Software development is at an inflection point: speed, quality, and cost are all being rewritten by AI.
AI isn’t just learning to code like humans; it’s dismantling the human-first model of software development and rebuilding it as machine-first. OpenAI’s Sam Altman predicts that by the end of 2025, AI will likely be better at coding than any human. Meta CEO Mark Zuckerberg expects AI to handle half of all software development at Meta within a year. Anthropic CEO Dario Amodei says we may be only three to six months away from AI writing 90% of code, and within a year, nearly all of it. Microsoft CTO Kevin Scott projects that 95% of all code will be AI-generated by 2030, while Satya Nadella notes that at Microsoft, 20–30% of code is already written by AI today.
The message is clear: AI isn’t a plugin for developers; it’s the new foundation of software itself. But here’s the reality: adoption remains limited. A 2025 McKinsey survey found that only 17% of organizations report regularly using generative AI in IT, even though IT is one of the leading functions for deployment.1
That is not because the technology isn’t capable, but because they’re applying it in the wrong way – too narrow, too tacit, without explicit culture change, and with challenges in measuring the progress. Instead of focusing on the handful of recurring friction points that cause the most delay and waste in end-to-end software development, teams focus on coding only, leaving the core bottlenecks untouched. The fastest way to realize AI’s promise is to target those high-impact areas and truly think end-to-end, and measure progress relentlessly. And once that’s in place, your outsourcing partners should be held accountable for meeting and proving the expected results.
Leaders Are Getting There. Most Aren’t.
A handful of organizations are already showing what’s possible. They’ve embedded AI across the entire software development lifecycle (SDLC), not just in code generation. They’re also driving adoption through management and culture change, and they can measure improvements in speed, quality, and cost. Agents help them prioritize backlogs, keep documentation current, expand test coverage, and make retrospectives actionable.
Leaders like Airbnb have demonstrated what’s possible when AI is embedded where it counts. Airbnb compressed 18 months of test migration work into just six weeks using large language models. Beyond single-company examples, research backs this up: GitHub reports that Copilot users complete coding tasks up to 55% faster, and McKinsey’s findings show generative AI can cut development time by nearly half. But for most teams, adoption is limited. Efforts stall after coding experiments, while bottlenecks in testing, maintenance, and deployment go untouched. Tool sprawl compounds delays, making core workflows like backlog grooming and reviews slower and more error prone.
The issue isn’t whether AI works. It’s whether you’re applying it to the work that matters most.
From Blockers to Breakthroughs
The real breakthrough isn’t adding AI to coding tasks — it’s rethinking the entire SDLC as AI-first. Instead of human-first workflows patched with tools, teams need to flip the model: let AI handle the repetitive flow and bring humans in for judgment and creativity. Breakthrough comes when AI is measured not at the task level but across delivery speed, quality, and cost end to end. It also depends on applying AI continuously from the first requirements through coding, testing, and deployment in one consistent flow.
That transformation depends on four dimensions:
- Mindset Shift: Put AI first, human validation second. Flip the model: let AI handle the routine flow, and reserve human effort for refinement and oversight. In regulated sectors like healthcare, finance, and defense, this shift must be grounded in rigorous human review.
- Measurement Discipline: Track delivery speed, quality, and cost end-to-end, not just isolated productivity gains. Strong validation is essential to prevent over-reliance as AI adoption scales.
- Lifecycle Coverage: Extend AI beyond code into planning, testing, deployment, and retrospectives.
- Sustained Improvement: Bake AI into governance so every sprint compounds learning and efficiency.
This framework elevates adoption from experiments to systemic transformation.
Each blocker in the SDLC is more than just a pain point, it’s a starting line. The exhibit below shows how those blockers, when reframed with the right AI interventions, become the raw material for measurable improvements in speed, quality, and cost.
