Synthetic media in the enterprise: ethics, quality, and use cases

A new hire sits through onboarding for the third week running and still freezes the first time a real customer pushes back. A patient hangs up after intake without mentioning the symptom that actually worried them. These are the conversations that decide whether someone trusts a company, and they rarely scale with people alone. 

Synthetic media is moving into enterprise workflows to help carry those moments through video, audio, images, and text that have been significantly altered or generated by algorithms. That shows up in patient intake, compliance training, customer support, and localization, where a slide deck, a static page, or voice alone often is not enough.

Synthetic media defined for the enterprise

Synthetic media spans four primary modalities: generated video, generated audio and voice synthesis, generated images, and generated text. Each modality carries a distinct risk profile and a distinct set of enterprise applications, from AI-narrated training content to dynamically localized marketing campaigns. The NIST definition in NIST AI 100-4 stays technology-agnostic, covering outputs from diffusion models, large language models (LLMs), and generative adversarial networks alike.

Synthetic media, generative AI, and deceptive synthetic media sit in a nested hierarchy. Generative AI is the technology class. Synthetic media describes the consumable outputs of that technology. Deceptive synthetic media is the subset defined by the impersonation of a specific real individual with the intent to mislead.

For product leaders, the practical test is whether the technology can represent a brand with enough quality, reliability, and care. AI humans, the most advanced expression of enterprise synthetic media, now hold live video conversations with customers and employees: seeing, hearing, understanding context, and responding in real time. What separates a deployment that earns trust from one that erodes it is presence, the felt sense that the person on the other end is actually paying attention. Poor deployments miss it, creating uncanny interactions. Stronger ones hold it and help teams carry high-value conversations without scaling headcount.

Synthetic media versus deceptive synthetic media

The distinction is legal as well as technical. The EU AI Act defines deceptive synthetic media as content that resembles real persons, objects, places, entities, or events and would appear to a person to be authentic, per Article 3. An AI-generated spokesperson delivering branded training content is synthetic media. A cloned executive voice authorizing a fraudulent wire transfer is AI-generated impersonation content. Same underlying technology, opposite intent, very different legal exposure.

Synthetic media adoption is accelerating in business

Enterprise AI adoption continued to climb in 2025, particularly in user-facing applications. By 2027, 40% of generative AI systems will be multimodal, up from 1% in 2023, according to Gartner.

Personalization and localization are driving much of that growth. Marketing and sales rank among the top AI revenue-generating functions, and generative AI can lift marketing productivity by an estimated 5 to 15 percent of total spend, according to McKinsey research. For global enterprises managing dozens of markets and languages, synthetic media can support localized content production across those markets within a single content system.

The quality problem: realism, consistency, and trust

Growing adoption raises the stakes on a question most buyers underestimate: what makes a deployment good enough to trust. Presence is the bar, and it is harder to clear than realism. As an artificial human approaches a realistic appearance, small imperfections trigger discomfort and reduce acceptance, a pattern documented across ACM-indexed research. 

A common failure point is mismatch across modalities: a photorealistic face paired with an unnatural voice, or natural speech paired with rigid gesture, breaks the experience more sharply than a uniformly stylized presentation would. The same body of work found that virtual humans using neural text-to-speech were rated significantly less trustworthy than those using human speech.

Pre-rendered versus real-time

The realism challenge sharpens in live settings. Pre-rendered content can use computationally expensive diffusion models for maximum visual fidelity because nothing is waiting on the result. Real-time conversational systems must generate responsive behavior within milliseconds while the person is still talking.

In live conversation, presence determines quality. The interaction has to feel attentive. The AI human needs to nod while you are speaking, hold the floor open when you pause to think, and adjust its tone when you sound confused. That is a full conversational problem, not a rendering problem, and it is the gap most synthetic media never closes.

How a full conversational stack closes the gap

Some platforms now package those capabilities as an integrated system for real-time conversations. Tavus, the human computing company, builds full-stack AI humans that see, hear, understand, and respond. The work is split across a behavioral stack rather than a single model.

Sparrow-1 governs conversational flow and timing. Raven-1 fuses audio and visual signals into a unified understanding of the user's state. The LLM layer reasons about what to say next, drawing on the platform's Knowledge Base for grounded answers. Phoenix-4 renders emotionally responsive facial behavior.

Picture a compliance training session. Raven-1 fuses an adjuster's hesitant vocal tone with their averted gaze, catching the mismatch between stated confidence and real uncertainty. That perception feeds the LLM layer, which decides to probe deeper. Phoenix-4 renders an attentive, patient expression, all within sub-second latency. The person on the other end never sees the stack. They feel an interaction that holds together. With that bar in mind, the question becomes where it earns its keep.

Enterprise use cases for synthetic media

The use cases gaining the most traction share a shape: high-volume conversations where quality matters and human availability constrains delivery. Four areas are furthest along.

• Learning and development is the most validated vertical. AI-driven roleplay, coaching, and compliance practice let teams rehearse difficult conversations at a volume that live trainers cannot match.
• Customer support and conversational interfaces represent a high-volume opportunity. Large organizations are extending AI across support workflows and into direct customer communication.
• Sales communications are gaining attention in financial services, where vendors are deploying AI-driven video and conversational tools for client-facing roles.
• Marketing localization uses AI-generated video to produce market-specific creative more quickly across regions.

