Executive Summary

Generative AI is exploding—and much of it remains speculative. In advertising, the stakes are higher than ever: more formats, faster timelines, and rising demands for content that scales without losing brand integrity. With so much hype surrounding AI’s creative potential, RiTE Media set out to test what this technology can actually deliver in a commercial context.

From late 2023 through early 2025, we conducted a real-world test of AI’s capabilities in producing branded content. Our goal was to determine whether a custom-trained LoRA (Low-Rank Adaptation) model could match or enhance traditional production methods—while maintaining legal and brand safety standards. The result: while 80% of AI-generated outputs fell short, 20% showed tangible, scalable value. This white paper outlines our methods, findings, tools, and takeaways for agencies, brands, filmmakers, and technologists.

Objectives

– Test whether LoRA-trained diffusion models can emulate a traditionally photographed food commercial.
– Explore AI’s capacity to generate high-volume deliverables from a minimal base of owned assets.
– Evaluate the cost, quality, and control trade-offs between traditional and AI-assisted workflows.
– Assess legal, ethical, and IP compliance standards around custom-trained models.
– Highlight the emerging convergence of AI with cinematographic motion and framing control.

Methodology & Workflow

Phase 1 – Traditional Production

– Shot a tabletop burger commercial using practical food styling, lighting, and photography.
– Captured dozens of high-resolution stills across multiple angles and lighting conditions.
– These stills served as the foundation for AI training.

– FLUX → excels at photorealism, needs fewer images, but is resource-heavy.
– SD 1.5 → widely adopted, good community support, but less photorealistic.
– SD XL → better realism, handles complex scenes, but needs more resources.

Phase 2 – Model Training & Generation

– Trained a custom LoRA model using exclusively client-owned assets (no internet-sourced imagery).
– Collaborated with Undercurrent AI, an advanced AI research and consulting partner, to guide technical implementation.
– Employed a variety of tools including ControlNet, IP Adapter, Depth-to-Image, and ComfyUI to enhance output fidelity and prompt control.
– Generated multiple iterations per angle, lighting setup, and background—resulting in over 500 images across four different models.

Phase 3 – Motion Simulation

– Captured camera motion using an iPhone and motion tracking software such as SynthEyes.
– Applied this data to simulate camera movement in the AI generation pipeline.
– This approach is nearing a phase of practical implementation for real-world commercial use, especially in high-efficiency or R&D-forward productions.

Legal Review

– All assets used for training were owned or created in-house.
– The workflow was developed in tandem with our legal counsel to ensure compliance with current IP and AI regulations.
– No third-party datasets or scraped internet content were used.

Key Findings

1. Image Quality & Realism

– AI-generated imagery was often passable at first glance but degraded under scrutiny (e.g. textures, light falloff, food physics).
– AI struggled with realism in areas like melted cheese, steam, or sauce dynamics.

2. Efficiency & Scalability

– LoRA training was completed in a matter of hours.
– Generated assets in 9:16, 1:1, and 16:9 without requiring reshoots.
– Creative teams could test new looks, compositions, and variants rapidly.

3. Cost Implications

– Significant reduction in cost for generating format-specific variations (especially social cutdowns).
– Traditional shoots still required for foundational product fidelity.

4. Motion & Cinematography

– Motion simulations based on tracked iPhone footage and SynthEyes tracking showed promise and are nearing practical viability.
– Lighting and motion consistency remain challenging for AI models but are improving rapidly.

5. Legal & Ethical Clarity

– By training on exclusively owned material, the workflow minimizes IP risk.
– Described by counsel as one of the safest current uses of generative AI in a commercial context.

Tools Used

– LoRA (Low-Rank Adaptation) for custom model training
– ControlNet for fine-tuned image control and pose structure
– IP Adapter for asset conditioning
– Depth-to-Image for lighting consistency and 3D approximation
– ComfyUI for node-based visual programming and model orchestration
– SynthEyes for camera motion tracking
– Undercurrent AI as consulting partner for technical R&D and AI ethics guidance

Recommendations for Creative Teams & Clients

– Use AI as a post-production multiplier—not a full replacement for craft.
– Train on your own assets to protect brand integrity and IP.
– Combine traditional production and AI generation for maximum control.
– Engage legal counsel early in pipeline design.
– Begin testing AI motion and framing control tools for use in R&D and early-stage creative visualization.

