LLMs at Work: Six Real-World Applications in 2025

The Problem: Legal and contractual documents are the lifeblood of business, but they're also often dense, lengthy, and require meticulous human review. Finding specific clauses, checking for compliance, extracting key dates (like renewals or termination deadlines), and comparing versions is incredibly time-consuming and prone to human error. Think of mergers and acquisitions due diligence, reviewing supplier agreements, or managing stacks of sales contracts.

How LLMs Help: They are exceptionally good at "reading" and understanding the structure and content of text, even complex, jargon-filled documents like contracts. You can train or prompt an LLM to:

• Identify and Extract: Pull out specific pieces of information like party names, dates, financial figures, governing law clauses, or payment terms.
• Summarise: Provide a concise overview of the contract's purpose and key obligations.
• Compare: Highlight differences between two versions of a document or compare a contract against a standard template.
• Analyse Risk: Flag clauses that deviate significantly from standard terms or represent potential risks.

In Detail: Instead of spending hours sifting through hundreds of pages for a single detail, use an LLM application to ask it questions like: "Find all indemnity clauses in this agreement," "What is the renewal date for contract number XYZ?", or “List all instances where our standard liability cap was changed.”

This isn't about replacing lawyers; it's about augmenting their capabilities, freeing them up for complex negotiation and strategic advice rather than tedious manual review.

The Value: Massive time savings in legal review cycles, reduced risk of missing critical details (which can lead to costly disputes or missed opportunities), faster deal velocity, and lower costs associated with manual document processing. It turns a bottleneck into a streamlined process.

Cautions: LLMs can still misinterpret nuanced legal language or "hallucinate" information not present in the text. Human legal review remains essential for validation, especially for critical clauses or high-stakes contracts. Regulatory compliance regarding data privacy (e.g., handling sensitive client information) must also be a primary consideration when choosing and implementing a solution.

Best LLMs: Models known for their ability to process entire or large chunks of lengthy documents (i.e., those with large context windows) and for strong instruction following and detailed extraction are best suited here. Leading examples currently include models from the Gemini 2.0 family (Google), the Claude 3.5 family and Claude 4 (Anthropic), and the latest powerful models from OpenAI, such as GPT-4o for multimodal capabilities and GPT-4.1 for its exceptionally large context window. Specialised legal AI platforms built on top of these foundational models are often the most robust solutions for dedicated legal use cases. Keep in mind that model capabilities are rapidly evolving, so it's always worth checking for the latest versions and benchmarks, with some cutting-edge models like Magic.dev's LTM-2-Mini and Meta's Llama 4 Scout now offering significantly larger context windows for handling truly massive datasets.

The Problem: Businesses collect customer feedback from countless sources: survey responses, product reviews, social media comments, support tickets, call transcripts, emails. The sheer volume is often overwhelming, making it difficult and slow to identify trends, understand sentiment, and pinpoint key issues or areas of delight. Critical insights get buried.

How LLMs Help: LLMs can digest large volumes of unstructured text and analyse the data to:

• Summarise: Provide concise summaries of large batches of feedback.
• Identify Themes: Automatically group similar comments and identify recurring topics (e.g., "checkout process," "delivery speed," "customer service helpfulness").
• Analyse Sentiment: Determine the overall emotional tone (positive, negative, neutral) associated with specific themes or the feedback as a whole.
• Extract Key Opinions: Pull out representative quotes or highlight particularly impactful comments.

In Detail: Think about a product manager trying to understand what customers think of a new feature launch. Manually reading through thousands of survey responses and app store reviews is impractical. Using an LLM-powered tool, they can feed in all the text data and instantly get a report summarising the main points of feedback, identifying common bugs reported, highlighting popular feature requests, and showing the overall sentiment trends. Similarly, a marketing team can quickly gauge public reaction to a campaign across social media channels.

This capability moves companies from reactively addressing individual complaints to proactively understanding broad customer sentiment and identifying systemic issues or opportunities for improvement much faster.

The Value: Quicker identification of customer pain points and areas for improvement, faster response to market sentiment, data-driven product development and service enhancements, improved customer satisfaction, and freeing up employees from manual data tabulation and analysis.

Cautions: Sentiment analysis can be tricky for LLMs, especially with sarcasm, cultural nuances, or domain-specific jargon. Summaries might occasionally miss subtle but important points. It's wise to use human spot-checks to validate the AI's interpretation, particularly for critical feedback. Data privacy and anonymisation of customer data are also important considerations.

Best LLMs: Models with strong summarisation and text analysis capabilities that handle diverse language styles well are widely used here. This includes models from the OpenAI GPT family (like GPT-4o or similar), the Anthropic Claude 3 family, and the Google Gemini family (like Gemini 1.5 Flash/Pro). Models specifically fine-tuned on customer feedback data can offer improved accuracy for domain-specific language and sentiment. Note that the LLM landscape is changing rapidly; check for the latest model releases and performance benchmarks.

