What is AI?
Skip the hype. Here's what's actually true.
The AI conversation in 2026 is almost evenly split between useful capability and overblown nonsense. This page is the grounded version: what AI actually does today, what it doesn't, and how to think about it for your business. Aimed at non-technical operators, but precise enough that a developer won't roll their eyes.
The short answer
When most people say "AI" today they mean large language models, software trained on enormous amounts of text and code that has, somewhat surprisingly, ended up able to do a wide range of language and reasoning tasks at near-human quality. ChatGPT, Claude, and Gemini are the consumer faces of this. Underneath, the same models power every serious business AI deployment.
These models are not magic. They predict the next chunk of text given a prompt, repeatedly, very fast. The surprising thing is how much useful capability falls out of doing that well at scale. Writing emails, summarising meetings, classifying documents, holding a voice conversation, generating code: all the same underlying trick.
The other things people call AI today (voice agents, workflow automations, integrations, autonomous agents) are software systems built on top of these models. The model is the engine. Everything around it is the car. Most of the work in production AI is engineering the car, not the engine.
What AI is good at
Where current models genuinely outperform human-only approaches, today, in production.
Language tasks
Reading, writing, summarising, translating, drafting. Anything where the input and output is text or speech, and 'good enough' beats 'perfect'.
Pattern recognition at scale
Spotting patterns in messy data: classifying emails, extracting structure from PDFs, tagging photos, scoring leads. Work humans find tedious because the volume is high.
Creative remixing
Generating drafts, variations, options. Brainstorming. Combining known patterns in new ways. Excellent at producing the first version of something a human will refine.
Voice and audio
Real-time speech recognition, voice synthesis, multi-speaker transcription, conversational responses. Mature enough for production phone agents and field-capture tools.
What AI is bad at
The capability gaps that haven't gone away yet, and likely won't soon. Pretending they don't exist is how AI projects fail.
Precision arithmetic
Models don't actually 'do' math reliably. They predict text. For anything financial or audit-grade, the AI delegates to a calculator or database. Never trust raw model output.
Knowing what it doesn't know
Models will confidently invent facts when uncertain. This is called hallucination. Mitigations exist (retrieval, citation, confidence thresholds) but the underlying tendency doesn't go away.
Genuinely novel reasoning
AI is brilliant at recombining patterns it has seen. It is not good at reasoning through truly novel problems with no analogous example in its training data.
Real-time accuracy
Without explicit retrieval, models only know what was in their training data, which is months or years out of date. Production systems wire up live data sources to compensate.
The major models
Four families dominate practical AI work. They're more similar than they are different, but the differences matter for specific use cases.
OpenAI (GPT)
Strong all-rounder, strong tooling ecosystem, fastest at adding new modalities. Good first choice when you don't have a specific reason to pick something else. Note: their data-handling stance has shifted over time, read the current commercial terms before sending sensitive data.
Anthropic (Claude)
Excellent at long-context work, careful and grounded in how it reasons through complex tasks, very good at code. Tends to have stronger safety defaults out of the box. Our default for production agent work and document analysis.
Google (Gemini)
Tightly integrated with Google Workspace. Massive context windows. Strong at multimodal (images, video, audio). The right call when you're already in the Google ecosystem.
Open-source (Llama, Mistral, others)
Run locally or in your own cloud. Slower to evaluate, more setup, but no per-token costs and no data leaving your environment. Right answer when data sensitivity or volume make commercial APIs infeasible.
Glossary
The terms people use most often when discussing AI for business, defined plainly.
- Large language model (LLM)
- A statistical model trained on huge volumes of text to predict the next word. The thing inside ChatGPT, Claude, and Gemini. Surprisingly capable for tasks far beyond next-word prediction.
- Foundation model
- Same idea, broader scope. Includes vision, audio, and code models alongside language. The big general-purpose models like GPT-5, Claude Opus, Gemini Ultra.
- AI agent
- Software that uses an AI model to make decisions and take actions on its own. A voice agent that handles inbound calls is one. An email-triaging assistant is another. Agents have a goal and a set of tools they can call.
- Workflow automation
- A defined pipeline that runs a fixed sequence of steps when triggered. May or may not include AI in the middle. 'When a form is submitted, summarise the message with Claude, create a CRM record, notify Slack.'
- Integration
- Connecting two systems so they share data. AI integration usually means: take data from System A, process it with a model, push the result into System B.
- Prompt
- The text instructions you give a model. The art of writing good prompts ("prompt engineering") matters less than people think for production use; what matters more is the system prompt, evaluation harness, and surrounding code.
- Retrieval-augmented generation (RAG)
- Pattern where the model is given relevant information from your data before it answers. Lets the AI work with your documents, knowledge base, or live data without re-training the model. Most enterprise AI uses RAG.
- Fine-tuning
- Adjusting a model's weights for your specific use case. Necessary less often than people assume; modern models with good prompts and retrieval cover 90% of practical cases without fine-tuning.
- Token
- Roughly a syllable or short word. Models charge per token. 1,000 tokens is about 750 words. Useful when estimating per-request costs.
- Context window
- How much text a model can read at once. Modern models handle 200,000+ tokens, enough to fit a 500-page document. Larger context isn't always better; cost and latency rise with size.
How to evaluate AI for your business
The right question isn't "should we use AI?" It's "which of our specific workflows would AI actually improve, and by how much?"
Three filters that separate real opportunities from hype:
Filter 1
Is the bottleneck genuinely a language or pattern problem?
AI is great when the work involves reading, writing, classifying, or recognising patterns at scale. It's a poor fit when the bottleneck is physical, regulatory, or genuinely creative judgment that resists automation.
Filter 2
Is the volume high enough to justify the build?
A workflow that happens five times a week, no matter how painful, rarely justifies the engineering cost. A workflow that happens 500 times a week almost always does.
Filter 3
Is 'good enough' acceptable, or do you need 100% accuracy?
AI delivers 'mostly correct' reliably and fast. Some workflows can absorb that with human review. Others can't. Knowing which kind you have is half the planning work.