Gulf-led, with Saudi Arabia and the UAE expected to continue anchoring regional AI-in-media spending. However, the longer-term growth opportunity is becoming more distributed across the wider MENA region as other markets progress towards higher levels of AI maturity.

As AI spending expands across the region, organisations are moving beyond technology acquisition towards embedding AI into media operations. AI software includes tools for content generation, distribution, audience analytics and workflow automation, while AI services provide the expertise needed to implement, operate and scale these technologies.

These services typically fall into two categories. Professional services cover consulting & advisory, implementation & deployment, support & maintenance and training & enablement, while managed services refer to the ongoing external operation and optimisation of AI-enabled solutions on behalf of the organisation.

In 2026, software remains the largest share of AI investment.

Within software, spending remains concentrated in capabilities that directly support content creation and distribution. Content distribution currently accounts for around one third of software spending across the MENA, followed by content generation. Together, these two categories represent almost 60% of software spending, which reflects the continued focus on using AI to create content and improve how it reaches audiences.

As adoption matures, spending is expected to broaden towards capabilities that embed AI more deeply into media operations. Content generation and distribution are likely to remain important, but greater emphasis is expected on audience analytics, workflow automation, and the integration of AI across editorial, production and commercial processes.

This shift is already becoming visible in the region’s more advanced media markets.

Leading organisations are moving beyond standalone AI tools and are beginning to integrate AI across how content is produced, distributed and monetised.

Building on the software outlook, the services market points to a similar shift from initial AI adoption towards more sustained and embedded deployments. In 2026, professional services account for the larger share of AI-in-media services spending, ahead of managed services, across the MENA.

This reflects the continued need for specialist expertise as media organisations define their AI priorities, integrate new technologies and deploy them across existing operations. At this stage, organisations still require significant support across strategy, use-case prioritisation, implementation and change.

As adoption matures, the role of professional services is expected to evolve rather than diminish. Greater emphasis is likely to shift towards systems integration, data and technology architecture, operating-model design and workflow redesign. This suggests a broader transition across the region as media organisations move beyond individual tools and isolated pilots towards AI that is more deeply embedded across core processes and ways of working.

At the same time, managed services are likely to become increasingly relevant as AI becomes embedded in business-critical workflows. Moving AI into production creates ongoing requirements around platform operation, workflow optimisation, monitoring, security, governance and access to specialist capabilities.

Across the MENA, managed services models are already evolving from discrete outsourced activities towards more integrated, technology-enabled support. A similar pattern is visible within media, where organisations are increasingly working with external technology and specialist partners to implement and operate AI-enabled platforms and workflows across content production, analytics, metadata, distribution and other functions.

Workforce enablement and responsible AI governance are also likely to become more persistent components of the services landscape. As AI tools are integrated more deeply into media workflows, organisations need to build the skills required to use them effectively, redesign ways of working and establish appropriate controls around issues such as accuracy, intellectual property, transparency and content integrity. Initiatives across the UAE and Saudi Arabia are already placing significant emphasis on AI training, executive capability building and responsible-use frameworks for media, while similar capability-building efforts are emerging across the rest of the region.

Taken together, this points to a broadening of the AI services lifecycle as the regional market matures. Rather than services being concentrated primarily around selecting and implementing new technologies, demand is likely to extend further into integration, operating-model transformation, ongoing management and workforce capability building.

The evolution of the services market therefore mirrors that of software: as AI moves from individual tools towards a more embedded role across media organisations, the supporting services ecosystem is also expected to become more continuous, specialised and closely integrated with day-to-day operations.

AI spending across the region shows that media organisations are already committing meaningful resources to the technology. However, the level of spend alone does not reveal how extensively AI is being used or how deeply it has been embedded into day-to-day media operations.

This section therefore moves beyond where and how much organisations are currently spending on AI to examine how that spending translates into actual adoption. It first assesses adoption across key Arab media markets, before complementing this market-level view with the perspectives of media professionals in the UAE in the following subsection.

AI adoption in Arab media is already substantial in several markets, although the depth of implementation varies considerably. For the purposes of the underlying assessment, adoption is defined as the share of media organisations using AI in at least one operational workflow across content production, editorial operations, audience engagement, translation and localisation, production automation or content analytics.

The adoption estimates are based on primary research with media companies and professionals across MENA, supported by secondary research on country-level AI adoption and market developments.

Among the markets assessed, the UAE currently records the highest estimated adoption at approximately 75% in 2026, followed by Saudi Arabia at 70% and Qatar at 65%. Egypt remains at an earlier stage, at approximately 40%, reflecting a wider gap between individual experimentation and more structured organisational deployment.

Looking ahead, adoption is expected to deepen as existing pilots and departmental deployments expand into broader production use. By 2030, AI adoption is estimated to exceed 95% in the UAE and Saudi Arabia, reach approximately 90–95% in Qatar, and rise to 75–80% in Egypt. These estimates point to a progression towards broader organisational adoption in the Gulf, while markets starting from a lower base are expected to advance as institutional capabilities, infrastructure and workforce readiness develop.

Beyond overall adoption levels, the evidence also points to differences in how AI is being applied across markets. Content creation, translation and localisation, newsroom support, audience analytics and workflow automation are emerging as common application areas, but the depth and breadth of deployment vary by country.

Saudi Arabia and Qatar stand out for particularly strong adoption in translation, newsroom and workflow applications, while the UAE shows the broadest adoption profile and is especially advanced in synthetic media, which includes the use of AI-powered spokespersons and AI-generated virtual characters for institutional communications and digital storytelling. On the other hand, Egypt remains at an earlier stage of institutional maturity, with adoption currently more concentrated in content creation and newsroom-related applications.

Across the rest of the MENA, adoption appears more uneven and is often comparatively less mature overall. Available evidence from the Levant and North African markets suggests that AI use is expanding across content creation, newsroom support and digital workflows, but adoption is frequently driven by individual professionals or selected teams rather than embedded across organisations. Gaps in training, access to tools and organisational support remain common constraints, indicating that experimentation is advancing faster than the capabilities required to scale AI consistently across media institutions.

