Due to the increase in AI tools and their availability over the last few years, Google Cloud found in 2024 that nearly two-thirds of media and entertainment organizations are actively using generative AI. This high adoption rate indicates that GenAI is no longer just an experimental technology but is being actively deployed in the industry.
While generative AI has captured attention for its ability to create content, translate queries and simulate human-like interaction, recommendation engines remain irreplaceable for personalization at scale. Instead of competing, these two technologies complement each other and when combined, they create more seamless, engaging and relevant user experiences.
AI and Recommendation Engines: Different Purposes, Different Strengths
Recommendation engines are designed to excel in one key area: analyzing vast amounts of behavioral and contextual data to identify patterns that predict a user’s next desired action. They power personalization at scale. Helping streaming platforms to keep users engaged by surfacing the most relevant options at the right moment. In this respect, we have seen substantial increases in relevant engagement indicators, i.e., time watched per user by more than 50% compared to solutions that do not have any or poorly executed recommendations. The strength of personalization solutions lies in understanding user behaviour and delivering a more personal experience, optimized for consumption and monetization.
Generative AI, by contrast, was never designed to replace these functions. Its value lies in producing new content and reframing existing information into different formats, such as translating vague user queries into structured signals that recommendation systems can act upon. If recommendation engines are about precision in matching, generative AI is about creativity and context.
However, together, generative AI can enhance recommendation engines even though they solve different problems. Today, generative AI is best used for automation and simplification of the personalization journey for operators and users alike. It’s particularly relevant to bring machine learning and output closer to the human understanding of entertainment. For example, generative AI can be effectively used to present results of the personalization solution in a more humanly readable and relatable way, using natural language rather than generically created text.
The Black Box Challenge in Personalization
Most personalization and recommendation solutions can be ‘black boxes’, especially if they include neural networks or other AI components, an issue that does not arise with classic machine learning models. Black box recommendation solutions lack the flexibility, which not only makes it harder to deliver a fully tailored experience but also lacks in showing the reasons why users will enjoy particular content. That opacity is less of a concern when a model is writing a summary, but it poses real risks when a business is trying to explain to users why certain recommendations are shown. Transparent recommendation models, even sophisticated machine learning versions, are far easier to interpret, evaluate against business KPIs and learned user preferences.
Generative AI as a full replacement for a recommender risks eroding trust, increasing operational risk and undermining reliability at scale. The answer is to find a flexible solution that can utilize generative AI, without the need to rely on it for recommendations.
Solving the Cold Start Problem
Generative and agentic AI together provide a toolset that can help address the cold start problem, which occurs when a recommendation engine lacks the necessary data to make tailored recommendations. Generative AI, with its broad training base and ability to generalize, paired with the agentic capabilities to reach out to relevant external platforms, can fill this gap. It can help generate natural, contextually relevant suggestions for a first-time visitor. AI can provide what feels like a tailored experience, with the help of statistics, within the platform or beyond the platform, like social trends. This ensures that even without prior data, users still feel understood and engage from the very beginning of their journey, providing a more meaningful experience from the start.
When consumers start to interact with content, this is when personalization can take over with the algorithms and tailor user experiences even further.
The Future is Hybrid: Recommendation Engines and Generative AI
Gartner has predicted that by 2026, 30% of new applications will use AI for personalized adaptive user interfaces, a massive leap from less than 5% in 2023. As more platforms adapt to AI, teams need to adjust to the best way to use tools to deliver enhanced user experiences. Generative AI just adds a layer of intelligence to enhance content discovery. According to a recent research from Statistica, AI has reduced the time in content discovery by 42%.
The combination of generative AI and recommendation engines is the most effective way to build a foundation for personalized experiences that are both scalable and effective. In this model, recommendation engines continue to provide the foundation of scalable personalization, while generative AI adds a layer of intelligence that improves discovery, supports natural language interaction and creates richer context around results. Together, they enable experiences that feel more intuitive, more responsive and ultimately more aligned with user expectations.
Flexible Solutions to Succeed
Our personalization solution is not a one-size-fits-all or black-box tool. The algorithms are designed as a flexible toolbox of use cases and features. This enables media companies to exert a high degree of control over their personalization and monetization strategies. With the help of AI and our Personal Editorial AI Assistant, we can automate editorial processes and user journeys, while offering the highest possible degree of customization and control to our partners and clients. Get in touch with us today to discover how we can power your content discovery plan with minimal resources required and in a more cost-effective way.
