At Diveria, we use AI-First with a very concrete meaning: integrating artificial intelligence as a cross-cutting tool throughout the entire software lifecycle, with human oversight and a clear focus on business outcomes.
No magic promises. No replacing people. No hype.
Below is how this translates into practice, phase by phase.
AI-First in Discovery: Understanding Better Before Building
The discovery phase is usually where the most decisions are made with incomplete information. An AI-First approach does not replace strategic thinking, but it reduces friction and improves the quality of early definitions.
Concrete practices:
- AI-assisted analysis of requirements, interviews, and existing documentation to detect inconsistencies, dependencies, and early risks.
- Creation of living artifacts: functional summaries, scope maps, initial acceptance criteria, and prioritized open questions.
- Support for more informed estimates by combining human experience with historical data and technical pattern analysis.
Result: fewer implicit assumptions, more explicit decisions, and a stronger starting point for delivery.
AI-First in Delivery: Speed Without Sacrificing Quality
This is where the difference between “using AI” and truly working AI-First becomes clear. It’s not just about writing code faster, but about improving the entire development flow.
Concrete practices:
- AI-assisted pair programming to speed up development, catch common errors, and improve code consistency.
- AI-supported creation and maintenance of automated tests, especially for regression and edge cases.
- Technical and functional documentation that evolves alongside the code, not after it.
- Sprint planning and backlog refinement supported by analysis of complexity, dependencies, and technical debt.
Result: shorter delivery cycles, less rework, and teams that spend more time solving real problems instead of mechanical tasks.
AI-First in Support and Evolution: Learning From the System in Production
Many products fail not during initial development, but during the maintenance phase. An AI-First approach extends the use of AI beyond go-live.
Concrete practices:
- Monitoring to detect anomalies, performance degradation, and incident patterns.
- Assistance with root cause analysis based on logs, metrics, and events.
- Support for product evolution decisions using insights derived from real usage and system behavior.
Result: less time firefighting and more capacity to continuously improve the product.
What AI-First Is Not
To be clear, an AI-First approach does not mean:
- Eliminating human roles or delegating critical decisions to models.
- Promising unlimited productivity without process changes.
- Applying AI in isolation, without metrics or control.
At Diveria, AI amplifies the team, always with responsibility, traceability, and sound technical judgment.
In Summary
Adopting an AI-First approach to software development is a way of working where artificial intelligence:
- Supports the process from discovery through support.
- Reduces operational friction.
- Improves decision quality.
- Frees up time for work that truly delivers value.
No hype. Concrete practices. And real impact on how digital products are built and evolved.