H&M has begun exploring a future where fashion imagery can be produced without traditional photoshoots, using AI-generated digital twins of real models. The Swedish retailer is reportedly partnering with an artificial intelligence provider to create lifelike digital replicas of 30 models for product campaigns and marketing, potentially transforming how campaigns are planned, produced, and distributed. The move reflects a broader industry shift toward AI-driven creativity, efficiency, and scalability, while raising questions about rights, compensation, transparency, and the role of human models. The plan includes watermarking AI-generated imagery to maintain transparency and to study audience reactions to campaigns that rely on digital likenesses rather than on real people. This development comes as fashion brands increasingly weigh the advantages and risks of generative AI in creative processes, marketing workflows, and talent management.
Background: digital twins and AI in fashion
Digital twins in fashion refer to high-fidelity, AI-generated representations of real people or objects that can be used to simulate, visualize, and produce content without demands for in-person shoots. Over the past few years, brands have experimented with virtual influencers, CGI models, and other synthetic front-facing personas to captivate audiences, test creative concepts, and accelerate production timelines. The emergence of sophisticated AI-driven image synthesis and motion capture technologies has made it possible to render dynamic, realistic figures that move with a natural cadence, reflect nuanced lighting, and adapt to various wardrobe ensembles.
In the fashion industry, these innovations intersect with broader trends in personalization, omnichannel marketing, and cost containment. Virtual models and AI-produced imagery offer potential benefits: faster turnaround for seasonal campaigns, more consistent image quality across markets, and the ability to tailor visuals for diverse audiences at scale. Yet the adoption also brings scrutiny. Ethical considerations arise around the portrayal of real people, consent, and the potential displacement of human models in certain contexts. Industry observers note that AI-generated imagery can blur lines between authenticity and imagination, which can affect consumer trust and brand perception if not managed with clear transparency and responsible storytelling.
A growing body of mass-market campaigns has featured virtual or AI-driven personas, with instances where brands deploy digital assets alongside traditional photography to achieve hybrid storytelling. The conversation around compensation and rights for models whose likenesses are replicated digitally is intensifying. Creators, agencies, and unions are examining how to adapt contracts, usage rights, and revenue sharing to reflect the expanding role of synthetic representations in advertising. The H&M initiative sits at the intersection of these evolving dynamics, signaling a potentially transformative approach to how fashion brands conceive, develop, and deploy imagery in a digital-first era.
In parallel, observers note that AI-enabled campaigns can enable more precise audience testing and analytics. Marketers can explore a wider array of creative directions while maintaining a controlled and auditable process. At the same time, concerns persist about whether AI-assisted content could diminish opportunities for emerging human talent or reshape talent pipelines in ways that favor scalable digital assets over in-person shoots. The balance between efficiency, creative expression, and fair treatment of models and agencies remains a central debate as more brands experiment with digital clones and AI-driven creative workflows.
Moreover, the conversation around transparency has matured. Watermarking AI-generated imagery is one response to concerns about undisclosed synthetic content. Audiences increasingly expect clear signals when imagery relies on AI, enabling viewers to distinguish between traditional photography and machine-generated visuals. This transparency is not only a trust-building mechanism but also a data point for evaluating campaign effectiveness. By marking AI-generated assets, brands can gather insights into audience reactions to non-human likenesses and compare them with responses to real-model imagery, ultimately informing future production strategies.
In this broader context, H&M’s plan can be viewed as a strategic test case for how digital twins might integrate with existing creative ecosystems. The initiative aims to optimize product campaigns and marketing content while maintaining a human-centric philosophy that underpins the brand’s identity. As fashion brands seek to reconcile rapid digital production with ethical and social considerations, the interplay between AI-driven capabilities and human talent remains a pivotal axis of evolution. The following sections examine the specifics of H&M’s project, the technical and creative processes involved, the implications for campaigns, and the broader industry context.
The H&M–Uncut partnership: scope, goals, and timeline
H&M’s collaboration with Uncut, an AI-focused creative technology partner, centers on the development of digital twins—lifelike digital replicas—of 30 real-life models. The stated objective is to use these AI-generated likenesses to produce product imagery and marketing content efficiently, reducing the need for physical shoots that can be costly, logistically challenging, and time-consuming. By leveraging digital twins, H&M aims to expand its creative capacity, accelerate campaign production, and enable rapid iteration across markets and channels. The partnership envisions a workflow where AI-generated visuals supplement, rather than replace, traditional photography, allowing the brand to scale its campaigns while preserving a human-centric design philosophy.
