Generative AI is the category of artificial intelligence that creates new artifacts rather than classifying existing ones. The breakthrough that drove the 2022-2024 wave of adoption was the transformer architecture and the foundation model paradigm: instead of training a custom model per task, you fine-tune or prompt a single large model that has learned general patterns from internet-scale text.
Where it fits in the enterprise
Five mature use cases: knowledge search over internal documents (via RAG), customer-facing assistants with safety guardrails, document understanding (extraction, summarization, classification), code generation and review automation, and decision support (drafting analyses for human review). Use cases that aren't yet mature: fully autonomous agents in regulated workflows, real-time low-latency inference at extreme scale, and cases where correctness must be 100% with no human in the loop.
What's hard about shipping it
Generative AI introduces failure modes most software teams haven't operated before: hallucination, prompt injection, jailbreaks, sensitive-data leakage, and silent degradation when an upstream model is updated. Production-grade deployment requires evaluation harnesses, retrieval grounding, output guardrails, audit logging, cost controls, and a process for handling model upgrades. Most pilot-stage projects skip this scaffolding and stall at the security review.
How Prosigns approaches Generative AI
We start every engagement with an AI Readiness Assessment that maps data, infrastructure, governance, and use-case priorities. We design system architecture (retrieval, eval, governance) before picking a model, because models change every quarter and the architecture has to outlast them. We deploy on infrastructure your security team will sign off on, and we operate the system after launch with continuous evaluation and quarterly model upgrade cycles.