Dear Lucas Eiras ,

When it comes to AI projects, every model training process is different. Scope, audience, technical resources, financial constraints, and even the speed and skill of the developers all factor into the equation, creating a wide range of challenges.

While each set of model training difficulties may be unique, some themes exist. This article reviews six of the most common problems found during AI model training and offers solutions and workarounds for both the development team and the organization as a whole.

https://www.oracle.com/artificial-intelligence/ai-model-training-challenges/

Regards,

Shafagat

Training AI models for automatic classification and escalation of IT incidents in ITIL-based environments involves several challenges. Key issues include poor data quality, ambiguity in incident categorization, and the evolving nature of incidents, requiring continuous model updates. Additionally, handling multi-label classifications, accurately predicting escalation paths, and integrating AI with existing ITIL processes can be difficult. Bias in the data and the need for real-time performance further complicate model training. Finally, ongoing monitoring and refinement are necessary to maintain model accuracy and reliability in a dynamic IT environment.

In ITIL-based environments, training AI models for incident classification and escalation presents a range of challenges rooted in data heterogeneity, label ambiguity, and dynamic infrastructure behaviors. One of the key bottlenecks is the lack of consistency in incident taxonomies and the evolution of classification schemas over time, which causes misalignment between historical ticket data and real-time classification pipelines.

In my research, particularly the paper "Deep Guard: Fortifying Digital Authenticity with Deep Q-Learning and Gorilla Troop Optimization" (June 2024), we addressed the challenge of learning robust, context-aware representations under adversarial and ambiguous environments. This is highly relevant to ITIL-based systems where escalations can vary based on nuanced infrastructure context. Our approach incorporated reinforcement learning to improve the decision boundary sensitivity in multi-tier escalation scenarios—an architecture that can be adapted to IT incident workflows.

My Paper on Deep Guard Research :- Article Deep Guard: Fortifying Digital Authenticity with Deep Q-Lear...

Additionally, in "Efficacy of Data Governance: A Cutting-Edge Approach to Ensuring Data Quality in Machine Learning for the Banking Industry", we introduced methods to handle data sparsity, duplication, and governance misalignment, all of which are critical when training models on operational ITSM data, which often suffers from noisy labels and missing escalation hierarchies.

My Paper on Data Governance :-

Conference Paper Efficacy of Data Governance a Cutting Edge Approach to Ensur...

These works suggest that embedding policy-aware escalation logic, improving feature integrity via governance-aware preprocessing, and adopting reinforcement-driven escalation modeling could significantly improve model performance and operational trustworthiness in ITIL-aligned ecosystems.