Machine Learning Approaches to Business Process Optimization: Identifying Redundancies and Enhancing Operational Confidentiality in Enterprise IT

This review critically examines the application of machine learning to enterprise business process optimization, with particular emphasis on identifying operational redundancies while preserving confidentiality, privacy, and security. The study adopts a structured narrative review of scholarly literature on business process management, process mining, predictive analytics, artificial intelligence governance, and privacy-preserving computation. The analysis evaluates supervised, unsupervised, deep, reinforcement, and federated learning approaches, alongside anomaly detection, clustering, sequence modelling, natural language processing, and explainable artificial intelligence.

The findings show that machine learning can reveal duplicated tasks, repeated data entry, unnecessary approvals, rework loops, inefficient handoffs, abnormal process variants, and resource-allocation weaknesses. However, repetition alone does not constitute redundancy, because some recurring activities perform essential regulatory, audit, quality-control, or cybersecurity functions. Effective optimization therefore requires contextual interpretation, process-domain expertise, human oversight, and transparent model outputs. The review further establishes that sensitive workflow data can expose proprietary knowledge, employee behaviour, customer information, internal controls, and system vulnerabilities, making confidentiality protection integral to the entire machine-learning lifecycle.

The study concludes that sustainable process improvement depends on the coordinated integration of machine learning, process mining, business process management, cybersecurity, and organisational governance. It recommends phased implementation, reliable data engineering, controlled pilot testing, privacy-by-design, model auditing, multidisciplinary oversight, and continuous performance monitoring. Enterprises should prioritise interpretable, secure, and context-sensitive systems rather than pursue automation as an end in itself. These measures can align efficiency gains with accountability, resilience, compliance, stakeholder trust, and enduring value. Future research should examine causal redundancy detection, privacy-preserving process intelligence, human–machine collaboration, model drift, and implementation conditions across emerging economies and resource-constrained organisational environments.