Hyundai TV Ads Are Using AI Production. Here’s What That Means for Marketers

Hyundai has been quietly using AI-assisted production in its TV advertising, and the results are worth paying attention to, not because the ads are spectacular, but because of what the production shift signals. When a major automotive brand starts integrating generative AI tools into broadcast-quality creative, it stops being a conversation about technology and starts being a conversation about how marketing gets made.

This is not about replacing creative directors or putting agencies out of business. It is about what happens when the cost and speed of producing TV-quality video changes fundamentally, and what that means for brands, agencies, and the marketers sitting in the middle of it.

What Is Actually Happening With AI in TV Advertising

Before getting into the implications, it is worth being clear about what AI-assisted TV production actually involves right now, because the term gets used loosely in ways that obscure more than they reveal.

Generative AI in video advertising can mean several different things depending on where it sits in the production pipeline. It might mean using AI tools to generate concept visuals during pre-production, cutting the time between brief and first creative review. It might mean using AI to produce background environments, reducing the need for location shoots or expensive CGI studios. It might mean AI-assisted editing, colour grading, or voiceover generation. Or, increasingly, it means generating entire sequences of footage from text prompts, which is where tools like Sora, Runway, and Kling are starting to operate at a quality level that can hold up on screen.

Hyundai’s experimentation sits across several of these categories. The brand has used AI-generated imagery and video sequences as part of broader production workflows, not as a wholesale replacement for traditional production, but as a component that changes the economics and speed of getting work made. That distinction matters enormously if you are trying to understand what this means for your own marketing operation.

If you want a broader view of how generative AI video tools are developing across the industry, HubSpot’s breakdown of generative AI video tools covers the current landscape clearly and without the hype that tends to surround this space.

Why Automotive Brands Are Early Movers in AI Production

Automotive advertising has always been expensive to produce. You are dealing with location shoots, precision vehicle photography, complex motion sequences, and the kind of visual polish that car buyers have come to expect from the category. A single TV spot for a car brand can cost more to produce than most brands spend on their entire annual creative output.

That cost structure creates a specific incentive to find production efficiencies. If AI can reduce the cost of generating high-quality vehicle footage, or compress the time between creative brief and finished asset, the economics of automotive advertising shift in ways that matter to a CFO, not just a creative director.

I have sat in enough agency reviews to know that production cost is one of the most consistent sources of tension between clients and agencies. Clients want more creative output. Agencies know that production quality costs money. That negotiation never fully resolves, it just gets managed. AI starts to change the terms of that negotiation, which is why brands with high production costs are paying attention to it first.

Hyundai is not unique in this. Brands across automotive, fashion, and consumer electronics have been piloting AI production tools because those are the categories where production costs have historically been highest relative to the volume of content needed. The pattern makes commercial sense.

The Honest Assessment of Where AI Video Quality Stands

There is a version of this conversation that treats AI-generated video as either a revolution that will replace all production or a gimmick that cannot match real footage. Neither position is useful, and both tend to be argued by people with something to protect.

The honest position is this: AI video generation has reached a quality threshold where it can produce broadcast-usable content in specific contexts, and it is improving faster than most people in traditional production want to acknowledge. The contexts where it works well are constrained but growing. Abstract sequences, environmental backgrounds, product visualisations, and stylised creative all sit within the current capability range. Photorealistic human faces in motion, complex physical interactions, and anything requiring precise continuity across long sequences are still areas where AI generation is unreliable.

What Hyundai and other early movers are doing is identifying the specific production contexts where AI output is good enough, and routing those elements through AI tools while keeping human production for the elements where quality control still requires it. That is not a revolutionary creative statement. It is sensible production management.

I spent a significant part of my agency career managing production budgets across multiple clients simultaneously. The discipline that mattered was not finding the most impressive production approach, it was finding the right production approach for each element of a campaign. AI is starting to earn a place in that decision framework, not as the answer to everything, but as the right answer to specific production problems.

What AI Production Means for the Agency Model

This is where the conversation gets commercially uncomfortable, and where I think a lot of agency leaders are being less than honest with themselves and their clients.

If AI tools can generate broadcast-quality video sequences at a fraction of traditional production cost, and if brands like Hyundai are demonstrating that this is viable in practice, then the production margin that has historically been part of agency revenue is under pressure. That is not speculation, it is a straightforward consequence of changing production economics.

