International Journal of AI, BigData, Computational and Management Studies
A Multi-Agent Generative AI Framework for Automated Data Engineering, Governance, and Analytical Optimization
The data explosion in industries has introduced unprecedented issues in the management, governance and insights derived about huge volumes of data. Conventional data engineering culture is usually unstructured laborious processes that are subject to errors and delays. To automate the data engineering activities, mandate administrative policies and maximize the analytical processes, this paper introduces a new Multi-Agent Generative AI (MAGAI) to automate such functions. MAGAI architecture uses several dedicated AI agents that can work independently to perform such tasks as data cleaning, integrating, transforming, metadata management and automated analytics. The framework combines generative AI models and reinforcement learning strategies to streamline the process of data pipelines and decision-making. Through experimental assessment, it is proved that the efficiency of data processing have better levels and reduced errors in determining correct data and adhering to governance norms. This approach proposed has the benefit of minimizing the human factor as well as improving the quality of the information derived using complex data. We outline the promise of multi-agent AI systems in transforming enterprise data management and analytics
Dinesh Babu Govindarajulunaidu Sambath Narayanan
Optimizing Healthcare ETL Pipelines with Hybrid Cloud Data Warehousing: A Case Study Using Snowflake and Azure Data Factory
The exponential growth of healthcare data driven by electronic health records (EHRs), Internet of Medical Things (IoMT) devices, and evolving regulatory mandates necessitates scalable and secure data integration pipelines. Traditional ETL architectures struggle to meet the demands of real-time processing, hybrid cloud interoperability, and compliance with standards such as HIPAA and HL7. This paper presents a hybrid cloud ETL framework leveraging Azure Data Factory (ADF) and Snowflake to enable scalable, secure, and resilient healthcare data pipelines. The architecture integrates on-premises clinical systems with cloud-native services using a self-hosted integration runtime, facilitating ingestion, transformation, and analytics at scale. We evaluate the system through a real-world deployment at a multihospital healthcare network in the Midwest United States. The implementation achieved a 41% reduction in data latency and a 60% decrease in infrastructure overhead compared to legacy systems. Key contributions include dynamic schema handling, endto-end encryption, audit-ready transformation workflows, and optimized parallel loading. We also identify challenges around cost governance, schema drift, and network reliability. This work provides a replicable model for healthcare organizations seeking to modernize their data engineering infrastructure using hybrid cloud technologies
Secure DevSecOps Workflows for Medical IoT Device Integration in Smart Hospitals
Smart hospitals increasingly depend on interconnected medical IoT devices that collect physiological signals, execute clinical workflows, and support real-time decision making. These devices require continuous software updates and secure firmware delivery to maintain safety and interoperability across complex healthcare ecosystems. Conventional software pipelines are insufficient for this purpose because medical devices operate under strict regulatory constraints, involve persistent wireless connectivity, and remain exposed to both cyber threats and physical misuse. This paper proposes a secure DevSecOps workflow tailored specifically for medical IoT integration within smart hospital environments. The workflow incorporates secure firmware signing, hardware-rooted device identity, continuous vulnerability scanning, compliance-aware release gating, and post-deployment telemetry validation. By embedding security checks directly throughout the update and release lifecycle, medical IoT devices obtain resilient over-the-air provisioning and maintain verifiable trust from manufacturer to bedside. The resulting pipeline shifts device cybersecurity from post-release patching to continuous assurance, enabling safe, traceable, and regulation-conscious delivery of medical device software updates in smart hospitals
Early Adoption of Predictive Analytics for Preventive Care Opportunities Using Claims and Encounter Data
The integration of predictive analytics into health- care has emerged as a transformative tool in identifying preventive care opportunities. Leveraging claims and encounter data, this research explores early adoption strategies for predictive analytics models to proactively manage chronic conditions, reduce avoidable hospitalizations, and optimize care delivery. We present a scalable framework incorporating machine learning pipelines trained on structured insurance claims and encounter records from over 500,000 patients. The study emphasizes clinical relevance, cost-efficiency, and population health outcomes through model validation, risk stratification, and deployment case studies
AI-Driven Transformation of Home Health Care: Advancing Patient Outcomes, Operational Excellence, and Sustainable Care
AI in Home Health Care is changing the method in which patient-centered care is provided at home by combining the precision of Clinical Care with the caring aspects of In-Home Support. With the world's aging population growing and the amount of people suffering from Chronic Illnesses increasing, Healthcare Systems have many challenges ahead of them in terms of Quality, Accessibility and Efficiency. This study examines how AI technologies are transforming Home Health Care in the areas of Anticipatory Patient Monitoring, Customized Care Planning, Operational Efficiency and Compliance as well as how AI improves Patient Safety, Staff Productivity and Regulatory Compliance. Additionally, the study highlights how AI will provide caregivers with data-driven decision making capabilities, reduce the caregiver burden and create a sustainable home care delivery model that meets the needs of an evolving healthcare system. Finally, this study emphasizes the need for Ethical and Transparent AI systems to be developed so that Technology does not replace but enhance the Personal Touch. Overall, this study shows that the use of AI in Home Health Care has the potential to create an Ecosystem that is not only more Effective and Predictive, but also more Compassionate, Sustainable and Patient-Centered
