AI Search Focuses More on Intent, Context, Relevance, Not Keyword Stuffing

Nobody knows yet precisely how much financial impact artificial intelligence is going to have on the search business, website clicks and traffic or the marketing effectiveness of search ads. The fear in some quarters (search providers; content providers; marketing firms) is that AI will reduce traffic, interaction and attention for many sites, if not most sites. 

It already seems self-evident that multimodal artificial intelligence will be most important for extending AI beyond chatbot use, particularly as AI is embedded into machines and appliances. That isn’t to say multimodal interaction will be unimportant for human-chatbot interactions. 

As already is the case, many users prefer spoken interaction with apps and devices. And it increasingly is possible to use visual or audio input to create outputs (“what is this?” or “where can I buy this?”). 

Still, machine use of AI will often require multimodal input. Embodied systems often operate in dynamic physical settings, requiring integration of multiple data types (visual, auditory, sensory) to make context-aware decisions.

At the very least, keyword-based optimization strategies will have to change. Such tactics will not work as well when the AI apps are looking for relevance and context. 

Traditional search engine optimization and content strategies relied heavily on specific keywords or phrases, so many content creators stuffed web pages with targeted keywords to rank higher on search engine results. 

AI enables search engines to understand the context and semantics of queries and content, taking into account assumed user intent, relationships between concepts, and the broader context of the content.

A query such as “best running shoes for beginners” is not matched only to pages with those exact words. AI interprets “beginners” as implying affordability, comfort, and durability, prioritizing content that aligns with those attributes, even if the exact phrase isn’t used.

So AI-based search prioritizes content quality, relevance and user satisfaction above keyword density. 

Embodied System	Use Case	Description	Multimodal Inputs Used
Autonomous Vehicles	Real-Time Navigation and Obstacle Avoidance	Processes camera feeds, LiDAR, radar, and voice commands to navigate roads, avoid obstacles, and respond to traffic signs.	Video, sensor data, audio, GPS, text
Smart Appliances  (Refrigerators)	Inventory Management and Recipe Suggestions	Analyzes images of fridge contents, user voice queries, and dietary preferences to suggest recipes or order groceries.	Images, audio, text
Home Robotics (Vacuum Cleaners)	Adaptive Cleaning and Obstacle Detection	Uses cameras, sensors, and voice instructions to map rooms, avoid furniture, and adjust cleaning modes.	Video, sensor data, audio
Industrial Robots	Assembly Line Automation	Combines visual input, sensor data, and task instructions to perform precise manufacturing tasks (e.g., welding, assembly).	Images, sensor data, text
Smart Wearables (AR Glasses)	Contextual AR Assistance	Integrates visual surroundings, voice commands, and user gestures to provide real-time information or navigation cues.	Video, audio, gestures, text
Drones	Autonomous Delivery and Surveillance	Processes video feeds, GPS, and environmental sensors to navigate, avoid obstacles, and perform tasks like package delivery.	Video, sensor data, GPS, audio
Smart Thermostats	Adaptive Climate Control	Analyzes temperature sensors, user voice preferences, and visual room data to optimize heating/cooling settings.	Sensor data, audio, images
Medical Robots (e.g., Surgical Assistants)	Precision Surgery Support	Uses imaging, sensor feedback, and surgeon voice commands to assist in precise surgical procedures.	Images, sensor data, audio, text