AI delivery best practices hold immense promise but also carry a unique risk: they often generate more technical debt than traditional software initiatives.
AI delivery involves data pipelines, evolving models, and infrastructure that must adapt to changing inputs and business needs. Without careful planning, these systems become brittle, costly, and difficult to scale.
Why AI projects accumulate debt quickly
AI projects combine data engineering, machine learning, and software development under tight delivery timelines.
Teams often take shortcuts when building MVPs with manual processes, one-off scripts, and tightly coupled components. While these approaches can produce quick wins, they rarely support long-term growth.
One critical challenge is that these MVPs frequently remain in production far longer than intended. Instead of being temporary prototypes, they become core business systems, accumulating technical debt until a full rewrite becomes inevitable.
This mirrors the themes discussed in our article on What is Software Refactoring?, and aligns with our perspective in Reducing Technical Debt: A Pathway to Effective Digital Transformation, where we emphasize that recognizing and managing technical debt enables strategic, sustainable transformation.
Hidden debt in AI delivery
Early AI successes can mask growing issues:
- One-off pipelines that fail when data changes
- Models deployed without monitoring or retraining plans
- Manual workflows that hinder repeatability.
- Knowledge trapped in silos across data science, engineering, and ops teams.
These issues surface only when scaling leads to rework, downtime, and missed opportunities.
Common causes
- Model drift: Performance degrades as data changes.
- Lack of standards: Each project reinvents ingestion, training, and deployment.
- MVP Longevity: Temporary solutions become permanent without proper refactoring.
Preventing debt: Best practices
- Reusable ML Modules: Standardize ingestion, feature storage, and deployment components.
- Observability: Built-in logging, tracing, and drift detection.
- Modular architecture: Decouple workflows to enable independent evolution.
- Governance: Implement policies for model approval, auditing, and lifecycle management.
- Planned refactoring: Schedule technical clean-up as part of the delivery roadmap, aligned with e‑Core’s strategy of
refactoring as a growth enabler.
Early actions for sustainable AI
- Define success metrics beyond initial accuracy.
- Invest in CI/CD for ML, data catalogs, and metadata tracking.
- Create cross-functional teams bridging product, engineering, and data science.
- Document architectures and decisions to close knowledge gaps.
- Treat MVPs as stepping stones, not a permanent solution. Refactor or rebuild when scaling.
Final thoughts
Technical debt in AI isn’t inevitable. By designing for scalability, reusability, and governance from the start, leaders can prevent future chaos.
Recognizing when MVPs must evolve and applying disciplined refactoring ensures your AI systems remain reliable and adaptable.