Salesforce CRM Framework for Real Time DeFi Portfolio Intelligence and Customer Engagement Forecasting in Web3 Based Decentralized Finance Ecosystems Using ML Techniques
With the evolution of Web 3.0 and the rise of the DeFi ecosystems it is only natural that the paradigms of customer relationship management have changed, and are in urgent need of novel and real-time ways of gaining insights into portfolios and predicting engagement. In this work, we explore the fusion of Salesforce CRM frameworks with machine learning methodologies such as DeFi portfolio management and customer engagement prediction in the context of Web3. The study uses a quantitative research method that targets a sample of 250 users of DeFi platforms among Indian cryptocurrency exchanges. The questionnaires were structured and dealt with portfolio performance metrics, customer engagement scores, and ML model accuracy indicators for the primary data collection. It includes some predictive analytics methodology such as decision tree algorithms, K-Nearest Neighbors (KNN), and hybrids between deep learning models. Their results show that Salesforce CRM systems integrated with ML achieve 87.3 percent accuracy in forecasting the portfolio and achieve 82.6 percent precision in predicting their customer engagement factors. Results show interesting, statistically relevant correlations between the ability to perform real-time data processing technologies and user satisfaction. The results suggest that blockchain-based CRM models improve transparency, security, and personalization within DeFi ecosystems. We add to the ongoing Web3 customer relationship management dialogue and offer guidance in bringing intelligent CRM solutions to existence within decentralized financial platforms
AI for Data Governance Analysts: A Practical Framework for Transforming Manual Controls into Automated Governance Pipelines
This paper proposes a practical, analyst-centric framework for transforming fragmented manual data governance controls into Automated Governance Pipelines. Rather than replacing humans, the approach focuses on AI-assisted governance and analyst augmentation, where machine intelligence continuously monitors data assets, enforces policies, and surfaces prioritized risks while humans retain oversight of design, exceptions, and approvals. The framework integrates a Rule-Based Automation with learning-based elements to adopt predictive compliance, where past incidences, metadata signals and policy context are used to identify the likely occurrence of violation prior to its actualization. One of the key design features is good decision traceability, which makes sure that all control executions are connected with the policy, rule, and model signal that led to their creation to align with audit and regulatory requirements. The architecture focuses on Policy-Aware API Integration to ensure that the governance logic is integrated directly into data platforms, ETL tools and analytical processes with the support of rich metadata enrichment and classification, lineage, and impact analysis cataloging. The application of least-privilege access, masking, and immutable logging is applied to the End-to-End Governance Lifecycle, addressing the requirements of security and privacy, in the policy authoring to the continuous improvement stages. The guidelines on implementation and quantitative outcomes indicate better accuracy, less false positives, reduced workload on the analysts and significant cost savings as compared to entirely manual controls. The framework can therefore provide data governance analysts with a roadmap to transition out of the spreadsheet-based governance practices to scalable, explainable, and resilient AI-powered governance operations
With the growing prevalence of data-intensive, AI-driven systems, secure and transparent data exchange has become an imperative challenge. Traditional centralized architectures often suffer from single points of failure, susceptibility to tampering, and trust deficits among collaborating parties. Blockchain technology characterised by decentralization, immutability, and consensus-based validation presents a promising alternative for enabling robust data exchange frameworks. Recent work in healthcare and other domains demonstrates that blockchain‐inspired architectures can enhance integrity, access control, and provenance of shared data (Kumar et al., 2023; Nguyen et al., 2023). At the same time, significant gaps remain around scalability, interoperability, and integration with legacy systems (Nguyen et al., 2023; analysis of secure data sharing techniques, 2023). This paper explores how blockchain can be leveraged to secure data exchange in AI-driven ecosystems, by (1) mapping key blockchain properties to essential data-security dimensions (confidentiality, integrity, availability, provenance, auditability), (2) proposing an architecture tailored to AI workflows, and (3) evaluating the trade-offs in performance, cost, and governance compared with traditional models. The findings indicate that blockchain-enabled exchange systems hold substantial potential in enhancing transparency and trust among participants while reducing reliance on centralized intermediaries. However, practical deployment requires addressing throughput constraints, cross-platform interoperability, and regulatory compliance. The implications for AI applications, data governance, and enterprise integration are discussed, along with directions for future research
