The Intelligent Enterprise: A Technical Examination of AI-Driven Analytics in SAP S/4HANA Environments

 Corresponding author: Siva Reddy Pulluru. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. The Intelligent Enterprise: A Technical Examination of AI-Driven Analytics in SAP S/4HANA Environments Siva Reddy Pulluru * Orora Packaging Solutions, USA. World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 Publication history: Received on 20 April 2025; revised on 27 May 2025; accepted on 30 May 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.2117 Abstract The integration of artificial intelligence into SAP ERP systems heralds a fundamental transformation in enterprise resource planning, elevating these platforms from traditional transactional engines to sophisticated decision support systems. This technical examination explores how SAP's AI-driven analytics capabilities are redefining core business processes through predictive insights, cognitive automation, and intelligent decision frameworks. By leveraging SAP's comprehensive AI technology stack—including SAP AI Core, Joule, and Analytics Cloud—organizations can implement self-optimizing supply chains, autonomous financial operations, and hyper-personalized customer engagement models. The article provides a structured implementation methodology addressing data architecture considerations, security frameworks, and change management strategies essential for successful AI adoption. As SAP's ecosystem continues to evolve, enterprises that strategically embrace these advanced capabilities will achieve unprecedented operational agility and competitive differentiation in an increasingly complex business environment. Keywords: Artificial Intelligence; SAP S/4HANA; Predictive Analytics; Intelligent Automation; Cognitive ERP 1. Introduction 1.1. The Evolution of SAP ERP into an AI-Powered Engine The transformation of SAP ERP systems represents a pivotal shift in enterprise technology, evolving from traditional data repositories to dynamic, AI-driven decision engines. This evolution fundamentally redefines how organizations leverage their enterprise systems, transitioning from retrospective reporting to predictive and prescriptive business operations that actively shape organizational outcomes. 1.2. The Strategic Imperative for Intelligent ERP S/4HANA's emergence has catalyzed a transformative period in enterprise resource planning, establishing new benchmarks for operational intelligence and business agility. Organizations implementing S/4HANA report significant improvements in financial closing processes, enhanced real-time reporting capabilities, and substantial gains in operational efficiency. These advancements stem from the platform's in-memory computing architecture, which enables processing speeds previously unattainable in legacy ERP environments. The integration of intelligent technologies within S/4HANA has become increasingly central to maintaining competitive advantage in rapidly evolving markets, particularly as organizations seek to extract actionable insights from expanding data volumes [1]. World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4422 1.3. Architectural Foundations for AI-Enhanced ERP The technical architecture underpinning S/4HANA's AI capabilities represents a significant departure from traditional ERP designs. The simplified data model eliminates redundant aggregates and indices, creating a streamlined foundation for advanced analytics. This architecture enables real-time data processing across complex business scenarios, supporting sophisticated machine learning applications previously impractical within ERP environments. The innovative column-oriented database structure optimizes both analytical and transactional workloads simultaneously, overcoming the historical compromise between these processing models. Organizations leveraging these capabilities report meaningful reductions in their total cost of ownership while simultaneously expanding analytical capabilities [2]. 1.4. Business Value Transformation Through AI Integration The business impact of AI-enhanced ERP extends far beyond incremental efficiency gains, fundamentally redefining organizational capabilities across core functions. Finance organizations implementing S/4HANA report substantial improvements in their forecasting accuracy, while supply chain operations benefit from increased visibility and predictive capabilities. Customer engagement functions leverage enhanced data integration to deliver personalized experiences at scale. The platform's embedded analytics capabilities democratize access to business intelligence, reducing dependency on specialized technical resources. As organizations progress in their intelligent enterprise journey, they increasingly report qualitative benefits related to organizational agility, innovation capacity, and talent optimization [1]. The integration of machine learning within core business processes represents a fundamental shift from reactive to proactive operational models, enabling organizations to anticipate challenges and opportunities with unprecedented precision [2]. 2. SAP's AI Technology Stack: Architecture and Integration Points The technological framework underpinning SAP's artificial intelligence capabilities represents a sophisticated ecosystem designed to transform traditional ERP functionalities into an intelligent enterprise platform. This architecture enables predictive analysis, cognitive automation, and intelligent decision support across the SAP landscape. 2.1. SAP Business Technology Platform: The Foundation for Intelligence The SAP Business Technology Platform (BTP) serves as the cornerstone for artificial intelligence integration, providing a comprehensive environment for developing and deploying intelligent applications. This platform seamlessly connects core ERP processes with specialized AI services through a unified integration layer. The BTP's architecture follows a microservices approach, enabling modular deployment and scalability while maintaining robust enterprise governance. This architectural model facilitates continuous innovation without disrupting core business processes, allowing organizations to implement new AI capabilities incrementally rather than through high-risk transformation initiatives. The platform offers integration with various data sources through standard interfaces and protocols, enabling organizations to incorporate both structured and unstructured data into their analytical models. Organizations implementing this approach report substantial reductions in integration complexity while simultaneously expanding their analytical capabilities across previously siloed data domains [3]. 