A critical element of the plan is the use of watermarking. Once images are created, they will be clearly watermarked to ensure transparency about their AI origin. This step is intended to provide visibility into when AI-generated imagery is used and to support researchers and marketers in analyzing audience engagement with campaigns that deploy digital twins versus those featuring real models. The watermarking approach aligns with broader industry discussions about disclosure standards for synthetic content, helping maintain trust with customers and industry stakeholders.
Industry reports describe the project as a multi-phase initiative. In the initial phase, H&M is engaging with the models and their agencies to craft digital twins that will appear in social media content and in broader advertising campaigns later in the year. The collaboration emphasizes a governance framework in which the digital twins live alongside real models, each contributing to different aspects of the brand’s storytelling and product presentation. While the project is positioned as experimental, it also signals a potentially scalable model for future campaigns across the company’s product categories.
From a strategic perspective, the plan aligns with H&M’s broader technology strategy, which Chief Creative Officer Jörgen Andersson describes as an exploration of how generative AI can augment the creative process without diminishing the human elements central to personal style and self-expression. Andersson emphasizes that the initiative is about identifying how AI can broaden creative possibilities while maintaining the brand’s commitment to individuality and accessibility. In his words, the technology is intended to enhance, not supplant, the human-centric approach that defines H&M’s ethos. This framing underscores the company’s intent to use digital twins as a complementary asset within a carefully managed creative ecosystem.
The project also entails a legal and commercial dimension related to usage rights. H&M has indicated that when digital twins are employed for campaigns, the models themselves retain ownership of the rights to their AI-generated clones. This arrangement implies that models could later leverage their digital likenesses to secure deals with other brands or agencies beyond H&M, thereby creating a potential new revenue stream. The specifics of compensation for the use of digital twins—whether through a flat fee, ongoing royalties, or other structures—remain an area for further clarification. Industry observers note that defining compensation for digital twins will require careful consideration of licensing models, consent, and the evolving legal landscape around synthetic media.
The partnership also reflects a close collaboration with models and their agencies. By involving stakeholders in the development process, H&M aims to foster trust and alignment with talent, ensuring that digital representations are accurate, authorized, and ethically sourced. This collaborative approach is intended to minimize misrepresentation risks and to establish a sustainable framework for the use of digital twins in future campaigns. As the fashion industry continues to experiment with AI-driven assets, the H&M–Uncut initiative is being watched closely as a potential blueprint for how large brands can balance innovation with responsible talent management and transparent practices.
In terms of timing, the project is positioned to roll out across campaigns later in the year, targeting both e-commerce platforms and mainstream advertising channels. The emphasis on social media suggests that H&M intends to leverage short-form formats and platform-native content to maximize reach and engagement. The multi-channel strategy is designed to maximize the impact of the digital twins while maintaining alignment with the brand’s overall marketing calendar. As with any ambitious AI-driven program, the timeline will hinge on successful collaboration with model representatives, the refinement of synthetic-visual technologies, and careful governance around usage rights and compensation.
The creation process: data capture, modeling, and rights
The process of creating digital twins begins with extensive data collection to capture a level of detail capable of producing highly realistic representations. H&M’s approach involves photographing models in motion across a variety of locations and lighting conditions, ensuring a comprehensive dataset. The goal is to document subtle features—such as birthmarks, distinctive movement patterns, and unique gait characteristics—that contribute to a model’s distinctive presence. The attention to detail is intended to empower the AI system to reproduce the model’s likeness with fidelity across different body poses, outfits, and environments, ultimately enabling the generation of convincing, flexible digital images and animations.
To achieve this, the project uses a combination of high-resolution imagery, motion capture techniques, and advanced rendering algorithms. The captured data informs the digital twin’s 3D model, texture maps, shading, shading, skin tone nuances, and micro-movements that contribute to realism. The resulting digital twin is designed to be adaptive, capable of projecting the model into a range of campaign contexts without requiring the model to physically appear in every shot. The technical objective is to create a scalable asset that faithfully mirrors the model’s appearance and movement while enabling efficient production workflows for images, short videos, and interactive content.
