Text-based support was once sufficient. By 2026, customers will expect help through voice, video, images, and chat, often all within a single conversation.
For a long time, omnichannel support was the main goal.
Email, chat, phone, and social messages all came into one system. Context followed the customer, and agents could see the conversation history. This was a big improvement over separate channels.
But omnichannel still relies on one idea:
It assumes that text is the main way customers explain their problems. This is no longer the case.
Customers now try to:
Omnichannel connects where conversations happen.
Multimodal support changes how problems are explained and solved. Many CX teams are beginning to notice this gap.
Multimodal customer support lets customers use different types of input in the same support experience.
That includes:
The main difference is flexibility.
Customers do not have to turn a visual problem into text. They can simply show it. They do not need long explanations if a voice note or quick call is easier. This is why the difference between omnichannel and multimodal support matters.
Omnichannel means connected channels. Multimodal means richer communication within those channels. In practice, multimodal support makes things easier because customers don't have to work around support limitations.
Text works well for simple, repeatable problems. But customers often switch to video or voice when:
This is why video support in customer service keeps coming up in CX conversations. A 20-second screen recording can replace ten back-and-forth messages. A short voice message often gives agents context that text cannot provide.
Customers do not want to use video or voice for every issue. They want these options when it makes things simpler.
Multimodal support only works if systems can understand more than text alone. This is where AI has advanced quickly.
Modern AI can now:
This is especially important for AI voice and chat support, where AI does more than route requests; it helps interpret them.
Instead of asking follow-up questions like “Can you explain what you’re seeing?”, AI can add context, summarise issues, and guide the next step. Platforms like Zendesk are already moving in this direction by combining AI with shared customer context across channels.
You can see how this fits into broader CX tooling on the Zendesk AI overview page.
Multimodal support is not just a theory. Teams are already using it in practical ways.
Common examples include:
In B2B support, multimodal input especially reduces time-to-resolution because customers do not need to simplify complex problems.
This is how multimodal customer support directly improves resolution speed and customer confidence.
Most teams do not need to rebuild their CX systems.
Preparing usually starts with a few basics:
The biggest mistake teams make is treating multimodal support as just a new feature instead of a workflow change.
The goal is not to add more channels. It is to reduce misunderstandings.
If you’re already using a central support platform, multimodal capabilities often layer on top of what you have.
Multimodal support is one of several changes that are reshaping customer experience.
It is closely connected to other 2026 trends such as AI-led resolution, smarter self-service, and fewer handoffs. We have brought these ideas together in our CX Trends 2026 report, which includes practical examples and advice for CX leaders.
👉 Download the CX Trends 2026 PDF
As a Zendesk Premier Partner, Gravity CX works with teams to apply these changes in real support environments.
Customers do not think about channels. They think in problems.
Multimodal support meets customers where they are by using the fastest and clearest way to explain what is wrong.
By 2026, text-only CX will not feel simple. It will feel limiting instead.
Multimodal customer support allows customers to use text, voice, images, and video together to explain issues and get help.
Omnichannel connects support channels. Multimodal support focuses on multiple input types within those channels, like voice, images, and video.
Video helps explain visual or complex issues faster, reducing back-and-forth and misunderstandings.
AI can analyse images, transcribe voice, detect tone, and summarise context before a human agent gets involved.
No. Many teams can start by allowing image uploads, voice context, and shared history without major system changes.