Hardik Jagda: AI Is Already Changing How You Watch Live Sports – You Just Don’t See It

Transforming Editorial Workflows

One of the most noticeable impacts of AI is in the way content is created. Traditionally, editorial teams would spend 15 to 20 minutes developing a single storyline that could be used in commentary or shared across platforms. This involved manually analysing data, identifying patterns, and structuring narratives.

With AI, this process has been significantly accelerated. Insights can now be generated in under a minute, and in much greater volume than before. Rather than replacing editorial teams, this shift allows them to focus on how stories are told rather than how they are discovered. It changes the nature of the work from manual compilation to intelligent curation, enabling faster and more engaging storytelling during live matches.

The Importance of Workflow Integration

Despite these advancements, Jagda emphasised that the real challenge lies in adoption rather than technology. AI systems, no matter how advanced, are unlikely to succeed if they require users to fundamentally change how they work.

To address this, modern AI solutions are designed to integrate into existing workflows. The same dashboards, tools, and interfaces that teams use daily remain unchanged, while AI-generated insights are delivered within those environments. This approach ensures that the technology feels natural and usable, particularly in high-pressure situations like live broadcasts where there is little room for experimentation.

The Infrastructure Behind Real-Time AI

Behind the seamless experience lies a complex infrastructure that must operate flawlessly in real time. Delivering AI-driven insights during a live match requires the coordination of multiple processes, including data ingestion, model inference, content generation, and delivery.

These processes must run simultaneously and within strict time constraints. As Jagda pointed out, even the most accurate insight loses its value if it arrives too late. Timing is critical, and ensuring that insights are delivered instantly is as important as generating them.

This is why infrastructure plays such a crucial role. It is not just about having powerful models, but about building systems that can operate reliably and at speed under real-world conditions.

Compute Close to the Action

To achieve low latency, compute resources need to be positioned as close to the source of the action as possible. This could involve on-ground infrastructure at venues, regional processing nodes, or optimised cloud environments.

The underlying principle remains consistent: minimise delay and ensure that insights are delivered in the moment they are relevant. In live sports, even a slight delay can reduce the impact of an insight, making proximity and efficiency key considerations.

The Role of Telecom Networks

Telecom infrastructure is an essential part of this ecosystem. With millions of users consuming live sports on mobile devices, network quality directly influences the effectiveness of AI-driven experiences.

Whether through 5G, fibre, or hybrid networks, connectivity determines how quickly and reliably content can be delivered. Better networks enable richer personalisation, faster updates, and a smoother overall experience for users. From the perspective of AI platforms, telecom networks form the foundation on which real-time intelligence is built and distributed.

AI as a Core Broadcast Layer

Another significant shift is in how AI is perceived within the broadcasting stack. It is no longer treated as an optional enhancement but as a fundamental component, similar to video processing systems.

Broadcasters and leagues are increasingly recognising that AI is essential for staying competitive. Those who have invested early are already seeing benefits in efficiency and engagement, while others are beginning to realise the urgency of adoption.

New Revenue Opportunities Through AI

AI is also opening up new avenues for monetisation. One of the key advantages is the ability to generate more content from a single event. This increase in output creates additional opportunities for engagement and revenue without a proportional increase in cost.

In addition, AI enables audience expansion through multilingual and multi-format content. This makes it possible to reach new markets that were previously difficult or uneconomical to serve.

Another important area is sponsor intelligence. By analysing audience behaviour and engagement in real time, AI provides more accurate insights into the effectiveness of sponsorships. This allows rights holders to optimise placements and demonstrate measurable value to brands.

Level Playing Field for Smaller Leagues

One of the more interesting outcomes of AI adoption is its ability to level the playing field. Historically, high-quality sports production required significant investment, giving larger leagues a clear advantage.

AI is changing that dynamic. Smaller leagues can now produce professional-grade content, generate insights, and engage audiences at a fraction of the cost. This shifts the focus from budget size to execution and strategy.

India’s Growing Role in AI-Driven Sports

India stands out as a particularly strong market for AI-driven sports innovation. With cricket’s massive popularity, rich data environment, and diverse audience, it offers unique opportunities for deploying and scaling AI solutions.

Jagda noted that Indian broadcasters have been proactive in adopting new technologies, particularly in areas related to fan engagement and digital experiences. The combination of scale, diversity, and demand makes India a key market in the evolution of AI in sports.

The Future of Personalised Viewing

Looking ahead, the next major shift is likely to be towards deeper personalisation. While current systems personalise content at a broad level, the technology now exists to deliver fully individualised experiences.

In the future, fans could receive customised highlights, tailored statistics, and narratives focused on their specific interests. This would effectively create multiple unique viewing experiences from a single match.

However, the challenge lies not in the AI itself but in delivering these experiences efficiently and sustainably at scale.

The Case for Invisible AI

Perhaps the most compelling idea from the discussion is that the best AI is invisible. Users do not engage with AI directly; they engage with the improved experience it enables.

If the technology can deliver better content, faster insights, and more meaningful engagement without disrupting the workflow, it has achieved its purpose.

A Practical Approach to AI Adoption

Jagda concluded with a simple but important piece of advice. Organisations should not begin with the technology. Instead, they should identify a specific operational bottleneck and focus on solving that.

Short, focused experiments — ideally within a few weeks — can help determine whether AI is delivering real value. Over time, this approach allows organisations to build capability, refine their strategy, and scale effectively.

What Comes Next

AI is quietly transforming live sports broadcasting, not through dramatic changes but through continuous improvements that enhance every part of the experience. From real-time insights to personalised content and new revenue models, its impact is already being felt.

As the technology continues to evolve, the real differentiator will not be access to AI but the ability to integrate it effectively into workflows, infrastructure, and business models. In that sense, the future of sports broadcasting will be shaped as much by execution as by innovation.