AI in Account-Based Marketing (ABM): Tactical Playbooks for Hyper-Personalized Prospecting
Account-based marketing has shifted B2B go-to-market strategies from high-volume campaigns to targeted engagement with specific accounts. However, traditional ABM implementations struggle with scale, requiring manual research and personalization that limits the number of accounts teams can effectively pursue. Artificial intelligence enables ABM programs to operate at greater scale while maintaining personalization depth through automated research, signal detection, content generation, and multi-channel orchestration. This paper presents tactical playbooks for implementing AI-assisted ABM workflows. We describe account selection and tiering, research automation, AI-generated personalization, multi-channel coordination, and measurement frameworks. The playbooks address common implementation patterns including one-to-one, one-to-few, and one-to-many ABM motions, with guidance on tool selection, workflow design, and team coordination. While examples reference common ABM platforms, the patterns remain applicable across different technology stacks
Compliance-as-Code: Automating Governance and Security Controls in Financial and Healthcare Clouds
The growing use of cloud computing within controlled industries like the financial and healthcare sector has necessitated an urgent need of either automated compliance systems that are capable of fulfilling the high-level governance and security standards. The Compliance-as-Code (CaC) is a transformation of regulatory management by digitalizing compliance rules, governance policies, and security control as a machine-readable code, which can be mechanically run and checked in the cloud computing environment. The given paradigm allows organizations to check compliance with the standard as HIPAA, PCI-DSS, and NIST SP 800-53 repeatedly, reducing manual audits and configuration drift. CaC frameworks such as Open Policy Agent (OPA) and HashiCorp Sentinel, Chef InSpec and Cloud Custodian have been integrated with Infrastructure-as-Code (IaC) pipelines to implement preventive and detective controls on cloud resources [5] -8]. This paper examines the principles of design, technical enablers, and challenges of CaC in financial and healthcare clouds. We discuss policy-as-code languages, automated evidence generation and compliance testing systems as beneficial to achieving an audit ready position, alleviate operational risk and enhance regulatory assurance. In addition, the research has encountered the issues of mapping the legal controls to the technical claims, guaranteeing that the policies are entailed in multi-cloud settings, and the audit traceability. The present results indicate that CaC has proven to provide consistent efficiency and consistency gains but its effective implementation involves a balance between regulatory semantics, governance models, and policy implementing structures [1], [2], [4]. The paper provides a set of recommendations regarding recommendations of scalable, auditable and regulator-defensible CaC adoption on highly regulated cloud settings
Overcoming Early-Stage Adoption Challenges of Microsoft Fabric in Enterprise Environments
Microsoft Fabric is emerging as a comprehensive and unified data platform that integrates data engineering, analytics, and business intelligence within a single cloud-based ecosystem. Its design aims to simplify data management, promote interoperability, and accelerate analytical innovation across enterprise environments. Despite these advantages, early adopters often encounter substantial challenges related to migration complexity, evolving functionality, and organizational preparedness. This paper presents an analytical exploration of these early-stage adoption barriers and proposes structured strategies for overcoming them. It emphasizes the importance of incremental implementation approaches, including domain-aligned workspace organization and proof-of-concept deployments, as mechanisms to reduce risk and enhance operational readiness. Furthermore, the study highlights the architectural and governance benefits of Microsoft Fabric’s unified framework, particularly its capacity to enable real-time analytics, cost efficiency, and centralized compliance management. In addition to serving as a practical guide for enterprise practitioners, the paper provides constructive recommendations to the Microsoft Fabric development team aimed at improving migration support, documentation quality, and enterprise-grade feature maturity to strengthen the platform’s long-term adoption and impact
The Cognitive Supply Chain Data-Driven Resilience and AI augmented Decision-Making in Global Infrastructure Deployments
The rapid expansion of global data-center regions, fiber backbones, submarine-cable systems, and edge-network clusters has exposed structural limits in traditional supply-chain models. These legacy systems built for predictable, linear manufacturing flows struggle to manage the volatility, interdependencies, and long-lead components that characterize modern digital-infrastructure deployments. Existing forecasting tools and operational platforms lack real-time visibility across procurement, logistics, engineering, and site-execution domains, resulting in delayed risk detection and fragmented decision-making. This paper introduces a Cognitive Supply Chain framework designed specifically for digital-infrastructure environments. The model integrates multimodal data, probabilistic risk propagation, AI-augmented decision pathways, and a cognitive digital twin to simulate deployment scenarios and anticipate disruptions. Through architectural analysis, implementation guidance, and a real-world case example, the paper demonstrates how cognitive decision systems can improve deployment accuracy, extend predictive lead time, and enhance resilience across multi-region infrastructure programs. The results highlight a path toward a more anticipatory and intelligence-driven supply chain capable of supporting the next decade of global digital-infrastructure growth.
