NAB Show Perspectives: The AI Wild West and why it needs a sheriff

Adoption of AI hasn’t followed neat, centrally managed transformation programs. Instead, it has spread organically, driven by teams trying to move faster. Today, AI tools are embedded across newsrooms, marketing departments, HR, legal, and strategy functions. Often, this adoption happens informally and without central oversight.

A producer indexes archive footage using an AI tool. A marketing team analyses sentiment. An editor runs a clip through a model to check for profanity. Each action feels efficient, helpful, and low risk. Yet collectively, these practices create a situation few organizations are prepared for: AI embedded in core workflows without visibility, control, or traceability.

Where does the data go? Which model was used? Was the output reviewed? Were any rights unintentionally waived in the process? In many cases, no one has a complete picture. AI hasn’t outpaced governance because organizations are careless; it has outpaced governance because the tools are frictionless, and governance isn’t.

Reputational risk now moves at machine speed

The reputational equation has fundamentally changed. One hallucinated output, one biased summary, one automated decision that shouldn’t have been automated — and it can be published, shared, and amplified instantly.

For media organizations in particular, this is high stakes. Publishing misinformation is damaging enough. Publishing it at machine speed, with unclear accountability, compounds the impact. When something goes wrong, questions arise immediately: Was AI involved? Was it checked? Who approved it?

If those answers aren’t clear and defensible, credibility suffers. AI doesn’t just scale productivity — it scales exposure.

Regulation is accelerating — and accountability is personal

At the same time, regulation is catching up quickly. New frameworks increasingly demand transparency, oversight, and traceability in AI-assisted decisions and content production. Executives are accountable even when outputs are generated by third-party models. Yet many organizations cannot currently evidence which model produced a specific output, what data informed it, what safeguards were applied, or how the output was reviewed before release.

Policies may exist. Ethical principles are often well-articulated. But unless they are embedded in operational systems, they provide limited protection. The gap between intent and implementation is where risk resides.

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Speed versus safety is the wrong debate

There’s a perception that governance slows innovation. In reality, the absence of governance creates far greater friction later: retractions, investigations, legal exposure, and long reputational repair cycles.

If AI adoption is intended to improve efficiency, reconstructing an audit trail across multiple disconnected tools defeats the purpose. Manually piecing together who used what, where, and how is time-consuming and unreliable.

The smarter approach is to integrate governance directly into workflows so that it happens automatically, not retrospectively. Organizations that do this can scale AI adoption responsibly while maintaining control and accountability.

Trust is the competitive advantage

For media brands, trust is the product. Audiences, clients, and regulators are increasingly asking the same questions: Was AI involved? Was it checked? Who is responsible?

Being able to answer clearly and confidently isn’t just a compliance exercise — it’s a commercial advantage. Organizations that will succeed in the next phase of AI adoption won’t be the fastest. They will be the ones that scale responsibly: controlling inputs, auditing outputs, and integrating AI intelligently.

Some commentators describe the current landscape as “the AI Wild West.” In that context, the winners will be those with sufficient sheriffs — not the fastest guns.

Julian Wright, Blue LucyJulian Wright is the founder of Blue Lucy, where he guides vision and strategy.