Overall, AI adoption across Arab media is advancing, but at different levels of institutional maturity. More advanced Gulf markets, notably Saudi Arabia, the UAE and Qatar, are increasingly moving towards AI being embedded across multiple media workflows, while other Arab markets are still progressing from individual and departmental experimentation towards more structured organisational deployment. The following subsection complements this market-level view with the perspective of media professionals in the UAE, examining how frequently AI is being used, where it is being applied and how professionals perceive its role in their work.

These differences become clearer when viewed through the media value chain. Across content creation, distribution and monetisation, AI is changing not only how video is produced, but how it reaches audiences and how value is ultimately captured.

AI is expanding how video content is conceived, produced and managed, from ideation and scripting through production, editing, post-production and aggregation. GenAI can support visual ideation, storyboarding, script development and the creation of imagery, video and other production assets, while synthetic media is enabling generated environments, voices, presenters and performers to become part of finished content. In films and scripted programming, these capabilities can extend into previsualisation, VFX, character transformation and entirely generated sequences; in news and traditional television, they can support summaries, visual assets, programme preparation and the conversion of longer material into short-form content for digital and social channels.

AI is also changing the economics and speed of production. Automated editing, transcription, metadata generation, archive retrieval and other repetitive workflows can reduce manual production hours and shorten turnaround times, while digitally generated assets can reduce reliance on selected physical sets, locations and production resources. For news organisations in particular, these capabilities are increasingly being connected through integrated newsroom environments, bringing research, archives, editorial preparation and production into a more coordinated AI-enabled workflow. As Agentic AI matures, systems could increasingly coordinate multiple steps across these workflows rather than supporting individual tasks in isolation.

However, the economics of AI-led production are not always straightforward. Studios are beginning to differentiate between AI-led and human-led production models in how they structure and price projects. AI-led production can reduce selected costs by lowering reliance on physical production inputs and accelerating parts of the workflow, but it is not necessarily cheaper in every case. For more complex films and audiovisual content, repeated generation and the combined cost of model subscriptions, compute and token usage can materially increase production costs and erode some of the expected savings. Human-led production also remains important where creative nuance, emotional judgement and a distinctive creative vision are central to the output, areas where AI-generated content can still struggle to replicate the human touch.

The expanding number of AI tools also creates an adoption challenge for studios. Production teams need time to test different models, understand where each performs best and determine which tools can be reliably integrated into specific workflows. This R&D and experimentation can slow near-term adoption, but it is also necessary to understand the true cost and performance of different models before studios standardise them across production workflows.

AI is also changing how existing content libraries are used. Multimodal AI can analyse footage, recognise people, speech, objects and scenes, and make large archives easier for production teams to search, retrieve and reuse when developing new programmes, news packages or audiovisual content.

AI is changing distribution from the movement of finished content towards a more intelligent process of packaging, adapting, scheduling and surfacing content to different audiences. On OTT platforms, recommendation engines already shape discovery, while semantic and conversational search can allow viewers to express what they want more naturally. Emerging Agentic AI capabilities could take this further by interpreting intent across interactions and increasingly coordinating how content is surfaced to individual viewers.

For news and broadcast organisations, distribution is also becoming faster and more dynamic. AI can support rapid clipping and short-form content creation for digital and social channels, automate translation and subtitling, and help determine how content is packaged across television, websites, apps and social platforms. Traditional broadcasters can apply audience intelligence to programme scheduling and channel planning, while AI can support the placement and sequencing of programmes based on expected viewing behaviour. AI capabilities combined with live broadcasting can also enable automated highlights, contextual information, on-screen data and interactive features around sports and other live content.

Additionally, localisation is becoming an increasingly important extension of this shift. AI and synthetic-media capabilities can accelerate translation, dubbing, voice generation and lip-synchronisation, while content can also be automatically adapted for different channels and formats, such as shorter social cuts, vertical mobile video or platform-specific trailers.

For films, cinema remains an important distribution channel alongside streaming and other digital windows. AI-enabled audience and demand analytics can support decisions around release timing, market prioritisation and promotional activity, although the theatrical exhibition itself remains predominantly a physical distribution model.

The result is a shorter and increasingly intelligent path between finished content and audiences across markets, languages, channels and platforms.

AI is strengthening monetisation by improving how media organisations understand audiences, optimise commercial decisions and connect content with revenue opportunities. Audience analytics can identify viewing patterns, preferences and engagement signals that inform advertising, subscriptions, programming, retention and release strategies. At the same time, as AI becomes more visible in the production of media itself, it could also begin to influence how audiences and buyers perceive the value of different types of content. For broadcast and traditional television, AI can support audience segmentation, addressable advertising, ad-inventory optimisation and traffic optimisation by helping determine which advertising inventory should be allocated, when it should run and against which audiences. AI-enabled audience intelligence can also inform media buying and placement decisions by improving the match between audiences, programming and advertising objectives. For OTT platforms, first-party audience data can support recommendation, churn prediction, retention, advertising optimisation and customer-value analysis, creating a more connected commercial layer around the viewing experience. As these capabilities mature, platforms can increasingly use AI to determine which proposition, offer or experience is most relevant to different audience segments. For films, audience analytics can support release and marketing decisions, while box-office performance remains a core monetisation mechanism for theatrical releases. AI can help studios and distributors better understand audience demand and optimise how films are marketed and released.

AI may also begin to influence the perceived value of video content itself. As audiences and buyers become more aware of how content is produced, human-led and AI-led productions may not always be valued in the same way. For premium films, documentaries and other creatively distinctive formats, human authorship, performance and craftsmanship may carry additional value, particularly where they shape the emotional, cultural or artistic quality of the experience. This could create a more differentiated market in which AI-generated and human-led content are not necessarily perceived or priced in the same way. At the same time, AI can increase the commercial value of existing intellectual property. Once archives and programme libraries are digitised, structured and searchable, content can be identified, packaged, reused and licensed more systematically rather than remaining a passive repository.