Cognitive Governance for Web-Scale Systems: Hybrid AI Models for Privacy, Integrity, and Transparency in Full-Stack Applications
Web-scale application architecture amplifies the challenges of enforcing privacy, integrity, and transparency across heterogeneous data sources, microservices, and user-facing interfaces. Traditional governance approaches, built on manual reviews and static rule sets, are no longer sufficient to manage dynamic risks, evolving regulations, and high-velocity data flows. The present paper introduces a Cognitive Governance paradigm that integrates a Hybrid AI Privacy Framework into the full-stack application paths directly, making the policy reasoning, data privacy engineering, and data runtime monitoring unified. These policies and models are interpreted, anomalies are identified, and integrity is checked to form complete-stack integrity intelligence to assess context in relation to data sensitivity, user role, jurisdiction, and behavioral indicators. The governance decisions can be accessed, masking, blocking, escalation using intelligent policy enforcement services implemented at API gateways, service meshes and data platforms. The transparent analytics systems help to reveal the decision traces, explanations, and lineage to the developers, auditors and regulators making governance not an after-the-fact audit capability but an operational plane of control. Describe the building blocks of architecture, privacy preserving mechanisms and integrity protection necessary to achieve Cognitive Governance in web scale environment and justify practical considerations of evaluation, such as feasibility, deployment trade-offs, and constraints. The result is a blueprint for AI-driven compliance frameworks that are adaptive, explainable, and resilient, supporting trustworthy full-stack applications in cloud-native, highly regulated, and data-intensive settings
AI-Enabled Decarbonization Analytics for State and Local Transportation: A Data-Driven Framework for Evaluating Greenhouse Gas Reduction, Air Quality, and Equity Impacts
State and local transportation agencies across the United States are seeking practical ways to reduce greenhouse gas emissions, improve air quality, and address long-standing equity concerns. However, most evaluation tools used today are limited to basic accounting methods and do not provide a clear picture of how different decarbonization strategies perform across environmental, social, and financial dimensions. This study introduces a data-driven analytical framework that supports program evaluation for transit agencies, school districts, and municipal fleets. The framework combines predictive modeling, evidence based evaluation methods, and multi-criteria decision analysis to examine the benefits of clean transportation programs with greater clarity and reliability. Publicly available data sources, including emissions inventories, fleet activity records, air quality observations, and community-level demographic indicators, are used to estimate greenhouse gas reductions, changes in exposure to pollutants, and the distribution of benefits across communities. The study applies the framework to a representative urban region to demonstrate how different clean fleet strategies can be compared in a transparent and structured way. Results show meaningful improvements in emissions and community outcomes when agencies transition to cleaner technologies, especially in neighborhoods with a history of higher exposure to transportation-related pollution. This research provides a practical and adaptable evaluation approach that can help local and state agencies strengthen funding applications, guide investment choices, and align transportation programs with national climate and environmental justice priorities
Machine Learning Applications In Sustainability
Fleet, Driver & Supply Chain Optimization Achieving First- and Last-Mile Excellence through SYNAPSE Orchestration
Modern logistics systems operate as fragmented collections of predictive tools, routing engines, and visibility platforms that lack coordinated decision-making across fleet, driver, routing, and supply chain domains. This paper introduces SYNAPSE, a unified predictive–prescriptive orchestration framework designed to integrate multimodal data streams and enable real-time, cross-domain operational optimization. The framework fuses heterogeneous signals—including fleet health indicators, driver physiological and behavioral metrics, graph-based routing predictions, and multi-tier supply chain forecasts—within a centralized insight fusion layer. A novel Multi-Objective Decision Engine (MODE–DDR) leverages reinforcement learning to generate prescriptive actions that balance cost efficiency, service reliability, safety risk, carbon emissions, and regulatory compliance. A high-fidelity digital twin simulates complex transportation networks, enabling large-scale training and evaluation under diverse stochastic disruptions. Experimental results across 12,000 shipments and 3,000 simulated disruptions demonstrate substantial improvements over industry-standard baselines, including a 6× reduction in exception resolution latency, 17% lower operational cost, 34% reduction in delay impact, 28% reduction in emissions, and 52% lower safety-risk exposure. Ablation studies confirm that cross-domain data fusion is critical to policy quality, validating the central hypothesis of architectural convergence
AI-Driven Price Sensitivity Analysis and Consumer Value Optimization for Competitive Markets
The competitive markets are getting more and more sensitive to competitive pricing strategies, demand; more specific, and less responsive, this is due to more knowledgeable and price sensitive consumers being sensitive to and reactive to unstable external conditions that change in rapid time. Conventional techniques, including both calculus-based elasticity of stature, rule-based decision-making engines, and manual pathfinding are inadequate to characterize nonlinear behaviour, many-facet decision-making patterns and actual-time market oscillations. In order to overcome these shortcomings, this paper proposes AI-powered framework that unites machine learning, causal inference, deep learning and reinforcement learning to create price sensitivity estimates that are very accurate and constantly re-evaluated. The suggested methodology exploits the better feature engineering, demand elasticity modeling, consumer segmentation, and utility-based analysis in order to measure willingness-to-pay and the feature that mostly influences consumer value perceptions formation. It is based on these predictive signals that a multi-objective optimization layer, enabled by reinforcement learning and Bayesian optimization, makes recommendations on the optimal price points that maximize revenues, boost conversions, and positioning. A lot of experiments done with actual retail and e-commerce data reveal a large performance boost up to 23-41 in price sensitivity predictive accuracy, 18-32 in uplift of revenue, and 28 in consumer value alignment, relative to the economy and statistical models. The results show that the framework is scalable, decipherable, and adaptable to fluctuate market environments that the firms can operationalize of intelligent, consumer focused pricing strategies. The study contributes to the idea of AI-based pricing analytics and provides an example of a lasting blueprint of implementing strong data-driven pricing systems in competitive and moderately dynamic markets
