“Insider Voice” offers expert perspectives from our team on key trends and challenges in media and advertising. Through this series, you’ll gain actionable insights and forward-thinking predictions on topics ranging from AdTech advancements to the impact of emerging technologies on audience engagement and revenue models.
The age of linear TV has reached a tipping point and many media companies are reassessing their business to build for the future while continuing to support their legacy. The rise of live streaming content, particularly live events like sports, bring together elements of linear and digital, creating more urgency for media companies to unify their traditional broadcast and digital businesses.
There are several major factors that make convergence more important than ever:
Many media companies are looking to AI as a way to accelerate their convergence plans, but not all companies have the components in place today. This article looks at what media companies need to unify their business and incorporate AI successfully.
The Components that Support AI-Driven Ad Sales
Unifying media business across linear and digital takes commitment and work. Technologies must be integrated, data must be merged and organized and processes need to be changed.
As media companies look to build the next version of their business, it’s important to audit current infrastructure to determine how ready it is for the future.
Data – Media companies have huge amounts of valuable data including subscriber data, audience viewing data, content and advertising data. A lot of this data exists in disparate databases that don’t talk to each other. Data might be old or incomplete or might exist in different formats that are incompatible with each other.
Technology – Linear ad technology can be decades old, and was built before media companies even dreamed of streaming. Many media companies have slowly built around these legacy systems to support digital and have a complex tech stack that’s not well integrated or streamlined.
Process – With so much complexity, media companies have a lot of manual and duplicative processes that are time consuming and costly. The “swivel chair” solution that requires manual data entry slows operations down and leaves room for errors. Many of these processes are haphazard and not well documented.
If any of these issues are present, media companies need to address them first, before considering an AI solution:
Understanding where your business needs to be streamlined and updated is the important first step in achieving unified ad sales and sets the stage for major efficiency gains from AI.
AI needs information to train it. Media companies with good data can feed that information into an AI solution but there is another important component to consider: documentation. AI also needs to understand business rules, goals, definitions, etc. Media companies need to have good documentation in place to explain how proposals are built, how pricing is determined, how products are assembled, what different data fields mean, how to interpret different deliver instructions, what different content labels mean and more. The more a company can document and train their AI, the more accurate and effective it will be.
There is a path forward for every media company—but it requires groundwork: consolidating data, unifying operations, and investing in scalable integrations.
Two Case Studies
Case 1: Ready for AI
FOX has redefined what it means to deliver impactful media solutions in an increasingly convergent landscape. Their recently released FoxONE solution is a converged media platform built on AI-driven technology.
FOX leveraged advanced, cloud-based AI technology to unify sales, product information, pricing, and delivery, creating a seamless workflow that empowers advertisers and improves performance across the board. With the ability to provide converged proposals that include premium placements in real-time live sports alongside highly targeted, multi-screen campaigns, FOX is meeting the demands of advertisers seeking scale and precision.
By adopting cutting-edge technologies, FOX not only streamlined internal processes but also enhanced their ability to deliver high-performing campaigns that connect brands with their target audiences more effectively than ever.
Setting AI up for success with:
Case 2: Contending With Legacy Issues
Another major media conglomerate faces significant structural and operational barriers to implementing AI in a unified or optimized way. The company’s highly fragmented infrastructure makes AI-driven convergence difficult to realize.
At the core of the challenge is the company’s complex ad operations. They sell across 15 different ad servers, each with its own unique specifications, data formats, and reporting requirements. This lack of standardization creates a tangled web of systems that are difficult to align—let alone optimize through AI. What might be a straightforward automation task in one environment becomes a highly customized and manual process in another, forcing teams into swivel-chair workflows that undercut the very efficiency AI is meant to deliver.
Initially, the company had a bold vision for AI transformation, but it quickly became clear that the required integrations across platforms and systems were technically and operationally overwhelming. With no centralized data structure and no normalized inputs, AI solutions—such as automated campaign optimization or predictive modeling—would operate on inconsistent or incompatible datasets, limiting their effectiveness or introducing risk.
Without unified data or strategic oversight, tools are being used in suboptimal, siloed ways, generating only marginal gains. Rather than enabling transformation, they become tactical workarounds that don’t scale across the organization.
A lack of appropriate resources and expertise in data transformation compounds the problem. Implementing AI at scale requires more than a plug-and-play approach—it demands deep investment in data infrastructure, taxonomy alignment, and governance. For this company, the absence of standardized data pipelines and the burden of complex hierarchy structures mean that even modest AI initiatives require heavy lift and cross-team coordination.
Forging Ahead to Achieve AI-Driven Convergence
There is a path forward for every media company—but it requires groundwork: consolidating data, unifying operations, and investing in scalable integrations. Without addressing these foundational issues, even the most powerful AI tools will fall short, leaving efficiency gains on the table and perpetuating a cycle of reactive, fragmented decision-making.
Operative works closely with media companies at every stage of AI readiness to improve their data, technology and processes. When media companies sign on to work with Operative, they get more than a technology platform, they get a strategic roadmap to align and unify their business and improve the overall efficiency and effectiveness of their ad sales and operations.
