Synthetic Media & Digital Twins: Marketing’s AI Future

The Synthetic Media Revolution in Marketing

The convergence of artificial intelligence and content creation has given rise to a new and transformative category of digital assets known as synthetic media. This technology, which enables the generation of highly realistic images, videos, audio, and text, is rapidly moving from the fringes of technological curiosity to the core of modern marketing strategy. For business leaders, understanding the nuances of this revolution is no longer optional; it is a strategic imperative for maintaining competitive advantage in an increasingly digital marketplace.

Defining the New Reality: From “Deepfake” to Strategic Asset

Synthetic media is a broad, encompassing term for any form of media that has been partially or fully generated using artificial intelligence (AI). This category includes AI-written music, text generated by large language models, computer-generated imagery (CGI), virtual and augmented reality (VR/AR), and voice synthesis. The defining characteristic that separates synthetic media from traditional, human-edited content—such as an image altered in Photoshop—is that the content is substantially or entirely created by computer algorithms, which are often trained on vast datasets of real-world examples.

The term “synthetic media” is itself a strategic repositioning. Its predecessor, “deepfake,” is colloquially used to describe realistic manipulations of video or audio but carries deeply negative connotations associated with malicious uses like political misinformation, fraud, and the creation of non-consensual explicit content. This association presents a significant barrier to corporate adoption. Consequently, the industry has deliberately shifted its lexicon towards more neutral and commercially viable terms like “synthetic media,” “AI-generated media,” or “personalized content”. This rebranding is a crucial effort to decouple the powerful underlying technology from its harmful applications, thereby legitimizing it as a tool for creativity, efficiency, and commercial innovation. For a Chief Marketing Officer to secure budget and stakeholder buy-in, the language must be sanitized of its negative origins. This linguistic shift is the first and most critical step in normalizing the technology for business, creating a clear and defensible boundary between its ethical and unethical applications.

The Technology Under the Hood: A Primer for Strategists

The remarkable realism of modern synthetic media is primarily driven by advancements in a subfield of AI known as deep learning. Among the most significant technologies in this space are Generative Adversarial Networks, or GANs. Understanding the basic principle of GANs is essential for any leader seeking to grasp both the potential and the risks of this technology.

A GAN consists of two competing neural networks:

• The Generator: This network is tasked with creating new, synthetic data (e.g., an image of a human face) by learning from a training set of real data.
• The Discriminator: This network acts as a judge. It is trained to distinguish between real data from the training set and the fake data produced by the generator.

The two networks are locked in an adversarial, zero-sum game. The generator constantly tries to create content that can fool the discriminator, while the discriminator continuously improves its ability to detect fakes. This competitive dynamic creates an exponential learning curve; for every improvement in the discriminator’s detection capabilities, there is immediate and intense pressure on the generator to improve its creative output. This inherent technological arms race means that the ability to create realistic synthetic content will almost always outpace the ability to detect it. The generator learns directly from the discriminator’s failures, giving it a persistent advantage. This dynamic suggests that relying solely on technological detection as a long-term strategy for combating malicious fakes is likely untenable. Instead, strategies must also incorporate elements of disclosure, regulation, and media literacy.

A Taxonomy of Synthetic Assets for Marketing

For marketing strategists, synthetic media is not a monolithic concept but a diverse toolkit of assets, each with unique applications and implications. The primary categories currently being deployed are:

• Virtual Personas (AI-Generated Influencers): These are entirely computer-generated characters, often with hyper-realistic appearances, who are given distinct personalities, backstories, and social media presences. They function as brand ambassadors and social media influencers, engaging with audiences and promoting products in a highly controlled environment. Prominent examples like Lil Miquela and Shudu Gram have amassed millions of followers and secured partnerships with major global brands, demonstrating their commercial power.
• Synthetic Voices: AI-generated speech has evolved dramatically from the robotic, monotone text-to-speech (TTS) systems of the past. Modern deep learning models are trained on extensive datasets of human speech, allowing them to learn and replicate the subtle nuances of natural communication, including tone, pitch, cadence, rhythm, and even emotional expression. For advertisers, this technology offers unprecedented efficiency and consistency. A single synthetic voice can be cloned and adapted for use across multiple languages and regional accents, ensuring a consistent brand voice in global campaigns without the need to hire numerous voice actors.
• Generated Environments (CGI, AR, VR): This category involves the creation of digital settings and the augmentation of physical ones.
  • Computer-Generated Imagery (CGI) allows brands to create photorealistic product visualizations and fantastical advertising campaigns that are impossible or prohibitively expensive to produce in the real world. From showcasing a new car driving on Mars to creating a 360-degree view of a product, CGI offers limitless creative freedom.
  • Augmented Reality (AR) overlays digital information or objects onto the user’s real-world environment, typically via a smartphone. Marketing applications include “virtual try-on” features, such as Amazon’s app that allows users to place virtual furniture in their homes to see how it fits.
  • Virtual Reality (VR) creates a fully immersive, completely simulated environment that replaces the user’s real-world surroundings. While less common in mainstream marketing due to hardware requirements, it offers potential for deeply engaging brand experiences.

The Rise of the Virtual Persona: A Deep Dive into AI-Generated Influencers

Among the most visible and disruptive applications of synthetic media is the emergence of AI-generated influencers. These virtual personas are rapidly carving out a significant niche in the multi-billion-dollar influencer marketing industry, forcing brands to reconsider the very nature of celebrity, authenticity, and audience engagement. They represent a new class of marketing asset: perfectly controllable, globally scalable, and free from the inherent unpredictability of human talent.

Anatomy of a Virtual Influencer: More Than Just Pixels

The creation of a compelling virtual influencer is a sophisticated process that blends advanced technology with classic storytelling. The visual appearance is typically generated using a combination of CGI and AI-powered text-to-image tools like Fooocus or Midjourney, which can produce hyper-realistic human features. However, the technology is only one part of the equation.

The true success of a virtual influencer lies in the meticulously crafted narrative that underpins their existence. Behind every major AI influencer is a team of human writers, designers, and brand strategists who develop their backstory, personality traits, interests, values, and social stances. For example, the Spanish virtual model Aitana Lopez was conceived by her creators at The Clueless agency not just as a 25-year-old with vibrant pink hair, but as a gamer and fitness enthusiast with a Scorpio astrological sign—all details carefully chosen to resonate with specific target demographics and maximize engagement. This reveals that the most effective virtual influencers are not merely digital mannequins; they are platforms for sophisticated, narrative-driven marketing. Their creators script intricate storylines, including feuds and alliances with other virtual characters (such as the rivalry between Lil Miquela and Bermuda), to generate an ongoing digital drama that fuels media buzz and audience investment far beyond what a simple product placement could achieve. For brands, this means an opportunity to participate in a continuing story, offering a much deeper form of integration than a one-off advertisement.

Market Landscape and Key Players: A Strategic Overview

The AI influencer market has matured rapidly, with a clear hierarchy of established players and emerging challengers. These personas are not just social media curiosities; they are highly profitable commercial entities driving significant revenue.

• The Trailblazer (Lil Miquela): Created in 2016 by the Los Angeles-based startup Brud, Miquela Sousa (or Lil Miquela) is arguably the most famous virtual influencer. Portrayed as a 19-year-old “robot celebrity” musician and activist, she has amassed over 2.5 million Instagram followers.

Her cultural impact is validated by her inclusion in Time Magazine’s “25 Most Influential People on the Internet” and her high-fashion collaborations with elite brands such as Prada, Dior, Chanel, and Calvin Klein.

The E-commerce Powerhouse (Lu do Magalu)

With over 7 million followers, Lu do Magalu is Brazil’s most influential virtual personality and the most followed AI influencer globally. She originated in 2003 as a virtual personal assistant for the Brazilian retail giant Magazine Luiza. Her primary function is to drive e-commerce sales through product reviews, unboxing videos, and direct customer interaction, demonstrating a clear and direct return on investment that few other influencers, human or virtual, can match.

