It literally took decades for technologies like the PC and the internet to reach billions of people, but generative AI reached 53% of the world's population in just three years. The Stanford AI Index 2026 reports that AI use among organizations increased from 78% to 88% in a single year, showing how quickly LLMs have moved into everyday business work.
Implementing AI and putting it to work at scale are two different challenges. The report shows that AI agent use is still in the single digits across most business functions. Many companies use AI, but only a few are using AI agents for regular business work at scale.
If you're looking for ways to use LLMs in your product or business, the biggest challenge is choosing the right use case and building it well. At GoML, our Applied AI FDEs help clients choose and test the right models, identify use cases worth building, and take ideas to a 4-day POC and then to production. This guide covers LLM use cases across different industries and job roles.
What is a Large Language Model (LLM)?
A large language model (LLM) is an AI system trained on large amounts of text to understand and generate language. It can answer questions, write content, translate languages, and write code.
Most LLMs use transformers, which understand relationships between words and context and use that to predict the most likely next word. During pre-training, LLMs learn grammar, language patterns, and general information from text without human labeling. Unlike older training methodologies that process text one word at a time, transformers process multiple parts of a sequence at once, making LLMs better at contextualizing content. .
How LLMs are different from traditional AI
Traditional AI and large language models (LLMs) can both help businesses automate work, but they work differently. Traditional AI usually follows fixed rules or learns from labeled data to perform a specific task, while LLMs learn from large amounts of text and can handle many different language tasks using one model.
What work can LLMs handle?
LLMs started as text tools, but they can now read, write, analyze, code, and take action when connected to business systems. Here are the main tasks they handle and how businesses use them.
1. Content generation and rewriting
LLMs can create new content or rewrite existing text quickly.
- Writing: Draft emails, blog posts, product descriptions, reports and documentation.
- Rewriting: Change the tone, style or length of existing text.
- Personalization: Adapt messages for different customers, roles, or markets.
Example: Ojje used AI for story generation to alter its publishing time by 98%.
2. Summarization
LLMs can turn long documents and conversations into shorter summaries.
- Summarize contracts, research papers, financial filings and policy documents.
- Turn meeting and call transcripts into action items.
- Create short summaries of lengthy reports.
Example: Patient7 improved clinical documentation with AI medical transcription and real-time SOAP notes.
3. Text analysis and data extraction
LLMs can turn unstructured text into structured data that businesses can use.
- Classification: Sort support tickets, emails or documents by topic or urgency.
- Sentiment analysis: Identify positive, negative or neutral feedback.
- Information extraction: Pull names, dates, amounts, and clauses from documents.
Example: Ledgebrook used AI for liability code extraction from insurance documents and achieved 95% classification accuracy.
4. Question answering and knowledge search
LLMs can search company knowledge and answer questions in plain language.
- Answer employee and customer questions.
- Search internal wikis, policies and document libraries.
- Use retrieval-augmented generation (RAG) to answer questions using company data.
Example: Ledgebrook's underwriting chatbot cut document retrieval time by 70%.
5. Conversational AI
LLMs can power chatbots and virtual assistants that handle multi-turn conversations.
- Answer customer support questions.
- Guide users through onboarding, troubleshooting or bookings.
- Support internal help desks and learning assistants.
Example: We built a conversational AI chatbot for Miden, a modern issuer processor, that reduced its support workload by 58%.
6. Translation and localization
LLMs can translate content while keeping its meaning and tone.
- Translate product content, support articles and documents.
- Adapt content for different markets.
- Support multilingual customer service.
Example: Lokal used Gen AI to summarize news in local languages, helping it serve readers in the languages they speak.
7. Coding and development
LLMs can help developers write, review, and improve code.
- Write code in Python, JavaScript, SQL and other languages.
- Find bugs, explain errors and suggest fixes.
- Generate tests and documentation.
- Turn business questions into SQL queries.