*OTT-specific AI impacts are concentrated primarily downstream. Content creation typically occurs upstream within film, scripted & non-scripted audiovisual content*

The concentration of impact differs across the four sub-segments.

For films and scripted or non-scripted audiovisual content, AI is having its most visible impact in content creation, where generative and synthetic capabilities are changing how content is conceived, produced and edited.

In news, the impact spans creation and distribution as integrated newsroom capabilities connect research, production, archives and rapid multiplatform publishing.

Broadcast and traditional TV are seeing AI spread more broadly across the value chain, particularly through production automation, programme scheduling, live-content enhancement and advertising optimisation.

For OTT platforms, the strongest impact remains in distribution and monetisation, where personalisation, discovery, localisation and first-party audience intelligence increasingly shape both the viewing experience and its commercial value.

Spending on AI applications related to video in MENA more than doubled between 2024 and 2026, increasing from approximately USD 182 million to USD 367 million. This represents a CAGR of around 42%, with spending projected to surpass USD 1 billion by 2030.

Content creation applications currently represent the bulk of AI spending in the video segment, driven particularly by film scriptwriting, VFX and storyboarding, post-production workflow editing and video production. This concentration is expected to persist, with some of the fastest-growing applications continuing to sit within creation and production workflows as generative and synthetic capabilities become more sophisticated.

Applications supporting content distribution and monetisation account for a smaller share of spending, including real-time OTT content moderation and predicting viewer preferences. While these applications are also expanding, the spending profile indicates that AI’s strongest near-term impact remains upstream, where it can directly augment creative processes.

Regional momentum is led by Saudi Arabia, consistent with its position as the largest individual market for AI spending in media more broadly, followed by the UAE. Looking ahead, Egypt is expected to record the fastest growth among the individual regional markets covered, suggesting that adoption is broadening beyond today’s leading spenders.

The opportunity for video organisations is therefore to build on the momentum in content creation while progressively connecting these capabilities to distribution and monetisation. As AI becomes more deeply embedded in how content is conceived, produced and edited, greater value can be captured when these capabilities are linked to localisation, discovery, audience intelligence and commercial decision-making across the wider value chain.

Building on where AI spending is concentrated across the video sub-segments, the following case studies illustrate how these investments are translating into practical applications at the points in the value chain where the impact of AI is currently most pronounced: content creation for films; content creation and distribution for broadcasting and news; content distribution for OTT; and content creation for scripted and non-scripted audiovisual content.

The model is also difficult to replicate quickly. AI models and individual tools are increasingly accessible to every publisher; however, decades of proprietary content, audience data and an integrated technology architecture are not.

The next phase of AI adoption in video will depend less on adding individual tools and more on building the foundations that allow AI to operate across content, workflows, audiences and commercial models.

Audio is moving from one of AI’s earliest applications—recommendation, scheduling and production automation—toward a model in which AI can increasingly influence the content itself, how it reaches listeners and how the listening experience evolves in real time. Generative AI is already being applied to music creation, synthetic voice, editing and radio programming, while AI-driven recommendation has become embedded in digital audio platforms. As agentic capabilities mature, these individual applications could become more connected: coordinating creation, programming, localisation, distribution and audience response rather than optimising each task independently.

This evolution plays out differently across music, radio and live audio, and podcasts and on-demand spoken audio. In music, AI increasingly touches the creative work itself and raises new questions around authorship and rights. In radio, it can reshape programming and presenting within a traditionally linear medium. In podcasts and spoken audio, it can significantly reduce the effort required to create, translate and scale content. Across all three, synthetic voice and multilingual generation create particular opportunities for Arabic-language and regional content, while also increasing the importance of consent, attribution and trust. The previous Arab Media Outlook already highlighted AI-driven personalisation, synthetic voice, radio automation and Arabic-language audio applications as emerging areas of adoption.

AI is expanding what creators can produce and how quickly they can produce it. In music, generative models can support composition, arrangement, sound design and mastering; in radio, AI can assist with scripting, scheduling, voice generation and programme assembly; and in podcasts and spoken audio, it can support research, scripting, editing, transcription and synthetic narration. Similarly, AI-assisted music composition is seen as an emerging entertainment application.

This directly supports two of the report’s core use cases: enhanced content creation and lower production cost & faster workflows. The next step, however, is not simply producing the same audio faster. Agentic systems could increasingly coordinate multiple production activities—from research and scripting through editing, rights checks and versioning—while synthetic audio could enable new forms of content that are generated or adapted dynamically.

The impact is particularly significant for spoken audio. Automated translation, dubbing and Arabic voice synthesis can allow a single piece of content to be recreated across languages and markets without repeating the full production process.

AI has already transformed audio discovery through recommendation, but the opportunity is expanding from recommending existing content to adapting the listening experience itself. Music platforms can sequence tracks around individual preferences and context; radio can increasingly combine live programming with personalised digital streams; and podcast platforms can tailor discovery, episode recommendations and potentially even versions of content to different listeners.

This is where hyper-personalised audience experiences, deeper audience engagement and faster distribution & localisation become particularly important. AI can help determine not only what a listener receives, but potentially the language, format, duration, presenter or sequence in which it is delivered.

For radio, this could be especially disruptive. The medium has traditionally relied on a largely shared, linear schedule, yet AI creates the possibility of retaining the immediacy and personality of radio while introducing greater personalisation across digital channels. This matters in MENA because radio continues to retain substantial reach—particularly in the UAE and Saudi Arabia—even as streaming and podcasts expand.

For podcasts and spoken audio, localisation can also materially change distribution economics. Translation and synthetic voice can allow Arabic content to travel into new markets—or international content to be localised into Arabic and regional dialects—far faster than traditional re-recording.

