How Enterprises Are Using GenAI Today
>_TLDR
Enterprises are leveraging GenAI to unlock insights from unstructured data, power conversational assistants and enhance business intelligence, driving an average ROI of 41% among early adopters.
While ChatGPT and other large language models (LLMs) are used to craft dinner recipes and personalized self-portraits, enterprises are unlocking far deeper value. Across industries, GenAI is reshaping how businesses operate, make decisions, and innovate. But what exactly are companies using GenAI for today? Before diving into use cases, let’s briefly step back and understand what LLMs are, how they work, and why they matter.
What Is a Large Language Model (LLM)?
A Large Language Model (LLM) is a type of artificial intelligence (AI) based on a deep learning architecture known as the transformer. These models excel at understanding and generating human language, making them an intuitive interface between people and machines. At their core, LLMs operate like traditional machine learning models: they take an input and generate an output. However, their true breakthrough lies in their ability to handle unstructured data — things like text, audio, and images — at scale. While structured data (commonly found in databases) had long been the domain of traditional AI, unstructured data was much harder to analyze effectively until LLMs arrived.
From Experiments to Value Generation: GenAI’s Enterprise Use Cases
Companies were quick to launch proofs of concept (POCs) to explore GenAI’s potential. Today, many are moving beyond experimentation into true value generation. At the enterprise level, GenAI is successfully being deployed across the following key areas:
1) Unstructured Data Insights
One of the most immediate and impactful applications of GenAI is extracting insights from unstructured data. LLMs are being used for Natural Language Processing (NLP) tasks, such as: • Document Classification: Automatically sorting and categorizing documents based on their content. • Entity Extraction: Identifying key entities within text such as people, organizations, locations, or products. • Notes Summarization: Distilling large volumes of text, like meeting notes, customer feedback, or research papers, into concise, actionable summaries. By turning unstructured information into structured knowledge, companies are gaining insights faster and at a greater scale than ever before.
2) Conversational Assistants
Another powerful use case is the creation of conversational assistants or so-called AI-powered agents that interact naturally with users through text or speech. Unlike earlier chatbots, these assistants can understand complex queries, maintain context across conversations, and deliver human-like interactions. Enterprises are deploying conversational AI in multiple ways: • Customer Support: Virtual agents handle everything from basic FAQs to complex troubleshooting. • Internal Chat Assistant: Internal help desks, IT support, and HR services increasingly rely on conversational AI to assist employees with requests, forms, and internal navigation. • Knowledge Management: Making various information sources or knowledge bases across the enterprise searchable in natural language and surfacing insights that were previously difficult to uncover. By grounding responses in company-specific data through semantic search and retrieval-augmented generation (RAG), enterprises ensure these assistants are accurate, secure, and aligned with internal knowledge.
3) AI-Augmented Business Intelligence (BI)
Business Intelligence has traditionally focused on structured data, dashboards, and historical reporting. GenAI is pushing BI into a new era where AI doesn’t just visualize data — it can interpret, advise, and even predict. With AI-augmented BI, self-service is starting to become a reality and companies can: • Text-to-SQL Generation: Reduction of the barrier to entry and increase of the number of users within an enterprise thanks to the use of natural language. • Augmented Insights: Traditionally BI queries are structured and recurring. GenAI enables more explorative analysis to augment BI results. This fundamentally democratizes data-driven decision-making, empowering both executives and front-line employees to act smarter and faster and removing technical barries to entry for generating business intelligence from data.
The Business Impact and ROI of GenAI
GenAI is no longer just an experimental trend — it’s delivering measurable returns. A global survey by Snowflake of over 3,300 organizations identified 1,900 early adopters, 92% of whom reported positive returns. Among those who quantified their results, the average return on investment (ROI) was 41%, prompting increased spending on: • Data infrastructure (+81%) • Large Language Models (+78%) • Supporting software (+83%) • AI talent (+76%) Companies that not only adopt GenAI but deeply integrate it into their operating models and culture are seeing the greatest rewards. The strongest returns are being achieved by those who invest heavily in robust data foundations, security, and continuous model tuning and governance.
The Start of the New Industrial Revolution
We are only in the early stages of a new, AI-powered industrial revolution. So far, businesses have largely used AI to enhance existing processes — making them faster, cheaper, and more accurate. But the next wave will bring entirely new capabilities, products, and services we haven’t even imagined yet. Enterprises that embrace GenAI systematically — combining data, strategy, and culture — will be the ones shaping the industries of tomorrow.