What is Natural Language Processing?

Most of a company's information doesn't sit in tables; it sits in language: emails, contracts, quotes, reviews, customer conversations, meeting notes — territory where classical software was blind. NLP — natural language processing — is the branch of AI that gave it sight. The discipline predates the current wave (decades of rules and statistics produced spellcheckers, machine translation, search), but large language models melted the ceiling: what once demanded research projects — grasping nuance, context, sloppy spelling and abbreviations — is now an API call. The concrete business uses, roughly in order of frequency: message triage and routing (the complaint email reaches the right person, prioritized, with no human dispatcher), information extraction from documents (key clauses out of contracts, order details out of free-form emails), voice-of-customer analysis (reviews and conversations aggregated into themes and sentiment — you learn what hurts systematically, not anecdotally), assisted replies (a draft answer proposed to the agent, a bot over the knowledge base), transcription and summarization (meetings and calls become searchable text and action minutes), and workable translation. Modern models handle many languages well beyond English — including smaller European ones — so the old language barrier around these tools has largely fallen. Calibration stays the same as for all AI: NLP systems work statistically — superb at volume and triage, with inevitable errors at the margins — so healthy workflows put people on exceptions and on validating high-stakes actions, not on rereading everything the machine already read.