Agentic AI without rights-aware data is a liability

In most industries, bad data creates bad outputs. In media and entertainment, it produces lawsuits.

Agentic AI is moving from experimentation into the operating workflows that determine how content is created, distributed, licensed, recommended and monetized. The question for M&E companies is whether the rights data, role design and accountability models beneath those workflows are mature enough to support AI systems that do more than generate suggestions.

A generative AI tool might produce a weak draft or an unusable image. An agentic AI system triggers decisions across connected workflows — and the consequences scale with every action it takes. When AI systems operate on incomplete, outdated or poorly structured rights data, they act on a version of “reality” the organization is not legally allowed to use.

For media and entertainment companies, rights data includes, but isn’t limited to:

• Licensing terms
• Provenance records
• Talent agreement parameters
• Territorial limits
• Usage windows
• Training-data attribution

These records have always mattered in M&E, but agentic AI makes them operational in a new way. Because the data no longer sits apart from workflows, the data can become the workflow’s decision authority.

AI in M&E is outpacing its workflows

As media and entertainment companies move autonomy into workflows that touch the industry’s most valuable assets, incomplete or outdated rights data creates commercial and legal exposure. With agentic AI entering operationally sensitive workflows, media and entertainment companies need rights-aware operating data, role design and accountability models mature enough to support that shift.

Grant Thornton’s 2026 AI Impact Survey findings show a sector with strong board commitment but an execution gap at the workflow level. Eighty-seven percent of M&E boards have approved major AI investments — the highest rate of any industry in the survey. Sixty-eight percent have integrated AI risk into ongoing oversight, 14 points above the cross-industry average. Fifty-four percent say frontline employees need the most AI adoption support, also the highest rate in the survey. And 17% say agentic AI is already fully integrated into enterprise workflows, the second-highest rate behind only technology.

Yet only 34% of M&E leaders cite regulatory or compliance risk as their primary agentic AI concern. Twelve percent report no agentic AI concerns at all, the second-highest zero-concern rate in the survey. The sector deploying agentic AI at the second-fastest rate is among the least concerned about governing it, 9.5 points below the cross-industry average on regulatory and compliance risk.

Media and entertainment companies are not waiting for AI to become relevant, as boards are approving investment and agentic tools are entering enterprise workflows. But the hardest work now sits closer to the content library, the rights desk, the production environment, the ad operations team and the employees who must determine when AI can act and when it must stop.

That is precisely where most M&E companies are underprepared. Commitment is ahead of execution and autonomy is moving into the business faster than workflow discipline is catching up. AI systems can scale the consequences of incomplete operating records before the business realizes something went wrong.

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A content library isn’t just content

Consider what a media library is and how it’s perceived by others. To a creative team, a media library is a resource and a repository of possibilities. To a finance leader, it is a long-lived commercial asset. To a legal team, it is a collection of negotiated permissions, restrictions, obligations and exceptions.

But to an AI agent, unless the data is structured properly, a media library is a content data source for use in its functions. The danger is that the AI agent has no way of “knowing:”

• A clip could be cleared for one market but not another
• A piece of content’s music rights might expire before visual rights
• A performer’s likeness can be used in promotion but not synthetic recreation
• A content asset can be recommended to one audience segment but not repurposed for a new commercial product

This is not a cybersecurity issue. The technology does not need to carry malicious intent to create risk. It only needs to be confident, capable, connected … and wrong.

The data conversation in media and entertainment needs to be more specific than the usual call for “clean data.” The sector’s defining AI constraint is rights data specifically. Generic data modernization can improve reporting or operational efficiency. “Rights-aware” data modernization can determine whether AI can operate safely inside the workflows that make the content valuable.

Grant Thornton's 2026 AI Impact Survey found that 20% of M&E leaders say insufficient data readiness has already contributed to AI underperformance. That figure is below the cross-industry average — but in M&E, data readiness failure doesn't produce a quality problem. It produces a liability event. The consequence is disproportionate to the percentage.

Rights-aware governance is a different discipline from compliance governance. In other more heavily regulated industries, AI governance is usually focused on formal compliance regimes. The media and entertainment industry has some legal and regulatory concerns, but much of its governance structures address contracts and reputational harm. Rights agreements, union structures, talent contracts, brand commitments, advertiser expectations and audience trust can function as binding constraints even when they do not look like traditional regulations. In M&E, governance centers on intellectual property (IP) rights and brand trust.