A crucial aspect of the rights framework concerns ownership and control over the AI-generated clones. The individuals who serve as the basis for digital twins will retain ownership of their likeness in the digital format. This arrangement is intended to ensure that models have ongoing control over how their digital representations are used and monetized. In practical terms, this means that the digital twin belongs to the model rather than to H&M or the technology provider, at least in terms of rights to exploitation. Consequently, the models can, in theory, license their digital clones to other brands or media outlets beyond H&M, creating potential opportunities for additional revenue streams.
Nonetheless, questions remain about compensation for the use of digital twins. H&M’s Global Business Development Manager, Louise Lundquist, has noted that compensating digital twins represents a new revenue stream, akin to the compensation models used for human models in ecommerce shoots. She indicated that the structure would mirror traditional usage rights for the digital twin, with payment for the rights to use the clone’s likeness. This parallel suggests that the financial terms for AI-generated twins could follow familiar patterns seen in human model agreements, though the details—such as whether compensation becomes ongoing royalties versus a one-time fee—still require formalization in contracts. The absence of a fully defined compensation framework underscores the ongoing negotiation and refinement of best practices in digital-twin economics.
The data-handling and consent processes are also pivotal. Model participants must approve how their digital likenesses are captured and deployed, including the contexts in which the digital twin may appear, the duration of usage, geographic scope, and the range of products and campaigns. Agencies and the models’ representation networks are likely to play a key role in negotiating these terms, ensuring that usage aligns with each model’s professional boundaries and brand safety requirements. The model’s representatives can safeguard against unwanted associations, ensure proper disclosure in marketing materials, and help determine the appropriate compensation model. These governance measures are essential to maintain trust among talent, agencies, and the brand.
From a data-security standpoint, securing the models’ biometric data and ensuring that the digital twins cannot be misused are critical considerations. The project likely includes robust access controls, audit trails, and usage monitoring to prevent unauthorized replication, distribution, or exploitation of the digital twins. While the technical details remain proprietary, the emphasis on transparency—through watermarking and disclosure—suggests a broader commitment to responsible AI practices in the fashion industry. The combined emphasis on fidelity, consent, and governance constitutes a comprehensive approach to deploying AI tools in a highly identity-sensitive domain.
An intrinsic challenge in the creation process is ensuring that digital twins genuinely reflect the model’s individuality without veering into over-synthesized or caricatured representations. The risk of producing a digital twin that deviates from the model’s true appearance or comportment could undermine authenticity and audience trust. To mitigate this, the collaboration with model agencies is critical, enabling ongoing reviews and adjustments at multiple stages of model development. The goal is to preserve the model’s distinctive characteristics while enabling the digital clone to perform across diverse creative contexts. The result should be a faithful, versatile digital twin that respects the model’s professional identity and creative boundaries.
In summary, the digital-twin creation process encompasses a rigorous cycle of data capture, AI-driven modeling, rights designation, consent and governance, and security measures. The emphasis on buyer-like control for the models’ digital clones, combined with a transparent broadcasting approach (watermarking), signals a thoughtful attempt to navigate the complexities of synthetic media in fashion. It also points toward a future where digital twins become established assets that work in tandem with human talent to expand the possibilities of product presentation, storytelling, and brand engagement without compromising ethical and professional standards.
Campaign implications: from product imagery to social media
The use of digital twins in campaigns reconfigures several core aspects of fashion marketing. First, it broadens the creative palette available to the brand. AI-generated twins enable rapid prototyping of imagery, offering the ability to test different wardrobe combinations, lighting setups, and environment concepts with minimal time and cost. Brands can experiment with avant-garde styling or diverse cultural cues without the logistical constraints of coordinating real shoots in multiple locations. This flexibility is especially valuable for global campaigns that require localization across dozens of markets. Digital twins can be adapted to suit regional preferences and seasonal variations, ensuring that campaigns remain resonant with varied audiences while maintaining brand coherence.