– 316 images across burger-focused sets.
– Detailed taggings for ingredients, angles, human interactions.
– High-quality, well-framed images.
– Multiple camera angles possible via prompting.
– Ingredient order and burger shape remained consistent.

The 80/20 Reality

Over 500 images were generated using four model variations. Each model took under 6 hours to train and test. Roughly 80% of the outputs were unusable without significant cleanup: distorted proportions, broken textures, or lighting mismatches. However, the remaining 20% delivered high-fidelity outputs that were:

– Consistent with brand look and feel
– Rendered in multiple aspect ratios
– Legally clean and copyright-safe

These assets proved valuable for supplemental deliverables, social content, and R&D previsualization.

This test shows that AI is not ready to replace human production—but it is ready to augment it in powerful, flexible ways.

Final Note

This test was conducted in March 2025, and the tools used are evolving at an exponential pace. Some techniques in this paper may become outdated in a matter of weeks. Our aim is not to present a fixed blueprint—but to document a moment-in-time experiment that opens doors to what’s next.

Special thanks to photographer Nate Dorn for capturing the process so vividly; our AI consultants at UndercurrentXR for their technical guidance; food stylist Vanessa Parker for elevating every detail; and the RiTE Media team—Kevin Christopher, Hoss Soudi, Nika Tabidze, Johnny Meyer, and Ryan Monolopolous—for bringing this vision to life.

The post Generative AI Meets Branded Content: A Field Test by RiTE Media first appeared on RiTE Media.

Opening Thoughts

In an era where technology continuously reshapes how stories are told, virtual production (VP) emerges as a groundbreaking tool, transcending traditional filmmaking barriers. The recent Sonic Drive-In ad campaign, spearheaded by Mother LA, Trevor TV, and RiTE Media, underscores this innovation. Tasked with producing five dynamic commercials within a stringent timeline, the team leveraged virtual production to not only meet but exceed creative goals. This case study delves into how virtual production has redefined commercial content production, offering a glimpse into the future of brand storytelling.

Shooting in real-time, all the time

Virtual Production: The Game-Changer in Commercial Filmmaking

Virtual production combines real-time visual effects with digital environments displayed inside an LED cave, allowing filmmakers to customize and render the background in camera as they shoot. Unlike traditional methods where post-production adjustments happen after the fact, virtual production integrates these elements upfront, drastically altering the production landscape. For Sonic, this meant creating a photo-realistic drive-thru without the unpredictability of on-location shooting, such as adverse weather or lighting issues.

Addressing Sonic’s Core Needs

The primary challenges Sonic faced included revamping their visual content and executing multiple commercials quickly without disrupting their operational restaurants. Virtual production addressed these needs by providing a controlled, efficient environment that mimicked real-world dynamics without the associated costs or logistical nightmares.

Trevor, the director, emphasized the adaptability of virtual production, stating, “Virtual production allows for the pre-design of sets, environments, and lighting, significantly reducing on-set time and allowing for real-time adjustments.” This flexibility is crucial for brands that need to maintain a fresh and engaging consumer presence.

Innovative Process and Streamlined Execution

The collaboration with Trevor TV and RiTE Media facilitated the transformation of a physical Sonic location into a digitized version, enabling the team to manipulate elements such as traffic flow and time of day seamlessly. Kevin Christopher, Virtual Production Supervisor from RiTE Media highlighted the process, noting, “We could control every aspect of the environment, from the cars in the drive-through to the lighting, making it possible to achieve perfect shots every time.” This approach not only enhanced creative freedom but also optimized shooting schedules.

Comparative Advantage over Traditional Methods

The efficiency of virtual production is evident when compared to traditional on-location shoots and even blue/green screen techniques. By eliminating extensive post-production work, such as rotoscoping and VFX, virtual production ensures that the final visuals are both captivating and authentic. The ability to make real-time adjustments and receive instant client feedback further streamlines the approval process, enhancing overall productivity.