The Hyper-Realistic Supermodel (Shudu)

Created by British photographer Cameron-James Wilson in 2017, Shudu is known as the “world’s first digital supermodel.” Her hyper-realistic appearance and collaborations with high-fashion brands like Balmain and Fenty Beauty have sparked important conversations about representation, diversity, and artistry in the digital age.

The New Guard (Aitana Lopez)

Aitana represents the next wave of virtual influencers, created explicitly as a business solution. Her agency, The Clueless, developed her in response to the challenges of working with human influencers, citing their demands and unpredictability. Aitana can reportedly earn up to tens of thousands of euros per month from advertising campaigns, proving the viability of a business model built on creating “perfect” influencers from scratch.

This emergence of successful virtual personas has catalyzed a new type of vertically integrated agency model, exemplified by firms like Brud, The Clueless, and Aww Inc. (creator of Japanese influencer Imma). These companies control every aspect of the asset: talent creation, narrative development, campaign execution, and performance analytics. This structure offers brands a highly controlled and predictable partnership, but it also represents a fundamental disruption to the traditional talent agency model, which is predicated on managing the careers of independent, and often unpredictable, human beings. As these new-age agencies consolidate their power, they may gain significant negotiating leverage over brands seeking to enter this space.

Influencer Name	Creator/Agency	Follower Count (Instagram)	Key Brand Collaborations	Estimated Price Per Post	Persona Archetype
Lu do Magalu	Magazine Luiza	7.3M	Adidas, Samsung, McDonald’s	Not Public	E-commerce Guru
Lil Miquela	Brud	2.5M	Prada, Dior, Calvin Klein, Samsung	$7,500	Fashion Icon / Musician
Leya Love	Cosmiq Universe AG	546K	Impact Investing Solutions	Not Public	Activist / Advocate
Imma	Aww Inc.	393K	IKEA, Porsche, Hugo Boss	Not Public	Cultural Trendsetter
Aitana Lopez	The Clueless	338K	Olaplex, Brandy Melville	Not Public	Digital Supermodel
Shudu	Cameron-James Wilson	239K	Fenty Beauty, Balmain, Ellie Saab	Not Public	High-Fashion Muse

Follower counts and collaboration data sourced from. Price per post data from.

The Business of Virtual Influence: Control, Cost, and Controversy

For brands, the strategic advantages of partnering with AI influencers are compelling. They offer complete control over messaging and brand representation, eliminating the risk of human error, off-brand behavior, or personal scandals that can plague campaigns with human celebrities. Virtual personas are available 24/7, can be in multiple places at once, and are not bound by physical or geographical limitations.

The economic model is also a significant driver of adoption. While the initial investment to create a high-quality virtual influencer can be substantial, the long-term cost-effectiveness is undeniable. Brands avoid recurring expenses for travel, accommodations, and logistics associated with traditional photoshoots. Furthermore, once created, the asset can be reused in limitless campaigns at a marginal cost.

However, this new frontier is not without its risks. The very artificiality that provides control can also be a liability. Audiences, particularly those who value authenticity, may perceive synthetic campaigns as deceptive or manipulative. A prominent case study in brand risk is Calvin Klein’s 2019 ad featuring Lil Miquela in a romantic scene with model Bella Hadid. The campaign sparked significant backlash, with the brand being accused of “queerbaiting” and inauthenticity. The public outcry was so strong that Calvin Klein was forced to issue an apology. This incident serves as a critical reminder of the fine line brands must walk between technological innovation and maintaining consumer trust.

