NAB Show Perspectives: Operational intelligence already at work, a proactive approach to AI

For years, the media and entertainment industry has been captivated by the promise of AI. Early momentum was driven by demos, proof of concepts, and speculative use cases. Today, we find ourselves far more grounded in reality: AI is no longer experimental. It is operational and is being measured by the value it delivers across production, news, post, and content supply chain workflows.

As adoption accelerates, a clear divide is emerging. Many solutions still rely on a familiar model. A user asks a question, and the system responds. While effective in isolated scenarios, this reactive approach places the burden on operators to identify issues, investigate root causes, and initiate action, often after delays or errors have already impacted workflows. At scale, this model introduces friction rather than removing it.

A more effective approach is beginning to take shape, one that embeds intelligence directly into workflows. Instead of waiting for input, AI can continuously monitor operations, anticipate needs, and provide suggestions for resolutions before small issues become larger disruptions.

This shift is less about consistently trialing new tools and more about redefining the role AI plays within the organization and assessing industry readiness to operationalize AI, at scale.

The concierge model

The next phase of AI in media is not about faster answers. It is about better operations.

When applied within the context of real workflows, AI can move beyond task-based assistance to deliver continuous, operational intelligence. This includes identifying patterns across large datasets, surfacing the most relevant actions, and resolving issues in real time.

The result is a system that supports smarter decision-making by leveraging AI analysis to share proactive suggestions without waiting for an operator to ping an AI agent to support an incident they may not have even known is a fault yet.

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A helpful way to think about this model is through the lens of a concierge. Not as a novelty, but as a design principle.

In the same way a concierge anticipates needs based on context and history, AI can analyze workflows, user behavior, and system conditions to guide actions before they are explicitly requested. In a media environment, this can mean surfacing the most relevant delivery scenarios, preventing avoidable errors, or identifying and resolving stalled processes before they impact downstream operations.

This is not about replacing human expertise. It is about removing the repetitive, low-value work that limits it.

Building a proactive foundation

A practical approach to AI starts with focusing on known operational challenges and applying intelligence where it can deliver immediate value. When operational datasets are unified and paired with user and system context, three capabilities begin to emerge.

AI can anticipate actions by analyzing historical workflows and recommending the next best step, reducing repetitive input and accelerating execution. It can identify and correct inconsistencies as they occur, ensuring that small oversights do not propagate through the supply chain. And it can continuously monitor workflows, detect anomalies, and resolve common issues automatically, maintaining system health without requiring manual intervention.

Together, these capabilities shift AI from a passive responder to an active participant in operations.

Why this approach matters now

Many organizations continue to invest heavily in AI without a clear path to returns. In some cases, these investments remain disconnected from the day-to-day realities of how work actually gets done. A more effective strategy is to focus on high-impact, operational use cases where AI can deliver measurable improvements today.

Reducing repetitive data entry, preventing common order errors, identifying stalled jobs, and accelerating exception handling are not theoretical opportunities. They are immediate, practical applications where AI is already capable of driving efficiency and improving outcomes. This approach prioritizes execution over experimentation, aligning AI investment directly to business value.

A shift in perspective

Adopting this model requires more than technology. It requires alignment.

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Organizations must first connect their data and workflows to create a reliable operational foundation. Platforms like TMT Insights’ Polaris are designed to unify the media supply chain across profile specifications, title and inventory analysis, operations, and task management, creating the visibility needed to operationalize AI in a meaningful way. From there, intelligence can be layered in incrementally, delivering value early while scaling over time.

Just as importantly, it requires clarity around intent:

• Why are we implementing AI?
• What problems are we solving?
• How should AI support the people and processes already in place?

When approached this way, AI becomes less of a standalone initiative and more of an integrated capability, one that strengthens decision-making and improves how the business operates as a whole.

The future of AI in media: Resolution before disruption

The next phase of AI in media and entertainment will be defined by how well it aligns with the realities of the supply chain.

At TMT, this means taking a pragmatic approach, focusing on embedding intelligence directly into workflows rather than investing in disconnected experimentation. AI should operate within the systems teams already rely on, continuously monitoring activity, identifying patterns, and resolving issues in real time.

The objective is not just faster response, but earlier intervention.

By introducing proactive intelligence into the supply chain, organizations can reduce manual investigation, maintain system health, and keep operations aligned with business intent. AI becomes a layer of operational support, providing the right context and actions at the right time without adding complexity.

For organizations ready to move beyond reactive models, the path forward is clear: focus on real operational challenges and build systems that resolve issues before they disrupt the business.

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Hannah Barnhardt, TMT InsightsHannah Barnhardt is the chief operating officer of TMT Insights.