Client and Agency Perspective on Virtual Production

From the client’s perspective, virtual production offers substantial cost and time savings. The agency noted, “It’s like I’m a kid in a candy store. The ability to see changes as they happen and keep our restaurants open during production is invaluable.” Ursula Camack, producer for Trevor TV describes the prospect of shooting the creative as written any other way as “impossible.”

In Closing

Looking ahead, virtual production is poised to reshape the landscape of brand campaigns and storytelling. The Sonic Drive-Thru commercials case study exemplifies the revolutionary potential of virtual production in the advertising industry. By overcoming traditional filming challenges and enabling more efficient, creative, and engaging storytelling, virtual production stands as a beacon for future advertising campaigns. As brands strive to captivate audiences in an increasingly digital world, virtual production may well become the standard, offering limitless possibilities in the art of brand storytelling.

Wander into any gathering of the creatives these days and you might find the usual gossip and industry chatter has shifted toward a more doom-like tone; the collective hyperventilating about the inevitable AI apocalypse. You’ll learn that entertainment industry jobs will soon be shrink-wrapped and the essence of human creativity will undergo a facelift so extreme, we might not recognize it when we pass it in the hallway.

The post Digital Drive-Ins and Dives: Sonic’s Journey into Next Gen Advertising first appeared on RiTE Media.

Wander into any gathering of the creatives these days and you might find the usual gossip and industry chatter has shifted toward a more doom-like tone; the collective hyperventilating about the inevitable AI apocalypse. You’ll learn that entertainment industry jobs will soon be shrink-wrapped and the essence of human creativity will undergo a facelift so extreme, we might not recognize it when we pass it in the hallway.

As the founder of RiTE Media, a creative content studio known for developing and executing ambitious tech initiatives for well over a decade, I’ve had a front row seat to the transformative power of innovation in filmmaking; from high-speed cinema, automation/motion control, to virtual production and mixed reality. I won’t bury the lead… the concerns about AI are valid, especially around the rise of deep fakes (that’s a whole different topic). Today, I want to share my insights specifically on the future of content creation… of course the challenges, but also opportunities presented by GenAI, and find the silver lining amongst the chaos.

The Journey to GenAI

If you’re like me and grew up reading futurist authors like Ray Kurzweil and Max Tegmark, the thought of computers teaching themselves to be creative would have seemed both inevitable and total science fiction. Thanks to the compounding nature of machine learning, the line between sci-fi and reality is not just blurred but obliterated. Creative AI was generally considered a pipedream until the 2010s when deep learning took a quantum leap due to a dramatic increase in computational power and the explosion of big data, which provided the fuel for training complex neural networks. In parallel, advancements in deep learning algorithms and the collaborative spirit of the open-source community accelerated innovation even further.

Demystifying GenAI: The Fear vs. The Reality

In February 2024, OpenAI dropped a slew of very impressive clips [still in research phase] of their new text-to-video AI model SORA on social media. Mere days later, Tyler Perry announced in the Hollywood Reporter he was halting his $800M Atlanta studio expansion, blaming GenAI for his move. This article was shared around to every person and their grandma. What followed was an avalanche of collective panic talk throughout creative circles. Yes, GenAI will disrupt traditional roles and industries. Yet, I argue net-net, this seismic shift isn’t a death knell for creativity; it’s a transformative leap into new realms of artistic possibility.

Commissioned by The Animation Guild (IATSE Local 839), Concept Art Association, The Human Artistry Campaign, and The National Cartoonist Society Foundation, this infographic is based on a comprehensive study by CVL Economics, exploring Gen AI’s profound impact on entertainment industry jobs.. Find the study here.

Creative Inflation

As text-to-video becomes ubiquitous, we’re now about to be inundated with wildly bizarre visuals. The Internet is about to undergo a meme renaissance. This transition will feel like creativity inflation, as if the Federal Reserve is printing too much premium content for our minds to comprehend. Sadly, this explosion of creativity isn’t without its casualties, particularly at the entry level of many professions. AI assistants are streamlining our workflows to the point where the traditional entry level jobs are becoming obsolete. Everyone will be forced to build our own creative voice. For instance, those who used to be CG animators will accelerate their careers by building their own GenAI models to kick out incredible original renders. 