Example: At GoML, AI coding is a part of how we form solutions for our clients. Our engineers use AI Matic, which is our in-house platform that generates production-ready code from proven blueprints, alongside AI coding agents such as Claude Code. We follow spec-driven development (SDD), defining what the system should do before coding begins so that agents can build against a clear specification. Over the past six months, AI Matic has been used in more than 95% of our AI builds which supports delivery of 4-day POCs to 12-week production systems. This year we have deployed more than 40+ AI systems into production.
7. Software testing and bug fixes
LLMs can help teams test software faster and catch problems before code goes live.
- Find bugs, explain errors, and suggest fixes.
- Generate test cases and review code for common issues.
- Create technical documentation.
Example: We helped DevPlaza use AI agents to review pull requests, identify code vulnerabilities, and suggest improvements to software testing.
8. Reasoning and problem-solving
Reasoning models can work through multi-step problems and compare different options.
- Break down business, math, and logical problems.
- Compare options against specific criteria.
- Support planning and analysis.
Example: Bosch boosted decision-making speed by 60% with a financial analysis AI copilot.
9. Multimodal tasks
Many LLMs can work with text, images, audio, and video.
- Read charts, scanned documents, screenshots, and diagrams.
- Transcribe and analyze calls and meetings.
- Answer questions about images and visual content.
Example: AI for retinal disease detection increased triage speed by 85% for Asia's second-largest eyecare chain.
10. Agentic workflows
When connected to tools, APIs, and business systems, LLMs can complete work instead of just answering questions.
- Break a goal into steps and carry them out.
- Update records, route work, and create actions.
- Pull live data from systems.
- Spin up or call other agents for specialized tasks.
Example: Dairyland Power uses a GoML-built AI powered work order automation that reviews and processes work orders uploaded as PDF or Word files, boosting efficiency by 70%.
Leading LLMs in 2026
Some LLMs perform better at complex reasoning and coding. Others are better at audio and video, offer lower costs, or can be hosted on your own servers. Closed models such as Claude, GPT, and Gemini still lead in overall performance, while open-weight models such as DeepSeek, GLM, and MiMo offer strong performance at a lower cost.
Types of LLMs
LLMs fall into two main types, based on how you access and use them.
- Proprietary (closed) models run on the provider's own servers and you can use them through an API or app. You can't see or change the model itself. The provider handles infrastructure, updates, and safety features, so teams can get started quickly. These models usually lead on convenience and breadth of capabilities, but they come with ongoing usage costs and limited control over token usage and inference residency.
- Open-weight models let you download the model and run it on your own servers or private cloud. You get full control over your data and can fine-tune the model for your needs, which makes them a strong fit for regulated industries and high-volume workloads. The trade-off is that you need the right hardware and skills to host and maintain them.
Many businesses now use both: proprietary models for broad, open-ended work and open-weight models for routine, data-sensitive work that needs to be performed at scale.
Top LLM providers 2026
These are the companies building the most widely used LLMs today.
Different models have different strengths
All the models have different capabilities and what they built to do sets them apart. Some are better at complex reasoning and coding, while others focus on speed, lower costs, multimodal tasks, or running on your own servers.
- Reasoning: Some models handle complex coding, analysis, and planning well.
- Speed and cost: Smaller models can respond faster and cost less, making them a good fit for simple, high-volume tasks.
- Multimodal tasks: Some models can work with images, charts, documents, audio, and video along with text.
- Agentic work: Many models can use tools, browse the web, and complete multi-step tasks.
Propr ietary models such as Claude, GPT, and Gemini still perform strongly across many tasks, while open-weight models such as DeepSeek, GLM and MiMo can offer strong results at a lower cost.
Top LLMs ranked by intelligence
GoML tracks these models and tests them on real enterprise AI client work. We have also listed the leading LLMs based on the Artificial Analysis Intelligence Index, along with what each model does best based our assessments.
How to choose the right LLM for your use case
Choose the right LLM according to your needs, not the model everyone is talking about. Define your requirements, compare models against them by testing their performance with real data before deciding.
