Blog Information

• Posted By : gleb andreev
• Posted On : Oct 04, 2025
• Views : 8
• Category : Technology
• Description : AI has reached a tipping point in software development. It’s no longer a sidekick that autocompletes a few lines of code, it’s an engine that accelerates planning, coding, testing, modernization, and release. The question for leaders isn’t whether to adopt AI, but how to turn localized boosts into end‑to‑end value.

Overview

• AI has reached a tipping point in software development. It’s no longer a sidekick that autocompletes a few lines of code, it’s an engine that accelerates planning, coding, testing, modernization, and release. The question for leaders isn’t whether to adopt AI, but how to turn localized boosts into end‑to‑end value.

  Stats That Matter

  The 2025 DORA Report finds 90% of software professionals now use AI at work (up 14% YoY), with a median 2 hours/day spent with AI tools. Over 80% report productivity gains and 59% see code‑quality improvements, yet a “trust paradox” persists about 30% trust AI outputs only a little or not at all.

  Crucially, DORA cautions that AI amplifies what already exists: strong teams get stronger; weak processes become bottlenecks unless platform engineering, testing, and feedback loops keep pace.

  Recent Updates to Know

  • Agentic app modernization (Microsoft): New AI agents in GitHub Copilot and Azure Migrate attack enterprise technical debt, delivering up to an 88% reduction in manual migration effort for .NET/Java upgrades compressing months of work into days.
  • Coding copilots level up (OpenAI & Microsoft): GPT‑5 Codex and deeper IDE integrations push beyond code suggestions to real‑world engineering tasks feature building, refactoring, tests, and code review. Microsoft previews AI “woven” into future Visual Studio rhythms, from test generation to pre‑PR insights.
  • Quality at AI speed (Greptile v3): A new AI code‑review agent claims 3× more critical bug catches than its prior version, integrating with GitHub/GitLab, Jira, and Notion to keep up with agent‑generated code volume.

  Together, these signal a shift from assistive snippets to lifecycle orchestration.

  How AI Is Rewriting the SDLC

  1. Requirements & Planning: Generative AI converts business goals into draft user stories, acceptance criteria, and test plans. DORA’s findings show throughput rises when teams pair AI with small batches, fast feedback, and clear AI usage stances otherwise velocity amplifies instability.
  2. Build & Refactor: Tools like GPT‑5 Codex and Copilot now tackle large‑scale refactoring and boilerplate elimination, freeing engineers for architecture and resilience work.
  3. Test & Review: AI‑generated tests plus independent validation layers (e.g., Greptile) catch regressions at the pace agents produce code.
  4. Operate & Modernize: Autonomous agents now plan migrations, fix dependency chains, and update runtimes turning technical debt into a programmable backlog.
  5. Measure Value: Bain’s analysis shows the biggest returns come when leaders redesign processes across the lifecycle not just code, so time saved is reinvested where it moves the business.

  Enterprise Lens: Data, Platforms, and ERP

  Successful AI requires platform thinking and good data plumbing. Digital Transformation Firms should prioritize platform engineering (golden paths, paved roads) and Data Warehouse Services that feed AI clean, governed context for test generation, RAG, and analytics. On the application side, ERP suites like Acumatica Cloud are weaving AI into forecasting, finance automation, and supply insights multiplying value when paired with modern data estates and lifecycle automation.

  Operating Playbook (What to Do Next)

  1. Treat AI as a systems change, not a tool swap. Publish a clear AI stance, instrument your pipelines, and measure outcomes beyond local productivity (DORA AI Capabilities Model).
  2. Shift left on quality. Combine AI‑generated tests, coverage gates, SAST/DAST, and independent AI code review to counter the trust gap.
  3. Modernize strategically with agents. Use agentic migration for legacy estates to unlock capacity (and budget) for innovation.
  4. Invest in data foundations. Empower AI with governed, discoverable data sets via your Data Warehouse Services; wire telemetry so AI sees real context.
  5. Keep humans in the loop. Make review, sign‑offs, and rollback paths non‑negotiable to reconcile speed with safety (DORA stability insight).

  The Bottom Line

  AI is rebuilding the software factory from agentic modernization to continuous code validation, but the winners won’t be those who copy/paste prompts. They’ll be the organizations that redesign their platforms, processes, and data to let AI amplify excellence at every stage of delivery.