The real game-changer here is how GenAI is making our interactions with computers more human-like, all thanks to advancements in natural language processing. The art of fine-tuning your LLM (Large Language Model) to grasp your creative vision can dramatically elevate the uniqueness and marketability of your output. This shift isn’t just about making old tasks easier; it’s paving the way for novel forms of communication and storytelling that were previously unimaginable.

AI in Content Advertising

At the time of this writing, brands are tiptoeing lightly in this space given ambiguity around the legalities of AI data training and copyright. However, we’ve already seen some early hints of AI commercials like Wes Walker’s UnderArmor ad, “Forever is Made Now.” Though it was met with controversy surrounding the origins of the footage used for the model, we all can see clearly that commercials will never be the same again. The days of cushy physical production sets and large crews are not necessarily over, but they are going to be downsized, budget cut, and become more niche. On the upside, the efficiency associated with content creation will be elevated to near epic proportion.

‘Forever Is Made Now’ by Under Armour inspires the relentless pursuit of greatness in the here and now.

I don’t anticipate large language models taking over the jobs of creatives. In fact, AI will be a valuable tool in gathering consumer behavior trends and informing the creative directors. Brands will use internally-trained and collected data to identify market opportunities that will need to be translated creatively. Many product-based commercials will be made on the computer versus traditionally with cameras and lighting. There will be more content than ever blasted across social platforms, being created faster, cheaper, and more captivating.

AI in Filmmaking

Storytelling thrives on authentic human experiences, something AI, for all its advancements, can’t replicate. It can produce content, but genuine storytelling—with its depths of struggle and joy—remains uniquely human. AI’s role is more of a creative collaborator, streamlining processes like storyboarding and editing, making pre-visualization swift and aligning cinematic visions with digital precision. This revolutionizes animation and reduces production costs, yet the core of storytelling’s value remains in its originality. I may be biased here, but I think the craving for real human connection persists. Audiences value the tangible presence of actors, both on-screen and in the real world, anchoring the live-action genre in genuine human performances. Of course I could be wrong, but I don’t see the demand for photorealistic versions of Pixar movies taking over gritty filmmaking anytime soon. AI enhances, but can’t replace, the magic of human storytelling.

Commissioned by The Animation Guild (IATSE Local 839), Concept Art Association, The Human Artistry Campaign, and The National Cartoonist Society Foundation, this infographic is based on a comprehensive study by CVL Economics, exploring Gen AI’s profound impact on entertainment industry jobs.. Find the study here.

Next Decade: The Overton Window of Authenticity

Virtual Production (VP) will play a growing role in how we execute content production. VP essentially merges physical sets with digital landscapes, expanding creative possibilities beyond traditional limits. Enhanced by Generative AI, this workflow offers accessibility and streamlined cost benefits not only to big studios but to a wider creative community.

As the line between how we create and experience content becomes increasingly blurry, I believe augmented reality will be the next silver screen. We’re yearning not just to witness stories but to live them, to experience them with a visceral intensity. What’s murkier, however, is the fate of our tried-and-true methods of filmmaking — the magic of capturing real people and places using lenses, lighting, big set builds, exhausting 12-14 days. Predicting this is tricky; it hinges on audience desires, which, in turn, is linked to the economics of producing content. The creators and brands who thrive in this rapidly changing arena will be agile enough to pivot slightly ahead of the times. In a world where high-quality visuals are the expectation at any price, the true differentiators in content will be authenticity, original perspective, and boldness. 

Navigating this new era, we’re committed to venturing beyond the known, to extend the boundaries of imagination, and to support creators in achieving their most ambitious visions. The future may be uncertain, but I’m hopeful about what lies ahead, convinced that together, we’re crafting a legacy of innovation and unparalleled creativity.

The post Generative AI: Content Apocalypse or Creative Renaissance? first appeared on RiTE Media.