Large language models (LLMs) are becoming a practical way for companies to work with documents, customer feedback, and business data. For example, finance teams can use an LLM-assisted tool to summarize budget variances, while marketing teams can use one to summarize customer sentiment on feedback surveys.

What Are Large Language Models (LLMs)?

Large language models (LLMs) are AI systems trained on large collections of text and other content to understand inputs, identify patterns, and generate text. Through the training process, LLMs learn vocabulary, grammar, and context, along with commonly used patterns and formats. As a result, they can understand text inputs and generate text outputs. Once trained, LLMs provide a range of functions to teams: They can query in natural language rather than strict formats or keywords, tools can analyze paragraphs and other unstructured text data, and generated outputs use clear and appropriate language.

Businesses can get more out of LLMs by applying contextual information such as organizational standards and policy documents. With that context, workflows can integrate LLM-based tools as natural language interfaces for analyzing documents and explaining data trends. However, remember that LLMs are not databases, accounting systems, or decision-makers on their own.

Key Takeaways

  • LLMs are AI systems that understand and process language to generate natural language outputs.
  • Companies can use LLMs for both internal and external tasks, from analyzing customer feedback sentiment to powering a customer service chatbot.
  • Because LLMs work with natural language, they make tools more accessible to nontechnical staff.
  • LLMs work best for text-heavy tasks with clear rules and easily accessible source data.
  • LLM outputs should always be checked by a person.

Large Language Models Explained

An LLM works by turning words, phrases, and other inputs into numerical representations that a machine can process. During training, the model learns relationships among terms, ideas, formats, and contexts to better understand inputs and produce text responses. Businesses can add more context, such as organizational data and internal workflow guidelines to improve the model's responses. Accuracy and quality can also improve when people write more specific prompts. In addition, the LLM should only source data from trusted systems to ensure accuracy. For instance, an LLM can help turn financial results into a plain-language summary, but accuracy depends on using internal numbers from trusted databases.

Why Are Large Language Models Important?

LLMs matter because most business workflows rely on text in some way. Internally, operations depend on emails and reports while tools such as analytics platforms can use natural language to expand access to nontechnical staff and generate easy-to-understand summaries. This flows to leadership teams, who can make decisions faster thanks to clearer explanations of dense operational data. Externally, LLM-powered AI agents can answer customer service requests in natural language while also answering simple product or logistics questions. Across all these use cases, LLMs work best when using accurate data, role-based access, and established review practices.

Benefits of Large Language Models

While LLMs can be powerful general-purpose tools, they excel when applying them to focused use cases. In particular, LLMs are strongest with repetitive language-heavy tasks and data explanation. The following are common benefits of LLMs:

  • Expanded access to data and knowledge: Teams can speed up research by asking questions in natural language to receiving a summarized answer.
  • Clearer data explanations: LLMs can help convert departmental data into narrative summaries for easier review.
  • More consistent communication: When paired with brand, legal, or support guidelines, LLM-assisted tools can review documents to ensure consistency and compliance.
  • Automation: LLMs can automate routine text tasks, allowing employees to spend more time on judgment-based work, relationship management, and exception handling.
  • Better analysis of unstructured text: LLMs can analyze unstructured data to categorize comments and process survey responses, so leaders can spot underlying themes more quickly.
  • Faster onboarding: New team members can use LLM-powered AI agents to learn processes and monitor training.

Steps to Using Large Language Models in Business

While LLMs have many business applications, most follow broad standards for usage and integration. The following provides a high-level view of common steps for using LLMs in business: Identify text-heavy work with clear value.

LLMs work best with tasks involving text-heavy processes, such as sorting through customer feedback or summarizing data. These tasks are good starting points for integrating LLMs.

  1. Define the source of approved information. Processes using LLMs are only as good as the data they use. Decide which systems, documents, and data sets the LLM can use; these should rely on current, governed information rather than open-ended responses.
  2. Set rules for privacy and access. Map LLM use to existing roles, permissions, and data policies to support privacy and security requirements.
  3. Build review into the workflow. LLMs are a tool, not a source of record. Employees should thoroughly review output, particularly for customer outreach, critical financial decisions, or external publishing. Because LLMs tend to use a smooth, professional tone, reviewers should also check the voice against audience and style requirements.
  4. Measure results by task. Is the LLM actually improving the process? There’s a difference between simply using tools and effectively improving workflows. KPIs can measure how much processes actually benefit from LLMs.
  5. Expand after proving the use case. After teams establish a reliable workflow, they can extend LLM support to adjacent tasks. For example, a finance team that starts with variance commentary might later add board-report summaries or forecast explanations.

NetSuite AI for Business Workflows

NetSuite embeds AI capabilities across its suite to assist business leaders with tasks, suggested actions, and business insights. With NetSuite, teams can use capabilities such as Text Enhance for content creation and refinement, Bill Capture for invoice scanning, Narrative Reporting for financial and transactional explanations, Planning and Budgeting features for forecasting, and AI Connector Service for connecting external AI platforms using Model Context Protocol. These features work with business data and processes inside NetSuite, helping teams apply AI to finance, operations, analytics, and other daily workflows.

Large language models can help companies work faster with documents, language-heavy processes, and data explanations, but they need structure to produce dependable results. The strongest uses combine LLM capabilities with accurate business data, clear ownership, and human review.

Large Language Models (LLMs) FAQs

What is the difference between an LLM and AI?

AI refers to a broader technology category of systems performing tasks that traditionally require human intelligence, including pattern recognition or data analysis. Within the umbrella of AI, an LLM is a specific type focused on understanding and generating language. LLMs are trained on language, which makes them ideal for language-based tasks such as summarizing documents and answering questions.