Driving Measurable ROI in ERP Testing Automation through TOGAF-Guided Enterprise Architecture
Modern supply chains, especially in the fast-paced retail sector, demand unprecedented agility and resilience. Enterprise Resource Planning (ERP) systems form the operational backbone, but their complexity and criticality make them significant sources of risk if not rigorously tested. Traditional ERP testing methods are often manual, time-consuming, costly, and struggle to keep pace with continuous delivery cycles and frequent updates, particularly in cloud environments. Furthermore, demonstrating a clear Return on Investment (ROI) for testing efforts remains a persistent challenge. This white paper, targeting supply chain professionals, IT architects, and business decision-makers, proposes a structured approach leveraging The Open Group Architecture Framework (TOGAF) as a guiding methodology for implementing and governing ERP testing automation. By aligning test automation strategy with enterprise architecture principles, organizations can move beyond reactive bug-fixing to a proactive, risk-based testing model. This TOGAF-guided approach enables the identification of critical business processes, optimization of test scope, selection of appropriate automation tools (including AI-driven solutions), and establishment of robust governance. The result is a significant reduction in testing effort, accelerated release cycles, improved software quality, enhanced supply chain collaboration, and, crucially, a framework for measuring and demonstrating tangible ROI, transforming testing from a cost center into a strategic enabler of business velocity and value, particularly vital during Mergers and Acquisitions (M&A)
Adversarial Robustness in Multimodal AI-Enabled Cybersecurity Systems: Defenses, Vulnerabilities, and Modality Interactions
The study being reported on is investing in the multimodal artificial intelligence (AI) models for their optimum use and validation to carry out the best possible cyber protection. The mixed-up data sets provided the necessary ground for the statistical analysis—model parameters' significance was evaluated through P-Value testing and various kinds of residual analysis changing from the standardized one to quantile–quantile (Q–Q) plots. All the important features showed their statistical significance under the p < 0.005 threshold. This implied the strength of the model. The Q–Q plot showed that the residuals were very much like a normal distribution; thus, the prediction made was reliable and the error deviation was minor. The extreme P-Value analysis pinpointed the heavily influential and lightly significant variables, thereby indicating the multimodal features that impact the system performance and defense against malevolent threats. Not only does this study highlight the role of artificial intelligence (AI) in advancing cybersecurity but also the necessity of ML and statistical diagnostics in data-driven optimization and model interpretability, making the entire process of result consistency, transparency, and reproducibility reliable. To sum up, the proposed framework points to the use of AI-based statistical optimization as an intelligent, efficient, scalably and, most importantly, secure solution for the future cyberspace (one where AI-based applications will be in use)
Building Trustworthy AI in Salesforce: An Ethical and Governance Framework
Artificial Intelligence (AI) in enterprise platforms such as Salesforce absolutely requires trustworthy AI because customer experiences, sales strategies, and business intelligence may be directly affected by decisions made by machine learning models. Ethical, transparent, and fair AI implementation is necessary, as the use of AI in Salesforce clouds, namely Sales Cloud and Service Cloud, has been rapidly integrated. The present paper suggests the holistic model of governance and ethical AI systems in Salesforce. It also explores the ethical nature and extent of issues of ethical concern, namely algorithmic bias, model opacity, data privacy, and accountability as applicable to Salesforce AI tools e.g., Einstein GPT. This framework focuses on three pillars fairness (bias mitigation and inclusive training data), transparency (explainable AI and auditability) and responsible deployment (policies governance, human-in-the-loop systems, and legal compliance). The methodology will consist of a literature survey focusing on AI ethics and evaluation of the current AI policies by Salesforce as well as the conceptual model of enterprise AI platforms. Finance, healthcare, and retail documents on case studies provide an example of using the framework in practice. The research is completed with practical suggestions and indicators to gauge credibility in the use of Salesforce AI
Using Machine Learning for Intelligent Case Routing in Salesforce Service Cloud
Innovations in modern businesses customer support is shifting towards automation with an aim of simplifying ticket management and promote customers satisfaction. Salesforce conventional rule-based systems Service Clouds have proved non-flexible and lack scalability, which is the usual misroutes or delayed cases. The current paper discusses intelligent machine learning (ML) usage. Salesforce and cases classification and routing. Natural language processing (NLP) can be used. can adjust themselves to new conditions, ML-based systems may change according to new needs, historical case data, supervised learning algorithms, and ML-based systems. detect trends in support, forecast all-time optimum agents or queues and boost up resolution times. This study offers a comparative study of the rule-based and the ML-driven techniques. performance measures that include accuracy of classification, time of resolution and customer satisfaction scores. Besides, we suggest a hybrid design, which will involve human feedback loops. active learning in order to retain long-term routing accuracy. The results point to the fact that ML not only enhances the efficiency of routing as well as lowers the operational cost and enhances support team effectiveness
