Market View
From archives to assets: Exploring media monetisation in the age of AI
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Meropi Kylika
Vice President of Commercial at CNBC International
About Meropi Kylika
Meropi Kylika is Vice President of Commercial at CNBC International, where she leads business development, strategic partnerships, and -content distribution across international markets. With over 20 years of experience in entertainment and consumer goods industries, she previously served as Senior Director of Business Development & Strategy at CNBC International, where she supported Commercial revenue across all ex-USA regions.
How do you see AI transforming content management and monetisation in the media industry?
From my experience in news media, I see three key approaches emerging. First, AI is enhancing content syndication through improved metadata and cataloguing capabilities. This enables us to service niche platforms more effectively when they request specific content, such as technology-focused stories from particular regions. We can now deliver highly targeted content packages to both B2B and B2C customers with greater precision.
Secondly, AI presents opportunities for resource optimisation, particularly in coverage of non-exclusive events. Consider financial news – there are regular scheduled announcements from institutions like the Bank of England that every outlet covers. These events aren't rare or exclusive, so there could be a scenario where AI manages the baseline coverage/ the breaking news, while our journalists focus on sense-making it, and delivering deeper analysis and insights.
The third application involves predictive analytics and sentiment analysis. To my knowledge, we haven’t experimented in this field yet, but we’ve observed developments where AI analyses historical news coverage patterns to identify trends and potential future developments. For instance, we've observed companies using AI to analyse pre-2008 financial crisis coverage, studying the terminology and sentiment patterns to identify similar trends in current reporting. However, it's crucial to maintain the human element in these processes – AI serves as a tool to enhance our capabilities rather than replace human judgment.
AI serves as a tool to enhance our capabilities rather than replace human judgment – it's about freeing our resources to focus on deeper analysis and unique insights.
What are the primary challenges media organisations face in implementing AI for content management?
The challenges are multifaceted, but IP protection and language capabilities stand out as key concerns. There's significant apprehension about submitting intellectual property to AI systems – questions arise about ownership and control once content is processed through these systems. Media companies need to carefully evaluate how to integrate with AI while protecting their unique assets and original content.
Language capabilities present another challenge. AI's effectiveness varies significantly across different languages. For instance, when we launched a partner channel in Mongolia, we discovered limited AI capabilities for Mongolian-English translation. Our partners had to resort to creative solutions, such as training AI systems using parallel texts of classic literature in both languages. This disparity in language support creates uneven implementation opportunities across different markets and highlights the need for continued development in multilingual AI capabilities.
The adoption timeline also presents a strategic challenge. Companies must assess whether to be early adopters or wait for more mature solutions. This decision involves careful risk assessment, considering not only technological capabilities but also workforce implications and industry dynamics. The reality is that even if organisations have reservations about AI adoption, market forces may necessitate engagement with these technologies to remain competitive.
How are changing viewer habits influencing decisions about content redistribution and monetisation?
While news and sports have traditionally anchored pay TV services, we're witnessing a fundamental shift in consumption patterns. This evolution has led us to develop a three-pronged strategy: direct-to-consumer products, B2B partnerships, and expanded reach through AVOD platforms.
The key challenge is timing our transition to newer platforms. For instance, FAST (Free Ad-Supported Television) channels have shown particular promise for news content, but they require different monetisation approaches. Success often depends on strategic positioning – such as securing prominent EPG placement – while carefully managing the shift away from traditional distribution models.
Different regions also present varying challenges and opportunities. While some markets remain predominantly subscription-based, others are rapidly shifting towards digital platforms. This requires a nuanced approach to content distribution and monetisation strategies, considering regional preferences and market maturity. The goal is to maintain brand visibility and revenue while adapting to evolving consumption patterns.
The challenge lies in determining when to transition from legacy revenue models to newer platforms, even if they might not initially provide the same returns.
While AI can help with basic coverage, its true potential lies in enabling our teams to focus on creating differentiated, high-value content that drives subscriber growth and retention.
What metrics should media organisations use to evaluate the success of AI implementation in content management?
We need to consider multiple dimensions when measuring success. Financial metrics are crucial – examining both revenue generation and cost efficiency. However, it's equally important to evaluate operational enhancements and resource optimisation. The third key metric involves product innovation – measuring our ability to develop new offerings that weren't previously possible.
For subscription-based services, success metrics extend beyond basic engagement to include customer acquisition and retention rates. Our value proposition centers on delivering unique insights and analysis that justify premium pricing. AI's role here is transformative – by automating routine coverage, it enables our teams to focus on creating the differentiated content that drives subscriber loyalty.
This dynamic is particularly crucial in news media, where we must maintain comprehensive coverage while delivering distinctive value. While automated content may not directly drive subscriptions, it supports overall brand presence and audience engagement. Success ultimately depends on achieving the optimal balance between efficient automation and insightful human analysis, measured through both audience metrics and operational effectiveness.
About CNBC International
CNBC International, the global division of CNBC, delivers real-time financial market coverage and business content across Europe, the Middle East, Africa and Asia Pacific. Operating from London and Singapore, the network provides comprehensive coverage through television channels, digital platforms and strategic partnerships, serving as the world's leading source for business and financial news.
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