Accountability cannot be outsourced

AI policies must define where AI agents can act inside company workflows. AI use policies should be able to answer questions such as:

• Who owns the outcome if a vendor-enabled agent recommends the wrong content, uses the wrong asset, produces an unauthorized derivative, misapplies a license, exposes unreleased material or places advertising against content in a way that creates brand harm?
• Who has authority to approve, pause or reverse that action?
• What evidence exists showing what data the system used and why it acted?

“Who owns the outcome when it does something they didn't expect?” said Deborah Newman, Grant Thornton's Head of Industry for Media and Entertainment. “In an industry built on rights, talent relationships and audience trust, that question determines whether AI creates value or erodes it.”

The accountability question is harder to answer when AI capability is bought rather than built. Most M&E companies are not creating every model internally but adopting AI embedded in production tools, marketing platforms, customer engagement systems, content management platforms, rights systems and ad technology. That accelerates transformation but creates a false sense of delegated responsibility. Buying AI does not outsource accountability for how it behaves inside the business.

Vendor governance is necessary but insufficient. A company still needs to know where AI touches rights-sensitive assets, what rights data the system can access, whether that data is complete enough to support action, and when human review is required. As Grant Thornton’s survey found, only 28.8% of M&E organizations have a tested AI incident response playbook — meaning most have documented a response but never rehearsed it. In a sector where an agentic incident means an IP breach or a talent contract violation, a plan that hasn't been tested is not a plan.

Governance must be embedded in the workflow

In an “assistive” AI environment, humans remain the primary control point. AI tools respond to employee prompts and produce outputs for review. In an agentic environment, the AI system can independently orchestrate tasks across applications, searching, retrieving, classifying, routing and triggering next steps on its own without human direction.

Human oversight must be designed into the agentic AI workflow — not left to hoping someone notices a problem after the system has already acted.

A risk and controls mindset ranks as an essential AI leadership attribute for only 20% of M&E leaders, 16.7 points below the cross-industry average, and the widest gap of any leadership attribute we measured in our 2026 AI Impact Survey. The sector most exposed to IP and contractual liability from agentic AI is the one least likely to prioritize risk controls as a leadership capability.

Rights-aware agentic AI helps media companies activate more of their content library, accelerate localization, improve metadata, support smarter ad placement and reduce manual friction in production and distribution. Faster AI adoption is the goal. Rights-aware discipline is what makes it defensible.

To begin, M&E leaders must map where AI already touches rights-sensitive workflows, which can include:

• Content production
• Post-production
• Localization
• Licensing
• Archive searches
• Recommendation engines
• Customer interaction
• Ad operations
• Any workflow involving talent likeness, copyrighted assets or audience-facing outputs

Next, M&E companies need to classify the rights data those workflows depend on. Which records determine whether an asset can be used? Which are authoritative? Which are incomplete? Which are trapped in contracts, spreadsheets, legacy systems or individual knowledge? Which rights fields must be machine-readable before AI can safely operate? In M&E, this is an AI control requirement.

Companies must define decision rights for AI — the decisions humans routinely make that now need to be embedded in agentic processes. These distinctions determine what the AI agent can do autonomously versus what it can only recommend, which actions require legal, creative, commercial or brand review, and what triggers escalation. Companies must be explicit about where accountability resides when AI makes a decision.

These questions determine enterprise value. Rights-aware operating discipline now shapes how companies are valued, acquired and integrated — AI sits at the front of every diligence conversation.

Winners will know what AI is allowed to do

Leaders should connect AI governance to business performance. The goal is to get the organization to move faster with assurance that AI-enabled workflows are accurate, authorized and commercially useful. Rights-aware governance helps teams use more of the content library, enter new channels with confidence and personalize audience experiences without creating hidden rights exposure.

Treating agentic AI as another technology layer — and hoping existing rights processes hold —  is a risk most M&E organizations cannot afford. Autonomy changes what rights data must do.  Rights-aware AI makes that shift manageable.

In media and entertainment, agentic AI does not know what the rights data does not know. The business has to know first.

The question worth asking before you scale further

Map your most rights-sensitive AI workflows and ask: who owns the outcome if the system acts on incomplete data? If you can't answer that for each one, or if the answers differ across your leadership team, that gap is worth closing now.

Read the full 2026 AI Impact Survey findings for media and entertainment.

If you haven't built the accountability architecture yet, our Media and Entertainment advisory team works with content leaders, CFOs, CIOs and legal teams to define rights-aware operating discipline before an incident forces the question.

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