2.2. In-Memory Computing and Predictive Analytics Framework SAP HANA's in-memory computing architecture fundamentally transforms how organizations approach business analytics, eliminating the historical separation between transactional and analytical workloads. This technical foundation enables predictive algorithms to operate directly on operational data without requiring traditional extracttransform-load processes. The platform combines column-store technology with sophisticated compression algorithms, optimizing memory utilization while maintaining exceptional query performance. This architecture supports advanced analytical workloads including complex pattern recognition, anomaly detection, and predictive forecasting within standard business process timeframes. SAP Analytics Cloud extends these capabilities by providing an integrated environment for business planning, predictive analytics, and visual exploration. Organizations leveraging these capabilities report meaningful improvements in forecast accuracy and decision quality while simultaneously reducing their analytical cycle times [4]. 2.3. API Ecosystem and Extensibility Framework The technical viability of AI-enhanced business processes depends significantly on seamless integration between specialized AI services and core ERP functions. SAP's API-first development approach provides standardized business World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4423 interfaces that expose S/4HANA functionality to both native and third-party AI services. These interfaces follow RESTful design principles, enabling predictable, secure integration while maintaining appropriate separation between systems. For organizations requiring specialized AI capabilities beyond SAP's native offerings, the BTP provides a comprehensive framework for integrating external services. This extensibility model allows enterprises to combine SAP's robust process management with specialized AI technologies from the broader ecosystem, creating unique capabilities tailored to specific business requirements. The platform's event-driven architecture facilitates real-time integration between ERP processes and AI services, supporting sophisticated scenarios such as dynamic pricing optimization and predictive maintenance without requiring batch processing windows [3]. This architectural approach enables organizations to incorporate emerging AI technologies while maintaining a coherent, manageable technology landscape [4]. Figure 1 SAP's AI Technology Stack: Architecture and Integration Points [3, 4] 3. Intelligent process automation: beyond basic RPA The evolution of process automation within SAP ecosystems represents a significant advancement beyond traditional automation approaches, incorporating sophisticated cognitive capabilities that enable autonomous decision-making and continuous process optimization. This section examines the technical foundations and implementation considerations that underpin intelligent process automation within modern SAP environments. 3.1. Advanced Process Intelligence and Mining SAP's process intelligence technologies provide unprecedented visibility into actual business process execution patterns rather than idealized process designs. These capabilities leverage sophisticated process mining algorithms that analyze system logs across the SAP landscape to reconstruct actual process flows with exceptional precision. The technical architecture implements specialized graph-based analytics to identify execution patterns, decision points, and process variants that impact operational performance. Recent implementations demonstrate that process mining technologies can effectively analyze complex business processes with thousands of variants, enabling organizations to target automation initiatives with precision. The integration of machine learning with process mining enables predictive capabilities that anticipate process deviations before they occur, shifting automation from reactive to proactive intervention models. Organizations implementing these technologies report significant improvements in process transparency and operational efficiency across their SAP landscapes [5]. World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4424 3.2. Cognitive Automation Framework The integration of cognitive capabilities fundamentally transforms process automation from rule-based execution to intelligent, adaptive workflows. SAP's cognitive automation framework incorporates natural language processing, machine learning, and knowledge representation to enable context-aware decision-making within automated processes. This technical architecture supports complex scenarios including unstructured data interpretation, contextual decision-making, and autonomous exception handling. The framework implements sophisticated reinforcement learning models that continuously improve automation performance through operational feedback. Implementations demonstrate particular effectiveness in semi-structured processes with significant exception rates, where traditional rule-based approaches typically fail. The cognitive layer enables automation to adapt to changing business conditions without requiring explicit reprogramming, significantly enhancing solution longevity and reducing maintenance requirements [6]. 