The Problem: Many roles involve writing – emails, reports, internal communications, marketing copy, job descriptions, meeting summaries. Facing a blank page can be daunting, and the process of drafting takes significant time, pulling employees away from other core tasks.

How LLMs Help: LLMs can serve as powerful co-pilots for content creation. Given a prompt, an outline, or key information, they can generate initial drafts of various types of text. They can:

• Draft Emails: Create professional emails based on bullet points or a brief description of the purpose.
• Write Internal Comms: Generate announcements, policy explanations, or project updates.
• Produce Marketing Copy: Draft social media posts, ad copy variations, or website snippets.
• Summarise & Draft: Turn meeting notes into a structured summary or draft action items.
• Create Boilerplate: Generate first versions of standard documents like job descriptions or simple proposals.

In Detail: Let's say you need to send an email summarising a project status update to stakeholders. Instead of writing it from scratch, you provide the LLM with the key updates, achievements, challenges, and next steps. It can generate a well-structured, grammatically correct draft email that you can then review, edit, and refine. Or a marketing specialist needing five variations of an ad headline for A/B testing can quickly generate options based on a core message.

Crucially, this is about generating drafts. LLMs are tools to overcome the initial inertia and provide a starting point. Human oversight, editing, fact-checking, and applying brand voice and nuance are still essential. But reducing the time spent on getting that first draft down is a massive productivity boost.

The Value: Significant time savings in writing tasks, overcoming writer's block, enabling employees to focus on higher-value creative or strategic work, ensuring a baseline level of clear and consistent communication across common tasks.

Cautions: LLM-generated content can sometimes sound generic, lack specific company voice or tone, or occasionally include factual inaccuracies ("hallucinations"). It's vital that human editors review and refine all generated drafts before publication or sending to ensure accuracy, brand consistency, and appropriate tone. Plagiarism checks may also be necessary depending on the source data the LLM was trained on and the intended use of the content.

Best LLMs: Models known for producing high-quality, creative, and varied text drafts are generally preferred. This includes models from the OpenAI GPT family (like GPT-4o or similar), the Anthropic Claude 3 family, and the Google Gemini family (like Gemini 1.5 Pro). Open-source models like Llama 3 or Mistral, potentially fine-tuned on company-specific writing styles, are also strong options, particularly for internal communications or when data control is critical. Note: check for the latest model releases as capabilities are constantly improving.

The Problem: Employees waste countless hours searching for information scattered across company wikis, shared drives, internal reports, emails, and databases. Traditional keyword search is often ineffective because users don't know the exact terms used in the document, or the search results are overwhelming and lack context. Important knowledge is trapped in documents.

How LLMs Help: LLMs greatly improve internal search by enabling semantic search and natural language querying. Instead of just matching keywords, LLMs can understand the meaning and intent behind a user's query.

• Natural Language Queries: Users can ask questions the way they think ("How do I book a holiday?" or "What's the policy on expensing client dinners?") rather than needing to search for "holiday policy" or "expense policy client meals."
• Contextual Understanding: LLMs can find relevant information even if the exact words aren't present, by understanding synonyms, related concepts, and the overall topic.
• Summarising Results: Instead of just returning a list of documents, the LLM can often extract the most relevant passage or even provide a direct, summarised answer based on the source material.
• Searching Across Silos: Integrated LLM search can potentially pull relevant information from different connected data sources.

In Detail: Imagine a new employee trying to find information on the company's benefits policy. Instead of navigating a complex intranet structure or trying various keyword combinations, they can simply type "Tell me about our health insurance options" into an internal search bar. An LLM-powered system can understand this natural language question, find the relevant section in the employee handbook or benefits document, and present the specific information or a summary. This significantly reduces friction in accessing crucial company knowledge.

Note that to understand the meaning of text, not just generate it, specialised AI models (different from the generative ones) are needed to convert documents and search queries into a numerical format that allows the system to quickly find semantically related information – in other words, finding relevant content based on ideas, not just exact words. These include OpenAI Embeddings, Cohere Embeddings, Google's Embedding models. The LLMs then formulate the answer based on retrieved documents.

The Value: Employees find information much faster, leading to increased productivity and reduced frustration. It decreases interruptions for colleagues who previously had to answer frequent questions. It helps surface and utilise existing company knowledge more effectively, aiding in onboarding and ongoing operations.

Cautions: The accuracy of the answers depends heavily on the quality and comprehensiveness of the internal knowledge base. If the source documents are outdated or incomplete, the LLM's answer will reflect that. There's still a risk of hallucinations if the LLM can't find relevant information and attempts to generate an answer from its general training data. Ensuring data security and access controls are respected is paramount when connecting the LLM to internal systems.

