Introduction to Artificial Intelligence in Software

What Counts as "AI" in Software Today

When a product team says "we use AI," they could mean any of half a dozen things: a recommendation model, a fraud-scoring classifier, a document-summary LLM, a chatbot, an image classifier, or an agentic workflow that strings several of these together. Each behaves differently, fails differently, and costs differently to run.

Where AI Earns Its Keep

The most reliable wins in 2026 come from a small number of patterns:

• Search and retrieval — semantic search and RAG over your own documents

• Classification and ranking — fraud detection, lead scoring, content moderation

• Generation in narrow contexts — drafting replies, summarising tickets, producing structured data from messy input

• Forecasting — demand, churn, capacity planning

What Makes a Good AI Use Case

Three signals usually matter:

1. Tolerance for fuzziness. AI is probabilistic. If a wrong answer 1 in 200 times is unacceptable, you need either a much better model or a deterministic fallback.

1. Volume. AI shines when humans can't keep up — millions of tickets, transactions, or documents per day.

1. Cost of being wrong. Pair AI confidence scores with human review for high-stakes decisions.

What to Build vs. What to Buy

Off-the-shelf APIs (OpenAI, Anthropic, Google, AWS) handle most generative use cases. Build custom models when you have proprietary data, latency requirements, or regulatory constraints that rule out third-party APIs.

The Bottom Line

AI in software isn't one thing — it's a toolbox. Pick the simplest tool that solves the problem, measure it ruthlessly, and design fallbacks for when it gets things wrong.

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