For anyone who has worked in media technology long enough, the current AI conversation feels familiar.

Today, organizations are debating whether to build their own AI assistants, agents, and workflows from scratch — or buy a purpose-built solution. Twenty years ago, they were asking a nearly identical question about media asset management. At the time, building a custom MAM seemed logical. It promised control, differentiation, and the ability to tailor the system to specific workflows.

But in many cases, what started as a strategic advantage became a long-term burden: brittle integrations, expensive maintenance, limited scalability, and platforms that struggled to keep pace as the market shifted to cloud, automation, analytics, and AI. AWS still frames MAM as a classic build-vs-buy decision, noting that buying is often the better path for organizations without the time, budget, or developer resources to build and maintain a full system, while MAM itself has only become more central as an integration point across the media supply chain.

That lesson matters even more now, because AI is moving far faster than MAM ever did. Media asset management evolved over years in the context of cloud migration, workflow automation, and remote collaboration. Agentic AI is evolving in months.

McKinsey recently argued that agentic AI is collapsing traditional IT planning horizons, forcing leaders to make foundational architectural decisions in months instead of years. At the same time, enterprises are adopting agentic AI quickly, often before strategy, governance, and operating models are fully defined. MIT Sloan Management Review and BCG describe this as a “tidal wave of adoption” arriving ahead of strategic clarity.

That speed changes the economics of “build.”

When many teams say, “We’ll build our own AI agent,” what they actually mean is, “We’ll prototype a workflow with a model.” But an enterprise AI solution is not just a model wrapped in a user interface. It requires orchestration, permissions, observability, governance, prompt and workflow versioning, model routing, fallback logic, evaluation, cost controls, and security.

As multi-step AI systems become more autonomous, the complexity rises sharply. Large language model applications are non-deterministic, difficult to test with traditional methods, and increasingly expensive to manage without tracing, monitoring, and centralized control; for multi-step agentic systems, those operational requirements expand into AgentOps. In other words, the hard part is not getting an agent to work once. The hard part is getting it to work reliably, securely, and cost-effectively every day in production.

Media organizations should be especially careful here, because their workflows are more complex than generic enterprise automation. Content pipelines are rights-aware, deadline-driven, format-sensitive, multilingual, and deeply interconnected.

MAM platforms became mission-critical precisely because they sat at the intersection of ingest, storage, metadata, editing, review, distribution, archive, and monetization. Industry research continues to highlight integration complexity, workflow interoperability, data governance, and cross-platform accessibility as persistent challenges in MAM environments, even before agentic AI is layered on top.

As the market continues to grow, the expectation is not for simpler environments, but for more intelligence, more automation, and more interoperability across the stack.

This is why the old MAM cautionary tale matters.

A custom-built MAM could look impressive in its first phase. It might solve a local problem beautifully. But over time, requirements changed. Teams wanted cloud deployment. Then they wanted remote collaboration. Then analytics. Then AI-assisted metadata. Then better search. Then integration with more editors, more storage tiers, more distribution endpoints, more rights systems, more channels.

Suddenly, the “perfectly tailored” system had become the bottleneck. The organization wasn’t just maintaining software; it was maintaining an evolving architecture. And every major market shift became a catch-up exercise. The same risk is now emerging with AI, except the cycles are shorter, the dependencies are broader, and the expectations are higher.

The trap is easy to fall into because building feels empowering.

It offers control. It offers flexibility. It can even feel cheaper at the beginning, especially when internal teams already have dev teams and cloud architects. But the true cost of building agentic AI is not the demo. The media industry has its own language and workflows that often hidden behind closed doors and “secrete sauce” that is not plastered on the internet for LLM’s to learn.

On top of that, it is the lifecycle. It is maintaining compatibility as models change. It is reworking prompts and tools when behavior shifts. It is monitoring token consumption, response quality, latency, and failure modes. It is applying governance to what an agent can access, what it can trigger, how it can explain decisions, and how humans stay in control.

Microsoft’s guidance on production AI agents emphasizes that moving from proof of concept to production requires robust lifecycle management infrastructure, while broader AgentOps and LLMOps commentary repeatedly points to tracing, debugging, evaluation, and policy controls as essential for safe scale.

So does that mean organizations should never build?

Not at all. The smarter lesson from the MAM era was never “buy everything.” It was “don’t rebuild the foundation unless that foundation is truly your differentiator.” Most media organizations are not trying to win by inventing their own orchestration frameworks, observability stacks, or rights-aware conversational layers from scratch. They win through storytelling, speed, operational excellence, audience growth, and monetization. The most pragmatic strategy is often to buy the core platform and customize the last mile: the unique business rules, templates, workflows, automations, and experiences that reflect your brand and operating model.

That is what makes a purpose-built solution more compelling than a generic AI toolkit.

In media, the real challenge is not simply generating text or answering questions. It is understanding that media industry unique context and taking actions across the entire workflow. We position Dalia as far more than a chatbot: a media-aware, agentic AI layer built on our core API, designed to unify workflows across ingest, production, distribution, and archive through a single conversational interface combined with integrated micro apps.

Through a simple prompt using media terminology or not, Dalia can locate assets, create clips based on user defined criteria, assemble collections based on rights status, trigger review workflows, and more. That distinction matters. Dalia is an AI layer designed for operational fluency in media, not just generalized interaction. Creating this knowledge and platform is not the only challenge though; maintaining, supporting and evolving it also a major factor.

And that may be the most important takeaway of all.

The build-vs-buy debate is never really about software. It is about time, risk, and focus. The organizations that built their own MAMs often believed they were buying themselves independence. In reality, many were taking on a permanent modernization burden and increased risk. With agentic AI, that burden grows heavier: the technology is changing faster, the operational requirements are broader, and the cost of falling behind is higher. The cautionary lesson from the MAM era is not that custom innovation is bad. It is that infrastructure debt has a way of disguising itself as control.

Media teams should absolutely innovate with AI. They should experiment, tailor workflows, and push for differentiation. But they should also be honest about what they are signing up to maintain. In this market, the winning move may not be to build your own agents from scratch. It may be to start with a solution that already understands your media world — and then use it to move faster than the teams still trying to assemble theirs.