Best Generative Models for RAG: Models strong in question answering and synthesising information from provided sources are good choices for the generative part of a RAG system. At the time of writing, this includes models from the OpenAI GPT family (like GPT-4o or similar), the Anthropic Claude 3 family, or the Google Gemini family (like Gemini 1.5 Pro). Many enterprise search or knowledge management platforms now integrate RAG capabilities using various underlying LLMs and specialised embedding models. As the field advances rapidly, newer models may offer improved RAG performance.

The Problem: Just like customer feedback, businesses generate a mountain of internal text: long reports, detailed meeting transcripts, extensive email threads, research findings, project documentation. It's impossible for employees to read everything in depth, leading to missed information, duplicated effort, and slow decision-making.

How LLMs Help: Similar to summarising external feedback, LLMs excel at condensing lengthy internal texts into digestible summaries.

• Report Summaries: Get the key findings, conclusions, and recommendations from a long research or project report without reading the whole thing.
• Meeting Minutes Summaries: Quickly grasp the decisions made, action items assigned, and key discussion points from a long meeting transcript.
• Email Thread Summaries: Catch up on lengthy email conversations quickly by getting a summary of the main points and outcomes.
• Extracting Key Data: Pull out specific figures, dates, names, or decisions buried within documents.

In Detail: Consider a manager who receives weekly status reports from multiple teams. Instead of reading each multi-page report, they can use an LLM to generate a one-paragraph summary for each, highlighting progress, roadblocks, and key needs. Or someone joining a project late can quickly get up to speed by summarising the project's documentation and past meeting notes. For anyone dealing with a high volume of reading material, this is a game-changer.

The Value: Saves significant time spent reading and processing information, ensures employees are aware of critical points from internal communications and documents, improves knowledge sharing, helps in making faster, more informed decisions by quickly accessing the core of information.

Cautions: Summaries are inherently reductive; they capture the main points but omit details. For tasks where every detail is critical, relying solely on a summary is risky. LLMs might occasionally misinterpret complex arguments or miss subtle nuances in the original text. Validation against the source document is still necessary for important information. Confidentiality and data handling policies must be strictly followed when using LLMs for internal document summarisation.

Best LLMs: Models with large context windows, which allow them to process longer documents effectively, are good choices here. This includes models from the Google Gemini 1.5 family (Pro/Flash), the Anthropic Claude 3 family (Opus/Sonnet), and the OpenAI GPT family (like GPT-4o or similar). Keep an eye on new model releases, as context window sizes and summarisation quality are continually improving.

The Problem: due to globalisation, companies often deal with information and communication in multiple languages – for example, customer inquiries, internal documents, marketing materials, and legal agreements. Manually translating, summarising, or understanding content across languages is time-consuming and requires specialised skills.

How LLMs Help: LLMs are inherently multilingual, having been trained on vast datasets from many languages. They can perform various text-based tasks across language barriers:

• Translation: Translate documents, emails, or customer interactions between languages.
• Cross-lingual Summarisation: Summarise a document written in one language into another language.
• Multilingual Analysis: Analyse feedback or documents written in multiple languages to identify common themes or sentiment across your entire global customer base or operations.
• Localisation Assistance: Help adapt content (like marketing copy or internal training materials) for different linguistic and cultural contexts, beyond just direct translation.

In Detail: Imagine a customer support team receiving inquiries in five different languages. Instead of needing a human translator for each, an LLM can provide quick (albeit imperfect) translations of incoming messages and help draft responses, allowing support agents to handle a wider range of customer requests. A marketing team can use an LLM to get a quick summary of how a product is being discussed on social media in different countries, or a legal team can get a summary of a foreign-language contract.

This capability breaks down language barriers, enabling smoother communication, faster processing of international information, and more effective global operations.

The Value: Improved efficiency in international communication and document processing, faster access to information regardless of language, enhanced customer service for non-English speakers, and enabling broader analysis of global data.

Cautions: While LLM translation is improving rapidly, it's not always perfect. Nuances, cultural context, and highly technical or legal jargon can still be misinterpreted. For critical or high-stakes translation (like legal contracts or medical information), human review by a qualified translator is still essential. Data privacy rules regarding cross-border data transfer must also be considered.

Best LLMs: Most leading general-purpose LLMs (from the OpenAI GPT family, Google Gemini family, and Anthropic Claude family) have strong multilingual capabilities due to their training data. Models specifically benchmarked for translation and cross-lingual understanding would be particularly well-suited. Given the rapid advancements, always verify the latest multilingual performance of models.

Note: Keep in mind that model capabilities are rapidly evolving, so it's always worth checking for the latest versions and benchmarks.