This article is the fifth in the “Media Strategy Reality Check” series.
Over the past few decades, the media industry has failed to make strategic investments in technology transformation, leaving it with inadequate infrastructure, leadership, and talent. This failure hampered its ability to compete with Big Tech and leverage technology as a differentiator. These issues are not only immediate but long-term, and the media’s lack of basic readiness is clearly to blame, especially as the industry pivots to generative AI. I am.
As an example, consider building a skyscraper on top of a building with cracks in its foundation. Adding new layers without addressing these structural flaws only increases the risk of collapse. For media companies, this shaky foundation means that any technological innovation, be it content management systems (CMS), data pipelines, or AI integration, requires fixing core weaknesses, slowing progress and increasing costs. will increase.
Despite the progress some exceptional companies are making, many media companies remain unprepared to take advantage of the AI revolution due to decades of inadequate investment in their technological backbone. yeah.
our ability to attract and retain technical talent;
The quality of technology talent that media companies can recruit directly impacts innovation and competitiveness. However, many media companies still view technology as a support function rather than a strategic driver. This outdated perception, combined with low budgets and a lack of a compelling vision, limits their ability to attract top engineering talent.
AI is making this problem even worse. The global AI talent pool is highly competitive, with resource-rich companies like Google, Meta, and OpenAI attracting top candidates. Without targeted interventions, media companies will struggle to recruit the talent they need to build innovative technology solutions.
But money is not the only challenge. Media companies need to reinvent themselves as attractive employers by presenting a compelling vision of how technology can drive creativity and innovation in media. Partnerships with academic institutions, targeted AI upskilling programs for current employees, and collaborations with startups can help close the talent gap. For example, organizations that establish in-house AI labs to test media applications such as automated fact-checking and audience behavior analysis may position themselves as dynamic, forward-looking employers in the technology ecosystem.
Technology as a strategic differentiator
For decades, Big Tech companies have invested billions of dollars in research and development, building competitive moats through technology and network effects. In contrast, media companies have historically focused on supply-side technologies such as automation aimed at reducing costs rather than creating new revenue streams. While these investments can help with cost efficiency, they often do not lead to long-term competitive advantage.
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Take content management systems (CMS) as an example. The New York Times, BuzzFeed, and the BBC have all developed custom CMS platforms, but these investments often serve as cost centers rather than strategic assets. Rather than differentiating their business with unique user experiences and data-driven algorithms, most media companies rely on tools that support their existing workflows. For example, the Washington Post’s Arc CMS and Vox Media’s Chorus have tried to sell CMSs with limited success. The chorus was eventually shut down.
Pixar’s story provides a powerful counterargument. Steve Jobs bought Lucasfilm’s struggling technology division and turned it into a company that not only revolutionized animation, but also generated enormous economic value. Pixar’s success stems from its ability to combine supply-side innovation (e.g., reducing production costs) with demand-side influence (e.g., producing groundbreaking animated films). This dual focus made Pixar an acquisition target, ultimately achieving a valuation nearly 83% higher than Lucasfilm in just half the time.
Media companies could emulate this approach by innovating technology that simultaneously improves operational efficiency and fosters audience engagement. This could include developing sophisticated recommendation algorithms, creating immersive interactive experiences, or using data to hyperpersonalize content delivery.
Peanut Butter Strategy: Thinly Diversified Investments
One of the recurring problems in the media industry is the so-called “peanut butter strategy” of spreading limited resources across too many initiatives. Diversification may reduce risk, but in this case no single project will have the resources it needs to mature to a true competitive advantage.
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For example, companies often pursue multiple emerging technologies such as blockchain, AR/VR, and AI without prioritizing efforts that align with core strategic objectives. The result is a collection of half-baked projects that fail to deliver meaningful benefits.
Instead, media companies should focus on a small number of high-potential initiatives and allocate resources accordingly. For example, companies may prioritize AI-powered content moderation and personalization tools that drive subscriptions over experimenting with unrelated technologies.
What is important is not only concentration but also patience. Big tech companies often invest years in projects before realizing significant returns, while media companies are frequently under pressure to deliver short-term results. Changing this mindset is critical to long-term success.
Organizational structures that inhibit innovation
Media companies rely on departmental organizational structures that often stifle innovation. These silos encourage individual leaders to prioritize their own department’s success over company-wide goals, leading to fragmented and ineffective technology strategies.
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Technology teams in particular often function as internal service providers, answering requests from different departments, rather than driving a unified strategic agenda. In contrast, companies like Amazon and Google employ matrix structures that embed technical expertise within business units while maintaining centralized oversight. This approach ensures alignment of innovation with broader organizational goals.
Media companies can make incremental changes to replicate these structures. For example, creating cross-functional teams that integrate technologists, editors, and marketers can foster collaboration and allow technology to play a more strategic role in decision-making.
Owned and operated platform infrastructure
Media companies have long recognized the importance of building audiences through direct-to-consumer platforms such as apps and websites. However, they failed to adequately invest in the infrastructure needed to monetize these owned and operated platforms.
first party data
Industries with well-defined business processes, such as telecommunications and fintech, are known to have very robust data pipelines. Data discrepancies can lead to direct financial losses. Instead, media companies have become heavily reliant on tools like Google Analytics, limiting their ability to perform real-time, “intraday” optimization and maintain control over their data.
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Some media companies still lack systems to integrate user data across brands, resulting in fragmented insights. Some companies lack consistent data dictionaries and audits, and different departments store data in different silos, resulting in data standards that don’t align with each other. Finally, poor error handling mechanisms often result in data loss and inaccurate reporting, which impairs decision-making.
AI for host viewers
Many companies lack systems to host audience-facing AI. This includes things like:
Design: Media companies often prioritize complex, visually appealing user interfaces, resulting in a complex mosaic of widgets that emphasize craftsmanship design over performance and data collection. In contrast, algorithmic marketplaces like Google and X maintain simple, straightforward interfaces like feeds and search bars. This simplicity allows front-end teams to focus on optimizing performance and collecting valuable user data, giving them a competitive edge in audience engagement and personalization. Servers: Compounding this challenge is the media’s historic reliance on content delivery networks (CDNs). While CDNs have facilitated content delivery at scale, they have also eroded the industry’s institutional knowledge of managing high-performance servers that can deliver personalized experiences. As a result, media websites and apps lack basic functionality common to algorithmic platforms, such as deleting content that has already been consumed or seamlessly resuming playback from where the user left off. This often happens. This gap further hinders the ability to effectively host AI-driven, audience-centric features.
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conclusion
The media industry is at a crossroads. Without decisive action to rebuild their technological foundations, media companies risk being permanently marginalized in an AI-driven future. However, this transformation requires significant financial investment, cultural change, and long-term commitment.
Talent acquisition and retention: We invest in recruiting top talent through a compelling vision and competitive compensation.
Intensive investment: Try not to spread your resources too thinly. Focus on initiatives with the most strategic potential.
Integrated data strategy: Build integrated, real-time data pipelines to enhance decision-making.
Rebuild for greater agility: Move to an organizational structure that allows technology teams to serve as strategic drivers.
The road ahead will not be easy, but it is essential. Media companies need to stop viewing technology as a cost center and embrace it as a core strategic asset. Only bold and innovative investments can compete with Big Tech and restore relevance in an increasingly AI-driven digital environment.
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The views expressed above are the author’s own.
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