Throughout my career, whether in process improvement, business analysis, or low-code solution design, one theme has been painfully clear: time is always the constraint, and technical debt is always the silent killer.
In recent years, AI has become less of a buzzword for me and more of a practical tool — especially when combined with platforms like Power Platform, Microsoft 365, and cloud services like AWS. AI, when applied correctly, doesn’t replace developers; it supports them in shipping better solutions faster and avoiding the future burden of rushed decisions.
AI as a Development Companion, Not a Shortcut
In the real world, most projects don’t fail because the team can’t write code. They fail because requirements evolve, businesses pivot, and quick fixes become permanent.
AI helps address that problem early by:
- speeding up repetitive or manual design tasks,
- improving decision-making with better data insights, and
- enabling automation of system behaviors that usually rely on human attention.
For me, the turning point was when I stopped thinking of AI as a separate “feature” and started seeing it as an assistant woven into the development lifecycle.
How AI Actively Reduces Technical Debt
1. Smarter Requirement Gathering and Validation
One of the most expensive sources of technical debt is misaligned requirements. AI services, especially in natural language processing (like AWS Transcribe, OpenAI’s APIs, or Azure AI), can turn voice notes, meetings, or unstructured conversations into actionable and reviewable documentation.
In one of my recent projects, I used this approach to streamline the initial stages of solution design — cutting down on revisions later and lowering the risk of “building the wrong thing fast.”
2. Predictive Automation for Maintenance
AI isn’t just about development; it’s about sustaining the solution. Predictive analytics can help spot bottlenecks, usage anomalies, or scaling issues before they become production incidents.
This proactive visibility reduces the need for hotfixes, rushed patches, and manual monitoring, which are all contributors to long-term technical debt.
3. Intelligent Integration Mapping
When connecting multiple systems (think SharePoint lists, document libraries, cloud storage, AI APIs) — one of the main causes of future rework is misaligned data mapping.
Using AI-powered tools, you can automate much of this mapping and even detect schema drift or inconsistent data patterns before they silently break your workflows.
AI + Low-Code: A Strong Combination
In the context of low-code development, AI feels less like “advanced tech” and more like an invisible design partner.
For example:
- Automating document classification in SharePoint using AI-powered models,
- Translating audio to structured text for form inputs using AWS Transcribe,
- Using AI to suggest improvements in workflow paths in Power Automate.
All these use cases save development time and dramatically reduce the kind of patchwork fixes that would otherwise turn into long-term technical debt.
Final Thoughts
AI isn’t about replacing developers or solving every problem automatically. It’s about giving development teams, business analysts, and solution designers the tools to avoid costly mistakes and repetitive tasks.
In my own experience, AI works best when you treat it as part of the solution architecture — not as a post-launch enhancement.
Less manual work. Fewer last-minute fixes. More sustainable solutions.
That’s how AI really helps reduce technical debt.