Artificial intelligence (AI) has quickly shifted from an innovation experiment to an operational necessity. Organizations are deploying AI copilots, automation platforms, customer service bots, analytics engines, and AI-generated software at record speed.
But many businesses are discovering an uncomfortable reality: the faster AI is ‘adopted,’ the faster technical debt can accumulate. And you can’t ask AI ‘what were you thinking?’ because AI doesn’t think. It determines what the next token of code is(most likely) and outputs it (with some random variability) without thinking.
This growing problem, known as AI technical debt, is quickly becoming one of the largest hidden costs in modern business technology, and one of the leading causes of frustration for developers.
What Is AI Technical Debt?
Technical debt traditionally refers to shortcuts in software development that create future maintenance problems. AI technical debt expands that concept beyond code.
It includes:
- Poorly governed AI tools
- Unmanaged data pipelines
- AI-generated code requiring extensive cleanup
- Duplicated automation platforms
- Undocumented workflows
- AI systems layered onto outdated infrastructure
Unlike traditional technical debt, AI debt compounds rapidly because AI systems continuously evolve and rely heavily on interconnected data and infrastructure.
A recent large-scale study analyzing more than 304,000 AI-authored commits found that over 15% of AI-generated commits introduced at least one issue, while 24.2% of those issues persisted into later software revisions.
What AI Technical Debt Looks Like in the Real World
AI debt is rarely obvious at first.
Until …
AI-Generated Code Cleanup
Many organizations are accelerating software delivery using coding copilots and generative AI tools. Companies like Airbnb reports that AI generates roughly 60% of new code, while Anthropic says AI writes more than 90% of its code.
That sounds efficient, right? Wrong!
When engineering teams inherit:
- Inconsistent architecture
- Undocumented logic
- Security flaws
- Code nobody fully understands
The productivity bottleneck doesn’t disappear, it shifts from coding to verification & maintenance.
Lightrun’s 2026 engineering report found:
- 43% of AI-generated code requires manual debugging in production
- 88% of organization need 2-3 redeploys to fix an AI generated change
Register to read the full report here: State of AI-Powered Engineering 2026
The business impact is real and staffing this shift is only part of it.
‘Shadow AI’ Across Departments
Marketing adopts one AI writing tool. HR adopts another. Finance deploys AI forecasting. Operations build internal automations.
Suddenly organizations have:
- Duplicate subscriptions
- Fragmented workflows
- Inconsistent governance
- Unclear ownership
This news is not new, Gartner warned in late 2025 that ‘shadow AI’ was contributing directly to rising technical debt and budget bloat.
Security and Compliance Risks
AI-generated code can introduce cybersecurity problems at scale.
Developers themselves remain skeptical as indicated in the C++ global developer survey where 78% worry about incorrect AI output and 50% cited privacy concerns.
A 2026 Sherlock Forensics report stated that 92% of assessed AI-generated codebases contained at least one critical vulnerability.
At the same time, Google recently warned that cybercriminals are now using AI-assisted development techniques to identify and exploit software vulnerabilities faster.
The cybersecurity risks are real especially for businesses operating in regulated industries, AI debt can quickly become compliance debt.
Which Departments Are Feeling It Most?
IT and Engineering
Technology teams are carrying the largest burden:
- Integrating AI systems with existing software including Legacy systems
- Validating AI-generated code
- Managing cloud costs
- Retraining models
- Refactoring unstable systems
- Managing innovation expectations and team engagement
According to Gartner, worldwide AI spending is expected to reach $2.5 trillion in 2026, with infrastructure spending alone increasing by nearly 50%.
The issue is not simply spending more. It’s spending more while inheriting more complexity.
Finance
Finance teams are struggling with:
- Unpredictable AI related operational costs
- Duplicated SaaS spending
- Unclear ROI measurements
Industry discussions on Reddit are suggesting that technical debt and fragmented infrastructure are among the top blockers preventing CFOs from scaling AI initiatives effectively.
Operations, Support and HR Teams
Operations departments often inherit broken automation chains and disconnected workflows.
HR teams are tasked with doing more with less. Compounded by AI literacy rates being out of alignment with AI Adoption, the burden of finding skilled AI talent falls on HR.
Customer support teams, face AI-system related challenges like:
- Tools that produce inconsistent responses
- Systems requiring constant oversight to ensure customer satisfaction
- Incomplete integration with legacy CRM systems, leading to duplicated effort
Operational friction disguised as automation is leading to decreased employee moral, frustrated customers, decreasing end user satisfaction and challenges filling talent pipelines.
The Legacy Problem
Deploying AI into clean, modern environments is a rare occurrence.
Most businesses are layering AI onto:
- Legacy ERP systems
- Aging databases
- On-prem infrastructure
- Decades-old workflows
Creating immediate integration debt.
Research on AI-enabled systems shows technical debt significantly impacts:
- System understandability
- Scalability
- Security quality
Legacy systems are not inherently bad, in fact, many remain as the backbone of businesses globally.
Having a clean modernization strategy that includes AI can help reduce the friction points for AI adoption and decrease possible DRP black holes.
Why The Right Software Partner Matters
Off-the-shelf AI tools solve isolated tasks. But businesses increasingly require:
- Custom integrations
- API modernization
- Centralized governance
- Architecture tailored to their infrastructure.
The challenge?
It is increasingly difficult to find truly experienced firms capable of handling:
This skills gap is becoming its own business risk and is where an experienced development partner become critical.
With over 20 years experience in all of the above, STEP can help.
Reducing AI Technical Debt
Organizations can curb AI debt by:
- Auditing all AI tools and workflows
- Implementing centralized governance
- Modernizing legacy infrastructure strategically
- Requiring human oversight for AI-generated code
- Investing in scalable custom software architecture
- Prioritizing long-term maintainability over short-term speed
AI debt behaves like financial debt: manageable early, expensive later.
Remember: not every shiny object is right for your business today. Adopting this mindset can save you from budget headaches down the road.
Final Thoughts
AI is already transforming how businesses build software, manage operations, and serve customers.
But speed without structure creates instability.
The organizations that succeed with AI won’t necessarily be the fastest adopters. They’ll be the companies who balance innovation with maintainability, connecting AI, custom software, and legacy modernization into a sustainable long-term strategy.
Because in the AI era, the real cost is not deploying too slowly.
It’s deploying too quickly, without a plan.
Drop us a line if you’d like to chat more about your AI technical debt.
