Artificial intelligence (AI) is evolving quickly, and the terminology being used to discuss it in the market is evolving too. Some terms in AI have been around for decades, while others seem to have popped up only this year. To help you understand AI and the types of AI out there today, we’ve put together this glossary of terms you need to know.
We hope this overview of AI terms helps you understand more about the different types and ways of implementing AI, as well as how they can benefit from effective orchestration. It’s very likely you’re either using some of these yourself already, or if not, that it would be easier than you think for your organization to adopt them today.
AI terms table of contents
Artificial Intelligence (AI)
Artificial Intelligence (AI) refers to computing systems that can perform tasks in some of the same domains as human intelligence, such as reasoning, learning, and decision-making. AI is increasingly being integrated into enterprise business processes to enhance decision accuracy, drive automation, and unlock operational efficiencies. AI orchestration enables AI to operate within governed, auditable workflows.
Machine Learning (ML)
Machine Learning (ML) is a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed. ML powers predictive analytics, intelligent document processing, and real-time personalization. When orchestrated within end-to-end business processes, it can ensure explainability, compliance, and adaptability.
Large Language Model (LLM)
A Large Language Model (LLM) is an advanced type of machine learning model trained on massive amounts of text to understand and generate human-like language. Popular LLMs include ChatGPT, Gemini and Claude. LLMs power enterprise use cases like automated customer support and content generation. Effective orchestration ensures they integrate effectively with deterministic business rules and human oversight.
Natural Language Processing (NLP)
Natural Language Processing (NLP) enables machines to understand, interpret, and respond to human language. It’s used in chatbots, document classification, and sentiment analysis. Within orchestrated workflows, NLP can help bridge the gap between unstructured communication and structured decision-making.
AI Automation
AI Automation is the use of artificial intelligence to perform tasks autonomously, from decision-making to process execution. It amplifies traditional automation by enabling systems to handle variability and ambiguity. When AI automation is well-orchestrated, it enables greater transparency, control, and alignment with business goals.
AI Workflow Automation
AI Workflow Automation integrates AI capabilities into business workflows to automate and optimize tasks such as document processing, classification, and decision support. An orchestration platform like Camunda makes it easier to deploy AI in mission-critical workflows securely and effectively by providing governance, visibility, and scalability.
AI Process Automation
AI Process Automation combines traditional process automation with intelligent capabilities like ML, LLMs, or NLP. By orchestrating both deterministic logic and AI-driven dynamic decisions, organizations can implement greater levels of automation while still maintaining compliance and agility in rapidly changing environments.
Intelligent Process Automation
Intelligent Process Automation (IPA) blends RPA, AI, and workflow orchestration to automate complex business processes. IPA works well when combining structured workflows with variable inputs, human interaction, and evolving rules. Given the complexity and variety of endpoints, orchestration is often required to manage IPA effectively and reliably.
AI Agents
An AI Agent is a form of AI that can understand its environment, apply reasoning, and take actions to fulfill specific goals, all without human intervention. Orchestration lets you take things to the next level by allowing AI Agents to easily and safely work together and integrate as part of a larger process.
Agentic AI
Agentic AI refers to artificial intelligence systems—often including multiple AI agents—that can operate autonomously, making decisions and executing tasks independently. Orchestration here enables you to align agent behavior with enterprise rules, integrate them with human workflows, and ensure operational reliability.
Agentic Orchestration
Agentic Orchestration involves coordinating one or multiple AI agents within structured business processes to achieve a defined goal. Agentic Orchestration blends traditional deterministic BPMN-based orchestration with dynamic agent behavior, enabling enterprises to take advantage of AI without sacrificing governance or compliance. Learn more here.
Enterprise AI
Enterprise AI is the application of various AI technologies—such as ML, NLP, and LLMs—within large organizations to optimize operations, enhance decision-making, and drive innovation. Robust orchestration enables enterprises to connect AI models with legacy systems, human workflows, and compliance requirements, dramatically boosting the effectiveness of AI.
Generative AI
Generative AI refers to models, such as ChatGPT, Gemini or Claude, that can produce new content such as text, images, or code based on training data. It enables powerful business use cases, but there are also risks, and orchestration can ensure it operates within governed workflows to ensure quality and align outputs with enterprise standards.
Learn more about Camunda and AI
We hope this glossary of AI terms was helpful for you. At Camunda, we’re thoughtfully considering how AI can help businesses orchestrate their processes with greater effectiveness, and rapidly implementing new features and capabilities to make it happen. Learn more about AI-enabled process orchestration and agentic orchestration and how they can help you realize the full potential of this technology in your business processes today.
Start the discussion at forum.camunda.io