What 'AI development' actually means in 2026 beyond the demos. Architectures, costs, evaluation, and how to ship value in 90 days.
Why AI projects still fail in 2026
The model is rarely the problem. Most AI projects stall because teams skip the unglamorous work clean data pipelines, retrieval that actually retrieves, evaluations that catch regressions, and product surfaces users trust. The good news is that 2026 has settled on a small, repeatable set of architectures that work in production.
This guide walks through the AI development patterns we ship most often at AISOVA, what each one costs, and a 90-day plan to get from "we should do something with AI" to a feature that drives measurable revenue or savings.
The four architectures that cover 90% of use cases