Second, the approach expands production efficiency. Traditional fashion shoots are resource-intensive, involving photographers, stylists, makeup artists, set designers, transport logistics, and extensive scheduling. In contrast, digital-twin workflows can compress timelines, reduce travel, and streamline post-production. Content can be rendered quickly in different poses, outfits, or backgrounds, enabling marketers to respond to trends and feedback in near real time. The ability to iterate rapidly translates into shortened launch windows for seasonal drops and more responsive marketing calendars, potentially delivering a competitive advantage in a fast-moving market.
Third, there are implications for channel strategy. AI-generated visuals can be tailored to suit a range of formats—from high-impact hero images for advertising to short verticals optimized for social media—without needing separate photo shoots for each format. The digital twin concept supports multi-platform optimization by providing interchangeable assets that maintain a consistent visual identity while adapting to platform-specific requirements. In addition, the content can be customized for different markets while preserving the brand’s core aesthetics, making it easier to achieve consistent worldwide messaging with localized nuance.
Fourth, the campaign design must account for authenticity and audience perception. Watermarking AI-generated images signals transparency but also invites scrutiny about the distinction between synthetic and real imagery. Campaigns that clearly disclose synthetic components may cultivate trust among audiences who value transparency, while others may respond differently to AI-based visuals. Brands may also use AI-generated content to experiment with audience engagement, studying how viewers react to digital twins in various contexts—ranging from product-focused shots to lifestyle scenes that emphasize personal expression and inclusivity.
Fifth, the relationship with talent and agencies is likely to evolve. As models contribute digital likenesses that can be licensed for multiple uses, agencies may renegotiate compensation structures and rights management to reflect this expanded set of capabilities. The potential for models to monetize their digital twins across campaigns with different brands introduces a new revenue stream that could restructure talent contracts and agency fees. It also requires clear governance to ensure consistent treatment across brands and to prevent conflicts of interest or over-licensing.
From a strategic vantage point, H&M’s plan to deploy AI-driven digital twins is not merely about reducing shoot costs; it is about expanding the scope and speed of creative development. The approach places a premium on data-driven storytelling, where performance analytics, audience insights, and iterative testing inform creative decisions. Marketers can deploy multiple iterations in parallel, gather engagement metrics, and quickly refine visuals to optimize impact. This data-centric capability aligns with broader marketing trends toward evidence-based creative optimization, where AI acts as a catalyst for experimentation, learning, and scale.
At the same time, the initiative raises practical questions about how to measure success and how to balance experimentation with risk management. Models, agencies, and legal teams must collaborate to define success criteria for digital-twin campaigns, including criteria for usage rights, compensation, and quality standards. Brands will need to establish governance protocols that ensure consistent, high-quality output while respecting the models’ rights and professional boundaries. The evolving process will require ongoing dialogue with talent representatives and third-party partners to ensure that the use of digital twins aligns with brand values, regulatory considerations, and ethical norms.
The campaign implications also extend to how content is distributed and consumed. AI-generated imagery can enable more diversified representation in a single brand narrative, allowing campaigns to feature a wider array of body types, skin tones, and characteristics while maintaining cohesive styling. The ethical dimension includes ensuring that digital twins do not perpetuate stereotypes or misrepresent cultural identities. Thoughtful curation, inclusive design choices, and transparent disclosure contribute to a responsible marketing strategy that respects audience expectations and fosters positive brand associations.
In practice, the use of digital twins for campaigns must be integrated into a holistic creative workflow. This workflow should respect the creative vision of designers and stylists, support the editorial direction of campaign teams, and incorporate feedback loops with models and agencies. A well-structured process will facilitate the seamless transition between AI-generated assets and traditional photography when appropriate, while also enabling the brand to maintain a consistent voice across campaigns and markets. The result can be a more agile, data-informed approach to fashion marketing that leverages AI capabilities without compromising the integrity of the design process or the human talent involved.
Transparency, watermarking, and consumer response
Transparency around AI-generated imagery is a central pillar of responsible marketing in the digital age. By watermarking AI-generated images, H&M intends to signal to consumers that certain visuals rely on digital likenesses rather than direct photographic captures of real people. Watermarking serves multiple purposes: it communicates the nature of the asset, helps protect the integrity of the brand’s content library, and invites audiences to consider the role of AI in fashion marketing. The watermark is not merely decorative; it is a tool for accountability, enabling easier differentiation between synthetic media and traditional photography.
