Quick answer: What is Gemini Omni and Veo 4?
Veo 4 has not been officially announced by Google yet, but the timeline from Veo 3 to Veo 3.1 suggests that Google is moving quickly toward AI video models with better audio, stronger visual consistency, more control, and more reliable scene generation. Gemini Omni matters because it points to a broader future where AI video isn’t just text-to-video, but a multimodal, conversational creation process that can use text, images, video, and audio as references. For businesses, the main takeaway is simple: better video models will make AI-generated clips more useful, but teams will still need a complete workflow to plan, edit, review, brand, collaborate on, and share finished videos. That’s where platforms like Visla remain valuable, especially as next-generation models become part of everyday video production.
Is Veo 4 coming?
As of May 2026, Google has not officially announced Veo 4. The most current official Veo model is Veo 3.1, which Google describes as its leading video generation model. We can talk about what Veo 4 might reasonably improve, but we don’t yet have release details, features, pricing, or availability.
Still, the timeline gives us useful context.
Google introduced Veo in 2024 as a major text-to-video model. Veo 3 followed in 2025 with a major step forward: native audio. Instead of creating silent clips that needed separate sound design, Veo 3 could generate sound effects, ambient noise, and dialogue as part of the video. That was a meaningful shift because audio isn’t a finishing detail for business video. It affects comprehension, tone, pacing, and whether a clip feels usable.
Then Veo 3.1 arrived in October 2025. Google positioned it around richer native audio, better narrative control, improved image-to-video generation, stronger prompt adherence, and better character consistency across multiple scenes. Google also added capabilities like reference images, scene extension, and first-and-last-frame control.
That progression tells us something. Veo isn’t just getting more realistic. It’s getting more controllable.
If Veo 4 follows the same direction, the most important improvements for businesses probably won’t be abstract model benchmarks. They’ll be practical gains: more consistent characters, better products and environments, fewer visual surprises, stronger motion control, and more reliable audio.
What Gemini Omni suggests about Veo 4 and Google’s AI video strategy
Gemini Omni isn’t Veo 4. Let’s be clear on that. It’s different Google model family built around multimodal creation and editing.
Veo 3.1 is Google’s specialized cinematic video generation model. Gemini Omni Flash points to something broader: AI video that can work from many kinds of inputs, including text, images, existing video, and audio. Instead of only asking a model to generate a clip from a prompt, users can start with source material, make changes in plain language, use references, and build toward a more coherent final output.
Google also says Omni combines Gemini’s reasoning and world knowledge with generative media models, which is why the company frames it around coherent scene editing, physical logic, and reference-based control. That makes Omni important to a Veo 4 conversation because it shows where Google may be taking AI video overall: away from isolated clip generation and toward editable, multimodal video creation.
For businesses, that may be the more important shift. Teams rarely start with a perfect text prompt. They start with product pages, decks, PDFs, scripts, screenshots, recordings, brand assets, and existing footage. A model that can understand and work across those inputs is much closer to how business video actually gets made.
So Omni does not prove what Veo 4 will include. But it does show Google’s larger direction: AI video is moving from one-shot clip generation toward multimodal, editable, reference-aware creation.
Competition in the AI video space that Google might worry about
Google isn’t developing Veo in a vacuum. The AI video market is moving quickly, and competition is pushing every major player toward better quality, more control, and easier workflows.
OpenAI’s Sora 2 raised the stakes around physical realism, controllability, synchronized audio, and the ability to include real people through consent-based likeness features. Even though Sora itself has been discontinued, the model showed how quickly expectations for AI video can rise. Once users see more realistic motion, dialogue, and physics, they start expecting those capabilities everywhere.
Runway has also been important, especially with Gen-4. Runway has focused heavily on world consistency, including consistent characters, objects, locations, styles, and cinematic elements across scenes. That’s directly relevant to business video because companies do not just need beautiful visuals. They need a product, spokesperson, environment, or brand style to stay stable.
Adobe Firefly takes a different angle. Its value proposition isn’t only generation quality, but commercial safety, creative controls, and integration with the Adobe ecosystem. That’s important because many companies care as much about risk and production workflow as they do about model quality.
