Accelerating productivity with AI is no longer optional — it’s a strategic imperative. The real risk isn’t slow adoption but failing to focus on initiatives that deliver measurable outcomes across the software development life cycle (SDLC).
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Use AI to Accelerate Productivity Throughout the SDLC
Why is AI-driven productivity the new mandate for software engineering leaders?
With mounting pressure to deliver faster, higher-quality software, engineering leaders face resource constraints and rising expectations. Productivity gains can no longer rely on incremental improvements — AI adoption across the SDLC is now essential to bridge capability gaps, reduce friction and drive measurable outcomes.
Download this guide to discover actionable insights and proven strategies that help you turn AI investments into real performance gains for your teams and your organization.
Key actions to accelerate AI-driven productivity across the SDLC
Software engineering leaders who strategically apply AI across the SDLC are 60% more likely to reduce time spent on engineering tasks and code review. Achieving this requires a disciplined, outcome-focused approach — aligning AI strategy, skill development and technology selection to measurable business value.
Gartner has pinpointed the essential actions and decisions for success: defining key metrics, building critical capabilities and prioritizing high-impact use cases. Our insights, benchmarks and tools guide leaders through each phase, offering a flexible framework that adapts to organizational needs and drives sustained productivity and performance improvements.
Define your AI software engineering strategy
Identify key impact metrics, required skills and high-value AI use cases and technologies for your team.
Roll out AI coding assistants and agents
Select the right AI tools, set usage standards and train developers for effective and compliant adoption.
Expand the use of AI agents across the full SDLC
Apply AI to more SDLC stages like requirements, documentation, code reviews, testing and spec-driven development.
Measure and improve the ROI of using AI tools across the SDLC
Analyze net present value (NPV), productivity gains and costs to refine AI use cases and establish best practices.
Scale and optimize agentic AI tools across the organization
Set governance standards, monitor adoption, address resistance and invest in platform engineering for scale.
Decision tools
Get benchmarks, assessments and guides to drive stronger performance.
Unparalleled, proprietary software engineering insights
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