Do not consider model at first, begin with the task
Before checking any leaderboard, answer three questions:
- What should the model do? Answer FAQs, summarize reports, extract data from forms, write code, or handle a multi-step agent? Each task needs different model capabilities.
- How complex is the task? A simple FAQ bot needs less reasoning than a tool that reviews contracts or supports complex decisions. Using a top-tier model for a simple task can also increase costs without adding much value.
- What type of data will it handle? Text, scanned documents, images, audio, or video? This can quickly narrow down your model choices.
Set you requirements before comparing models
Use benchmarks to shortlist, then test with real data
Public leaderboards can help narrow dozens of models down to two or three. But a high score does not guarantee good results for your use case. A model may perform well on general tests but struggle with industry terms, document formats, schema, or customer questions.
The final choice should come from testing the models on your own work. Collect real examples from your workflow and define what a good answer looks like for each one. Run them through every shortlisted model and compare:
- Accuracy and completeness
- Latency
- Performance on edge cases
- Token and cost per activity
Use the same scoring criteria for every model. For larger test sets, an LLM can help evaluate responses, with human reviewers checking the results. Keep adding real failures to the test set so future model and prompt changes are tested against known issues.
Score each model side by side
Rate each shortlisted model from 1 to 5 on the factors that matter most to your use case, such as accuracy, speed, cost, privacy, and integration. Give higher weight to the factors that matter more. For a legal review tool, accuracy may count twice. For a high-volume support bot, cost and speed may matter more.
This process may show that no single model performs best across every category. That is normal. Many teams use more than one model, with a stronger model for complex tasks and a faster, lower-cost alternative model for routine work.
Test one use case first
When your model choice is made, built out the first iteration or prototype of your use case. Set clear goals such as time saved, accuracy, or faster results. If it meets your goals, you can expand its use.
At GoML, we adopt the same methodology with our clients. We define the ask, set the requirements, test a few models with real data, and then compare the results. This helps us choose a model based on actual results. After that, we move from a quick proof of concept to a pilot and finally to production.
The drawbacks with subjective model testing
Many teams test an LLM by trying a few prompts, reading the answers, and choosing the model that feels best. I get it, it's easy and we’re trained to form immediate impressions. The approach can be disastrous for picking LLMs for enterprise use cases. It may work for an initial check, but it is just not enough to choose an LLM for production scale. Here’s why.
The same prompt can give different answers
Traditional software is deterministic. The path from input to output is clear, programmed, and verifiable. But LLMs can give different outputs each time because their responses are based on probabilities.
Even small changes in wording or punctuation can affect the result. Model providers regularly release updates that can change how a model responds, so a prompt that worked well on a different model may behave differently on a newer (or older) model without manual testing catching the change. Prompt regressions are still not an established practice.
People judge answers differently
Most LLM tasks do not have one correct answer, so an individual may find a response clear and useful while another may not. Reviewers also prefer answers that sound confident or look well written even when the facts are wrong. And LLMs are very good at confidently sharing responses that are plain wrong.
Without the same scoring rules for everyone, test results can depend more on who reviews the answer than on the quality of the model.
A few prompts are not enough
Real users ask questions in many different ways. They may make typos, leave out details, ask vague questions, or have long conversations. Testing only a few prompts covers a small part of real use. This means the problems that appear in production can easily be missed during manual testing.
Manual testing takes too much time
Writing prompts, checking responses, and recording issues by hand takes time. Every new model or prompt change means repeating the same work. This can slow down testing and make it harder to catch problems before launch.
Hidden risks can be missed
Quick manual tests may not catch issues such as prompt injection, attempts to bypass safety rules, sensitive data leaks, biased responses, or confident but incorrect answers. These issues need planned testing to find them.
Benchmark scores are not enough
Public benchmarks can help compare models, but they do not show how a model will perform in your system. Your prompts, data, retrieval setup, and tools also affect the final response. A model with a high benchmark score can still fail when working with your documents, terms, or workflows.
