Software Improvement Group named Gartner leader on debt

Software Improvement Group has been named a Leader in Gartner's Magic Quadrant for Technical Debt Management Tools, as companies increase their use of AI coding tools.

The Amsterdam-based software consultancy said the recognition reflects growing demand for tools that track and manage technical debt across software portfolios, with particular focus on architectural debt rather than isolated code defects.

Technical debt has long described the cost of poor or rushed software decisions that later require extra work to fix. In SIG's view, that pressure is now moving beyond developer teams into broader business oversight as AI-assisted development increases the volume of new code entering enterprise systems.

That shift is central to the market SIG is targeting with Sigrid, its software portfolio governance platform. The platform analyses architecture and code quality across more than 300 technologies and is designed to help organisations detect structural issues before they affect resilience, security or scaling.

As SIG describes it, Gartner's assessment points to a market that is consolidating several strands of software analysis, including static and dynamic analysis, software composition, and architecture observability. The goal is to help organisations identify debt and decide what to remediate first.

SIG argued that AI coding assistants can reduce some forms of code-level debt while also contributing to a broader architectural problem. That issue emerges across systems and architectural layers rather than in single files or applications.

Architectural debt, SIG said, appears in the way systems connect, how teams depend on one another's software, and whether the resulting structure still matches the wider platform's original design. In that view, the challenge is less about syntax errors or isolated vulnerabilities and more about whether increasingly complex systems remain understandable and maintainable over time.

The business case is straightforward. Enterprises can often identify code defects with existing testing and scanning tools, but they may have less visibility into how multiple applications, services and teams interact as AI-generated code expands across a portfolio.

Luc Brandts, Chief Executive Officer, Software Improvement Group, linked the company's position in the market to a broader shift in how software risk is viewed.

"We believe this Gartner recognition reflects a major shift in software engineering," said Luc Brandts, Chief Executive Officer, Software Improvement Group. "Technical debt management has evolved from a niche engineering concern into a strategic business priority. As AI accelerates software creation, organizations need visibility into architectural drift, maintainability, security risks, and long-term software sustainability more than ever before."

Architectural focus

SIG said the market is moving towards tools that address architectural technical debt, which it described as debt spanning multiple systems or architectural layers. The company cited an expectation that this category will account for 80% of all technical debt by 2027.

That claim highlights a broader debate in enterprise software about the side effects of AI-assisted development. Coding assistants can produce or suggest code quickly, but they work within limited context and may not reflect the wider design logic of an organisation's technology estate.

SIG said this creates a risk that software teams and AI agents fill gaps independently, leading to systems that function in the short term but become harder to manage at scale. The result, it argued, can be a gradual drift from intended architecture, with knock-on effects for maintainability and operational stability.

The company also linked the issue to governance concerns around quality, security and legal exposure when AI-generated code is deployed without oversight. Its argument is that organisations need visibility across their full software estate, not just within individual repositories or development teams.

Brandts expanded on that point in a second statement.

"AI coding assistants can improve developer productivity and reduce certain forms of code-level debt, but they also increase the risk of architectural technical debt accumulating at scale," said Brandts. "Organisations need software portfolio governance that helps developers, AI agents and IT leadership make informed engineering decisions. We believe Sigrid is uniquely positioned to support that need through architecture analysis, benchmarking, and governance capabilities."

Market pressure

SIG said it has spent 25 years analysing billions of lines of code and is using that experience to position itself in a market where buyers are seeking broader software oversight. The emphasis is shifting from fixing defects after they appear to identifying systemic weaknesses earlier in the software lifecycle.

For large organisations, that may resonate with broader concerns about software sprawl. As businesses add cloud services, internal applications, third-party components and AI-generated features, interdependencies tend to increase, making structural problems harder to detect through conventional code review alone.

The company's message also reflects a change in who is expected to care about technical debt. Rather than being treated solely as an engineering maintenance issue, it is increasingly being framed as an operational and business risk with implications for resilience, scalability and software sustainability.

That framing may help explain why technical debt management is becoming a more defined category in enterprise software. Vendors are no longer just selling tools that flag bugs or vulnerabilities in code; they are increasingly offering visibility into how software portfolios evolve, where hidden complexity is building, and which areas carry the greatest risk if left unaddressed.

SIG said organisations need portfolio-wide visibility to detect, prioritise and govern architectural debt before it destabilises critical systems.