From a consumer behavior perspective, the presence of watermarks can influence trust and engagement. Some viewers may appreciate the transparency and feel more comfortable interacting with AI-assisted content, while others might react differently depending on the context and presentation. Brands can gauge the impact of AI-generated visuals by comparing engagement metrics, conversion rates, and sentiment analysis across campaigns that incorporate digital twins versus those that rely entirely on human models. The data gathered from such experiments can inform future marketing decisions, including creative direction, messaging strategies, and audience segmentation.
The use of AI-generated imagery with watermarking also opens opportunities for experimentation with storytelling formats. For instance, marketers can explore how viewers react to behind-the-scenes narratives that reveal the existence of digital twins, or to campaigns that emphasize the fusion of technology and personal expression. These narratives can help build a unique brand voice that acknowledges the role of AI in creative processes while celebrating individual style and authenticity. The resulting content may appeal to tech-savvy audiences as well as to consumers who value transparency and ethical considerations in marketing.
However, watermarking alone does not fully address potential consumer concerns about synthetic imagery. Brands must consider broader disclosure practices, including clear labels on product pages, campaigns, and video content where synthetic assets are used. Establishing a consistent disclosure policy across channels can help prevent confusion and protect the brand’s credibility. It may also be helpful to provide education for consumers about how digital twins work and why they are used, framed in a way that aligns with the brand’s values and storytelling goals. Thoughtful communication can turn a potential skepticism into curiosity and appreciation for the technology’s role in fashion innovation.
In addition to customer-facing transparency, internal governance must ensure responsible AI use. Brands should implement robust review processes that assess not only aesthetic outcomes but also ethical implications of digital twin deployment. Questions to consider include whether the use of digital clones could inadvertently reinforce biases or stereotypes, and how to monitor and mitigate any negative social impact. Establishing clear policies and oversight mechanisms can help ensure that AI-driven creative practices are aligned with the brand’s ethical standards and social responsibilities.
The long-term success of AI-generated campaigns, including digital twin usage, depends on a positive consumer reception and ongoing dialogue with audiences. Brands should be prepared to adapt based on feedback, refine storytelling strategies, and continuously improve transparency mechanisms. By adopting a proactive and thoughtful approach to disclosure, brands like H&M can navigate the evolving landscape of AI-assisted marketing while maintaining trust with customers and sustaining brand equity in a crowded, highly competitive market.
Models’ rights, compensation, and agency roles
A central question in H&M’s digital-twin initiative is how models will be compensated for the use of their AI-generated likenesses and what rights they will retain. According to statements from H&M’s leadership, the digital twins will be owned by the models in terms of usage rights for the cloned likeness, presenting a model where the digital twin becomes a separate revenue asset. This arrangement implies that models could leverage their digital clones to negotiate deals with other brands beyond H&M, potentially unlocking additional income streams while preserving control over their digital identity.
The compensation framework for digital twins is described as a new revenue model that parallels traditional compensation for human-model usage, with decisions about usage rights and payments to be negotiated through the models’ agencies. The specifics of compensation—whether it will be ongoing royalties, one-time license fees, or a hybrid structure—remain to be finalized. The absence of a fully defined compensation scheme at this stage underscores the need for clear contractual terms that reflect the unique nature of AI-generated likeness rights, while ensuring fairness and consistency across campaigns and clients.
Model agencies play a pivotal role in negotiating terms for digital twins. They act as intermediaries in clarifying how digital clones can be exploited, the scope of permitted usages, geographic distribution, duration, and the types of campaigns in which digital twins can participate. Agencies are also responsible for safeguarding models’ professional boundaries and ensuring that digital twins are deployed in contexts that align with the models’ brand and career aspirations. This collaborative framework helps maintain ethical standards and protects the reputational value of the models involved.
From the agency and talent perspective, the emergence of digital twins invites a rethinking of contracts and career trajectories. Models may see new opportunities to monetize their likeness over longer time horizons, potentially across multiple brands and campaigns. However, this new paradigm also introduces risk—models could face pressure to license their digital clones broadly, raising concerns about overexposure or misalignment with personal career goals. Talent representatives and brands will need to strike a careful balance between maximizing the benefits of digital twins and safeguarding models’ creative control and professional development.
