Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.

Managed AutoML platforms package feature engineering, model selection, training, deployment, and monitoring into a guided workflow so teams can ship predictive models quickly without owning a full bespoke ML stack.

Workflow Automation with AI embeds models such as LLMs, OCR, and ML classifiers into orchestrated, multi-step business workflows. It uses triggers, AI-powered tasks, human-in-the-loop approvals, and system integrations to execute processes end-to-end with minimal manual effort. Traditional workflow or orchestration engines coordinate the sequence, while AI steps handle perception, understanding, and decision-making. Monitoring, governance, and exception handling ensure reliability, compliance, and auditability in production environments.