AI can also change how audio audiences are monetised. In music, personalisation can support subscription retention, discovery and catalogue utilisation, while AI-generated music creates new questions around licensing, royalties and the value of human-created IP. In radio, better audience intelligence can improve advertising relevance, inventory planning and sponsorship opportunities. In podcasts, AI can support dynamic advertising, audience matching and more scalable monetisation of niche or localised content.

These opportunities connect primarily to new monetisation opportunities, but also to deeper engagement and personalisation: better understanding what listeners value can help platforms move beyond simply maximising reach toward optimising retention, willingness to pay, advertising relevance and lifetime engagement.

The MENA context makes this particularly relevant. Music already accounts for the majority of regional audio revenues, while podcast monetisation remains comparatively immature and radio retains broad reach. AI could therefore have different commercial implications by sub-segment: strengthening mature streaming economics in music, improving monetisation of radio audiences, and helping podcasts and other spoken formats reach the scale and localisation required to build more sustainable business models.

The overall shift is from using AI to make audio production and distribution more efficient toward using it to create audio experiences that can become increasingly generated, localised, personalised and adaptive—with significant implications for creativity, rights and the economics of audio.

Spending on AI applications in audio is concentrated around two broad areas: music, and spoken-audio production spanning radio and podcasts. Music has a distinct creative and rights-driven value chain, while radio and podcasts are often analysed together because both rely heavily on spoken-word production, scripting, editing, voice, programming and distribution workflows, even though their consumption models differ. Across both areas, AI is moving quickly from workflow automation into content generation, localisation and personalisation.

Between 2024 and 2026, MENA audio AI application spend more than doubled from approximately USD 19 million to USD 39 million. Music generation and production rose from around USD 12.0 million to USD 25.2 million, while podcast and radio content production increased from approximately USD 6.9 million to USD 14.1 million over the same period.

The composition of spending also reflects the relative maturity of the sub-segments. Music accounts for around two-thirds of audio AI application spend in 2026, consistent with its position as the largest part of the regional audio economy. The remaining third sits within podcast and radio content production, where AI is increasingly supporting scripting, editing, programming, synthetic voice and localisation.

Across the sub-segments, the implications differ. In music, investment is increasingly supporting AI-assisted composition, production and sound creation. In radio and live audio, AI is being applied to programming, scheduling, archiving and synthetic presenting. In podcasts and on-demand spoken audio, the opportunity is increasingly around lowering production effort and scaling content through transcription, translation and synthetic voice. Existing regional adoption already spans radio automation, podcast translation and multilingual audio applications.

Looking ahead, the current trajectory implies growth of around 40% annually through 2030 across these audio AI applications, with music generation and production growing slightly faster than podcast and radio production. The direction of spend points less to simply generating more content and more toward AI becoming embedded in how audio is produced, localised and adapted for different audiences.

Regional momentum is strongest in Saudi Arabia and the UAE, which together account for approximately USD 17.5 million—just under half of MENA audio AI application spend in 2026. Both markets more than doubled between 2024 and 2026, reflecting the wider development of their digital audio and creative ecosystems.

The opportunity is not simply to automate audio production, but to invest in capabilities that expand the reach and relevance of regional content. For MENA, this is particularly important across Arabic-language creation, multilingual and dialect localisation, personalised discovery and scalable spoken-audio production, while rights, attribution and consent become increasingly critical as synthetic music and voice mature.

AI remains a relatively small technology layer within the wider audio economy, but it is growing rapidly and moving closer to the voice, sound and listening experience itself.

The most disruptive applications of AI in audio are emerging across three connected shifts. First, AI is becoming better at understanding audio itself, improving how music and spoken content are classified, discovered and recommended. Second, generative and synthetic technologies are beginning to reshape how audio is created and who, or what, can create it, from virtual presenters to synthetic artists and voices. Third, AI is making the listening experience increasingly dynamic and personalised, with content that can be assembled, localised and adapted around individual audience preferences.

The following cases span music, radio and podcasts, illustrating this progression from content intelligence to synthetic creation and personalised audio across creation and distribution, and showing how AI is beginning to move from supporting the audio value chain to shaping the audio experience itself.

The next advantage in audio will come from using AI not simply to produce more content, but to expand the reach of Arabic audio, unlock more adaptive listening experiences and scale synthetic voice responsibly.

Gaming is becoming one of the clearest examples of AI moving beyond production support and into the experience itself. Generative AI is already accelerating how games are developed, while emerging agentic capabilities allow characters, systems and environments to respond more dynamically to players. Looking ahead, synthetic media could take this further by enabling increasingly personalised experiences that evolve in real time. Across MENA, this creates opportunities not only to produce games more efficiently, but also to deepen engagement, expand reach and unlock new sources of value.

AI is expanding what development teams can create while reducing the effort required to bring games to market. Generative tools can support concept development, character and environment design, dialogue, localisation, testing and other asset-heavy activities, enabling enhanced content creation at greater speed and scale. At the same time, automation across development and production workflows can reduce repetitive effort, shorten iteration cycles and lower barriers for smaller studios—supporting lower production costs and faster workflows.

As agentic capabilities mature, AI can move beyond assisting individual tasks toward coordinating multiple activities across the development process and becoming increasingly embedded within gameplay itself.

AI is also changing how games reach and interact with audiences. Recommendation, matchmaking, localisation, moderation and player intelligence allow platforms and publishers to deliver hyper-personalised audience experiences, shaped increasingly by individual preferences and behaviour.

AI-enabled localisation and automated content adaptation can also support faster distribution and localisation, helping games and associated content reach multiple markets, languages and communities more efficiently. This is particularly relevant in MENA, where linguistic and cultural relevance can be an important factor in expanding regional reach.

Gaming generates continuous signals around how players interact, compete, spend and engage with content. AI can turn these signals into a deeper understanding of audiences, supporting deeper audience engagement through more relevant interactions, live experiences, communities and personalised content.

