5 Critical Elements You Can’t Afford To Overlook In Your AI-powered Content Discovery Strategy: From Automation to Editorial Control

Improving content discovery has become mission-critical for video streaming services. With more services, more content, and more competition than ever, viewers are overwhelmed by choice. The result? Frustration, decision fatigue, and churn. When users can’t easily find something to watch, they close the app, unsubscribe, or scroll away.

That’s why leading video services are shifting their focus from simply pushing more content to making that content easier to find and consume in different forms. At the heart of that shift is the use of deep metadata: granular, contextual, real-time information that powers better search, smarter previews, and more relevant recommendations.

To help streaming and broadcast platforms understand how to harness metadata for maximum impact, we’ve created The New Playbook for Content Discovery. This is a practical guide to reducing viewer fatigue and boosting engagement through smarter discovery.

But transforming discovery isn’t just a technical exercise. It requires careful attention to safety, compliance, localization, and editorial needs. In this blog, I’ll look at a few key considerations that often get overlooked by video services and which we always highlight in our discussions with them to ensure their projects succeed.

1. Balancing Automation and Editorial Control

Automation is what makes scale possible. As we’ve seen, no one has the resources to manually correct program start times, chapter thousands of hours of content, or handcraft previews or thumbnails for thousands of programs, especially when delivery speed is a factor. However, for some premium content assets, editorial teams may want to maintain control, particularly when it comes to tone, style, and brand alignment. What we see in practice (most recently with our launch of Previews for Sunrise), is that fully-automated solutions in many cases provide excellent results if you scope and tune them well, and are clear about their intended use.

As such, when looking for solutions, it’s smart to look at your own requirements and look for tools designed to work with editorial preferences, not against them. For some platforms, full automation is the goal. For others, metadata and preview suggestions are just a starting point for editorial review and to free up time for more valuable activities. Make sure the solutions you employ are flexible enough to support that.

2. Alignment: What are good results?

Automated previews and chaptering must respect the content’s age classification, licensing terms, and cultural context. A great clip isn’t useful if it accidentally contains violence, nudity, or spoilers - especially if it’s going to autoplay on the homepage. On the other hand, does a preview for an action movie that doesn’t show any action give the viewer a good representation of what that movie is about?

Look for solutions that apply safety filters to avoid common pitfalls. Clips should typically be selected from the early part of a program, and scene-level detection is essential to avoid problematic material. Make sure your filters can be tuned to your policies and target audiences, reflecting cultural sensitivities across markets. But most importantly, always evaluate results with key stakeholders from your editorial team and have an open discussion about what makes a preview or chapter title appealing, good or bad, because in the end, there’s subjectivity involved.

3. Balancing Rights and Regulatory Compliance with Effective Content Discovery

Depending on your content licensing agreements, you may not have the right to generate preview clips from certain shows, enable timeline navigation, or overlay additional metadata during video playback. These rules vary by market, by partner, and even by genre.

In some cases, you can’t provide new video snippets, but you can enrich the metadata to enhance search in your app. In others, you can use chaptering to improve the UX, but that should not interfere with "trick play" restrictions in ad-supported content. Perhaps your license doesn’t allow you to generate a new clip from third-party content, but if you provide timestamps to your app, it can play just a snippet of the full asset in the UI as a form of preview.

This kind of complexity means content discovery solutions aren’t one-size-fits-all. That’s why we work closely with each customer to define the right level of automation and the optimal way to use the data to help viewers - without breaching content licensing agreements

4. Implementation Considerations: Build vs Buy vs Hybrid

There’s no single right way to roll out content discovery improvements to your service:

You can build and maintain your own solution, fully built in-house or using cloud APIs. This will give you full control, so it sounds interesting. But if you are looking for this approach, don’t just look at the cost of the tools, but at the total cost of ownership:

• You need to maintain the solution. Do you have the know-how and staff to do this? Is that the best use of their time? Several of our customers have started with building their own solutions, but concluded that it was too difficult to scale, and required time from their teams that they’d rather spend elsewhere.
• Can the tool provider be reached in case of issues? Will they implement your requested features or improvements? Are they looking at your specific need or do they offer one-size fits all approaches?

You can partner with a specialist (like Media Distillery), who will manage the full solution. You may not have full (in-house) control, but on the other hand, you reap the benefits of a solution that is maintained for multiple clients, is proven to scale, and provides high-quality results. And your team is free to work on more strategic priorities than AI models.

You go for a middle ground, where you leverage the advances without a full migration. Instead of a big-bang approach, you start on a smaller scale, where you augment your current Content Discovery tools with features offered by a third party, interleaving the results in your platform. For instance:

• Feed AI-enriched metadata into your existing search engine
• Combine automated preview clip suggestions with editorial curation
• Start with one content type or user group and scale gradually

5. Start specific and small

Two significant pitfalls I have seen across the years of working with deep video analysis come when people:

• Look for AI, instead of a solution for a concrete business, user problem, or need. That will leave you disappointed (as was recently made clear in an MIT Study)
• Focus on using AI to get as much metadata as possible, instead of the minimal actionable data that helps fix your problem, with as few costs as possible.

Improving content discovery should be a continuous activity, not a big bang. Iteration and experimentation are key, along with the understanding that perfect is the enemy of good. We know that AI is not yet sophisticated enough to make exactly the same, nuanced choices that a human editor or curator would make, but it offers a scale that’s unattainable otherwise.

What matters is flexibility. As such, Media Distillery’s systems are designed to integrate with the existing tech stack within a video service, support their workflows, and scale at a pace that’s appropriate to their individual business goals.So, while you may not be able to meet 100% of your desired content discovery goals from day one, that doesn’t mean you should wait to get started. Many of our clients are pleasantly surprised with the range of benefits they can bring to their viewers today, and the prospect of more to come as AI technologies continue to evolve.

Don’t be afraid to start small

What matters is flexibility. Media Distillery’s systems are designed to integrate with the existing tech stack within a video service, support their workflows, and scale at a pace that’s appropriate to their individual business goals. Improving content discovery should be a continuous activity, not a big bang. Iteration and experimentation are key, along with the understanding that perfect is the enemy of good. We know that AI is not yet sophisticated enough to make exactly the same, nuanced choices that a human editor or curator would make, but it offers a scale that’s unattainable otherwise.

Having considered all the challenges you’ll face along the way, let’s look at what we call the Deep Metadata Spectrum. Where is your streaming service on the road from beginner to advanced?

Few services are at the advanced stage right now. Many won’t ever get there due to rights restrictions. And while you may not be able to meet 100% of your desired content discovery goals from day one, that doesn’t mean you should wait to get started. Many of our clients are pleasantly surprised with the range of benefits they can bring to their viewers today, and the prospect of more to come as AI technologies continue to evolve.

Want to Know More?

Download the e-guide: The New Playbook for Content Discovery: How to Reduce Viewer Fatigue & Drive Engagement

Or book a meeting with our solutions team to explore implementation models that fit your content rights, editorial needs, and operational setup.