The regulatory and legal landscape surrounding AI-generated likeness rights is still evolving. While some jurisdictions are beginning to clarify IP and personality-rights issues, many questions remain unsettled, particularly around ownership, consent, and the transferability of rights across brands and platforms. In this context, H&M’s approach to compensating digital twins and protecting model rights could influence industry norms. Brands that adopt transparent, fair compensation frameworks and robust consent processes may set higher standards for how digital assets are treated in fashion marketing. Cooperation with industry bodies, unions, and legal experts can help establish best practices for digital-twin arrangements that are equitable and sustainable over time.
In practice, the digital twin model represents a shift in the value chain for fashion talent. It reframes the way models contribute to campaigns, not only as live performers but also as enduring digital assets with licensing potential. As the market evolves, models may increasingly negotiate terms that recognize the ongoing value of their likeness beyond individual shoots. Agencies and brands that embrace this shift with transparent agreements and protective safeguards can foster a healthier ecosystem in which talent, technology, and commerce reinforce one another. The H&M initiative thus serves as a case study in balancing opportunity with accountability, showing how digital twins can be integrated into talent-management strategies while preserving personal and professional autonomy.
Industry context: virtual models, case studies, and lessons
H&M is not alone in exploring digital twins and AI-driven fashion imagery. The industry has already seen a wave of virtual models and synthetic personas achieving prominence on social media and within brand campaigns. A notable example involves a digitally created influencer who rose to thousands of followers and collaborated with well-known brands, illustrating how AI-generated likenesses can build audience engagement and deliver marketing value without the constraints of physical travel and schedule alignment. This broader industry context highlights the potential for AI-generated models to play a substantial role in brand storytelling and audience reach, while also underscoring the need for careful governance and ethical considerations.
Case studies across fashion and retail reveal a spectrum of outcomes when AI-driven synthetic media enters the marketing mix. Some campaigns have been met with enthusiastic reception and positive business results, including improved engagement metrics, expanded creative experimentation, and heightened efficiency. Others have triggered backlash or criticism related to the perceived replacement of human talent, concerns about authenticity, or questions about the fairness of compensation for models whose digital clones contribute to multiple campaigns. These mixed outcomes underscore the importance of transparency, thoughtful messaging, and robust policy development as AI-enabled marketing becomes more pervasive.
From a business perspective, the digital-twin approach can complement existing talent models by extending the reach and flexibility of campaigns. For brands, the ability to simulate myriad scenarios—changing demographic targets, testing cultural cues, or exploring different fashion lines—offers valuable strategic insights. The sales impact of AI-driven imagery depends on how well the visuals resonate with audiences, how effectively they convey the product’s features, and how convincingly they align with the brand’s identity. The potential for cross-campaign reuse means that the initial investment in digital twins can yield long-term benefits if the assets remain consistent with the brand’s voice and quality standards.
External industry observers also note the importance of monitoring public sentiment and consumer perception as AI technologies become more integrated into marketing. Brands should be prepared to adapt based on feedback, adjusting usage guidelines, disclosure practices, and creative strategies to maintain trust and appeal. Dialogue with consumer communities, talent representatives, and industry regulators can help shape norms and expectations for AI-enabled fashion campaigns. The H&M initiative, as one of the more prominent high-street fashion examples, may influence how other brands approach digital twins and AI-assisted creativity in the near term.
The lessons drawn from real-world experiments include the importance of establishing clear consent, rights management, and compensation structures from the outset. Early efforts demonstrate that models and agencies value control over how digital likenesses are used, and brands that adopt transparent governance are better positioned to build long-term partnerships with talent. The revenue opportunities associated with digital twins—ranging from licensing to brand collaborations—require robust contract frameworks that protect the interests of models, agencies, and companies. Brands that invest in these foundations are more likely to succeed in leveraging digital twins to augment, rather than undermine, human talent.
In sum, the industry context surrounding digital twins shows a dynamic landscape where fashion brands are testing, refining, and expanding AI-enabled practices. The H&M initiative reflects a maturing approach to synthetic media in fashion marketing, balancing innovation with transparency and model rights. As other brands observe outcomes and best practices emerge, the fashion industry is likely to see more formalized standards, contract templates, and governance models that support ethical implementation and sustainable value creation for all stakeholders involved. The ongoing dialogue among brands, models, agencies, and regulators will play a pivotal role in shaping how AI-generated digital twins integrate with traditional talent ecosystems and creative workflows in the years ahead.