In each case, the presence bar from the previous section decides whether scaling the interaction helps or backfires. AI video agents become especially relevant when text and voice alone fall short, particularly in conversations that require visual presence.

Ethical considerations and responsible use

The same technology that powers a trustworthy AI human can power the deceptive impersonation described earlier, so responsible use is part of the deployment decision, not a separate concern.

Consent and likeness rights

Consent and likeness rights sit at the foundation. In the U.S., right-of-publicity protections vary by state with no federal standard. Tennessee's ELVIS Act grants every individual a property right in their voice, including its simulations. By 2024, 24 states had passed regulations targeting deceptive synthetic media, according to Stanford HAI's AI Index.

Provenance and disclosure

Provenance and disclosure are maturing through the Coalition for Content Provenance and Authenticity (C2PA). Content Credentials work like a nutrition label for digital content: they cryptographically record who produced content, when, and which tools were used. C2PA's own guidance flags "Made with AI" as insufficient because it does not clarify whether content is fully or partially generated.

Controls engineered in before deployment

Synthetic media also makes it easier for bad actors to challenge authentic evidence as fabricated. Proactive disclosure and provenance infrastructure protect against both directions of that risk. Those governance needs have to be built into the system before deployment, not bolted on afterward. On the Tavus platform, Objectives and Guardrails address this at the infrastructure level: compliance boundaries are built directly into the Conversational Video Interface (CVI) before deployment. Guardrails provide governance controls and content moderation for real-time AI human interactions.

Governance and compliance for synthetic media programs

Regulation is tightening. The EU AI Act's Article 50 sets out transparency rules, including disclosure obligations for AI-generated or manipulated content, with the obligations becoming enforceable on August 2, 2026, and penalties reaching the higher of significant fixed fines or a share of global turnover. California's SB 942 carries penalties of $5,000 per violation, with each day of noncompliance treated as a separate violation.

Article 50 reaches ordinary commercial uses, including marketing content, training materials, and customer engagement, wherever an average person could reasonably believe the content to be authentic. Internal governance tends to follow a three-lines-of-defense model: engineering builds responsibly, risk and compliance set policy, and internal audit provides independent assurance. Enterprises operating across multiple jurisdictions generally align with the strictest applicable standard to simplify compliance. Meeting that standard on the ground depends on what a platform can actually enforce, which turns the regulatory picture into a procurement checklist.

Evaluating a synthetic media platform

Enterprise procurement requires evaluation across dimensions specific to synthetic media, beyond those used in standard SaaS vendor assessments. Three criteria should anchor any evaluation.

• Security certifications: SOC 2 Type II, not Type I, attesting that controls operated effectively over a sustained period. HIPAA with a signed Business Associate Agreement for any deployment touching protected health information.
• Data residency and model training exposure: Verify whether user-submitted assets, including voice samples and likenesses, are used to train the vendor's models. Address this through an explicit clause in the executed data processing agreement.
• Content authenticity controls: C2PA compliance, watermarking, provenance metadata, and disclosure labels are enforced at the platform level before distribution.

Guardrails without technical enforcement in the platform itself do not satisfy enterprise compliance requirements. Security certifications, training data clauses and authenticity controls help teams distinguish a demo from infrastructure capable of supporting an enterprise deployment.

Viewed through that procurement lens, Tavus addresses several of these criteria through its CVI infrastructure: SOC 2 and HIPAA compliance on enterprise plans, built-in consent mechanisms for Custom Replicas, white-label capability so AI humans carry the customer's brand, and bring-your-own-LLM flexibility through OpenAI-compatible endpoints.

The platform also includes components built for grounded, stateful conversations. Knowledge Base, its proprietary retrieval-augmented generation (RAG) model, grounds responses in the customer's own data at roughly 30ms retrieval speed. Memory and Evolution retain context across sessions, so a returning employee in a training program picks up where they left off. Objectives define measurable completion criteria, such as confirming a client understands the fee structure, so every conversation has a trackable outcome. Those capabilities matter most when a team is evaluating infrastructure for deployment, not a polished demo.

The trajectory for enterprise synthetic media

Task-specific AI agents are forecast to be embedded in 40% of enterprise applications by the end of 2026. At the same time, more than 40% of agentic AI projects are expected to be canceled by 2027, undone by escalating costs and unclear business value. Adoption can expand even as weak projects are still canceled; both happen at once.

The deployments that last create a presence in the conversation. A compliance trainee rehearsing a difficult client call needs the AI human to hold the floor open when they stumble over their words, to register their frustration without judgment, and to remember next week that this particular scenario was hard for them.

In that moment, the model architecture is not what matters. What matters is whether the person felt heard, whether the response met them with care, and whether the conversation held together when it counted. Enterprises have conversations that matter and cannot always scale them with people alone. The enduring test is still human: whether the interaction feels attentive, careful, and worth trusting. See it for yourself. Book a demo.