Introduction
In an age where content is unlimited, but attention is scarce, media companies face a growing paradox: how to deliver stories that capture attention without drowning audiences in noise. At the same time, the pressure to produce content faster has intensified, particularly in journalism where deadlines often cut into depth. Together, these twin challenges—the demand for personalized engagement and the need for speed—have forced the industry to search for new solutions. Increasingly, the answer lies in artificial intelligence. Far from replacing creativity, AI is emerging as a tool to repurpose content, personalize experiences, and streamline production, allowing creators to focus on what matters most: telling stories that resonate. Industry leaders, including those speaking at Avasant’s “Empowering Beyond Summit,” have echoed this view—arguing that AI is becoming not a threat, but an enhancer of creativity (Avasant Summit Video, 2025).
Revitalizing Archives: Giving Old Stories New Life
One of the most promising uses of AI lies in how it revives archival content. For decades, media companies have amassed vast backlogs of footage, articles, and recordings,—much of it untouched. Traditionally, reusing this material required significant manual effort to find and edit. Now, AI can process archives at scale, tagging metadata, identifying themes, and transforming forgotten assets into formats fit for today’s audiences (SDVI, 2024).
Some outlets are already experimenting. ArcAI, for instance, assists journalists by surfacing relevant archived articles during the writing process, helping them reuse material that might otherwise be overlooked (LSE, 2024). This does not disrupt creativity; it extends it, giving journalists new ways to recycle high-quality work and reach broader audiences. By unlocking archives, AI not only creates efficiency, but also extends the lifespan of stories that still matter.
Personalization and Real-Time Engagement
If archive mining helps media do more with less, personalization addresses the heart of media’s attention crisis. Static, one-size-fits-all content no longer satisfies fragmented digital audiences. AI’s strength lies in its ability to track user behavior, learn preferences, and adjust recommendations in real time.
This technology is already reshaping storytelling. TIME launched its “TIME AI” platform, blending generative AI with interactive features to create multilingual, personalized versions of flagship projects like Person of the Year (TIME, 2024). The BBC has also invested in a dedicated AI unit to experiment with personalized delivery, particularly for younger audiences (The Guardian, 2025). By making content feel more intimate and relevant, personalization fosters not just engagement, but loyalty. When readers feel seen, they are more likely to return and subscribe. Still, this strategy raises concerns about manipulation and filter bubbles, underscoring the need for strong editorial ethics.
Efficiency Without Compromise
Beyond engagement, AI addresses the practical challenge of time. Newsrooms and production studios are under pressure to do more with fewer resources, often sacrificing depth for speed. AI helps resolve this tension by automating repetitive tasks—transcription, translation, summarization, or even drafting initial copy.
Reuters has integrated generative AI into parts of its workflow, with strict disclosure rules and human oversight to preserve journalistic integrity (Reuters, 2024). Similarly, Bloomberg and the Associated Press have long used AI to generate earnings reports and sports summaries, freeing reporters to focus on analysis and storytelling (Digital Content Next, 2019). These examples show AI not as a competitor to journalists, but as an efficiency multiplier—one that allows professionals to concentrate on judgment, nuance, and voice while machines handle the routine.
Looking Ahead: The Future of AI in Media
While AI is already solving immediate challenges, it also hints at more radical possibilities. Interactive narratives could soon adapt to a reader’s mood, location, or behavior in real time. News could be hyper-localized, tailored to individual neighborhoods or communities. Media executives are already experimenting with real-time A/B testing that adjusts headlines or formats mid-distribution, optimizing performance while stories are live (Business Insider, 2025). These innovations point toward a future where stories are not just consumed but co-created. Yet they also blur the line between personalization and manipulation, raising urgent questions about transparency and editorial responsibility.
Conclusion
The challenges facing media—scarce attention and mounting production demands—are unlikely to fade. But artificial intelligence offers powerful tools to navigate them. By revitalizing archives, personalizing content, and streamlining workflows, AI helps media companies deliver more meaningful stories, more quickly, to more people. Importantly, this does not diminish the role of human creativity. On the contrary, it strengthens it, allowing creators to focus on vision and voice while machines manage the routine. The future of media will belong to those who balance AI’s efficiency with human authenticity, crafting stories that are both technologically agile and emotionally resonant.
References
- Avasant Summit Video. “Empowering Beyond Summit: AI & Media Innovation.” YouTube, 2025. Note: video unavailable for internal citation.
- Business Insider. Neate, Rupert. “Media execs on AI-driven content.” June 2025.
- Digital Content Next. “The bots among us: AI and automation in media.” May 23, 2019.
- LSE Polis. “ArcAI: JournalismAI Collaboration.” 2024.
- Reuters. “Reuters and AI: Our Approach.” 2024.
- SDVI. “Unlocking Hidden Value in Archives.” 2024.
- The Guardian. Hern, Alex. “BBC launches AI department to personalize content.” March 6, 2025.
- TIME. “TIME AI: Redefining Storytelling.” December 2024.
By Peter Turk, Associate Consultant