Digital Twins as a Marketing Catalyst

Parallel to the rise of synthetic personas, another powerful concept is reshaping the marketing landscape: the digital twin. Originally an engineering concept used to model physical assets like jet engines, the digital twin has been adapted for marketing to create virtual replicas of both products and consumers. This technology serves two distinct but complementary functions: revolutionizing the efficiency and creativity of content production, and enabling a new era of predictive, hyper-personalized customer engagement.

Part A: The Product Digital Twin – Revolutionizing Content Creation

In a marketing context, a product digital twin is a photorealistic, high-fidelity 3D model of a physical object. This model serves as the single source of truth for all visual marketing assets. It is crucial to distinguish this from its engineering counterpart; while an engineering twin is fed real-time sensor data to simulate operational performance, a marketing twin prioritizes photorealistic accuracy and creative autonomy to generate sales-driving content like images, videos, and interactive experiences.

The primary impact of this technology is the obsolescence of the traditional commercial photoshoot. Instead of organizing complex and expensive location shoots, marketing teams can now operate in a “Virtual Photo Studio.” Within this digital environment, they can generate an unlimited number of product visuals, changing lighting, backdrops, camera angles, and compositions in a matter of minutes. This approach yields quantifiable improvements in efficiency and cost. Case studies show that this method can be 5 to 25 times more efficient than traditional production. For example:

• Daan Tech, a home appliance company, saw its average cost-per-image plummet from over $50 to just $1 and reduced its total visual content costs by up to 60%.
• Misencil, a beauty brand, accelerated its campaign production speed by 7.5 times (from weeks to days) and increased its monthly content output tenfold.

This efficiency is not merely a cost-saving measure; it is the enabling technology for hyper-personalization at scale. The marginal cost of creating a new visual variation of a product is near zero. Consequently, brands can move away from producing a single “hero” image for a campaign and instead generate hundreds of tailored visuals for different audience segments. As Daan Tech demonstrates, a brand can show its product in a minimalist setting for urban design enthusiasts, a rustic kitchen for rural homeowners, and a camper van for travelers—a level of visual segmentation that would be economically unfeasible with traditional photography.

Furthermore, these high-fidelity 3D models are the foundational assets for creating immersive customer experiences. They power Augmented Reality (AR) features like virtual try-ons for fashion or furniture placement apps, and they enable the interactive 360-degree product viewers on e-commerce websites that allow customers to examine a product from every angle, significantly boosting purchase confidence and reducing return rates.

Part B: The Consumer Digital Twin (DToC) – The Next Frontier of Personalization

While product twins revolutionize how brands create content, consumer digital twins (DToCs) are set to revolutionize how brands understand their audience. A DToC is a dynamic, virtual replica of an individual customer or an entire customer segment, constructed from real-world data to simulate and predict their behaviors, preferences, and future actions.

The creation of a DToC is a multi-stage, data-intensive process:

• Data Aggregation and Integration: The foundation of a DToC is a comprehensive, 360-degree view of the customer. This requires integrating vast streams of both historical and real-time data from every touchpoint, including website visits, chatbot interactions, social media engagement, call center records, purchase history, and even data from IoT devices and wearables.
• AI and Machine Learning Modeling: Once aggregated, this data is fed into sophisticated AI and machine learning models. These algorithms analyze the data to identify patterns, infer preferences, and construct a predictive virtual persona. This model is not static; it continuously learns and evolves as new data flows in, allowing it to reflect the customer’s changing needs and behaviors in real time.
• Simulation and Analysis: The true power of the DToC lies in its use as a simulation environment. Marketers can run “what-if” scenarios to test hypotheses before deploying them in the real world. For instance, they can simulate how a specific customer segment might respond to a new product launch, predict the likelihood of churn and test intervention strategies, or A/B test different marketing messages on a virtual population to identify the most effective approach.

This capability marks a fundamental shift in the function of marketing, moving it from retrospective analysis (studying what customers did) to predictive simulation (modeling what they will do).

