Industry
Communications, Media and Information Services
Connect. Curate. Comprehend: Unlocking Value Beyond Boundaries
Highlights
- Media and entertainment (M&E) enterprises face a shifting landscape with surging content, audience fatigue, streaming wars, regulations, and economic pressures disrupting workflows.
- Agentic AI, applied ethically, goes beyond automation by reasoning, planning, and acting with autonomy to transform content operations.
- Core building blocks include seamless data integration, human-centric AI for purposeful action, and experience-first workflow.
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A new media paradigm
Creating personalized promotional trailers based on user data; ensuring an ever-increasing volume of video content is indexed in detail; generating and distributing sports highlights on multiple platforms in seconds
The demands placed on M&E enterprises are changing, and traditional, manual and linear workflows are struggling to keep pace. Enter agentic AI: intelligent systems that take autonomous actions at every step of the way, from content creation, ingest, and tagging to editing, scheduling, and distribution.
Depicted in fiction as characters with goals and agency – think Ava in Ex Machina or Samantha in Her – agentic AI in real-world media production is increasingly used as a creative collaborator, from generating story concepts, plot arcs, character developments, and entire scripts, to music, art, and even virtual influencers. Meanwhile, interactive entertainment increasingly features agentic AI that adapts to user input, powering immersive games, chatbots, and VR experiences.
Agentic AI will be able to generate personalized movies, games, or music for individuals or niche audiences based on taste, mood, or cultural background.
It will enable increasingly sophisticated dynamic pricing, release strategies, and audience segmentation. AI agents can design marketing campaigns, optimize ad placement, A/B test messaging, and even negotiate distribution deals autonomously.
In short, M&E is moving into a new era, where orchestration not only needs to be automated, but also be purposeful and human-oriented.
AI powers the flow
Built on human-centric AI principles, agentic media workflow systems are designed to enhance rather than replace human creativity.
They are capable of perceiving, reasoning, and acting with semi-autonomy and autonomy to achieve content or operational goals. An example of agentic AI at play is Netflix’s machine learning (ML)-driven promo generator that creates trailers based on audience behavior patterns, genre appeal, and visual attention metrics.
Agentic systems also learn, adapt, and evolve in real time. So, in contrast to traditional automation, which follows a hard-coded, rigid framework that constrains workflows to pre-defined options, agentic workflows are continually updated as the system learns and improves with each decision.
Cogntive Media Ops
Adopting intelligent workflows is crucial in a rapidly changing media landscape.
M&E production companies are grappling with an explosion in content volume and must also handle a variety of formats, adapting them for multiple geographies and languages.
Time-to-audience requirements are also changing. Consider live news, sports highlights and episodic content, where timing is intrinsic to successful delivery. Sports highlights, for example, are no longer just about the post-event show presenting the key moments; today, sports broadcasters rely on real-time AI clipping tools during events to create short highlights and push them out on social media platforms within seconds.
Finally, as platforms battle for user attention in a crowded marketplace, personalization at scale is key. Tailored and predictive content suggestions that adapt to each user’s experience and history are now the norm across over-the-top (OTT) streaming, mobile and social platforms.
In this new world of M&E, businesses that adopt TCS’ Perpetual Adaptive Enterprise™ ethos will thrive by designing self-learning workflows that adapt continuously to viewer behavior, market trends, and operational signals.
AI-Driven workflow
The core building blocks of success in agentic orchestration are threefold: seamless integration of data and systems; cognitive intelligence for purposeful action; experience-first workflow design.
First, seamless integration of data and systems. This requires cloud-native pipelines to unify content management systems (CMS), digital asset management (DAM), broadcast, OTT, and rights systems, along with APIs and an event-based architecture, to ensure real-time interoperability.
Second, cognitive intelligence for purposeful action. For example, human-centric AI ensures metadata is enriched intelligently, audience insights are broken down in detail, and compliance tagging is complete. Additionally, decision-making agents can recommend content variants, push notifications, and optimal schedules.
Third, an experience-first workflow design. Humans and AI collaborate to design experiences that adapt to the platform and audience context under a framework of governed autonomy; humans define the purpose, while AI executes within the guardrails.
Agentic AI in Action
The power of agentic AI can be demonstrated at every stage of the media workflow.
Take, for example, video production. At ingest, agentic-AI auto-tags the content in detail using AI object and speech recognition, ensuring the content is well-prepared for the following production stages. It then facilitates real-time production, automatically detecting scenes, cleaning up background noise, and generating subtitles. The final product is then distributed during the optimal timeslot recommended by the system, along with thumbnails and potentially even regional versions. Viewers then feed back into the machine learning models, creating a continuous feedback loop for future iterations.
All of this is powered by media orchestration accelerators, cloud-native frameworks, and ethical AI governance models.
The balancing act
While agentic AI delivers undeniable productivity benefits, it also raises ethical considerations.
From a creative labor perspective, there are questions about authorship, royalties and job displacement. Meanwhile, there are concerns about the misuse of AI to create deepfakes, with realistic AI voices and faces, challenging trust and copyright principles.
Balancing creativity, authenticity, and adoption of AI requires using AI as a tool to enhance rather than replace human imagination and emotional depth. Clear guidelines and transparency about AI-generated content are essential to maintain trust and artistic integrity, while collaboration between creators, technologists, and policymakers is vital to ensure AI supports innovation while respecting creative ownership and cultural values.
Human-AI collaboration
Ultimately, agentic systems are not designed to replace humans, but to enhance creativity and empower meaningful experiences in a radically different industry from that of just even a decade ago.
The media enterprises of the future must deliver seamless engagement, be perpetually adaptive, and place humans at the heart of every decision-making loop. The moment to shape that future is now.
