Demystifying the types of AI: Industrial, Physical, Generative, and Agentic

When generative AI went big after the release of ChatGPT in November of 2022, many people were under the impression that this artificial intelligence (AI) came out of nowhere. But generative AI can be traced back to 1964, when MIT developed ELIZA as a virtual psychotherapist. The first physical AI was invented in 1966, with the first AI Robot – Shakey by the Stanford Research Institute.

Yes, there are multiple types of AI! Going forward, their presence and familiarity will increase. However, the use cases, IT stack, power and cooling, and techniques used (computer vision, machine learning, reasoning, etc.) differ for each. Here is a simplified breakdown of the different types of AI and what they do.

Simple definitions for different types of AI

• Industrial AI: AI for analytics, quality, maintenance, and optimization in manufacturing, energy, and supply chain logistics.
• Physical AI: AI systems that perceive, decide, and act in the physical world, controlling robots, vehicles, smart infrastructure, and devices.
• Generative AI: (GenAI): AI that generates new content or outputs – text, image, code, audio, video, etc.
• Agentic AI: An approach using one or multiple AI models that can plan and execute tasks with varying autonomy levels and minimal human supervision. 

Now that we’ve defined the most popular and emerging types of AI, let’s dive into use cases to understand them more fully.

Use cases for multiple AI types

Industrial AI:  

Predictive analytics using IoT sensors and data analytics to monitor equipment condition in real-time and predict potential failures before they occur is a popular industrial AI application. Companies are leveraging Industrial AI for process improvement and optimization, leveraging machine learning and computer vision for efficiency, energy reduction, and throughput improvements. The application of digital twins to virtually replicate physical assets, processes, or systems and use real-time data, machine learning, and simulation to predict, optimize, and automate operations is gaining momentum. Optimizing logistics and supply-chain planning across transportation and warehouse operations has been extremely effective in realizing just in time manufacturing and reducing delivery times.  

Physical AI:

An example of physical AI is autonomous robots for warehouse picking and sorting, transport, palletizing, and automated logistics for labor intensive tasks. There are also robotic lawn mowers and construction automation like demolition and bricklaying. It can be used for harsh or toxic environments, keeping humans out of danger. AI is being used in independently operating drones for inspections and security patrols for electric grids, pipelines, farms, construction sites, etc. The drones can perceive their surroundings, navigate autonomously, and make real-time decisions. The application that gets the most press is autonomous vehicles and self‑driving trucks for transportation and distribution for safe, 24/7, driverless operation. Surgical robots are being deployed for increased precision and control and hospital logistics automation.

Generative AI:

By far the most common application is Gen AI for transforming content creation to produce text, images, videos, and code based on user prompts and queries. It has been highly leveraged to speed up asset generation, allowing people to produce more personalized content at scale. Some popular content generation includes personalized marketing emails, blog posts, landing pages, and social media content. The other application people encounter in their daily lives is customer support chatbots utilizing conversational AI. While the goal is to provide instant, human-like, and context-aware responses, AI chatbots are easy for most people to recognize. Customer support functions will continue to improve and the day will come when an AI chatbot will be indistinguishable from an actual human. Where generative AI excels is data summarization and analysis when AI can instantly analyze large datasets, legal documents, or call transcripts to generate summary reports – something that would take most people months or years to accomplish. A popular application revolutionizing software and firmware development across industries is Gen AI for automating code creation, debugging, documentation, and testing, which dramatically increases productivity and accelerates coding, testing, and deployment cycles.

Agentic AI:

Agentic AI is meant to operate autonomously, but it seldom works as a single agent. It performs best when handling complex, multi-step tasks because it orchestrates a team of specialized AI models and agents, sometimes by function, such as researcher, coder, or content writer. This approach is called inter-agent collaboration or multi-agent functionality or mixture of agents (MoA). Agentic AI excels in scenarios requiring reasoning, planning, and acting. It can break down complex problems into smaller steps using chain of thought (CoT) reasoning to deliver very high accuracy. Unlike static models, agentic AI uses reinforcement learning (RL) to adapt to new information and changing environments over time through trial and error.

How use cases differ and a compute overview

Industrial AI is largely data‑centric, integrating information technology (IT) and operational technology (OT) data to run analytics, operational decision making, or suggestions to improve process or system efficiency. Industrial AI can include real-time monitoring and acting but it often emphasizes governance rather than split-second physical actuation. Use industrial AI for demand forecasting, inventory management, logistics management, predictive analytics, condition-based maintenance, and quality control to optimize production. 

Demand Forecasting: AI analyzes vast, complex data sets – including weather, trends, and market shifts – to predict demand with high accuracy, reducing overstock and stockouts. The IT technology stack must connect legacy OT (sensors, PLCs, and cameras to collect real-time data from industrial machines) processed by modern GPU-based used in AI data center infrastructure, accelerated compute IT systems using machine learning frameworks and specialized industrial AI libraries.

Physical AI is usually embodied into machines and robots neural processing units (NPUs) because it must sense and interact with the physical world with low latency and high degree of safety, often deployed in edge computing environments. Modern on-device NPUs can deliver 2 to 35+ TOPS (trillion operations per second). Physical AI may demand real-time responsiveness and fail-safe designs as many of the use cases can have catastrophic consequences if they fail. Consider autonomous mobile robots (AMRs) for bomb deactivation or poisonous gas detection, robotic surgery, pandemic response and exposure reduction to deliver food, medication, supplies, and autonomous emergency vehicle navigation. 

Generative AI focuses on content generation (text, image, code, videos) and creates from scratch or augments output created hy humans. For the most part, this content does not need to be created in real‑time. Gen AI is also used in knowledge augmentation, conversational assistants, tech support, or development productivity improvements like firmware, software, and code development. The size of the IT stack is dependent on the size of the model used, how fast the content is needed, the scope, scale, accuracy, and quality of the output or response needed. Some IT stacks running generative AI inference run on the latest NVIDIA Blackwell Ultra NVL72 SuperPods while some run on very modest accelerated compute single GPU servers. As generative AI applications mature, companies will verticalize and leverage more domain-specific models trained on their own data and integrate them into their integrating into existing workflows.

Agentic AI emphasizes autonomy with the goal of little or no human oversight, sometimes called Human-in-the-Loop (HITL). It also employs orchestration of multiple agents working together to solve complex tasks. A key way for agentic AI to become more accurate is to leverage active or reinforcement learning. When the agents receive feedback, they adapt and adjust. Agentic AI may require low latency for some applications including telecom network operations, real‑time control and safety in manufacturing, fraud detection, risk management, and conversational agents in life or business critical support. Each agent has its own IT stack; therefore, the speed of the agentic response is gated by the slowest model. Examples of non-time critical agentic AI include intelligent contract processing, compliance and audit reviews, and customer satisfaction survey analysis and response. Although the goal is to have agentic AI agents act alone, planning, acting and adapting, the reality is that’s a very long way off. Today the focus is to figure out which tasks agentic AI is best suited to do then determine how agents and humans or robots should be combined to deliver the greatest output. How well people, agents, and tools work together is the key to successful implementations. 

When the types of AI will converge into one fully autonomous AI agent

AI has already started to make an impact on our everyday lives but we are still at the beginning stages. Hardware innovations and AI functionality will advance and mature at a very high rate. While generative AI has been the focus, industrial AI with its targeted decision-making capabilities will be the next wave of impact. Physical AI is here and will gain momentum rapidly as well. Agentic AI will take generative, industrial, and physical AI towards autonomous operations and will start to converge generative, industrial and physical into one fully autonomous AI agent.

A condensed version of this article was previously published in Forbes.