Here’s a content problem for your marketing team, and a potential solution. 78% of marketers don’t have enough customized content to meet their needs. Multimodal AI can fill the gap by bringing different formats of content together in one digital workspace, combining text, images, audio, and video. But another transformation needs equal time. When brands leverage AI to customize experiences for customers at the point of contact, it makes little difference how AI gets there what it makes and when it arrives are equally important. Cloud-first approaches are what we’re comfortable with, but edge inference is a new way of thinking about speed, agility and privacy. Here’s how marketers can leverage both.
What Multimodal AI Means for Martech?
AI models that are able to understand, interpret and produce, multiple different formats/types of data/information such as text, images, audio and video. Multimodal AI delivers contextual customer experiences by combining and leveraging content from multiple formats rather than working in silos, with the focus still on text.
What this means for marketers is that campaign creation can become more streamlined. You could have a text-based creative writer create your ad headline, then have a second platform do your image selection, video editing, etc. Multimodal can start to integrate this process so you can write your ad copy, align it with an image, create a voice-over, and much more.
The power is in the interaction, and the different combinations. A campaign might have a common creative theme across a social media clip, a banner, and a radio spot, and reshuffle the format between channels. However, there is an element of human oversight that is necessary in order to achieve consistency across various platforms by ensuring good information about the brand and the customer, and clear instructions on what you would like to communicate. The role of AI here is to assist with implementation.
The Four Pillars of Multimodal AI in Marketing
Copy, images, audio and video have a distinct function. The true power of multimodal AI in marketing is at the intersection of these resources, where marketing teams can generate creative assets that communicate the same message across multiple formats and channels.
Text and image is often the foundation for many campaigns. AI can generate a headline, product description and ad copy, and match these with appropriate images. Marketers can experiment with variations without having to create all versions from scratch, but the words and pictures still need to support the position of the brand and the product.
Audio and video go into this a notch. Google announced on the earnings call that there were close to 70 million assets created through Gemini for advertising during Q4 2025 that were used in AI Max and Performance Max campaigns, indicating the incredible volume at which AI-assisted creative production is being utilized.
However, volume by itself does not make a campaign. It’s these four skills that make a campaign stand out by working in tandem to produce a message that is crystal clear, targeted and relevant to the channel.
Also Read: How Brands Are Engineering Their Way into AI Answers (and Winning Demand)
Why Marketers Are Moving Inference from Cloud to Edge
Cloud AI has democratized powerful models – but there is still some catch. Each time a request requires a remote server, a latency penalty can be added; each time new infrastructure is built, a cost penalty can be added; and to preserve privacy, data may be routed away from user devices. These tradeoffs can constrain how much personalization a campaign can bring to bear.
This is another alternative. Rather than sending everything up to a cloud server, the marketer could utilize an AI model operating on a smartphone, desktop, or even your browser to perform more relevant tasks directly. This could be any device that is able to personalize content preferences or determine the local context without sending everything up to a cloud server. The benefit will vary based on the model, device, and task.
Apple’s AI model is a good demonstration of this hybrid setup. Apple’s on-device Foundation Models will utilize the Private Cloud Compute to draw information from the server, and will operate independently when it is the most suitable option.
For marketers, the bigger question is not ‘should we quit the cloud?’ Content that takes a lot of horsepower to generate may still require a robust remote model. The opportunity is in knowing what must be done centrally and what can be done locally. That decision can determine the pace, costs, and trust of a campaign.
Accelerating Creative Production and Personalization at Scale
Campaign production can become a drag if the campaign has to work in numerous languages and markets and across channels. Every iteration is going to require modified copy, visuals, voiceover or format. Multimodal AI can ease the burden by providing teams with a way to repurpose a single creative idea into many formats without having to start from scratch.
How marketing gets better 74% of martech leaders surveyed said they are experiencing moderate or significant efficiency gains in ideation and creation of content – compared to where they were before Generative AI. That efficiency gain may give marketers back time that can go toward campaign planning and creative direction and assessing quality of output.
Dynamic creative optimization (DCO) expands this to optimize assets of a campaign based on varying audiences. A campaign might specify a video with a different background based on location, for example, or use a different voiceover language or text overlay to suit a certain country or region. Inference may deliver faster, on-device inference when the necessary context exists locally, and the device has the processing power.
But personalization has to be bounded. What a customer wants aren’t a given in device data, and too many variations can dilute brands and undermine their consistency. Performance marketers will demand audience signals, contextually relevant creative rules, and data about how well each variation works.
Improving Creative Testing and Automating Content Workflows
The problem is that producing many variations of creative material is beneficial only when the marketer can determine which ones succeed. Conventional A/B testing involves testing only a limited number of variations that involve making changes to one thing at a time. The multimodal AI technology can increase the scope of testing by allowing the marketers to generate a number of versions of visuals, voiceover, messaging, and tone. As an example, the marketing campaign team can make 50 various variations of a video and test them out to a particular target market.
However, the more versions you create, the more comprehensive of an insight you will get. Marketers should have a testing hypothesis and solid control groups when measuring things like engagement, conversions, or completed views.
The stakes are significant because irrelevant personalization can push customers away. Research by Adobe revealed that 50% of those surveyed would disengage from the experience if it was irrelevant. This means that relevance is a matter of performance, and not creativity.
AI algorithms could be used to identify patterns and preselect certain variations, with edge inference being applied to enable decision-making on the back of context. But the prediction metrics alone won’t tell you which ad will work for the business. Real-world data on live campaigns, experiments, and oversight are essential in validating winning ads.
The Future of the Edge-Powered Marketer
Multimodal AI in marketing is turbo-charging creative production, but that’s not necessarily the game-changer for brands. The real advantage is to pair content-creation capabilities across formats, with smarter choices for where that content gets processed. Cloud AI will be required for more complex processing, while edge inference can enable faster and more private experiences where local processing makes sense.
However, technology can’t hide weak data or poor decision making. For any company, the best solution is to test different channels, safeguard against eroding consumer trust and ensure your brand always comes first. Then audit your marketing stack to see where multimodal AI and edge inference can address genuine business needs, not a new engine for the latest tech buzzword.
