Product design has always been a fundamentally multimodal activity. Designers sketch, prototype, discuss, test, and iterate across visual, spatial, and textual dimensions simultaneously. So it makes sense that multimodal AI — models that process and generate across text, images, video, and increasingly 3D — would find fertile ground here.

What’s different in 2026 is that these tools aren’t theoretical anymore. They’re embedded in actual design workflows at companies ranging from consumer electronics firms to automotive manufacturers. And the way they’re being used is often not what the early demos suggested.

Beyond Text-to-Image Party Tricks

The first wave of multimodal AI in design was dominated by text-to-image generation. Type a prompt, get a concept render. That was impressive as a demo but limited in practice. Professional product designers don’t work from vague text prompts — they work from constraints. Material specifications, manufacturing tolerances, brand guidelines, ergonomic requirements, cost targets.

The current generation of tools understands this. Google’s Gemini models can now ingest a design brief (text), existing product images, CAD file previews, and user research data, then generate design variations that respect specific constraints. It’s not magic — the outputs still need refinement — but the speed of initial concept exploration has increased dramatically.

A product team that previously spent three weeks developing five concept directions can now explore twenty directions in a week. That doesn’t mean the AI is doing the design. It means the designers are spending less time on the mechanical work of rendering variations and more time on the judgement calls that actually matter.

The Real Workflow Changes

Here’s where it gets interesting. The most significant impact isn’t in concept generation — it’s in the feedback loops between design, engineering, and manufacturing.

Design-to-engineering translation. One of the persistent friction points in product development is the handoff between industrial design and engineering. Designers create forms; engineers figure out how to build them. Multimodal models are starting to bridge this gap. Feed the model a design render and it can flag potential manufacturing issues, suggest material alternatives, and even generate preliminary engineering specifications. It doesn’t replace the engineer, but it catches obvious problems earlier.

User testing synthesis. Product teams run user testing sessions that generate hours of video, pages of notes, and stacks of survey responses. Multimodal models can process all of this — the video facial expressions, the verbal feedback, the written responses, the interaction patterns — and surface patterns that would take a human researcher days to identify. MIT Technology Review highlighted several consumer electronics companies using this approach to cut user research synthesis time by 60%.

Cross-disciplinary communication. Design teams use visual language. Finance teams use spreadsheets. Marketing teams use decks. Multimodal AI is becoming the translation layer between these formats. A design concept can be automatically contextualised with cost estimates, market positioning data, and manufacturing feasibility notes — all generated from the visual input combined with the company’s internal data.

What’s Working and What Isn’t

Let me be specific about the maturity curve.

Working well: Concept exploration and variation generation. Design review documentation. Competitive product visual analysis. Basic manufacturing feasibility checks based on visual geometry.

Working but imperfect: Style transfer across product lines (maintaining brand language). Automated accessibility evaluation of physical products. Cost estimation from visual designs.

Still overpromised: Full generative design that produces manufacture-ready outputs. AI that can independently judge aesthetic quality aligned with brand values. Autonomous design iteration without human guidance.

The gap between the second and third categories is significant. The tools that work well are assistive — they speed up tasks that humans already know how to do. The ones that are overpromised require the AI to make subjective judgements that even expert designers disagree on.

The Tooling Landscape

The market is fragmenting rapidly. Autodesk has integrated multimodal capabilities into Fusion 360. Adobe’s Firefly for 3D is gaining traction in consumer goods. Figma’s AI features are expanding beyond 2D into spatial design. And a crop of startups — Vizcom, Kaedim, Masterpiece Studio — are targeting specific niches within the product design pipeline.

What’s notable is that no single tool covers the full workflow. Most product teams are stitching together three or four AI tools alongside their existing CAD and rendering software. It’s functional but messy, and there’s a clear opportunity for better integration.

The hardware side matters too. Real-time multimodal AI inference on a designer’s workstation requires serious GPU power. The shift to cloud-based rendering and AI processing is happening, but latency remains a frustration for designers accustomed to instant visual feedback in their tools.

Where This Goes Next

I think we’re about eighteen months away from multimodal AI being considered standard infrastructure in product design — like CAD software or a 3D printer. Not because the AI will be perfect, but because the productivity difference between teams using it and teams not using it will become too large to ignore.

The more interesting question is what happens to the design profession itself. If concept generation becomes cheap and fast, the value shifts toward design strategy, brand thinking, and the kind of integrative judgement that connects user needs to business goals to technical constraints. The designers who thrive will be the ones who were already thinking at that level. The ones who were primarily executing will need to adapt.

That’s not unique to design, of course. It’s the same story playing out across every knowledge profession. But product design, with its inherent multimodality, is one of the fields where the transition is happening fastest.