The “patterns of AI” are repeatable ways artificial intelligence is used to solve real problems. Thinking in patterns makes it easier to match the right AI approach to the job—whether that’s spotting fraud, generating content, or automating decisions. Below are seven widely used AI patterns, along with what each one is best at.
This pattern uses historical data to estimate what will happen next, such as demand forecasting, churn prediction, or delivery-time estimates. The output is usually a probability, score, or numerical forecast.
Classification assigns an input to a category—like “spam vs. not spam,” “defective vs. pass,” or “high-risk vs. low-risk.” It’s one of the most common patterns for operational triage and routing.
Recommendation systems suggest products, content, or actions based on behavior and similarity signals. Personalization extends this by tailoring what each person sees, when they see it, and how it’s presented.
This pattern focuses on identifying what looks unusual compared to normal behavior, such as fraud spikes, sensor failures, or suspicious logins. It’s especially useful when “bad” examples are rare or constantly changing.
Here, AI extracts meaning from text or speech to answer questions, summarize, route support tickets, or power chatbots. The goal is to interpret intent and respond in a helpful, context-aware way.
Computer vision enables AI to interpret images and video—detecting objects, reading text (OCR), checking quality, or measuring movement. It’s often paired with edge devices and real-time decisioning.
Generative models create new content—text, images, audio, code, or structured outputs. This pattern excels at drafting, brainstorming, transforming formats, and producing variants quickly when guided by clear constraints.
For a deeper walk-through and more context on how these patterns show up in real applications, visit the main guide on AI patterns.
They provide a practical shortcut: instead of starting from scratch, a team can map a business goal (like reducing fraud or improving support) to a proven AI approach, required data, and expected outputs.
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