When I was growing an agency from a small team to over a hundred people, production revenue was a meaningful part of how the business model worked. The margin on production was not the largest margin in the business, but it was consistent and it helped fund the strategic capability that clients valued. If that production margin compresses because AI tools change the cost base, agencies need to be honest about what that means for their pricing, their staffing, and their value proposition.

The agencies that will handle this well are the ones that have always competed on strategic thinking rather than production execution. The ones that will struggle are the ones whose value proposition has been built primarily around production quality and the relationships that come with it. That is not a comfortable thing to say, but it is the commercially grounded read of where this is heading.

For a broader perspective on how AI tools are being integrated into agency workflows and productivity, the Moz analysis of AI tools for automation and productivity is worth reading alongside the creative production conversation.

The Creative Strategy Question That AI Cannot Answer

There is a risk in the AI production conversation that the focus on tools and cost efficiency obscures the more important question, which is whether the creative strategy behind the work is any good.

I have judged the Effie Awards, which means I have spent time evaluating advertising effectiveness at a level of rigour that most marketing conversations do not reach. The pattern that emerges from looking at genuinely effective advertising is consistent: the work that drives business results is almost always built on a clear, specific, defensible strategic position. The production quality matters, but it is downstream of the strategic clarity.

AI can generate impressive video sequences. It cannot tell you whether your brand positioning is differentiated enough to cut through in a competitive category. It cannot identify the specific customer insight that makes a creative idea resonate rather than just look good. It cannot tell you whether you are solving a real marketing problem or producing content that will be ignored at scale with greater efficiency than before.

Hyundai’s TV advertising has been strategically interesting in recent years because the brand has worked hard to shift its perception from budget alternative to genuine quality competitor. That strategic work is what makes the creative output worth producing. The AI production tools are in service of that strategy, not a substitute for it.

If you are thinking about AI in the context of your own marketing, the question to start with is not which AI tools to use. It is whether you have a clear enough strategy that better production tools would actually help you. If the strategy is unclear, AI production will just help you produce unclear work faster and more cheaply.

How Brands Are Structuring AI Production Workflows

The practical question for most marketers reading this is not whether AI production is theoretically interesting, but how brands are actually integrating it into their workflows in ways that produce usable results.

The pattern that is emerging from brands doing this well involves three distinct phases. First, AI tools are used in pre-production to generate concept visuals and storyboard alternatives at speed, compressing the time between brief and creative review from weeks to days. This is where AI is delivering the most consistent value right now, because the quality threshold for concept visuals is lower than for finished broadcast content.

Second, AI-generated assets are used selectively within broader production workflows, specifically for elements where the generation quality is reliable and the cost saving is significant. Background environments, abstract sequences, and product visualisations are the most common examples. These elements are integrated with traditionally produced footage rather than replacing it entirely.

Third, AI tools are being used in post-production for tasks like colour grading assistance, music generation, and asset adaptation for different formats and markets. This is where the efficiency gains are often largest relative to quality risk, because the AI is operating on finished footage rather than generating new content from scratch.

The brands getting the most from this approach are the ones that have been specific about which phase of production each AI tool is appropriate for, rather than treating AI as a general-purpose production solution. That specificity requires someone in the production process who understands both the creative requirements and the current capability limits of the tools being used.

The HubSpot guide on which AI models to use for different tasks is a useful reference point for thinking about tool selection with this kind of specificity, even if the focus is broader than video production specifically.

The Measurement Problem Nobody Is Talking About

There is a measurement question sitting underneath the AI production conversation that I have not seen addressed clearly in most of the coverage of brands like Hyundai using these tools.

If AI-assisted production changes the cost structure of TV advertising significantly, it also changes the economics of testing. Traditional TV production costs have been high enough that most brands could only afford to produce and air a small number of executions per campaign. The cost of being wrong was high, which meant the risk tolerance for creative experimentation was low.

If AI production compresses the cost of generating broadcast-quality content, the number of executions a brand can afford to test increases. That is genuinely interesting from a measurement perspective, because it means TV advertising could start to operate with the kind of iterative testing discipline that digital channels have had for years. You could run multiple creative variants, measure response differences, and use that data to inform future production decisions with a speed and volume that was not previously economical.