| SDLC Stage | Common Friction Points & Bottlenecks | Generative AI/Agentic AI Application | Key Metrics to Measure Impact |
| Plan & Design | Vague or incomplete requirements. Inefficient backlog grooming. Time-consuming user story creation | AI-Assisted Requirements Analysis: Refine raw ideas into structured user stories and acceptance criteria Automated Backlog Prioritization: Suggest priorities based on historical data and business impact | Speed: Reduced planning cycle time Quality: Improved story point accuracy; fewer requirement-related bugs Cost: Less time spent by product managers on manual tasks. |
| Development | Slow code generation for boilerplate tasks. Inconsistent coding standards. Outdated or missing documentation. | Code Generation & Autocompletion: AI agents write routine code, functions, and unit tests. Automated Code Refactoring: Suggest improvements for readability, performance, and maintainability. Real-time Documentation Generation: Create and update documentation (e.g., READMEs, API docs) as code is written. | Speed: Increased developer velocity; reduced time-to-first commit. Quality: Lower code churn; better adherence to standards. |
| Review & Test | Manual, time-consuming code reviews. Inadequate test coverage. Difficulty identifying security flaws. Longer test cycles and lack of complete test automation | AI-Powered Code Reviews: Automatically scan for bugs, style issues, and security vulnerabilities. Automated Test Case & Script Generation: Create comprehensive unit, integration, and E2E tests based on code and requirements. Intelligent Test Execution: Prioritize which tests to run based on code changes. | Speed: Faster code review and test execution cycles. • Benchmark: Good = 4–12 hours review time (LinearB, 2025c). Quality: Increased test coverage; lower defect escape rate; improved security posture. • Benchmark: Good = 1–4% change failure rate (LinearB, 2025d). Cost: Reduced manual QA effort; lower cost of fixing bugs post-release. |
| Deploy & Operate | Complex deployment configurations. Manual incident root-cause analysis. Reactive monitoring and alerts | Intelligent CI/CD Pipeline Analysis: Predict and flag potential deployment failures. AI-Driven Root Cause Analysis: Analyze logs and metrics to quickly identify the source of incidents. Proactive Anomaly Detection: Identify performance issues before they impact users. | Speed: Higher deployment frequency; reduced Mean Time to Resolution (MTTR). • Benchmark: Good = 0.5–1 deployments per day (LinearB, 2025e). Quality: Lower change failure rate; improved system reliability and uptime. • Benchmark: Good = 1–4% change failure rate (LinearB, 2025d). Cost: Reduced operational overhead and downtime-related losses. • Benchmark: Good = 6–11 hours MTTR (LinearB, 2025f). |
| Measure & Iterate | Biased or incomplete retrospective feedback. Difficulty synthesizing lessons learned. Manual analysis of project metrics | AI-Assisted Retrospectives: Analyze commits, PRs, communication logs (e.g., Slack, Jira) to identify actionable insights. Automated Insights Generation: Synthesize metrics across the SDLC to suggest process improvements. | Speed: Faster, more efficient retrospective meetings. Quality: More objective, data-driven insights for process improvement. Cost: Maximized value from team feedback sessions. |
Breakthroughs last only with the right foundation. AI-first development depends on elements that turn those dimensions into practice.
Building the Foundation
Getting started with AI-first development isn’t about edge experiments — it’s about establishing the right foundation from day one. That begins with choosing an end-to-end platform that spans requirements, coding, testing, and deployment, and putting a coaching and enablement team in place to drive adoption. Pilots and playbooks help teams shift the culture from “I build, AI assists” to “AI builds, I guide” until it becomes second nature.
From there, measure relentlessly. Every sprint should track improvements in speed, quality, and cost against a pre-AI baseline. Signals from issues, tickets, commits, chats, and logs provide the proof that adoption is working and compounding over time.
Yet even the strongest internal foundation isn’t enough if external partners aren’t aligned. Providers must be held to the same standards of discipline and transparency as internal teams.
The Provider Accountability Test
Long-term outsourcing contracts are often the elephant in the room. They lock leaders into cost structures without delivering the promised results. But even within those contracts, leverage exists:
- Redefine Incentives: Shift provider compensation toward measurable business outcomes (time-to-market, defect rates, cost per feature) rather than hours worked.
- Insert AI Clauses and Escalate Transparency: Require AI-first practices and demand regular, benchmark-aligned reporting. If a provider resists, that resistance is its own signal.
- Bring in a Challenger: Even if you can’t switch today, introducing a challenger provider signals to incumbents that complacency is no longer acceptable.
A high-performing provider should guide you toward these benchmarks, share metrics openly, and be willing to tie compensation to measurable results.
The Bottom Line
The gap between AI’s hype and delivered results isn’t about the technology; it’s about whether organizations apply it end to end, embed it into culture, and measure progress relentlessly. The winners won’t be those that limit adoption to one part of the lifecycle, but those that extend it across planning, coding, testing, and deployment. That’s how AI moves from promise to impact.
The choice is simple: keep waiting for AI to live up to the promise, or start building the systems, metrics, and partnerships that make it happen now.
¹ Whatfix. (2025, January 15). AI adoption by sector: A comprehensive analysis. Whatfix Blog. https://whatfix.com/blog/ai-adoption-by-sector/
2 Table
LinearB. (2025). What is cycle time in software development? LinearB. https://linearb.io/blog/cycle-time
LinearB. (2025). Software engineering benchmarks report. LinearB. https://linearb.io/resources/engineering-benchmarks
LinearB. (2025). Engineering metrics community benchmarks. LinearB. https://linearb.helpdocs.io/article/d2v8kqzxzd-metrics-community-benchmarks