Taken together, the competitive landscape is pushing AI video in a clear direction. The winning tools will not just create better clips. They will help users control the output, keep assets consistent, reduce legal and brand risk, and fit AI generation into real production workflows.
That’s exactly why a future Veo 4 would matter. It wouldn’t only compete with other models on realism. It would compete on whether it can become a dependable ingredient inside business video systems.
How businesses are using AI video models now
Businesses are already using AI video, but usually in practical ways. The clearest use cases aren’t full cinematic films. They’re repeatable business content where speed, scale, and easy updates matter.
Marketing teams are using generative AI to create video ads, campaign variations, ecommerce visuals, product content, and social media assets. This is one of the strongest documented areas of AI video adoption. Advertisers want more creative versions for different audiences, channels, formats, and contexts. AI helps teams produce more variations without starting from scratch every time.
L&D teams are using AI video for training, onboarding, compliance, process education, and internal knowledge sharing. This makes sense because training content changes constantly. Processes update, policies shift, products evolve, and teams need clear materials that can be refreshed without a full production cycle.
Communications teams are using AI video for company updates, project summaries, executive messages, and meeting replacements. This is less flashy than AI-generated ads, but it may be one of the most durable business uses. A short video can often explain context better than a long email or meeting invite.
Customer success and support teams are using AI video to turn help articles, onboarding flows, support documentation, and knowledge-base content into short videos. This helps customers and support agents understand processes faster.
Sales teams are beginning to use AI video for client communication, product explainers, account updates, and expert-led content. The strongest evidence here isn’t mass sales prospecting yet. It’s client-facing communication where experts don’t have time to record every message manually.
Where business teams are using AI video today
AI video adoption is showing up most clearly where teams need speed, scale, repeatability, and easier updates. Bubble size represents the strength of available evidence, not exact adoption rate.
| Team | Campaign videos and ads | Training and onboarding | Internal updates | Customer education and support | Sales and client communication | Docs and process to video |
|---|---|---|---|---|---|---|
| Marketing | 3 | 1 | 1 | 1 | 2 | 2 |
| L&D | 1 | 3 | 2 | 2 | 1 | 3 |
| Comms | 1 | 2 | 3 | 1 | 1 | 2 |
| Customer Success / Support | 1 | 2 | 1 | 3 | 2 | 3 |
| Sales | 2 | 2 | 1 | 1 | 2 | 1 |
Marketing has the strongest survey-backed signal.
AI video is most clearly documented in ad creative, campaign variation, social content, and ecommerce marketing assets.
Training and support are strong operational uses.
L&D and customer teams use AI video where content needs to be repeatable, searchable, updatable, and easier to understand.
Comms and sales are growing, but less quantified.
Internal updates, meeting recaps, client videos, and expert-led explainers are emerging uses, mostly supported by public examples.
Why businesses need more than a better AI video model
A better model solves one problem: the raw generation gets better. That matters, but it isn’t the whole job.
Business video has more requirements. A team needs the message to be accurate. The video has to match the brand. Stakeholders need to review it. Legal or compliance teams may need to approve it. The script may need edits. The format may need to work for LinkedIn, an LMS, a product page, an email, or a help center. Someone may need to replace one scene next month without rebuilding the entire video.
That’s why model quality alone ins’t enough.
The difference between an impressive AI clip and a useful business video is the workflow around it. Without that workflow, teams can end up with beautiful fragments that are hard to revise, hard to approve, and hard to connect to a real business goal.
What complete AI video workflows should include
A complete AI video workflow should help teams move from source material to finished video with control at every stage.
It should support many inputs, including prompts, scripts, webpages, PDFs, slide decks, recordings, audio, images, and existing footage. It should turn those inputs into a structured video plan. It should let users control scenes, visuals, voiceover, captions, music, aspect ratio, branding, and pacing. It should make editing simple after generation, not force users to regenerate everything. It should support collaboration, approvals, sharing, and versioning.
