Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are moving from experimental projects into the operational core of organizations, where they increasingly execute, inform, and redesign business processes. Their effect is frequently described in terms of automation and efficiency, yet the actual consequences for a business process depend on where the technology is placed, how its outputs are routed into decisions, and whether the surrounding data, security, and governance arrangements can support it. This paper examines how modern AI, ML, and DL tools affect business processes across the full process lifecycle of design, execution, decisionmaking, monitoring, and improvement. Drawing on applied studies of ML-based face recognition, image-based electronic toll collection, peer-to-peer energy trading simulation, robotic process automation (RPA) and AI-bot identity management, low-code/no-code intelligent automation, real-time AI decision processing, human–bot discrimination, honeypot-based threat intelligence, and federated learning, the paper distinguishes three effects: task automation, decision augmentation, and process transformation. It then proposes a six-layer sociotechnical framework comprising process data and integration, AI/ML/DL intelligence, automation and execution, security, identity and privacy, governance and human oversight, and business value. A cost-sensitive, three-zone decision-routing model is introduced to show how confidence thresholds should be derived from business costs rather than from model accuracy alone, and a process-level evaluation framework is defined that combines quality, time, cost, risk, and human-oversight measures. The analysis concludes that AI improves business processes most reliably when it is applied to well-understood processes with standardized data, when automated decisions are bounded by explicit thresholds and human review, and when software bots are governed as accountable identities.

Laraib Tabish, Falak Ali, Maitikuerban Abuduaini,"The Impact of Artificial Intelligence, Machine Learning, and Deep Learning on Business Processes: A Socio-Technical Framework for Intelligent Process Automation, Decision Support, and Governance" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(981-988) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.