This transforms marketing from a primarily creative and executional function into a strategic, R&D-like discipline where campaigns and product ideas are rigorously stress-tested in a virtual environment before any significant budget is committed. Leading brands are already leveraging this approach. Coca-Cola, for example, uses digital twin models to simulate consumer personas, allowing it to test campaign strategies and fine-tune messaging to predict audience response and achieve a significantly higher return on investment. Analysis from McKinsey suggests that organizations successfully implementing DToCs have realized revenue increases of as much as 10 percent. This evolution elevates the strategic importance of the marketing department but also demands a profound transformation in its talent and processes, requiring deep expertise in data science, simulation modeling, and AI.

Performance & Efficacy Analysis: Synthetic vs. Human-Centric Marketing

The decision to integrate synthetic media into a marketing strategy requires a clear-eyed assessment of the trade-offs between the efficiency offered by AI and the established efficacy of human-centric approaches. While AI-generated content and influencers provide unprecedented scale and cost control, current data indicates they face a significant deficit in the very metrics that define genuine influence: engagement and trust.

The Engagement and Trust Deficit

Quantitative analysis reveals a stark performance gap between AI and human influencers. A 2025 analysis by the Financial Times found that, on average, human influencers generate approximately 2.7 times more engagement per post than their AI counterparts. This suggests that while audiences may be intrigued by the novelty of virtual personas, their level of interaction and investment remains deeper with human creators.

This engagement gap is rooted in a more fundamental issue: a “trust deficit.” A 2024 consumer survey by The Influencer Marketing Factory revealed that only 15% of consumers report having high trust in endorsements from AI influencers. This skepticism directly impacts commercial outcomes, with only 27% of respondents stating they would consider purchasing a product recommended by a virtual persona. This lack of trust is the primary factor limiting the current effectiveness of synthetic influencers. The absence of genuine “lived experiences,” vulnerability, and spontaneity prevents the formation of the deep parasocial relationships that are the bedrock of human influence and long-term brand loyalty.

While familiarity with virtual influencers is growing—53% of consumers follow at least one—and younger demographics like Gen Z show significantly more interest, their opinions remain deeply polarized. A survey found that while 37% of Gen Z consumers believe AI influencers make a brand more appealing, an equal 37% believe they make a brand less trustworthy, indicating that even among the most digitally native audience, authenticity remains a critical concern.

The ROI Equation: Efficiency vs. Efficacy

The core strategic dilemma for marketers can be framed as a trade-off between production efficiency and marketing efficacy.

• AI Excels in Efficiency: The business case for synthetic media is strongest in terms of speed and cost. Companies using AI platforms to generate content report up to 90% faster video production and cost savings of as much as $10,000 per video compared to traditional studio shoots. This allows brands to scale content creation at an unprecedented rate.
• Humans Lead in Efficacy: Despite these efficiencies, human influencers continue to dominate in their ability to drive commercial results. The market’s valuation of their influence is reflected in their earnings; human influencers earned an average of $78,777 per campaign, while AI influencers earned just $1,694. This vast disparity indicates that brands are willing to pay a significant premium for the trust and conversion power that human creators currently command.

The return on investment (ROI) calculation is complex and context-dependent. Some data suggests that AI-selected influencers can achieve a 15% higher conversion rate and a lower cost-per-acquisition ($25 vs. $35 for human-selected influencers). However, other cases show that human-selected influencers can deliver a 20% higher overall ROI, likely due to their ability to drive higher-value conversions and foster long-term brand loyalty.

Metric	AI Influencer Benchmark	Human Influencer Benchmark	Key Insight	Data Source(s)
Avg. Engagement Rate	1.0x (Baseline)	2.7x higher	Human influencers generate significantly deeper audience interaction.
Consumer Trust Level	15% (High Trust)	Not specified, but implied higher	The “trust deficit” is the primary barrier to AI influencer effectiveness.
Purchase Intent	27% of consumers would consider	Implied higher	Skepticism about AI endorsements directly impacts conversion potential.
Avg. Earnings Per Campaign	$1,694	$78,777	The market places a ~46x higher value on the commercial impact of human influencers.
Content Production Speed	Up to 90% faster	Baseline	AI offers unparalleled efficiency and scalability for content creation.
Avg. Cost Per Video	Up to $10,000 less	Baseline	Synthetic media provides a significant cost advantage over traditional production.