This creates opportunities for new monetisation models across in-game experiences, advertising, subscriptions, live operations, sponsorship and competitive gaming. Esports developments in the region are particularly relevant in this context, where tournaments, teams, brands, media platforms and fan communities extend the monetisation potential of gaming beyond direct player spending. AI can strengthen this ecosystem through audience analytics, sponsorship measurement, content optimisation and increasingly personalised fan engagement.

Across the value chain, the direction is consistent: AI is moving from helping gaming companies produce more efficiently toward helping them create, distribute and monetise increasingly adaptive experiences.

Spending on gaming and immersive AI applications in the MENA more than doubled between 2024 and 2026, increasing from approximately USD 28 million to USD 58 million. Growth was broad-based: NPC behaviour modelling almost doubled, while gaming content optimisation and immersive entertainment both more than doubled over the period.

The composition of spending also points to where AI is gaining relevance. Immersive entertainment accounts for around half of the market, while increasing investment in NPC behaviour and content optimisation reflects growing demand for more intelligent characters, adaptive gameplay and personalised experiences. AI is therefore moving beyond supporting production to becoming increasingly embedded in the game itself.

Looking ahead, maintaining the current trajectory would imply growth of around 36% annually through 2030. Agentic characters, real-time game optimisation and increasingly responsive environments could sustain this momentum, while synthetic experiences may open the next wave of demand.

Regional momentum is strongest in Saudi Arabia and the UAE, which together account for around half of MENA gaming and immersive AI application spend in 2026. Both markets more than doubled from 2024 to 2026, while Egypt grew from a smaller base at an even faster pace.

The opportunity is not solely to spend more on AI, but to shift investment toward capabilities that can differentiate the gaming experience. As intelligence becomes more embedded in characters, worlds and player interactions, AI can increasingly become part of how games engage, adapt and create value.

AI remains a relatively small technology layer within a sizeable gaming economy, yet it is growing far faster and moving into increasingly strategic parts of the experience.

The most disruptive applications of AI in gaming emerge when technology moves beyond helping humans perform a task and starts changing the experience, the economics or the product itself. Early examples are now appearing across gameplay, monetisation and the creation of entirely new types of gaming environments.

Scaled agentic gaming deployments in MENA remain limited, but the enabling ecosystem is developing quickly. In the UAE, initiatives linked to the Dubai Program for Gaming 2033, NVIDIA technologies and AI-agent development are exposing local studios to advanced gaming capabilities. Saudi Arabia is similarly expanding access to AI tools, cloud infrastructure and development platforms through ecosystem partnerships and training.

The opportunity for the region is therefore not simply to adopt the technologies already proven elsewhere, but potentially to build directly for the next generation of AI-native gaming experiences.

The next competitive advantage in gaming will come not from adding more AI, but from designing experiences, operations and commercial models that continuously learn and adapt with the player.

Strategic communications in this chapter focuses on how governments and public institutions shape national narratives, manage reputation and influence perception across domestic and international audiences. It establishes the narratives to advance, the stakeholders that matter and the perceptions to shape or protect. PR sits within this broader remit as an execution discipline, translating those priorities into media engagement, campaigns, public-facing communications, reputation management and crisis response.

This deep dive examines that evolution across creation, distribution and value impact, focusing specifically on government-to-stakeholder communications across MENA. It considers how AI is changing national narrative development, government campaigns, stakeholder and perception intelligence, media monitoring, multilingual engagement, crisis response and reputation management—and how emerging capabilities could move government communications from periodic campaigns and reactive response toward a more continuous, predictive and adaptive model for shaping and protecting national perception.

AI is expanding both layers. Generative AI can accelerate message and content development, while predictive analytics can help governments understand how narratives are evolving, identify emerging issues and assess shifts in stakeholder sentiment. As agentic capabilities mature, AI could increasingly connect monitoring, analysis, content development and response—supporting a more continuous and adaptive model of national communications.

This is also a particularly sensitive area for AI. Governments are not only users of AI-enabled communications; they operate in an information environment increasingly shaped by misinformation, deepfakes, synthetic content and coordinated influence. AI therefore becomes both a communications capability and a means of protecting the credibility, authenticity and trust that underpin national reputation.

At the strategic communications level, AI can combine insights from media coverage, stakeholder sentiment, policy priorities and emerging narratives to help governments determine what needs to be communicated, to whom and with what intended effect. This can strengthen the development of national narratives and ensure communications remain grounded in policy intent, cultural context and a current understanding of the information environment.

At the PR and campaign execution level, these narratives are translated into speeches, media materials, public-information content, campaign assets and crisis-response messaging. Generative AI can accelerate drafting, adaptation and localisation across formats and stakeholder groups, while keeping human judgement and editorial oversight at the centre.

Strategic communications determines which stakeholders matter, which perceptions need to shift and how narratives should be coordinated across audiences and channels. AI can support this through stakeholder intelligence, audience segmentation and real-time understanding of how different groups are responding to an issue or policy.

This is where PR becomes particularly visible operationally. Media relations, public-information campaigns, multilingual communications and crisis response translate the broader narrative into engagement with citizens, media, investors, visitors, international institutions and other governments. AI can help adapt communications by stakeholder, geography, language, channel and context, while continuous monitoring enables messages and interventions to evolve as media coverage, sentiment or emerging issues change.

For government communications, value is not primarily financial. It lies in whether communications are strengthening awareness, trust, reputation, policy understanding and national perception, and whether governments can identify and respond when those outcomes are at risk.

AI can connect media exposure, sentiment, stakeholder response and narrative performance to provide a richer view of communications effectiveness. PR metrics such as reach, coverage, engagement and sentiment remain important inputs, but strategic communications looks beyond these outputs to understand which narratives are resonating, how perception is evolving, where reputational risks are emerging and whether communications are contributing to broader national objectives. The overall shift is therefore from using AI primarily to produce and distribute government communications more efficiently toward using it as an intelligence layer that helps governments continuously understand, shape and protect national narratives—with PR providing a critical mechanism for activating and managing that narrative in the public domain.

