This Week's Term: Multimodal AI - AI systems that can understand and generate content across multiple types of input and output: text, images, audio, video, and code, processing them together rather than in isolation.
For years, AI models were specialists. One model for text generation, another for image recognition, a third for speech-to-text. You'd use DALL-E for images, Whisper for transcription, and GPT for text - each in its own silo. Multimodal AI changes this fundamentally by processing multiple data types simultaneously and understanding the relationships between them.
The business impact isn't just convenience - it's about eliminating translation losses between modes. When a customer sends a photo of a damaged product along with a text description, a multimodal AI processes both together, understanding that "the screen is completely shattered" relates directly to the crack visible in the photo. Traditional systems would route the image to one system for visual inspection and text to another for sentiment analysis, losing critical context in translation.
This capability transforms how businesses handle real-world complexity:
Customer support can analyze voice tone, facial expressions in video calls, and written complaints simultaneously - delivering truly personalized responses that understand the full context of customer frustration or satisfaction.
Product development teams can describe a feature in writing, show a competitor's interface, and sketch a rough wireframe - and have AI generate a detailed prototype incorporating all three inputs. This is exactly what we saw with Miro AI in this issue's tool deep dive.
Market research becomes richer when AI can analyze interview transcripts alongside facial expressions in video, presentation slides alongside speaker notes, and customer feedback text alongside product usage screenshots. The patterns emerge from connections between modalities, not just within each one.
For business leaders, the question isn't whether to adopt multimodal AI, but where the connections between different data types create unique value in your operations. Start by identifying one workflow where you're currently forced to process different data types separately - customer feedback with screenshots, sales calls with slide decks, quality inspections with photos and reports. That's your first multimodal opportunity.
Google just launched Gemini 3, which they describe as "the strongest model in the world for multimodality and reasoning.". Gemini has been multimodal from the beginning - able to see, hear, understand vast amounts of information, and natively generate across modalities. Gemini 2 added advanced reasoning, enabling AI agents to think, code, and take action. Gemini 3 combines both at unprecedented levels.
What makes Gemini 3 remarkable is how it enables entirely new interfaces - like coding interactive simulations custom-built for your Google search, or analyzing complex videos while helping you learn, create, plan, and take action, all within a single conversation.