Context is Key: A Strategic Framework for Deployment

The choice between synthetic and human-centric marketing is not a binary one. The most effective strategy is not to replace humans with AI, but to build a hybrid, portfolio-based approach that deploys each for its unique strengths. The data suggests a clear framework for this strategic allocation:

• Deploy AI Influencers for Scale and Aesthetics: AI influencers perform best in product categories where aspirational visuals, novelty, and objectivity are key drivers. This includes sectors like technology, consumer electronics, high fashion, and luxury goods, where a perfect, controlled aesthetic is paramount. They are also ideal for high-volume, low-risk activities like generating diverse product imagery for A/B testing or maintaining a consistent brand presence across global social media channels.
• Deploy Human Influencers for Trust and Emotion: Human influencers remain indispensable in emotionally driven sectors where authenticity, relatability, and genuine connection are critical. This includes lifestyle, wellness, food, parenting, and socially sensitive campaigns, where personal stories and lived experiences are necessary to build trust and credibility. High-stakes, trust-based initiatives like major product launches or brand narrative campaigns should be reserved for high-cost, high-impact human partnerships.

By adopting this tiered approach, brands can optimize both the efficiency of their content production and the efficacy of their audience engagement, leveraging AI for scale and humans for impact.

Navigating the Ethical and Regulatory Minefield

The power of synthetic media to create convincing, artificial realities brings with it a host of profound ethical challenges and a rapidly evolving regulatory landscape. For brands, navigating this minefield is not just a matter of legal compliance but a critical component of risk management and brand reputation. Failure to address these issues proactively can lead to severe financial penalties, public backlash, and an irreversible loss of consumer trust.

Part A: The Ethical Imperative – Beyond Deception

While the benefits of synthetic media are clear, the potential for misuse necessitates a strong ethical framework. Brands must consider several key areas of concern:

• Consent and Privacy Violations: The most fundamental ethical breach is the use of an individual’s likeness or voice without their explicit consent. This issue is most acute in the creation of non-consensual deepfake pornography, which currently constitutes the vast majority of deepfake content online and serves as a tool for harassment and severe psychological harm. While marketing applications are different, the underlying principle of consent remains paramount.
• Misinformation and Manipulation: Synthetic media can be used to create highly realistic but entirely false depictions of public figures, business leaders, or events. Such content can be used to influence elections, manipulate stock prices, or damage corporate and personal reputations. A brand associated with deceptive practices, even unintentionally, risks catastrophic reputational damage.
• Erosion of Public Trust: The widespread proliferation of synthetic media threatens to create a “liar’s dividend,” a scenario where it becomes so difficult to distinguish real from fake that all digital content becomes suspect. This erodes public trust not only in media and institutions but also in brand communications.
• Brand Risk and Authenticity: Even well-intentioned campaigns can backfire if audiences feel they have been deceived. Consumer demand for transparency is high, with 36% expecting clear disclosure when content is AI-generated. As the Calvin Klein case demonstrated, a campaign perceived as inauthentic can alienate consumers and lead to public relations crises.

Part B: The Global Regulatory Framework – A Compliance Deep Dive

In response to these risks, governments worldwide are beginning to implement legal frameworks to govern the use of synthetic media. The regulatory landscape is fragmented but is rapidly converging around principles of transparency and disclosure.

The EU AI Act: The New Global Standard

The European Union’s AI Act, the world’s first comprehensive law on artificial intelligence, sets a new global benchmark for regulating synthetic media.