The most disruptive applications of AI in government communications emerge across two connected layers. At the strategic communications level, AI helps governments sense the information environment, understand perception, anticipate risks and shape or protect national narratives. At the PR level, those strategic priorities are translated into campaigns, media engagement, crisis response, public communication and reputation management. Some of the strongest examples sit at the intersection of both—using AI not only to understand how a narrative is evolving, but also to determine how, where and when government should respond.

The following cases therefore span Strategic Communications-led, PR-led and hybrid applications, illustrating how AI is beginning to connect narrative intelligence with communications execution across creation, distribution and value impact.

The next evolution in government communications will come from connecting Strategic Communications and PR more closely, using AI to continuously sense and shape the narrative, while translating that intelligence into faster, more targeted and trusted engagement with the audiences that matter.

The next advantage in government communications will come from using AI not simply to communicate faster, but to continuously understand perception, shape the narrative, respond to emerging risks and protect trust.

AI is creating regulatory challenges that existing media frameworks were not designed to address, from determining whether content is authentic and who owns AI-generated outputs, to protecting human identity and establishing accountability when AI participates in content creation and distribution. This shifts the regulatory debate from a broad question of “How should AI be regulated?” towards a more fundamental set of choices for the media industry: What needs to be protected? Who owns what? Who is accountable? When should audiences know that AI was involved? And how can these protections coexist with freedom of expression, creative freedom and continued innovation? For Arab media, the opportunity is not simply to replicate regulatory models emerging elsewhere. It is to learn from how leading markets are addressing these questions and develop a framework suited to the region’s media ecosystem - protecting trust, rights and accountability without constraining the innovation AI can enable.

For most of media history, an image, voice recording or video provided at least some evidence that an event or interaction had occurred. Synthetic media breaks that assumption. AI can now generate convincing video of an event that never occurred, recreate the voice of a political leader, alter a journalist’s interview or deploy entirely synthetic presenters indistinguishable from humans. The regulatory problem is therefore moving from policing false information towards establishing content provenance: Can audiences determine where content originated, whether it has been materially altered and who stands behind it? This matters most in news, current affairs, political communication and other public-interest content, where authenticity itself carries informational value.

Copyright traditionally protects works: a photograph, recording, performance, script or programme.

Generative AI can reproduce something different—the person themselves. A model can imitate someone’s voice without reproducing a particular recording, recreate an actor’s appearance without copying a film, or construct a new performance that the performer never gave. This reveals a gap between intellectual-property rights and identity rights. The UK government’s 2026 review, as part of the consultation on Copyright and Artificial Intelligence which ran between 2024 and 2025, explicitly recognises this problem: the copyright owner of a photograph, recording or film may not be the individual whose face or voice is replicated, and the government is consequently considering stronger digital-replica or personality protections. AI therefore creates a new media asset: the digital self.

Generative AI depends on existing human knowledge and creative production. This raises a fundamental economic question: When does learning from content become commercial exploitation of that content? The issue initially centred on AI training. But agentic AI makes the challenge larger. An AI agent may not need to train permanently on a publisher’s archive. It can retrieve an article, analyse it, extract information and construct a personalised answer in real time—potentially satisfying the audience’s need without the user ever reaching the original publisher. The regulatory question therefore evolves from: “Can AI train on this content?” to: “Under what conditions can AI access, interpret, transform and commercially use this content?”

A future piece of media may be researched by an AI agent, written by another model, translated by another, voiced by a synthetic presenter, personalised by an algorithm and distributed automatically. When something goes wrong, responsibility can become fragmented among:

Yet an AI system cannot carry editorial responsibility. For public-interest media in particular, accountability must ultimately remain attributable to a human or legal entity.

AI models can be trained in one country, hosted in another, produce content concerning an individual in a third and distribute that content globally within seconds. This is particularly relevant for Arab media, which is already inherently cross-border. A programme produced in Dubai or Riyadh may simultaneously reach audiences across the Gulf, Levant and North Africa. Different standards concerning copyright, culturally sensitive content, political speech, misinformation or individual rights can therefore apply to effectively the same piece of AI-generated media. The regulatory challenge is consequently not only what rules should exist, but how those rules become interoperable across jurisdictions. Taken together, these challenges point to a fundamental limitation of regulating AI as a single technology. The risks AI creates in media vary significantly depending on what is being created, whose rights are affected and how the content is ultimately used. A synthetic news report, an AI-generated film character and a cloned actor’s voice may rely on similar technologies, but they raise fundamentally different questions of trust, ownership and harm. The regulatory response should therefore shift from regulating AI itself to protecting the rights and interests affected by its use. This requires distinguishing between different types of media content and assets—and determining where stronger safeguards are justified and where creative freedom should prevail.

A blanket approach to AI-generated or AI-assisted media would be both difficult to enforce and increasingly irrelevant as AI becomes embedded across everyday production workflows. The appropriate level of intervention should instead reflect the nature of the asset, the rights at stake and the potential harm created. Five areas warrant distinct treatment.

This includes news, journalism, government communication, documentaries, factual reporting and public-service information. Here, the audience’s right to understand what is authentic should carry substantial weight. Regulation should therefore focus on:

The EU is already moving toward this distinction. From 2 August 2026, AI Act Article 50 transparency obligations cover deepfakes and certain AI-generated public-interest content, while recognising that ordinary production effects or clearly fictional content should not necessarily be treated identically. AI may create journalism. AI should not eliminate editorial accountability.

A person’s recognisable face, voice and performance should increasingly be considered separately from conventional copyright. The emerging framework could be built around three rights: consent, control, compensation.

Individuals should know and agree to:

California has already introduced consent protections around performers’ digital likenesses, while the U.S. Copyright Office has recommended federal protection addressing unauthorised digital replicas. This suggests an important future principle: Owning the content should not automatically mean owning the person depicted in it. A studio could own a film without owning an actor’s face forever. A broadcaster could own an interview recording without acquiring unlimited rights to synthetically regenerate the interviewee’s voice.

News archives, films, television programmes, music, photography, scripts and other professional content represent valuable inputs into AI systems.

