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The media industry has spent decades perfecting the art of the partnership. Relationships built over years, over deals negotiated across boardrooms, over handshakes that turned into contracts that turned into campaigns. And while that partnership model built the industry, it’s also starting to hold it back.
Every major media organization wants to move faster: faster insights, faster activation, faster iteration on what's working. But speed requires connection, and connection in the media ecosystem is still, overwhelmingly, a manual process. Data sits in silos, integrations are bespoke, and every new partnership triggers months of negotiation, legal review, and technical work before a single workflow can run.
Only 30% of agencies, brands, and publishers have fully integrated AI across their media campaign lifecycles, with nearly two-thirds citing data fragmentation and disconnected tooling as the primary barrier. The IAB Tech Lab estimates that that obstacle costs the industry $12 billion annually in measurement losses alone, with $4 billion attributable to manual reporting costs as teams reconcile data across systems that were never designed to talk to each other.
Meanwhile, the industry keeps treating this as a workflow problem when it’s not; it’s a structural challenge.
For years the fragmentation was manageable, expensive and frustrating, but manageable, until AI came along.
Adobe's 2026 report found that 75% of organizations cite data integration and quality as their top challenge for implementing agentic AI, and 52% say current data unification limits their AI progress. When AI is embedded across campaign strategy, audience building, performance measurement, and optimization, this friction lives in the connections, or the lack thereof.
In fact, nearly two-thirds of organizations are experimenting with AI agents, but fewer than one in four have scaled to production, a gap between experimentation and scale that is almost entirely an infrastructure story.
The organizations that recognize this challenge are not weighing "which AI tool should we use," but "what does our architecture need to look like for AI-powered collaboration to compound over time."
The traditional model treats every partnership as a discrete project, scoped, negotiated, integrated, and eventually deprecated when priorities shift. That worked when partnerships were occasional and workflows were linear. In a connected, AI-native media ecosystem, it doesn't.
To be successful in an AI environment, media companies must acquire infrastructure that makes collaboration repeatable. Competition for audiences and ad dollars is pushing the industry toward alliances built around interoperable measurement frameworks and authenticated data, not because it's strategically elegant, but because it's the only model that scales.
The companies that build infrastructure capability now will move faster, collaborate more effectively, and unlock opportunities that simply aren't accessible to those still negotiating every connection from scratch.
Akkio's AI infrastructure runs on-premises within media cloud environments, sitting directly on top of data and purpose-built for the complexity of collaboration at enterprise scale. Secure, interoperable, and governed, so that every agentic workflow is repeatable, auditable, and ready to scale across a partner network that is already live and growing.
To learn more about how Akkio enables organizations to inherit the interoperability, the governance, and the scale without the integration work, read our recent partnership announcement with LiveRamp.
Transform your campaign workflows with powerful AI that delivers measurable results.