The key transparency obligations are outlined in Article 50 and are scheduled to become fully applicable on August 2, 2026. The Act establishes a dual-obligation system:

1. Obligation for Providers: Companies that provide the AI systems used to generate synthetic content must ensure that the outputs are marked in a machine-readable format (such as a digital watermark or metadata) so they can be automatically detected as artificial.
2. Obligation for Deployers: Companies that use these systems to create and publish content (the “deployers,” which includes brands and marketing agencies) must clearly and conspicuously disclose to the public that the content is artificially generated or manipulated, especially if it constitutes a “deepfake”.

The Act’s reach is extraterritorial, meaning it applies to any company whose AI-generated content is accessible to users within the EU, regardless of where the company is headquartered. This provision effectively makes the EU AI Act a de facto global standard. For multinational corporations, the operational complexity of creating different content policies for different regions makes it far more practical to adopt the highest regulatory standard—that of the EU—across all global operations.

Exemptions exist for content that is part of an “evidently artistic, creative, satirical, [or] fictional” work, where the disclosure obligation is limited to a manner that does not hamper the display of the work. Penalties for non-compliance are severe, with fines reaching up to €15 million or 3% of a company’s total worldwide annual turnover, whichever is higher, elevating this from a marketing compliance issue to a C-suite-level financial risk.

The U.S. Regulatory Patchwork

In contrast to the EU’s comprehensive approach, the United States currently has a fragmented “patchwork” of regulations. There is no single federal law governing synthetic media in advertising. Instead, oversight comes from two main sources:

1. Federal Agency Action: The Federal Trade Commission (FTC) has the authority to regulate synthetic media when it is used for “unfair or deceptive acts or practices” in commerce, such as in fraudulent advertising or scams.
2. State-Level Legislation: A growing number of states have passed or introduced legislation, but these laws are often narrow in scope, primarily focusing on two areas: the use of deepfakes in political advertising to influence elections, and the criminalization of non-consensual explicit imagery. States like California, Michigan, Minnesota, Texas, and Washington have enacted laws requiring clear disclaimers on political ads that use AI-generated content.

This legal fragmentation creates a complex compliance environment for companies operating across the U.S. The table below summarizes key state-level regulations.

State	Bill/Law Number	Key Provision	Scope	Disclosure Requirement
Arizona	S 1359	Prohibits deceptive synthetic media of a candidate.	Political Ads	Required: Must include a clear disclosure that content is AI-generated.
California	AB-730 / AB-2355	Prohibits deceptive media in election ads with “actual malice.”	Political Ads	Implicitly required to avoid liability.
Florida	CS/HB 919	Requires disclaimers on political ads using generative AI.	Political Ads	Required: Mandates a specific disclaimer on ads.
Michigan	HB 5141	Requires disclosure for political ads generated by AI.	Political Ads	Required: “This message was generated in whole or substantially by artificial intelligence.”
Minnesota	HF 1370	Criminalizes dissemination of deceptive media of a candidate within 90 days of an election.	Political Ads	Implicitly required to avoid criminal liability.
New Mexico	HB 182	Mandates disclaimers on ads containing materially deceptive media.	Political Ads	Required: Must include a disclaimer.
Texas	SB 751	Makes it a crime to create and distribute a deepfake video to influence an election.	Political Ads	Implicitly required to avoid criminal liability.
Washington	SB 5152	Prohibits deceptive or fraudulent deepfakes of candidates within 60 days of an election.	Political Ads	Required: Must include a disclosure statement.

Data compiled from.

The emergence of these regulations is also creating a new B2B market for “Trust and Safety as a Service.” The technical requirements for watermarking, detection, and compliance monitoring are beyond the capabilities of most marketing departments. This has created a business need for third-party vendors that provide AI detection software and content authentication services, meaning a portion of future marketing technology budgets will need to be allocated to content verification and compliance.

Strategic Framework and Future Outlook

As synthetic media and digital twins move from nascent technologies to integral components of the marketing toolkit, leaders require a structured framework for adoption and a clear vision of the future. The path forward involves a phased approach to implementation, a robust governance structure to mitigate risk, and an understanding of how these technologies will converge to create the next paradigm of customer interaction.

