<p>Media has always been an industry ahead of its own curve. Long before "digital transformation" became a mandate, the media industry was already living it. </p>
<p>Books, periodicals and music were among the first content forms to move from physical to digital. That first great migration didn’t just change the medium; it rewired how content was created, managed, distributed, consumed and monetized. </p>
<p>Today, media is at the threshold of a second, more consequential shift: generative and agentic AI. </p>
<p>Most of the industry conversation so far has focused on the visible part of that shift: content generation and dissemination processes, including faster edits, voiceovers and auto-generated recaps. But the bigger opportunity lies elsewhere, in the core business functions that keep a media company running: advertising, contract management, copyrights and digital rights management, fulfilment and subscription management, to name a few. </p>
<p>These functions rarely make headlines, but they carry the real revenue leakage and operational drag that agentic AI is best suited to fix.</p>
<p>Here is a look at why agentic AI is a necessary new step for media organizations, where AI is already reshaping content and audience experience, where it's poised to transform the less-visible business functions, and what separates the organizations pulling ahead from those still running isolated pilots.</p>
<h3><span class="h4">Why the time is right for agentic AI in media organizations</span></h3>
<p>In an industry already grappling with audience fragmentation and higher content costs, monetization is being reshaped as AI chat becomes a new discovery channel. Search referrals to publishers have fallen sharply over the past year; traffic that once arrived through a search results page is increasingly arriving via an AI assistant that summarizes or cites a story instead of sending a reader to it. </p>
<p>Roughly 60% of searches already end without a click. This changes what "distribution" means for a publisher measuring success in page views and ad impressions. </p>
<p>The economics of AI have also shifted industry realities. Video generation costs have reportedly fallen by around 90% in under two years, and 2025 alone saw roughly $12 billion in AI investment across the industry.</p>
<p> What used to be prohibitively expensive—searching decades of archived footage, localizing a title into 30 languages simultaneously, generating dozens of campaign variants for a single launch—is now commercially viable. That's turning agentic AI from a cost-saving tool into a genuine revenue lever. </p>
<h3><span class="h4">Where AI is being used in media today </span></h3>
<p>Agentic AI initiatives are moving past pilot into full production. The pace is slow but steady, with all indications of picking up speed as this space matures. </p>
<p>The following are the most visible areas where we see media organizations focusing their AI efforts:</p>
<ul>
<li><b>Content and production.</b> Agents are moving past simple generation into post-production activities, such as verifying metadata, routing footage across platforms, flagging rights conflicts and managing archives without constant human prompting.<br>
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</li>
<li><b>Audience and experience.</b> Agents can adjust personalization in real time—anticipating what a viewer wants next, powering conversational content discovery and adapting the experience itself (recap generation, dynamic content length) to the individual.<br>
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Fan engagement research shows more than half of audiences already follow their favorite franchises across multiple platforms, which is pushing agents to work across fragmented touchpoints rather than a single app or dashboard.<br>
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Such activities always necessitate responsible AI and ethical use of customer data.<br>
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</li>
<li><b>Advertising.</b> Advertising has always been the largest contributor to revenue <a href="https://www.deloitte.com/us/en/about/press-room/deloitte-survey-digital-media-trends-consumption-habits.html" target="_blank" rel="noopener noreferrer">regardless</a> of the media segment—from publishing to broadcasting to next-gen platforms. Programmatic advertising is shifting from systems that merely surface insights to ones that autonomously plan, price and optimize yield across the full revenue stack, including testing subscription prompts at moments of peak engagement, personalizing ad placement by reader context and identifying which topics drive the most revenue per reader.</li>
</ul>
<p>And a new category of "AI-native ads," served contextually inside agent conversations, has moved from concept to reality. OpenAI began placing sponsored responses inside ChatGPT in early 2026, with Microsoft Copilot following, and Google Gemini expected to do the same. This gives publishers and platforms a way to monetize attention that now lives inside the assistant rather than on a results page.</p>
<h3><span class="h4">How AI can benefit core media business functions</span></h3>
<p>These functions rarely make it into the AI-in-media headlines, but they carry real revenue leakage risk and operational drag—which makes them some of the most defensible early ROI cases for AI.</p>
<ul>
<li><b>Circulation and subscription fulfilment.</b> Subscription businesses are moving from static renewal reminders to agents that manage the full lifecycle: predicting churn from usage signals, triggering win-back offers at the right moment and running dynamic, usage-based pricing rather than one-size-fits-all tiers. Agents can scan subscriptions and auto-cancel or renegotiate, with that capability now being built directly into general-purpose AI assistants.<br>
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</li>
<li><b>Contract management.</b> Media contracts, deals, licensing agreements, ad vendor terms and distribution agreements are exactly the kind of high-volume, template-heavy, exception-driven workloads suitable for agentic systems. AI-native contract lifecycle management platforms are designed to handle drafting, redlining and multi-round negotiation on routine agreements. Purpose-built contract lifecycle platforms are now marketed specifically around licensing and royalty agreements, given that revenue leakage and payment disputes stem from poor tracking across a large contract book.<br>
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</li>
<li><b>Copyright and rights management. </b>This is where agentic AI can potentially be useful to track rights acquisition by territory, window and platform, calculate royalties across complex deal structures, flag unauthorized reuse of content and maintain the audit trail regulators and rights-holders expect. Digital rights management, largely in broadcasting and entertainment segments, and to a smaller extent in the book business, is an area where agentic AI is poised to deliver a meaningful shift.<br>
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</li>
<li><b>Entertainment.</b> Movie and game studios require interoperable systems that integrate rights/royalties management, amortization calculations (general ledger) and scheduling systems. This will necessitate a need for a workflow analysis to identify integration points between rights management and financial systems; middleware or API gateways to facilitate real-time data exchange and factoring in industry-standard data models for rights/royalties; and financial reporting to ensure compatibility of systems.</li>
</ul>
<h3><span class="h4">Enterprise transformation requirements for agentic AI</span></h3>
<p>Underneath all of this sits the harder question: How do you go from a handful of proven agents scattered across these functions to a coordinated, governed portfolio of a multitude of agents? </p>
<p>Media enterprises pulling ahead treat this as a business-led journey—discovering high-value opportunities function by function, prioritizing by ROI, building in low-code environments, grounding agents in real enterprise context and cataloguing what works for reuse—rather than a series of disconnected proofs-of-concept. </p>
<p>Enterprises need a mature governance model for autonomous agents as usage scales. Composable, standards-based architecture is less of an architectural nice-to-have and more of a baseline requirement for operating when channels, privacy rules and AI capability all keep continuously changing. </p>
<p>Reported outcomes for companies that get this right include materially faster deal and proposal cycles, meaningfully fewer production incidents and faster time-to-revenue on new AI-enabled offerings.</p>
<h3><span class="h4">The real differentiator for media organizations using agentic AI</span></h3>
<p>Every media and information company can now access broadly similar AI capability — the models, the tooling, even many of the accelerators are converging across vendors. What won't converge is execution: which organizations pair autonomy with real governance, build composable architecture instead of one more brittle point solution and treat agentic AI as an operating model rather than a feature. </p>
<p>For an industry built on capturing attention and turning it into revenue, agentic AI isn't just another production tool. It's becoming the operating layer the rest of the business runs on.</p>