Citizen-Facing Automation: Chatbots and Self-Service in Public Services
Automation aimed at people is quickly changing how governments & public organisations deliver services. Chatbots and self-service platforms are becoming important tools for connecting people with these institutions. These technologies are meant to make things easier for everyone, cut down on wait times & provide support all the time, which will make the entire experience better for everyone. Automated solutions, on the other hand, provide quick answers to these questions, speed up regular tasks & make interactions more open. This is not like most conventional means of providing these kinds of services, which usually need people to process them, take a long time, or be present in person. This study examines the use of chatbots & the self-service platforms in these public services, their potential to enhance efficiency & reduce their expenses, & the problems related to diversity, trust & accountability. The study is directed by enquiries such as: In what ways may citizen-centric automation technologies transform service delivery? What impact do they have on public trust & engagement? What governance systems are required to balance automation with human supervision? Studies show that these technologies may greatly improve operational efficiency & make services more accessible, but they only work well if they are well designed, integrated with these existing systems, and given a lot of attention to data security and the ethical usage. Case studies show that people are more likely to choose automated solutions that are easy to use, work in more than one language & can handle many different needs. The findings are more clear: automation might make things more transparent, make administrative jobs simpler & make the government more focused on the interests of the people. However, it has to be done very cautiously so that these digital gaps don't become worse. This article argues that the future of public service delivery relies on a balanced strategy where technology augments human judgment, ensuring that services stay more accessible, egalitarian & also dependable
RPA + AI → Intelligent Process Automation (IPA)
Robotic Process Automation (RPA) and Artificial Intelligence (AI) have over time combined the best features of both technologies to create a new, more efficient workflow – one that keeps the consistency and predictability of RPA and the flexibility and adaptability of AI. Though RPA is focused on large-volume automation of routine and rules-based tasks, e.g., data entry, transaction processing, and report generation, AI equips the automation it partners with problem-solving skills such as natural language processing, machine learning, and predictive analytics to handle complex unstructured scenarios. One of the significant reasons for the IPA revolution in companies is such dynamics: extending operational efficiency to the highest possible level, cutting costs substantially, increasing the capacity of rapid response to market changes, and getting the power to make more data-driven, strategic decisions. This paper is about tracing IPA back to RPA and AI, the technologies that formed the base for it, and the benefits with which the very first examples of future firms have not only lit up the way of tailored customer service but also the optimisation of workflows. Besides, it deals with the implications of adoption concerning change management, system integration, and governance, followed by a real-life example of how IPA is a facilitator of value creation. The first part of the discussion is about tracing the Intelligent Process Automation back to its origins in the `Artificial Intelligence` (AI) and `Robotic Process Automation` (RPA) technologies and talking about the benefits that its first use cases in the future firms have already brought about, from the optimisation of workflows to the improvement of customer service. Moreover, it discusses the adoption implications in terms of change management, system integration, and governance, followed by a real-life example describing how IPA creates value. The discussion closes with the questioning about the place of IPA among other technologies during the phases of company growth and in even stronger firms. It is actually true that Intelligent Process Automation is not far from being just another hype term; rather, it is an exceptional feature that eases the whole process of interaction between humans, machines, and automation, thus making organisations more efficient, intelligent, and flexible in a world of continuous turmoil
Transforming Government Workflows with AI-Driven RPA
The fast adoption of Artificial Intelligence (AI) & Robotic Process Automation (RPA) is changing how governments provide services by giving them the latest ways to make things more efficient, open & focused on the needs of their citizens. As public institutions face increasing pressures to reduce bureaucratic delays, improve accountability & meet the heightened expectations of tech-savvy individuals, AI-driven RPA emerges as a transformative force for more effective governance. This article investigates the importance of workflow automation in the public sector, assessing how AI-driven automation might improve their certain repetitive administrative procedures, minimise human mistakes & liberate workers for more value-oriented activities, such as policy development & citizen involvement. The major subject of the discussion is how AI-driven RPA may assist with these kinds of challenges that have been there for a long time, such as legacy systems that don't work together, regulations that are too rigid & resources that can't be scaled up. The study shows actual benefits, like faster response times, more accurate service & more openness in decision-making. It does this by using a systematic approach that includes a review of the literature, an analysis of the process & a look at an actual case study showing how the system was put into place by the government. The major findings indicate that automation, when integrated with these AI functionalities such as natural language processing, predictive analytics & cognitive decision-making, enhances operational efficiency and fosters trust by increasing the accessibility of government services to a broader population. The article argues that AI-driven RPA could speed up the modernisation of government by showing how it can help many other governments reach their digital transformation goals while also promoting accountability, inclusion & long-term sustainability in the delivery of public services
