AI tackles accumulated technical debt to boost business efficiency

Technical debt has accumulated to staggering levels in modern enterprises, with global figures exceeding $1.5 trillion according to industry estimates. A new report from technology research firm HFS and Publicis Sapient suggests artificial intelligence may finally offer organizations the capabilities needed to break through this costly burden, acting as a “jackhammer” against decades of accumulated system inefficiencies. The findings come at a critical juncture as companies struggle to modernize while simultaneously adopting transformative AI technologies.

The big picture: AI appears poised to help organizations tackle technical debt rather than add to it, with 80% of executives believing AI will significantly advance modernization efforts.

• Technical debt—defined as shortcuts or workarounds taken to meet delivery deadlines—has concrete-hardened many companies’ systems for decades.
• The Consortium for Information and Software Quality estimates accumulated software technical debt exceeds $1.52 trillion globally.

Key findings: Nearly three-quarters of surveyed executives (74%) plan to enhance business requirements through AI-driven service delivery, with nearly half (49%) intending to increase their use of AI-led agentic services.

• The survey included 800 executives who see AI as a potential solution to break through longstanding technical barriers.
• Rather than treating AI as another layer on top of existing systems, forward-thinking organizations are rebuilding their foundations with intelligence at the core.

Challenges remain: A majority of organizations (55%) lack the talent, data quality, or governance capabilities needed to build a truly AI-driven enterprise.

• Ethical and compliance concerns trouble 39% of executives surveyed.
• Integration difficulties with legacy systems continue to challenge 41% of organizations attempting AI implementation.

Strategic recommendations: The report advises organizations to stop merely managing technical debt and instead focus on demolishing it completely through disciplined approaches.

• Companies should treat technical debt similarly to financial debt by tracking it systematically, prioritizing what to address, and implementing disciplined “repayment” plans.
• AI tools can be leveraged to understand, refactor, and ultimately retire legacy systems that are holding organizations back.
• Rather than layering AI on top of existing architecture, organizations should rebuild their core systems with intelligence as the foundation.

Why this matters: How organizations approach technical debt will significantly impact their ability to capitalize on AI’s transformative potential, with those addressing fundamental architectural issues likely gaining competitive advantages.

• Companies failing to address the underlying technical debt may find their AI initiatives severely constrained regardless of investment levels.
• The intersection of technical debt and AI adoption represents a critical strategic challenge that most executives now recognize they must address.