Risks, ethics, and workforce impact
As fashion brands experiment with digital twins and AI-generated imagery, several risk dimensions demand careful consideration. First, the potential impact on creative jobs remains a central concern. While AI can streamline production and enhance creativity, there is concern that extensive use of digital twins could reduce demand for certain kinds of live-shoot labor, including photographers, stylists, makeup artists, and other production roles. The industry must address these concerns by identifying opportunities for collaboration, retraining, and redefinition of roles rather than simply displacing talent. A constructive approach would emphasize the continued importance of human judgment, artistry, and experiential knowledge in shaping compelling campaigns, even as AI handles repetitive or scalable tasks.
Second, authenticity and trust are at stake. Consumers may scrutinize the gap between synthetic visuals and real-world representation, especially for campaigns that aim to convey emotion, personality, or cultural nuance. Brands must manage expectations and ensure that digital twins do not misrepresent real people or mislead audiences about the nature of the imagery. Transparent labeling, consistent disclosure practices, and thoughtful storytelling can help mitigate confusion and preserve trust. The risk of misalignment between a model’s real persona and their digital twin’s portrayal is an area that requires ongoing oversight and governance.
Third, there are concerns about consent and ongoing control over how a model’s likeness is used. Clear authorization processes, contract terms, and post-usage reviews with the models and agencies are essential to safeguard personal rights. The ability to revoke consent or limit future usage of a digital clone could become a critical consideration for talent management in the AI era. The industry may need to develop standard consent models that accommodate the evolving nature of AI-generated representations and licensing arrangements, ensuring models can maintain autonomy and protect their professional reputations.
Fourth, there are broader social and cultural implications associated with AI in fashion. The deployment of digital twins must consider the potential for stereotyping or the reinforcement of narrow beauty standards. Responsible creative direction requires vigilance to ensure that AI-generated imagery promotes inclusive representation and avoids perpetuating harmful biases. Brands must integrate ethical guidelines into creative processes, establishing checks and balances that prioritize fairness, equality, and social responsibility.
Fifth, data privacy and security are paramount. The collection of biometric-like data and the creation of digital avatars entail sensitive information that must be safeguarded against misuse or unauthorized replication. Robust data governance, secure storage, access controls, and audit trails are essential to prevent leakage or theft of assets. Brands should implement comprehensive security measures and incident-response plans to protect both models’ rights and corporate data.
Finally, there is the reputational risk associated with AI-driven campaigns. Public perception can be influenced by how a brand positions its use of synthetic media. If audiences view digital twins as a gimmick or as a substitute for human artistry, brand reputation could suffer. Conversely, a well-executed, transparent, and ethically grounded AI initiative can reinforce a forward-looking image and position the brand as a responsible innovator. The balancing act between innovation and social responsibility is central to the long-term viability of digital-twin programs.
In light of these risk considerations, H&M’s approach emphasizes transparency, consent, and collaboration as foundational principles. By marking AI-generated imagery, detailing usage rights, and engaging with models and agencies in governance discussions, the brand is aiming to establish a responsible model for AI in fashion marketing. Yet many questions remain about the long-term implications for the workforce, compensation models, and the broader ecosystem. The industry’s response to these questions will shape how AI-generated digital twins are perceived and adopted in the future.
Regulatory and governance considerations for AI in fashion
As AI becomes more integrated into fashion marketing and production, regulatory and governance frameworks will play a critical role in shaping industry practices. While specific regulations vary by jurisdiction, several common themes are emerging in discussions about AI-generated media. These include transparency requirements for synthetic content, disclosure standards for AI-generated imagery, consent and rights management for digital likenesses, and protections for model and talent rights. Brands that implement clear governance structures and comply with evolving standards are better positioned to navigate the legal landscape and maintain trust with consumers and talent.
One governance pillar involves rights management and licensing. The use of digital twins requires explicit, enforceable agreements that define the scope of usage, duration, geographic reach, and compensation for the digital clone. Contracts should specify whether rights are exclusive or non-exclusive, whether the brand has the right to sublicense, and how revenue sharing is allocated. As digital twins can be used across multiple campaigns and platforms, agreements must address licensing terms for various media and contexts, ensuring that models retain meaningful control and fair compensation.