I remember the first time I ran a properly structured paid search test at scale, adjusting variables systematically and measuring the response differences with enough data to make confident decisions. The discipline that made that work was not the technology, it was the willingness to treat the campaign as a learning exercise rather than just an execution exercise. AI production creates the conditions for TV advertising to develop that same discipline, but only if brands approach it that way intentionally.

The Semrush perspective on AI optimisation tools for content strategy touches on some of the measurement and iteration thinking that applies here, even though the context is content rather than broadcast production.

What the Hyundai Example Tells Us About Brand Confidence

One thing that tends to get overlooked in the technology conversation is what a brand’s willingness to experiment with AI production says about its internal confidence and culture.

Hyundai is not a brand that has historically been associated with creative risk-taking. It is a brand that has built its position through product quality, value credibility, and consistent execution over a long period. The fact that it is willing to integrate AI production tools into its broadcast creative is less a statement about AI and more a statement about the brand’s internal appetite for operational experimentation.

That appetite matters because AI production tools are not plug-and-play. They require investment in capability, tolerance for early-stage quality inconsistency, and a willingness to learn in public to some extent. Brands that are risk-averse in their internal culture tend to struggle with that, even when the technology is genuinely useful.

The brands I have seen get the most from new technology in marketing have consistently been the ones with internal cultures that treat experimentation as a normal part of operations rather than a special project. That cultural factor is more predictive of success with AI tools than the size of the budget or the sophistication of the technology stack.

For anyone thinking about how to build that kind of capability within their own organisation, the conversation about AI marketing more broadly is worth engaging with seriously. There is a lot of useful thinking being developed across the industry right now, and the AI marketing coverage at The Marketing Juice is a good place to follow how these conversations are developing in practice rather than in theory.

The Visibility Question: How AI Changes What Gets Found

There is a separate but connected question about how AI changes not just how advertising is produced, but how it is discovered and evaluated by audiences.

As AI-powered search and recommendation systems become more significant in how audiences find and engage with brand content, the way advertising is structured and distributed matters in new ways. TV advertising that generates social conversation, search interest, or content engagement feeds into AI recommendation systems in ways that extend its reach beyond the original broadcast.

Hyundai’s advertising, when it generates the kind of cultural conversation that good creative work can produce, creates a downstream effect in search and social that compounds the value of the original broadcast investment. That compounding effect is increasingly mediated by AI systems that decide what content to surface and recommend.

Understanding how to structure content so that it performs well in AI-mediated discovery environments is becoming a genuine marketing discipline. The Ahrefs webinar on improving LLM visibility covers some of the emerging thinking on this, and it is relevant to anyone thinking about how TV advertising connects to broader brand discoverability in an AI-shaped media environment. The Semrush piece on driving LLM visibility approaches the same question from a different angle and is worth reading alongside it.

What Marketers Should Take From This

The Hyundai AI production story is useful not because it tells you to start using AI in your TV advertising immediately, but because it illustrates a set of questions that are worth asking about your own marketing operation.

The first question is where production cost or speed is currently limiting your creative output. If there are campaign ideas that are not getting made because the production economics do not work, AI tools are worth evaluating seriously as a way to change those economics. That is a specific, commercially grounded reason to invest time in understanding the tools.

The second question is whether your creative strategy is clear enough that better production tools would actually help. If the strategic foundation is weak, AI production will help you execute weak strategy more efficiently, which is not a useful outcome.

The third question is whether your organisation has the internal culture to experiment with new production approaches without the risk-aversion that tends to produce cautious, compromised results. AI production tools work best when the people using them have enough confidence to push them into territory where the output is uncertain, because that is where the interesting results tend to emerge.

Early in my career, I built a website myself because the budget for a proper build was not there. The result was not perfect, but it taught me more about what was actually possible with limited resources than any vendor presentation ever had. The same principle applies to AI production tools. The marketers who will understand them best are the ones who get their hands on them and produce something real, not the ones who wait for the technology to mature to the point where there is no risk.

If you want to keep following how AI is reshaping marketing practice across production, strategy, and measurement, the AI marketing section at The Marketing Juice covers these developments with the same commercially grounded perspective. The technology moves fast, but the underlying business questions stay consistent.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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