The State of AI Integration in 2026 — What Actually Works

Not every product needs an LLM. We've shipped chatbots, search enhancements, and content tools — and we've also pushed back on ideas that sounded good but didn't deliver. Here are the patterns that actually create value in 2026 and the ones that usually don't.

What works

Narrow, well-scoped tasks: summarisation, classification, and structured extraction from text. RAG over your own docs and knowledge base for support and internal tools. Assistants that hand off to deterministic flows (e.g. form filling, booking) rather than trying to do everything in natural language. We also see strong results when the model output is validated or edited by rules or humans before it affects state.

What often doesn't

Replacing entire UIs with a single chat box usually frustrates users who know what they want. Fully autonomous agents that make irreversible decisions without guardrails are still risky. And slapping "AI" on a feature that doesn't need learning rarely justifies the cost. We prefer to add AI where it clearly reduces effort or improves quality, and keep the rest simple and predictable.

How we choose

We start with the user goal and the minimum reliable solution. If AI can materially improve that (and we can handle failures and cost), we integrate it in a bounded way with clear fallbacks. That approach has kept our AI features useful and maintainable.