For developers, the best coding happens when they’re in a state of flow. Yet, with all the other tasks developers must handle—finding documentation and understanding processes—it’s much harder to reach than one might think.
When AI exploded onto the scene, the expectation was that developers would be able to reach that state more easily and often. But flow doesn’t happen by simply adding another tool. In order to be effective, AI must actively remove toil, surface the right context at the right time, and nudge better decisions, so developers spend the majority of their day in creative problem‑solving mode, not coordination.
The status quo of AI as it is just won’t cut it anymore. Tool sprawl, manual handoffs, and opaque systems tax every team. AI should be deployed to eliminate that friction—codifying best practices, automating the boring, and turning scattered signals into clear guidance that speeds teams up.
AI as a Teammate, Not Just a Tool
AI is not a bolt‑on. It must be embedded across the software development lifecycle (SLDC) to make every step easier, smarter, and faster—from backlog to prod. When AI has context, it can recommend, generate, and guardrail; when it doesn’t, it just adds noise.
Success requires an AI‑native operating model—shared data, shared context, and shared automation—so insights and actions compound across tools and teams.
It should behave like a teammate with opinions: propose plans, flag risks, and take safe actions—then learn from outcomes. The goal isn’t more code; it’s higher‑quality changes, delivered confidently, with fewer interrupts and faster feedback loops.
Practically, this means opinionated agents at key junctures—planning, PR review, CI/CD, and incident response—that either automate the step or present a one‑click decision with traceable rationale.
The payoff is fewer defects, faster cycle times, and happier developers—because the system optimizes for outcomes, not activity.
Measuring What Matters
In an AI-powered world, high-quality software engineering is non‑negotiable. That’s why leaders are doubling down on AI across R&D and engineering. But with all that investment, the real questions are where AI should be embedded in the SDLC and how to prove it’s actually delivering outcomes.
We advocate measuring value with metrics like lead time for change, change failure rate, time‑to‑restore, then tracing how AI interventions shift each metric over time.
But numbers alone aren’t enough. The quantitative metrics need to be paired with continuous dev‑pulse surveys and postmortems to understand not just where friction occurs, but why—and how AI should adapt. Understanding developer sentiment alongside productivity helps leaders make data-informed decisions to unlock developer flow.
The Future is an AI-Native SDLC
An AI-native SDLC lets context flow freely, builds guardrails into every step, and makes the fastest path the default. Productivity is tracked continuously, not after the fact. In practice, this means a unified data layer, standardized events, and intelligent agents that orchestrate work and surface insights to maximize outcomes.
We’re early, but the direction is clear. Teams that embrace an AI‑native operating model will ship faster with higher confidence and more developer joy. That’s the future we’re building toward.
Andrew’s bio: Chirag Shah is the Head of Product for Developer Solutions at Atlassian, where he leads the vision and execution for Atlassian’s developer-focused products, including Compass, Bitbucket Cloud, and ADO AI. He is responsible for driving Atlassian’s investments in the Atlassian System of Work and delivering the Software Collection to empower software teams worldwide.