Software Improvement Group has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Technical Debt Management Tools.
The trends and insights IT leaders need to govern what AI is building in 2027.
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90% of technology professionals now use AI at work, and the volume of code entering organizations is rising faster than at any point in software history. Review capacity hasn't kept up. Where quality is measured and managed, AI accelerates delivery. Where it isn't, AI accelerates debt, cost, and exposure at the same speed. Six chapters break down where that pattern is already showing up.
It's also less maintainable than human-written code, especially in large systems, and non-agentic AI spend for a 50-developer team already costs the equivalent of nearly one extra developer per year.
That's what's recovered by moving a system from 2-star to 4-star maintainability. Systems with lower code-level technical debt also see up to 72% stronger security compliance.
That's above the 86% global average. The average portfolio also runs 3 to 4 core technologies, adding complexity most teams haven't accounted for.
Large systems, the ones most likely to be business-critical, score lowest of all. The average system carries 20 critical security findings.
Second-worst of any industry benchmarked. Nearly 60% of AI leaders cite legacy-system integration as their top barrier to deploying agentic AI.
Only 1.5% of enterprise systems in production currently qualify as AI systems, but human-in-the-loop builds already score far higher than fully autonomous ones.
The findings on the pages that follow come from our own benchmark data on IT and software services systems specifically, set against the best independent research available. Some of it is encouraging. Much of it should be food for thought. All of it is meant to help you go faster — responsibly.
Each chapter speaks to a different seat at the table. Start with yours, then read the rest to understand what your peers are working against.
Technical debt already consumes 21–40% of total IT spend. Find out where that capacity sits, and what recovering it is worth.
Chapters 02, 03Understand what agentic coding actually costs in tokens, and where AI output needs a harness before it reaches production.
Chapters 01, 06See where AI-generated and open-source code is carrying more risk than your review process is built to catch.
Chapter 04Legacy-system integration is already the top barrier to agentic AI adoption. See what that means for your roadmap.
Chapter 05