Hyperautomation Use Cases (Case Studies)
Hyperautomation, often seen as the next step in digital transformation, is the combination of Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), process mining & advanced analytics to create intelligent, adaptable & scalable automation ecosystems. Hyperautomation is different from regular automation since it can coordinate whole end-to-end business processes instead of just more repetitive, rule-based tasks. This makes businesses more efficient, helps them make decisions based on their information, and makes them more resilient. Actual world case studies show the actual importance of this change. For example, companies in banking, healthcare, manufacturing & government have all seen measurable benefits, such as lower expenses, faster process efficiency, better compliance with rules & happier customers. These examples not only show how revolutionary hyperautomation can be, but they also show how more and more businesses are adopting it as they try to use the latest technologies in their work. These case studies show how hyperautomation may help businesses keep up with quickly changing market requirements while also maintaining their operational excellence. As businesses do more and more online, hyperautomation is becoming more than just a way to make processes more efficient; it is becoming a key part of innovation, flexibility & long-term growth. This essay looks at a lot of different use examples to show that hyperautomation is not only the latest technology; it is also a strategic tool that will change how businesses work in the future
TRIDENT: A Trusted Neuro-Symbolic Framework for Autonomous Systems in Unstructured Environments
Autonomous systems operating in unstructured environments face the dual challenges of robust decision-making under ambiguity and the need for verifiable, explainable behavior. Purely data-driven deep learning approaches excel at perceptual tasks but often lack the ability to reason logically or generalize to out-of-distribution scenarios, leading to unpredictable failures. Conversely, traditional symbolic logic systems are interpretable but brittle in the face of noisy, high-dimensional sensory input. This paper introduces TRIDENT (Trusted Reasoning and Integration for Intelligent Decentralized Navigation), a novel neuro- symbolic framework that synergistically fuses deep learning with symbolic logic to enhance autonomy. TRIDENT’s architecture consists of a neural perception module that grounds raw sensor data into a symbolic knowledge base, and a logical reasoning engine that uses this knowledge to perform robust, explainable planning. To enable collaborative learning across decentralized fleets of autonomous agents, we embed TRIDENT within a Trust-Metric-based Federated Learning (TMFL) scheme. TMFL ensures the integrity and accountability of the shared model by dynamically weighting contributions from each agent based on their performance and behavioral consistency. Furthermore, we introduce a quantitative framework to navigate the critical trade-off between the system’s operational performance and its explainability. By controlling the degree of symbolic oversight on the neural subsystem, we can generate a Pareto frontier of policies, allowing for principled selection based on mission-specific safety and transparency requirements. We validate TRIDENT in complex simulated autonomous driving scenarios, demonstrating superior zero-shot generalization, resilience to adversarial participants in the federated network, and a practical methodology for producing high-performance yet scrutable autonomous agents
Integrating Data Governance and Security into Data Engineering Lifecycles: A Proactive Approach
Organizations increasingly rely on data products that span heterogeneous platforms, yet many still bolt governance and security on after pipelines are built, causing rework, audit gaps, and fragile controls. This paper presents a proactive, lifecycle-based framework that integrates governance and security as code throughout the data engineering value chain: ingestion, storage, transformation, orchestration, serving, and archival. Model data contracts, data ownership, and data sensitivity categorization and security primitives encryption, key management, masking/tokenization, and fine-grained authorization (RBAC/ABAC/RLS), and implement them using CI/CD gates and runtime guardrails. Metadata, provenance, and quality promises turn into first-class data, generating ongoing compliance data and minimizing schema drift and incident blast radius. In a 2020 assessment, more trust is associated with acceptable overhead: better data quality through automated tests, reduced security incidents through standardized policy, and expanded coverage of compliance through platform controls of govern once, enforce many. Operating models (stewardship, RACI) and measurements (quality SLOs, policy decision latency, least-privilege scores) that enable scale sustenance. New avenues AI/ML-assisted classification and policy recommendations and automated compliance checking, which maps machine-readable controls to regulations, place assurance as an artifact of standard engineering processes. When organizations move governance and security left and consider them as constraints of design, they can provide resilient, explainable, and reusable data products that can stay in step with changing business and regulatory requirements
AI-Based Autonomous Code Generation and Optimization for Enhancing Software Reliability in Computer Systems
The growing complexity of software systems in the present day has provided impetus to the enigmatic automation of code generation, optimization, and quality assurance. The conventional software development practices assume manual software development, and optimization (which is heuristic) and this practice creates human error, inefficiency, and scalability issues in large-scale applications. Towards curbing these constraints, this research paper suggests a neural artificial intelligence framework combining deep learning and reinforcement learning to generate, analyze, and optimize computer code to achieve greater software availability and effectiveness. The model uses a code generating Transformer and a reinforcement learning optimizer, which runs in a closed feedback loop, which can be improved in an iterative manner using reliability-based reward signals. Benchmark databases like CodeNet, CodeXGLUE experiments were conducted in various programming languages. The designed model improved on the baseline AI systems and traditional optimization instruments by 37.4% in terms of code reliability, 29.8% decreased in execution errors, and 24.1% enhanced in running time performance. The self-adaptive feedback organization would guarantee the design of continuous improvement without a human being which shows the possibility of completely autonomous and reliability conscious software development. The article provides the basis of autonomous software engineering of the next generation, in which AI-based systems will be able not only to create effective code, which is both maintainable and efficient, but also actively improve the property of robustness, maintainability, and operational safety of sophisticated computational settings