Most importantly, it should help teams decide what should be generated, what should be recorded, what should use stock footage, what should use an avatar, and what should be edited from existing media.
That mix matters. The future of business video will not be 100 percent AI-generated footage. It will be hybrid production, with AI helping teams choose the fastest and most appropriate path for each message.
How Visla brings next-gen models into real video production
This is where Visla’s role becomes important.
Visla isn’t just a place to generate one single AI video clip. It’s a start-to-finish video production platform for recording, creation, editing, collaboration, and sharing. That makes the platform more valuable as models improve, not less.
As models like Veo improve, Visla can bring stronger generation quality into workflows teams already need. A marketing team might start with a campaign brief and generate a video draft. An L&D team might start with an SOP or slide deck and turn it into a training video. A customer success team might turn a help article into a customer-facing tutorial. A sales team might turn a product explanation into a polished, shareable video.
AI Director Mode is especially relevant in this next era. Instead of jumping straight into random generation, teams can plan a video scene by scene, define characters, objects, environments, and brand elements, then decide which scenes should become AI-generated clips. That matters because better models still need direction.
Visla’s value isn’t only that it can use powerful AI models. It’s that it wraps them in a workflow designed for business output. That’s why when Veo 4 releases, Visla will become even better.
What teams should do now to prepare
The best way to prepare for Veo 4, Gemini Omni, and the next wave of AI video isn’t to wait for a model announcement. It’s to get your video workflow ready.
First, organize your inputs. Gather brand assets, logos, product screenshots, approved images, voice guidelines, intro and outro templates, and example videos that show the style you want.
Second, define your repeatable video types. Marketing might need product launch videos, campaign variants, social ads, and demo clips. L&D might need onboarding, SOPs, compliance refreshers, and process explainers. Customer success might need tutorials, troubleshooting videos, and knowledge-base explainers.
Third, create a review process. Decide who approves accuracy, who checks brand, who reviews compliance-sensitive topics, and who owns the final publishing decision.
Finally, treat AI video as a workflow change, not just a new content button. The teams that benefit most will be the ones that know what they want to make, how it should sound, who needs to review it, and where it will be used.
Veo 4 may bring a major step forward when it arrives. Gemini Omni already shows that AI video is becoming more multimodal, editable, and reference-aware. But the real business opportunity is bigger than any single model. The winners will be teams that pair better AI generation with better planning, editing, collaboration, and distribution.
FAQ
No. As of May 2026, Google has not officially announced Veo 4. Google’s current public Veo page identifies Veo 3.1 as its leading video generation model, and Google’s developer blog says Veo 3.1 was released on Oct. 15, 2025 with richer native audio, better image-to-video generation, improved prompt adherence, greater narrative control, and stronger character consistency across scenes. A future Veo 4 would likely build on that direction, but any specific Veo 4 features, release date, or pricing should be treated as unconfirmed until Google announces them.
Gemini Omni is Google’s multimodal creation and editing model family, starting with video through Gemini Omni Flash. Unlike a basic text-to-video tool, Omni can use different references, including image, text, video, and audio, to create or revise a single cohesive output. Google describes it as combining Gemini’s reasoning with generative media models, with natural language editing, stronger world understanding, precise video editing, and better character consistency across scenes. Veo is best understood as Google’s specialized cinematic video generation model, while Gemini Omni points toward a broader workflow where users can generate, edit, and refine video from multiple input types.
Better AI video models can generate higher-quality clips, but businesses still need a full workflow to turn those clips into usable videos. Teams need scripts, brand assets, captions, voiceover, review steps, approvals, scene-level edits, collaboration, sharing, and ways to update videos after they’re published. Veo 3.1’s progress around audio, narrative control, reference images, scene extension, and character consistency shows that models are becoming more useful for production, but business teams still need a platform layer that helps them plan, edit, brand, approve, and distribute finished videos.
May Horiuchi
May is a Content Specialist and AI Expert for Visla. She is an in-house expert on anything Visla and loves testing out different AI tools to figure out which ones are actually helpful and useful for content creators, businesses, and organizations.