Artificial intelligence (AI) is the umbrella term for software systems that perform tasks once associated with human cognition: understanding language, recognising images, making predictions, planning actions and writing code. In 2026, the term mostly refers to machine learning systems trained on enormous datasets, with large language models like GPT-5, Claude Sonnet 4 and Gemini 2.5 dominating public attention.
A useful way to picture AI is as a stack with three layers. At the bottom is infrastructure (GPUs, training clusters, vector databases). In the middle sit models (transformer LLMs, diffusion image models, speech models). On top live applications — chat assistants, copilots, agents, marketing tools — that you actually interact with.
For a US business buyer, "AI" typically means buying access to a hosted model through an API or chat interface and wiring it into existing workflows. A marketing team might use ChatGPT for first drafts, an SEO team might use Claude inside Cursor to ship landing pages, and a support team might use a RAG pipeline over Zendesk tickets. None of this looks like the science-fiction "general AI" — it looks like fast, statistical pattern matching that earns its keep when the workflow is well-defined.
Three distinctions worth knowing:
- Narrow vs general: today's products are narrow AI — extremely good at specific tasks. AGI (artificial general intelligence) is the still-hypothetical goal of human-level performance across any domain.
- Discriminative vs generative: discriminative models classify or score (spam vs not-spam). Generative models produce content — text, images, audio, video, code.
- Symbolic vs statistical: classical AI used hand-coded rules. Modern AI is statistical and learned from data; rules are emergent, not written.
The practical implication for any team adopting AI is that the model is rarely the bottleneck. The bottleneck is usually data quality, prompts, evaluation, and integration. Picking the right model matters less than building the workflow around it: where does the input come from, how do you validate the output, and who owns the result?
If you are getting started, the cleanest entry path is to learn three things in order: how to write a clear prompt, how to attach your own data via retrieval-augmented generation, and how to chain steps into an agent. Every other skill — fine-tuning, evaluation, infrastructure — extends from those primitives.