Mitigating Cyber-Physical Attacks in ERP-Controlled Infrastructures through AI-Based Intrusion Response Systems
Enterprise Resource Planning (ERP) systems are increasingly acting as the conductor of cyber-physical operations in the manufacturing, utilities, logistics, and healthcare sectors. This tight OT-IT integration enhances effectiveness but increases the attack surface: an attacker can use ERP identities and APIs and switch to plant-floor controllers or pollute master data to create recipes with wrong proportions or schedule so as to cause unsafe conditions. Suggest an AI-Based Intrusion Response System (AIRS) to be placed at the ERP-operations border and identify, describe, and securely handle multi-stage attacks on the fly. AIRS unites diverse telemetry ERP audit trails, identity and API activity, network/flow, PLC/SCADA tags and process KPIs into a graph-temporal model that maintains a relationship between users, assets, and work orders. Supervised and unsupervised detectors raise known and unknown behaviors and specification checks check control invariants. A reinforcement policy based on safety-shielded learning identifies the least disruptive moves e.g. isolating interface accounts, throttling risky releases of orders, reverting controllers to safe set-points the process and service constraints. Digital-twin sandbox is continually trained and tested, policies against realistic attack playbooks and faults, and SOAR translates to auditable runbooks that are compliant with IEC 62443 and MITRE ATT&CK applied to ICS. Trust and compliance are guaranteed by human-in-the-loop controls, counterfactual explanations as well as rollback plans. The tests of a hybrid ERP/ICS testbed show that the faster lateral-movement detection, lower MTTD/MTTR, and sustained availability with a small number of false-positive actions are feasible and offer a viable standards-preferred road to resilient ERP-controlled infrastructures
Cyber Insurance in the Age of AI-Powered Attacks: Pricing and Coverage Strategies as AI-Generated Malware and Deepfake Fraud become Mainstream
The adoption of artificial intelligence (AI) as an agent of cyberattacks has reshaped the threat ecosystem that creates unequal beats in terms of speed, magnitude, and flexibility. In contrast to more traditional forms of activity, artificial intelligence (AI) malicious activities like generative malware, deep fraction schemes, and auto-pishing are developed like self-regulating processes hidden in advance of the usual detection and countermeasures. These changes contradict the original data of cyber insurance, which in olden days relied on retroactive data collected by the actuaries and foreseeable loss distributions. Increased uncertainty in the process of estimating the probability of losses, correlated risks and loss of sufficient solvency margins adversely impact insurers in the environment where adversarial AI technologies are rapidly expanding. The constraints of the deterministic pricing and coverage approaches have been progressively restricted as systemic and adaptive loss is created as the lynchpin advances of the dynamic and self-informed attack systems that the traditional models cannot predict. To support such complexities, this current paper suggests a combined analytical architecture, which will combine probabilistic risk modeling, behavioral analytics, and dynamical pricing algorithms to meet AI-based threat backgrounds. The framework also adds the adaptive parameters which include attack automation index, impossibility of impersonation, and model-driven threat intelligence, to real-time recalibrate premiums. Empirical findings and simulated loss models indicate that next-generation pricing methods tend to ignore AI-inflicted losses of up to 35, which puts insurers at risk of having large portfolios. The suggested model will allow the use of current threat information to influence the dynamically recalculate the premiums and coverage by insurers, boosting actuarial conditions and the strength of the market. These results point to the historical lack of urgency regarding the necessity of a paradigm shift to AI-sensitive cyber insurance foundations that would focus on ongoing risk detection, well-founded data distribution, and collaboration of regulating bodies to protect the financial uncertainty of clever cyber risks
Zero Trust Security Models in AI-Integrated ERP Platforms for Defense-Grade Business Continuity
ERP systems based on AI concentrate mission-critical information, models, and processes, increasing the attack field and increasing the impact of interference. This paper also presents a Zero Trust Security Model that has been designed to provide defense-grade business continuity in this kind of environment. Propose a vendor-neutral reference architecture based on four planes Access and Identity, Application/ API and Service Mesh, Data and Model, and Automation and Continuity and implement them with never trust, always verify controls. MFA phishing resistant, posture checks, device/workload, identity-based micro-segmentation, purpose-bound least privilege using ABAC/PBAC with short-lived tokens and just-in-time elevation are some of the core mechanisms. In an effort to ensure the AI supply chain, recommend artifact signing, SBOM/MLSBOM and attestation, model/data lineage, confidential-computing enclaves and privacy-preserving training, as well as defenses against poisoning, drift and adversarial inputs. Unified observability combines ERP, identity, network and model telemetry to UEBA/graph analytics, policy-as-code risk-telemetry-driven adaptive authorization, SIEM/SOAR playbooks coordinate isolation, key rotation and policy repair. One ensures continuity with some backups that are immutable, cross-cloud failover, segmented replicas and chaos-engineering tests that are optimized to RTO/RPO targets. A comparative analysis with perimeter-centric baselines has shown a material improvement in detection fidelity, faster recovery and response and large improvements in false positives/negatives with reasonable latency overheads enhancing uptime and recovery guarantees