Another governance dimension concerns data ethics and privacy. The capture and storage of biometric-like data must adhere to privacy regulations and best practices for data protection. Brands should implement data minimization, informed consent, and robust security protocols to protect the sensitive information used to train and refine digital twins. In addition, there should be clear guidelines on data retention, deletion, and rights of deletion to address evolving privacy expectations and regulatory requirements.
Transparency policies also demand careful consideration. Beyond watermarking, brands may pursue broader disclosure practices that inform consumers about when AI-generated imagery is used and how digital twins contribute to campaign development. Public-facing guidelines and internal workflows should emphasize responsible communication about synthetic media, enabling audiences to understand the role of AI in fashion marketing and reducing potential confusion or misinterpretation.
Industry standards and collaborations with regulatory bodies or industry groups could further shape best practices for AI in fashion. Brands that participate in ongoing dialogue about governance, ethics, and accountability can contribute to the development of widely accepted norms that benefit talent, consumers, and the broader ecosystem. Such collaboration can lead to standardized consent templates, licensing models, and disclosure frameworks that streamline adoption while preserving ethical safeguards.
At a practical level, brands implementing AI-driven campaigns should establish internal governance committees with cross-functional representation—creative, legal, privacy, communications, and talent management—to oversee digital-twin initiatives. These committees can provide ongoing oversight, assess risks, approve campaigns, and ensure alignment with brand values and regulatory obligations. They can also serve as a bridge between technology teams and non-technical stakeholders, translating technical capabilities into responsible business practices.
In summary, the regulatory and governance landscape for AI in fashion is evolving, with a growing emphasis on transparency, consent, rights management, privacy, and accountability. H&M’s approach—centered on watermarking, clear rights to digital clones, and collaboration with models and agencies—reflects an intention to implement a responsible, governance-forward model for AI-enabled marketing. As the industry continues to navigate this rapidly changing terrain, brands that engage proactively with regulators and industry peers are likely to establish a strong foundation for sustainable AI adoption that respects talent, consumers, and broader social values.
Conclusion
H&M’s strategy to deploy AI-generated digital twins of its models for product campaigns signals a significant advancement in how fashion imagery can be produced, tested, and distributed. The initiative illustrates a broader industry shift toward leveraging generative AI and digital replicas to expand creative possibilities, improve efficiency, and enable highly scalable marketing. By collaborating with Uncut and grounding the work in a transparent framework—complete with watermarking—the project seeks to balance innovation with accountability and brand integrity. The approach emphasizes that digital twins are intended to complement human models rather than replace them, preserving the brand’s human-centric philosophy while embracing technological progress.
Key considerations include the creation process, data capture, and the meticulous attention to detail required to faithfully reproduce a model’s likeness, movement, and personal characteristics. Ownership of digital clones rests with the models, while compensation discussions remain in flux, signaling a potential shift in how talent rights and revenue streams are managed in the AI era. The initiative also raises important questions about how digital twins will affect production workflows, creative processes, and the broader talent ecosystem. Agencies, brands, and models will need to navigate licensing, consent, and usage terms carefully to ensure fair compensation and ongoing collaboration.
Transparency remains a central pillar of responsible AI adoption. Watermarking AI-generated imagery and disclosing the use of digital twins can help maintain consumer trust and enable robust analysis of campaign performance. This openness supports evidence-based marketing, enabling brands to assess the relative impact of synthetic versus real imagery and to optimize campaigns accordingly. As the fashion industry tests and refines these practices, it will be essential to monitor audience responses, ensure alignment with brand values, and uphold standards of ethical marketing.
Looking ahead, the deployment of digital twins could become more widespread across campaigns, with models retaining control over their digital likeness and benefiting from new licensing opportunities. The development of robust governance frameworks will be crucial to safeguarding talent rights, guiding compensation structures, and ensuring responsible AI use. The H&M initiative provides a compelling case study of how a major brand can navigate the complexities of AI-enabled marketing—balancing efficiency, creative exploration, and human-centered values while establishing a path forward for the broader fashion industry. The coming years will reveal how digital twins evolve, how audiences respond to synthetic imagery, and how brands scale AI-driven campaigns in ways that respect talent and deliver meaningful value to consumers.