3.3. Intelligent Document Processing Document processing represents one of the most significant automation opportunities within enterprise operations. SAP's intelligent document processing combines computer vision, natural language understanding, and machine learning to transform unstructured documents into structured business data. The technical architecture implements multi-modal deep learning models specifically trained on business documents across industries and languages. These models utilize transformer-based architectures that understand document context beyond simple text extraction, enabling interpretation of complex business documents with implicit semantic relationships. Recent implementations demonstrate exceptional accuracy in extracting structured information from invoices, purchase orders, contracts, and other business documents across formats and languages. The framework supports continuous learning capabilities that enable extraction models to adapt to organization-specific document formats over time, enhancing accuracy through operational usage. Organizations implementing these capabilities report substantial reductions in manual document processing while simultaneously improving data accuracy and process compliance [5]. The integration with SAP's workflow management enables end-to-end automation of document-centric processes across the enterprise landscape [6]. Figure 2 Intelligent Process Automation: Beyond Basic RPA [5, 6] World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4425 4. Industry-specific ai applications: technical deep dive The implementation of AI-enhanced SAP solutions demonstrates significant variation across industries, with specialized applications addressing unique business challenges in each sector. This section examines the technical architecture, implementation considerations, and business outcomes of industry-specific AI applications within the SAP ecosystem. 4.1. Manufacturing Intelligence: Digital Twin Implementation The manufacturing sector has embraced SAP's intelligent technologies to transform traditional production environments into connected, data-driven operations. The implementation of digital twin technology represents a particularly transformative application, creating virtual representations of physical assets that enable advanced simulation and predictive capabilities. This approach integrates real-time sensor data with historical performance metrics to create comprehensive asset profiles. The technical architecture typically implements a three-layer framework comprising physical assets, data acquisition systems, and analytical models working in concert. SAP's Manufacturing Intelligence leverages this architecture to enable condition-based maintenance strategies that significantly reduce unplanned downtime while extending equipment lifecycle. The integration with S/4HANA allows these insights to automatically trigger maintenance workflows, spare parts requisitions, and production schedule adjustments based on equipment health predictions. Organizations implementing these capabilities report substantial improvements in overall equipment effectiveness and maintenance cost optimization across diverse manufacturing environments [7]. 4.2. Retail Analytics: Customer Intelligence Framework The retail sector faces complex challenges related to customer behavior analysis, demand volatility, and omnichannel engagement that AI-enhanced SAP applications specifically address. SAP Customer Activity Repository (CAR) serves as the foundation for retail intelligence, consolidating transaction data, customer interactions, and inventory movements across channels. This unified data platform enables sophisticated customer segmentation models that identify purchasing patterns, channel preferences, and price sensitivity across diverse customer cohorts. The technical implementation leverages machine learning algorithms that analyze basket composition, purchase frequency, and promotional response to generate customer-specific recommendations and personalization strategies. These capabilities extend beyond traditional demographic segmentation to incorporate behavioral patterns that predict future purchasing intent with remarkable accuracy. Organizations implementing these technologies report significant improvements in marketing campaign effectiveness, customer retention metrics, and share-of-wallet expansion. The integration between customer intelligence and inventory optimization creates closed-loop systems that automatically align assortment planning with evolving customer preferences [7]. 4.3. Healthcare Operations: Patient Flow Optimization Healthcare organizations implement specialized SAP solutions that address unique challenges related to patient flow, resource utilization, and care coordination. SAP's healthcare intelligence framework integrates admission data, resource availability, and clinical protocols within predictive optimization models that enhance operational efficiency while maintaining clinical quality. The technical architecture implements discrete event simulation combined with machine learning to predict patient volumes, optimize staff scheduling, and anticipate resource constraints before they impact care delivery. These capabilities enable healthcare organizations to reduce wait times, optimize bed utilization, and enhance overall patient experience through more efficient operations. The implementation typically follows a phased approach beginning with historical pattern analysis before progressing to near-real-time optimization and eventually predictive resource allocation. Organizations implementing these technologies report meaningful improvements in resource utilization, staff satisfaction, and patient throughput. The integration with core ERP functions enables comprehensive visibility across clinical and administrative processes, creating a foundation for continuous operational improvement while maintaining focus on patient-centered care delivery [8]. World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4426 Figure 3 Industry-Specific AI Applications: Technical Deep Dive [7, 8] 5. Implementation Methodology and Technical Requirements The successful implementation of AI-enhanced SAP systems demands a structured methodology addressing technical architecture, organizational readiness, and change management considerations. This section examines critical implementation components and success factors for organizations seeking to leverage artificial intelligence within their SAP environments. 