Multimodal AI
Multimodal AI refers to models that process and generate more than one type of data—text, images, audio, video—within a single system. GPT-4o can read a photo, transcribe speech, and respond in text in one call. Gemini can reason across video frames. These are multimodal models. The significance is integration: instead of chaining a speech-to-text model into a language model into an image generator, you get one model that handles the translation between modalities internally. For practical applications, multimodal capability means an AI can analyze a screenshot of a dashboard and explain the trend, or watch a product demo and write the marketing copy. The gap between multimodal demos and production reliability is still wide—image understanding is good, video understanding is inconsistent, and audio reasoning is early—but the trajectory is clear.
Related terms:
Embeddings
Embeddings are numerical representations of text—vectors of hundreds or thousands of floating-point numbers—that capture semantic meaning in a form machines can compare mathematically. They power semantic search, recommendation engines, clustering, anomaly detection, and the retrieval half of RAG architectures.
AI Strategy
AI strategy is an organization’s plan for how it will use—and what it will not use—AI to achieve business outcomes, answering concrete questions about workflow automation, human judgment, ROI measurement, infrastructure, and ownership. It means sequencing investments (for example, custom models to leverage proprietary data versus off-the-shelf solutions) rather than simply selecting vendors.
LLM (Large Language Model)
A large language model is a neural network with billions of parameters trained on massive text corpora to predict the next word in a sequence, powering tasks from coding and summarization to translation and conversation. Though general-purpose by default, LLMs require prompting, fine-tuning, or data integration to excel at specific tasks.