Video has always carried a quiet imbalance. While it often delivers information with visual depth, tonal nuance, and narrative clarity, it does so on fixed terms. The burden of interpretation sits with the viewer. No matter how sophisticated the surrounding ecosystem of recommendation systems, autoplay loops, or short-form formats, the underlying interaction has not changed. You press play, watch, and leave.

However, the rise in AI innovation is beginning to overturn that model. As systems embed AI across digital products, they introduce the ability to respond, clarify, and adapt in real time. Text has already undergone this shift through conversational AI. Video, until recently, remained the exception.

At the consumption layer, AI is turning video into something closer to a dialogue, where viewers can interrogate content, request context, and reshape the flow of information as they engage with it. At the production layer, AI is compressing and reconfiguring the creative process itself, replicating capabilities that once required full-scale studios—camera systems, editing workflows, visual effects—and integrating them into programmable, iterative pipelines.

Video is now moving beyond its role as a delivery format and beginning to function as an operational layer — one where interaction, creation, and feedback are tightly coupled.

New York–based video creation and real-time interaction technology company D-ID is tackling that constraint by reengineering how video behaves at its core. The company is introducing what it calls “ Agentic Videos ,” embedding a real-time AI agent directly into the viewing experience. The agent sits within the video layer itself—anchored to the content, aware of its context, and designed to respond as part of the experience rather than alongside it.

A viewer can interrupt at any moment and ask a question. The agent processes the query in real time, drawing from the video’s script and connected knowledge sources to generate responses that remain accurate and aligned with the original message. The interaction does not end when the video finishes; the agent persists, allowing the viewer to continue exploring the topic beyond playback. That seemingly simple shift changes the structure of the experience. The video no longer dictates a fixed sequence of information—the viewer does.

“The instinct of a creator is always to protect the narrative arc. The agent doesn't interrupt the story; it extends it. The interaction layer activates when the viewer chooses it - a question mid-video, or a conversation that continues after it ends. So the creator's intent is preserved, and the viewer's need for clarity is also met,” Gil Perry, co-founder and CEO of D-ID, told me. “In practice, what we're seeing is that the questions viewers ask reveal where the narrative wasn't landing, which is actually invaluable feedback for creators.”

Converting Video Playback Into Engagement Engines

The system builds on D-ID’s V4 expressive visual agents, which pair sub-second latency with human-like avatars capable of natural, real-time conversation. In this model, the avatar is no longer just the presenter—it becomes the interface itself. Perry said the real shift is not only technological but conceptual. For years, success in video has been measured through views and completion rates—metrics that say little about whether the content actually resonated, influenced understanding, or prompted action. “The presenter inside the video can now actually respond and use the arising questions as a hook to deepen early-level interest.”

D-ID argues that the disconnect is already visible at scale. Enterprises spend millions annually on video-based communication, yet engagement remains structurally broken. Comprehension and retention are inconsistent, and even short-form video often captures only fragmented attention. Perry realized that video’s one-directional nature felt like a structural limitation.

D-ID’s push into agentic video aims to close that gap by reframing video as a responsive system—one where interaction ultimately drives impact. The shift is already resonating with large enterprise customers experimenting with interactive, avatar-led engagement, including Tata Group and Microsoft. Moreover, the company has also introduced a new analytics layer that captures what users ask and where they engage, transforming video into a queryable, data-generating system rather than a static asset.

“What you're capturing is intent, not just behavior. A viewer who asks 'does this integrate with my CRM system?' has told you something qualitatively different from a viewer who watched 87% of a video. That's a buying signal, a readiness signal, a confusion signal - depending on when in the experience it appears and what came before it,” said Perry. “Agentic videos can consolidate intent signals across all viewer interactions, group them by theme, sentiment, and moment in the experience, and surface patterns that would otherwise be invisible. You're no longer guessing what resonates, you're reading it directly from the questions people couldn't help but ask.”

The Interactive AI Avatar Market Is Heating Up

The interactive AI avatar market has grown markedly more competitive in 2026, as both startups and platform giants converge on what is quickly becoming a core enterprise layer. A study by Precedence Research projects the AI avatar market is expected to reach approximately $142 billion by 2034, expanding at a 31.95% CAGR. It identifies D-ID, HeyGen, Synthesia, DeepBrain AI, Soul Machines, UneeQ, and Microsoft among the leading enterprise companies shaping the category.

Competition is fragmenting along functional lines. Companies such as Tavus, HeyGen, and DeepBrain AI are advancing real-time, conversational avatars designed for live interaction, while Synthesia continues to dominate scripted, enterprise-grade video production. Each of these approaches captures a different layer of the content stack. Likewise, larger platform players such as Microsoft and NVIDIA are increasing investment in digital human and AI infrastructure, signaling that the category is moving from niche to foundational.

