人工智能开发生命周期的兴起

From autocomplete to autonomous agents. By Purna Doddapaneni, Bill Radzevych, Chris Bell, CyrilleVincey, and Michael Blake Brief The Rise of the AI Development Life Cycle From autocomplete to autonomous agents. By Purna Doddapaneni, Bill Radzevych, Chris Bell, Cyrille Vincey, and Michael Blake8min read At a Glance. AI is creating a seismic shift in software development.. Today’s AI-assisted development will give way to AI-led development, with hybrid human-agent teams delivering 5 times to 10 times productivity gains.. Successful pilots are satisfying, but real value comes from end-to-end transformation thatreinvents workflows, teams, and measurement.. The traditional divide between product and software development is breaking down, givingrise to an integrated, AI development life cycle. Over the past several years, we’ve tracked steady progress in software development productivity.In 2024, we observed gains in the range of 10% to 15%, with leaders reaching as high as 30%. By2025, it became clear that some companies were achieving sustained improvements beyond thatrange through transformational change, rearchitecting the software development life cyclearound AI. Today, those benchmarks already feel outdated. In the wake of what many are calling the “Anthropic moment,” which is a shift from pointsolutions or individual cases to AI that can execute end-to-end workflows, expectations areaccelerating dramatically. This evolution isn’t incremental; it is redefining what’s possible fromAI assisted to AI led. Expectations for software engineering are accelerating at an unprecedented pace. The idea of the“5 times to 10 times engineer” is no longer theoretical, quickly becoming reality. Executivesentiment is evolving just as fast. In our 2024 survey, leaders projected 20% to 30% gains insoftware development productivity. Today, those expectations have surged, with many nowanticipating improvements of 5 times to 10 times over the next several years. At the same time,AI’s role in the software development life cycle is expanding rapidly. What was once seen as asignificant contribution by roughly half of executives is now approaching near-universaladoption. This shift is redefining the role of engineering as the foundation for broader enterprisetransformation. It’s not enough for engineering teams to deliver codefive times faster; businessteams must generate demand at the same pace, and operations must match that speed to deploy,scale, and support solutions in production. Unlocking the full value requires an end-to-end transformation across the entire delivery chain.Organizations that rise to this challenge will realize meaningful cost savings, higher throughput,and faster time to market, turning engineering velocity into a true competitive advantage. AI assisted era Synchronous engagement with developers querying and engaging with AI as they continue tolead development workflows•AI assists developers in the integrated development environment•Humans still drive sequencing, review, and integration• AI-led era Asynchronous engagement with agents orchestrating and completing work independently•Agents generate code, tests, and documentation•Pull requests created and iterated with human review•Humans focus mostly on direction setting, architecture, and work packaging (cleaner tickets,criteria)• What once looked like ambitious progress now represents the baseline for a fundamentallydifferent era of software development. Where efforts falter Bain’s researchfinds that while most companies are still seeing only single-digit improvementsin efficiency, their expectations are much higher. About half are hoping for faster time to marketand more productive engineering teams. Many already see benefits, with 63% reporting higheroutput per engineer and 53% seeing faster release cycles and shorter time to market. Beyondthose primary goals, executives also believe that AI can help improve market position, improveuser experience, strengthen security, and make developers’ work more enjoyable. Where do most efforts stall? Most companies start by optimizing a single activity, such as code generation, test creation, orrequirements drafting. That may be satisfying, but sometimes the bottleneck just moveselsewhere. Unlocking real value requires broader changes in behavior and organization. Rolling out lots of pilots may also feel like success, but pilots don’t necessarily translate into realusage or business impact. Without new workflows, measurement, and guardrails, companies arelikely to see adoption plateau and only minimal new value. Others focus too narrowly on code completion, which is important but not the end game. Ahybrid model unlocks more value: integrated development assistance for tight loops, combinedwith agent mode for multistep work. Teams also resist change. This isn’t a fad; it’s the skill set of the future. But change is always hard,and teams need enablement, examples, and reassurance to learn a new