These should not necessarily be removed from the AI ecosystem. Instead, AI may create an opportunity to make those rights more liquid and monetisable. Rights holders could determine whether their assets can be used for:

Permission could eventually become machine readable. An AI system could effectively ask: May I use this content, for this purpose, in this market, at this price?

Certain uses require stronger intervention. Examples include:

For such content, merely attaching an “AI-generated” label is unlikely to be sufficient. Regulation must address the underlying harm through prohibition, platform response, removal requirements and accountability.

This should receive the lightest regulatory intervention. Film effects, fictional characters, artistic experimentation, parody, games, animation and AI-assisted production should not require the same safeguards as synthetic news or fraudulent impersonation.

The EU approach already recognises this distinction by providing more proportionate treatment for evidently artistic, fictional, satirical or creative deepfake content. This is critical because regulation designed to protect creators can itself become a restriction on creativity.

Balancing these interests is precisely where global approaches begin to diverge. Some jurisdictions have prioritised transparency and provenance; others have focused more strongly on creator and personality rights, while others are experimenting with licensing and market-based solutions.

There is no single global leader in AI-media regulation. Leadership depends on what is being protected: the EU leads in comprehensive AI governance; China has moved further in operationalising synthetic-content traceability; the US is developing stronger protections around digital identity and performers; while the UK is exploring how regulation can be complemented by new licensing markets.

The comparison reveals that no market has yet built the complete AI-media regulatory model.

The emerging answer is likely to combine elements of these approaches: risk-based intervention, content provenance, digital identity rights, clear accountability and mechanisms through which AI can legitimately access and license creative assets.

These approaches provide useful building blocks, but they cannot simply be transferred into the Arab media ecosystem. The region’s linguistic diversity, culturally varied audiences and highly cross-border media market create additional considerations for how global regulatory principles are implemented.

The question therefore becomes not whether Arab markets should follow one international model, but which elements should be adapted to the characteristics of the regional media ecosystem.

Global approaches provide useful building blocks, but cannot simply be transferred to the Arab media ecosystem. The region’s linguistic diversity, varied audiences and cross-border media market create additional considerations for applying global regulatory principles.

Importantly, not every AI-related challenge requires new regulation. Many outcomes associated with AI-enabled media—including misinformation, privacy violations, fraud, copyright infringement and harmful content—are already addressed, at least partly, through existing media, data, intellectual-property and digital regulation.

The first question should therefore be whether existing regulation remains fit for purpose when AI is involved. New AI-specific rules should focus where AI creates genuinely different questions, such as synthetic identity, content provenance, AI access to copyrighted content and accountability across increasingly automated media workflows.

Regulatory compliance cannot simply mean a system “supports Arabic”. AI safeguards and moderation need to work across Modern Standard Arabic and different dialects, recognising context, humour, satire and culturally specific expressions. Safeguards should therefore be tested for linguistic equivalence, not simply translated.

Arab markets share important cultural characteristics but are not homogeneous. A single interpretation of “Arab values” could constrain legitimate journalism, satire or artistic expression. Regional coordination is therefore better focused on common rights and technical standards—such as provenance, consent and identity protection—while individual markets retain flexibility over content standards.

Arab media is inherently cross-border and increasingly multilingual. Content can be translated, synthetically dubbed, modified and redistributed across markets almost instantly. Rights and provenance should therefore remain attached to the asset as it moves across languages, platforms and jurisdictions.

As AI becomes embedded across media, regulation increasingly intersects with data and technology sovereignty: where strategic data and content are stored, who can access them, which platforms process them and who captures the value created.

For media, sovereignty extends beyond infrastructure or data residency. It also concerns the region’s ability to retain agency over its news archives, audiovisual libraries, intellectual property, language assets and cultural content as these become valuable inputs into AI systems. The objective should not be to restrict these assets from global AI ecosystems, but to establish clearer terms for how they are accessed, used and monetised.

The emerging regulatory landscape should therefore be viewed as an ecosystem rather than a standalone “AI-media law”. Existing media regulation can continue to address areas such as misinformation and content standards; intellectual-property and personality rights can govern ownership and identity; data and technology frameworks can address privacy, infrastructure and sovereignty; while AI-specific regulation fills the genuinely new gaps around provenance, synthetic media and automated accountability. The priority is not more regulation in every area, but greater clarity on how these frameworks work together.

AI does not require media regulation to start from scratch. Many of the issues it amplifies - from misinformation and copyright infringement to privacy and content standards - are already addressed by existing frameworks. The priority is to determine where those frameworks remain sufficient, where AI creates genuinely new questions, and how media regulation should work alongside wider AI, technology and sovereignty policies.

This calls for a coordinated rather than additive regulatory approach. Targeted provisions will be needed where AI changes the nature of the issue - such as synthetic identity, content provenance, automated accountability and AI access to protected content. At the same time, regulation should preserve media and creative freedom and provide sufficient certainty for organisations to adopt AI responsibly.

For Arab media, the opportunity is to move beyond regulation as a set of guardrails towards an AI-native rights and trust ecosystem - one that protects audiences and creators, enables legitimate access to regional content, strengthens agency over strategic media and cultural assets, and creates the foundations for new models of licensing and monetisation.

The leadership imperative is therefore not to regulate every new application of AI, but to build the rules, institutions and infrastructure that allow AI, media and human creativity to coexist and create value on trusted terms.

AI is transforming how media is created, but its workforce impact is more nuanced than a simple substitution of people by technology.

Technology has historically created jobs as well as displaced them, and current evidence suggests AI could follow a similar pattern. The World Economic Forum estimates that AI and information-processing technologies could create approximately 11 million jobs globally by 2030 while displacing around 9 million. More broadly, technological change remains one of the largest contributors to job creation globally.

Within media, the distinction between tasks and jobs is particularly important. AI can increasingly perform individual activities that previously required substantial human effort, but the automation of a task does not necessarily eliminate the occupation around it. Instead, existing roles are being redesigned, new roles are emerging and human contribution is moving towards activities requiring greater judgement, creativity, cultural understanding and accountability.