DeepBrain AI comes closest to D-ID in narrative, pushing real-time AI video agents into enterprise environments across financial services and large organizations. Still, its framing centers on the avatar as an interactive assistant, rather than redefining video itself as an interactive medium. Other players differentiate along narrower dimensions. Beyond Presence emphasizes rendering fidelity and low latency, while Life Inside focuses on authenticity and analytics, combining real employee footage with conversational AI to extract engagement insights.

D-ID’s key differentiator is collapsing these modes into a single, continuous experience. Its “watch-to-interact” continuity, where presenter and agent are the same, eliminates the traditional handoff between content and chatbot, creating a more cohesive and context-aware experience. The positioning is reinforced by D-ID’s integration with simpleshow following its 2025 acquisition, embedding the product directly into enterprise training, internal communications, and customer education workflows, an advantage that API-first competitors often lack.

“We are entering a moment where the interaction layer becomes the most strategically significant, because it's where intent is expressed and decisions are made.,” said Perry. “The upside is people get information that actually maps to their situation. The risk is that the same capability can be used to narrow rather than expand understanding. That's the obligation that comes with building the interface layer.”

Rebuilding The Video Production Model

While D-ID focuses on interaction, Higgsfield AI is reworking the creation side—how video is produced, distributed, and tested. The agentic and generative AI-powered video platform, which gained early traction on Instagram and TikTok last year, integrates multiple generative models—both proprietary and third-party, including Sora, Veo, Kling, WAN, and Seedance, into a single workflow. Within that system, users can control camera movement, lensing, shot composition, color grading, and character consistency in one place.

Alex Mashrabov, co-founder and CEO of Higgsfield, said that the gap between creation and audience feedback is collapsing, thanks to AI. “The interface layer is where that abstraction becomes real for users and where most AI video platforms have fundamentally underinvested. The prevailing design assumption in this space has been: expose model capabilities, let sophisticated users figure out the workflow. We’ve taken the opposite position,” he told me. “40% of the Higgsfield team are filmmakers, producers, and creatives, who define our product roadmap and work side by side with our ML engineers in a constant feedback loop.” Mashrabov revealed that the platform’s AI-powered reasoning engine collects real preference signals from actual user generations, over 700 million to date. “Over time, that lets us fine-tune and optimize for specific creative use cases in ways generic providers simply can’t replicate. That feedback loop between production behavior and model performance is the deepest moat in this space, but it takes time to accumulate and compound,” he said.

The platform’s level of control aims to address a persistent issue of inconsistency in AI video tools. By introducing more deterministic workflows and persistent character systems, the platform is helping creators move closer to repeatable, production-grade output. Moreover, through its “Original Series” initiative, Higgsfield has introduced a crowdsourced model for content development. Instead of relying on internal greenlighting, the platform allows audiences to watch pilot concepts and decide which ones move forward. Creators generate ideas, audiences evaluate them, and the strongest concepts advance to further production and distribution.

“What’s emerging is directorial intelligence, the ability to hold a complete creative vision, decomposing it across character, tone, optics, pacing, world-building, and executing it with precision using these (AI-powered) tools. In some ways, it’s more demanding because the abstraction layer removes the excuse of technical limitation. You can’t blame the budget or the equipment anymore. The work is a direct expression of your creative judgment,” said Mashrabov.

Within a year of launch, the platform claims it has expanded across more than 240 regions, reaching a $300 million annual run rate. “When you have 24 million users generating 5 million videos per day, the scarcity that once defined creative value - production access, technical skill, distribution reach - has effectively dissolved,” Mashrabov revealed. “The same platform that a solo filmmaker uses to build an original series pilot is what a Fortune 500 marketing team uses to produce campaign content at scale. The same tooling serves both.”

When Interaction And Creation Meet

Viewed together, D-ID and Higgsfield represent two sides of the same transformation. D-ID redefines how users engage with video, turning it into an interactive interface, while platforms like Higgsfield are turning video generation into a programmable system that evolves based on data and feedback.

As video becomes more adaptive, it also introduces new questions around accuracy, transparency, and control. Ensuring that responses remain grounded in verified content becomes critical. Making the logic behind those responses visible, through citations, assumptions, or validation layers, becomes equally important. D-ID addresses part of this challenge by anchoring responses in the original script and controlled knowledge sources.

“Responses are anchored in the video script first, so the agent isn't free-associating. External knowledge sources are additive, not primary. It's more like a subject matter expert who has studied a specific document deeply and can reference broader context when needed,” said Perry. “No system eliminates drift entirely, but the architecture is designed so that creators can deliberately add the relevant additional information as knowledge, and therefore set boundaries, allow broader context, or narrower limits.”

The transformation underway is less about improving video and more about repositioning it within the digital stack. As AI integrates across both consumption and creation layers, video is quietly beginning to operate as a living system—responsive, adaptive, and continuously evolving.