From BPM to AIM Transforming Business Process Management into Adaptive Intelligence Models with Pega
DOI:
https://doi.org/10.15662/IJARCST.2024.0703005Keywords:
Adaptive Intelligence Models\, Predictive Decisioning, Contextual Analytics, Next-Best-Action, Pega CDH, Continuous Learning, Explainable AI, Decision Governance, Intelligent Process Automation, Event-Driven Orchestration, Enterprise AI StrategyAbstract
Traditional Business Process Management (BPM) frameworks have served as the backbone of enterprise automation by standardizing workflows, enforcing compliance, and improving operational efficiency. Yet, in an era dominated by hyper-personalization, streaming data, and intelligent decisioning, BPM’s rule-based rigidity is proving insufficient. The emergence of Adaptive Intelligence Models (AIM) marks a paradigm shift where processes are not only automated but are self-optimizing, data-driven, and contextually aware.
This paper explores how Pega’s Adaptive Intelligence Models (AIM) integrate artificial intelligence, machine learning, and decision governance to evolve BPM into a Cognitive Process Orchestration (CPO) ecosystem. Through Pega Customer Decision Hub (CDH), Next-Best-Action (NBA), and adaptive analytics, organizations can dynamically tailor interactions, continuously learn from outcomes, and maintain explainability and compliance in real time. The study provides a comparative analysis of BPM and AIM architectures, discusses decisioning strategies within Pega CDH, and presents a reference model for implementing adaptive, compliant, and explainable automation in enterprise environments.
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