The workforce question is therefore shifting from “How many media jobs will AI replace?” towards “How will AI change what media professionals do, which new roles will emerge and how should the industry develop the talent to fill them?”

Within media, AI is affecting the full content creation lifecycle, but not evenly. Its most immediate impact is concentrated at the beginning and end of it, where activities are typically more digital, structured and repeatable. Development and editorial creation are increasingly supported by AI across research, transcription, ideation, first drafts, translation and storyboarding, while post-production is being transformed through AI-assisted editing, clipping, subtitling, dubbing, localisation and versioning. By comparison, production itself remains more dependent on human interaction, physical execution and real-time creative judgement.

This barbell effect also creates an important workforce consequence. Many of the activities most exposed to AI sit precisely where junior professionals have traditionally entered the industry: junior journalists conduct research and prepare first drafts; assistant editors log footage and assemble initial cuts; production assistants manage schedules and documentation; junior creatives produce concepts for senior review.

The challenge is therefore not simply whether these tasks become more efficient. They have also historically been the mechanism through which junior talent learned the profession. Removing repetitive execution can increase productivity, but removing the experience behind it risks creating an experience gap: senior professionals can use AI effectively because they already possess the judgement to challenge weak outputs, unreliable sources or poor creative choices; junior professionals are still building that judgement.

The implication for media organisations is not to preserve repetitive work, but to redesign the apprenticeship model. Junior journalists can move from transcription towards source verification and original angles; assistant editors from manual logging towards narrative experimentation and senior critique; and production assistants from documentation towards earlier participation in budgeting, coordination and decision-making.

AI can therefore do more than make content creation more efficient; if used deliberately, it can move junior talent earlier into higher-value work and accelerate how professional judgement is developed. The apprenticeship task may change, while the apprenticeship itself should become stronger.

AI’s workforce impact extends beyond changing junior work.

Three broader shifts are likely to reshape the media workforce.

Much of the transformation may happen without job titles changing. Journalists, editors, filmmakers, producers, designers and commercial teams will increasingly integrate AI into everyday workflows.

Journalists will still rely on investigative and editorial skills, while using AI for research, data interrogation and analysis. Editors will continue to apply narrative judgement while supervising automated editing, localisation and asset generation. Producers will coordinate creative and production teams while increasingly orchestrating AI tools and agents.

AI fluency could therefore become similar to digital literacy today: a baseline capability embedded across existing professions rather than a standalone technical specialism.

AI is also creating roles at the intersection of media and technology, including:

Many will become specialisms within existing journalism, creative, production and technology teams rather than entirely new professions.

As AI becomes more agentic, further roles are likely to emerge. Media organisations may need professionals to supervise autonomous agents, direct persistent synthetic characters, audit AI-produced content, optimise production economics or design AI-native media experiences.

Job titles will evolve, but the enduring shift is towards professionals who increasingly create, direct, verify and orchestrate combinations of human and machine capability.

Changing jobs require changing education. Preparing media talent for AI is not simply about teaching people to use today’s tools. Technologies will continue to change; the more enduring priority is to develop people who can work effectively with AI while retaining the judgement, creativity and human perspective that define strong media.

AI literacy needs to start early. Schools should introduce responsible AI use alongside critical thinking, source evaluation, originality and media literacy. The UAE’s introduction of AI education across schools provides an important foundation, allowing universities and specialist institutions to focus increasingly on how AI is applied within journalism, film, broadcasting, gaming and other creative disciplines.

At the same time, media education must continue to teach the craft itself. Students should learn how AI can strengthen research, production and editing, while still developing storytelling, editorial judgement and creative decision-making independently. The objective is not simply to know how to use AI, but to know when to use it, when to question it and when human judgement matters more.

For today’s workforce, vocational reskilling will be equally important. The pace of change is too fast to rely primarily on multi-year qualifications. Short, modular and continuously updated courses can help journalists, creatives, production professionals and media leaders develop practical capabilities in areas such as AI-enabled workflows, verification, production, localisation, agentic systems and AI governance.

For Arab media, the opportunity is therefore to build a continuous learning model: start AI education early, embed it within media disciplines, provide practical vocational pathways throughout the career lifecycle, and ensure that the skills that make media distinctly human remain at the centre.

Not every activity automated by AI represents an equivalent labour-cost saving. Human labour costs are relatively predictable, while AI introduces a more variable cost model driven by model choice, token consumption, reasoning, data retrieval, storage and agent interactions.

This becomes more significant as media organisations move towards agentic workflows, where multiple actions and iterations increase consumption. Gartner expects inference costs per agentic workflow to rise more than fivefold through 2028, even as underlying technology becomes cheaper. AI can therefore become cheaper per unit while more expensive per completed workflow.

The implication is not that people are cheaper than AI, but that technical feasibility should not determine workforce decisions. Leaders need to assess the full economics of each workflow, including AI consumption, human oversight, quality assurance and the value of retaining internal expertise.

The question is therefore less about “human or AI?” and more about what combination of human and AI capability creates the greatest creative, operational and economic value.

AI creates capacity: journalists can research faster, editors produce more versions, producers coordinate more efficiently and creative teams generate more ideas.

Media organisations can use that capacity primarily to reduce effort, or reinvest it in stronger content, new formats, new audiences and their people. The latter offers greater long-term value.

The leadership agenda should therefore move from workforce efficiency to workforce renewal: redesign roles, continuously reskill talent, create pathways into emerging professions and protect the entry points through which future creative and editorial leaders develop.

If AI removes the first rung of the career ladder without replacing the learning it provided, productivity gains today could create a talent gap tomorrow.

The opportunity is to use AI to remove repetitive work, not learning, and use the capacity it creates to move people earlier towards judgement, creativity and responsibility.

For Arab media, the ambition should be not only to optimise today’s workforce around AI, but to build the next generation of media talent around what AI makes possible.