AI-native development uses AI throughout exploration, design, implementation and improvement. Ideas, requirements and working software can evolve together.
The stages still matter.
Explore, define, design, build, test, deploy and improve still structure the work. Each stage helps establish whether the problem is understood, the scope is clear and the software is ready.
AI can turn an early idea into a working example sooner. That example exposes assumptions and gives people something concrete to review. Their feedback shapes the next iteration.
More speed, more decisions.
People still need to understand the business process, recognize awkward handoffs, choose an architecture and decide which tradeoffs are acceptable.
AI helps produce alternatives and revise them quickly. Human direction keeps the build connected to the purpose of the application.
Quality belongs throughout.
Test core workflows, exceptions, permissions and failures. AI can help create tests; people decide what the results need to establish.
Data, access, operation and support requirements depend on the application and its users. Those decisions should shape the build before launch.
A build in practice.
Vision was built in approximately 10 weeks by one developer and is currently in beta with a client. Its scope includes portfolio intake, evaluation, capacity planning, scenario modelling and project execution.
The example shows what focused scope and AI development made possible in one project. Other applications will have different requirements.
Clear scope, regular review and accountable decisions give that speed a purpose.
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