A Framework for Adoption: The Synthetic Media Maturity Model

Organizations should approach the adoption of synthetic media not as a single decision but as a journey through stages of increasing sophistication and strategic integration. A maturity model can guide this process:

• Stage 1: Crawl (Internal Experimentation & Efficiency): Begin with low-risk, high-value internal applications. Use synthetic voice technology for corporate training videos or internal communications. Leverage AI image generators to create conceptual visuals for internal campaign pitches and mood boards. The focus at this stage is on familiarizing the organization with the tools and realizing initial cost savings in non-customer-facing contexts.
• Stage 2: Walk (Content Production & Optimization): Adopt product digital twins as the core of the content creation workflow. Replace traditional product photography with virtual photoshoots to dramatically increase content velocity and reduce costs for e-commerce sites, social media, and digital catalogs. Use the efficiency gains to begin A/B testing different visual presentations for various market segments.
• Stage 3: Run (External Engagement & Innovation): Deploy synthetic media in customer-facing campaigns. This could involve partnering with an established AI-generated influencer for a product promotion or creating a novel CGI or Augmented Reality experience. The focus is on leveraging the novelty and creative freedom of the technology to capture audience attention and generate buzz, always accompanied by robust and transparent disclosure.
• Stage 4: Fly (Predictive Personalization & Simulation): Achieve the highest level of strategic integration by developing and deploying Consumer Digital Twins (DToCs). Use these virtual customer models to run predictive simulations for campaign strategies, product development, and customer journey optimization. At this stage, marketing transitions from a reactive to a predictive function, driving hyper-personalized experiences at scale based on simulated outcomes.

Governance and Best Practices: Building a Resilient Strategy

As organizations advance through the maturity model, a parallel focus on governance is essential to manage legal, ethical, and reputational risks.

• Establish an AI Ethics Council: Create a cross-functional governance body comprising leaders from marketing, legal, public relations, technology, and compliance. This council should be responsible for establishing a corporate-wide policy on the ethical use of AI and synthetic media, and it must review and approve all significant customer-facing synthetic media campaigns before launch.
• Mandate Radical Transparency: Adopt the EU AI Act’s disclosure requirements as the global corporate standard. All AI-generated or substantially manipulated content should be clearly, conspicuously, and proactively labeled. Rather than viewing this as a legal burden, brands should embrace transparency as a core tenet of their relationship with customers, using it as a tool to build trust and differentiate themselves in an increasingly artificial media landscape.
• Maintain a Human-in-the-Loop: While AI can automate content creation, it should not operate without human oversight. All AI-generated content intended for public dissemination must undergo a process of human review and editorial control. This “human-in-the-loop” approach is critical for catching factual errors, preventing brand misalignments, ensuring ethical standards are met, and providing a final layer of judgment that algorithms currently lack.

The Converging Future: GenAI, Digital Twins, and the Metaverse

Looking ahead, the individual technologies of synthetic media and digital twins will not evolve in isolation. Their convergence, accelerated by advances in generative AI, points toward a future where the boundary between the physical and digital worlds becomes increasingly permeable.

Generative AI will democratize and accelerate the creation of both product and consumer digital twins, making these powerful simulation tools accessible to a wider range of businesses. The ultimate application of this convergence will likely be the creation of persistent, immersive digital environments—often referred to as the metaverse. In these spaces, customers (or their DToCs) will be able to interact with hyper-realistic digital twins of products in simulated worlds. They will be able to try on virtual clothing that drapes perfectly on their digital avatar, test drive a virtual car in a simulated environment, or walk through a virtual replica of a store.

This paradigm shifts marketing from an act of communication to an act of simulation.

Brands will compete not just on the quality of their products or the cleverness of their advertising, but on the fidelity, utility, and immersiveness of the digital experiences they provide. The synthetic frontier is not just about creating new types of ads; it is about building new realities in which to engage with customers.