5.1. Data Foundation and Governance Framework Establishing a robust data foundation represents the cornerstone of successful AI implementation within SAP environments. Organizations must conduct comprehensive data quality assessments across master data domains including customers, materials, vendors, and financial structures to identify integrity issues before they impact analytical outcomes. Effective implementation methodologies incorporate dedicated data preparation phases that systematically address quality challenges through automated cleansing pipelines and governance frameworks. This approach establishes data as a strategic asset rather than a technical byproduct, fundamentally shifting organizational perspectives on information management. Organizations implementing formal governance structures report substantial improvements in data reliability metrics while simultaneously reducing preparation effort for analytical initiatives. The implementation typically establishes data stewardship roles with clear accountability for quality across domains, supported by technical monitoring frameworks that identify quality degradation before it impacts business processes. This proactive governance approach proves particularly valuable for complex AI initiatives that span multiple data domains and rely on cross-functional information integration [9]. 5.2. Technical Architecture and Infrastructure Requirements The infrastructure supporting AI-enhanced SAP applications requires specialized architectural considerations beyond traditional ERP implementations. Organizations must carefully evaluate compute capacity requirements for machine learning workloads, which differ significantly from standard transaction processing in both resource utilization patterns and scaling characteristics. Effective implementations typically establish dedicated environments for model development and training while integrating inferencing capabilities within production systems. This hybrid approach enables data scientists to experiment with complex models without impacting operational performance. Storage World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4427 architecture represents another critical consideration, with high-performance storage required for training datasets while inference operations often prioritize low-latency access over raw throughput. Network architecture must support both batch processing for model training and real-time data flows for operational integration, often requiring specialized configurations to maintain performance under variable workload patterns. Organizations implementing well-architected infrastructure report significant improvements in model development efficiency and operational reliability compared to those retrofitting traditional environments [10]. 5.3. Organizational Capabilities and Change Management The human dimension of AI implementation often determines success more definitively than technical considerations. Organizations must develop specialized capabilities across both technical and business domains to effectively leverage AI within SAP environments. Successful implementation methodologies incorporate structured skills assessment and development programs that address capabilities including data engineering, model development, business process redesign, and performance management. These programs typically combine formal training with experiential learning opportunities that build practical expertise through incremental implementation. Beyond individual capabilities, organizational structures must evolve to support cross-functional collaboration between technical specialists and business process experts. Effective implementations establish governance mechanisms that prioritize use cases based on both technical feasibility and business impact, ensuring resources target opportunities with meaningful returns. Change management represents another critical success factor, with structured approaches addressing both technical transitions and fundamental shifts in decision-making processes. Organizations implementing comprehensive capability development and change management programs report substantially higher adoption rates and business value realization compared to those focusing exclusively on technical implementation [9]. Table 1 Technical Architecture Components for AI Implementation [9, 10] Architecture Layer Primary Function Design Considerations Implementation Requirements Data Processing High-performance data analysis and transformation Memory capacity, parallel processing capabilities Sufficient resources for in-memory computing with growth headroom Model Management Development, deployment, and governance of AI models Version control, reproducibility, automated testing MLOps framework with CI/CD integration for model lifecycle Integration Services Connection between AI capabilities and business processes API design, event architecture, security model Standardized interfaces with appropriate authentication and monitoring User Experience Delivery of insights through appropriate interfaces Accessibility, context awareness, response time Embedded analytics with roleappropriate visualization and interaction 6. Future Technology Roadmap and Strategic Considerations The evolution of AI-enhanced SAP systems continues at a rapid pace, with emerging technologies poised to further transform enterprise operations. This section examines the future technology landscape, governance frameworks, and strategic recommendations for organizations seeking to maintain competitive advantage through intelligent ERP capabilities. 6.1. Emerging AI Governance Models for Enterprise Applications As artificial intelligence becomes increasingly embedded within core business processes, organizations must establish comprehensive governance frameworks addressing technical, ethical, and operational dimensions. Effective AI governance establishes clear accountability structures through defined roles and responsibilities that span both technical teams and business stakeholders. These governance models identify appropriate decision rights across the AI lifecycle, from use case identification through deployment and continuous improvement. Contemporary frameworks incorporate structured review processes that systematically evaluate AI applications against established criteria including technical feasibility, business impact, and ethical implications. This approach ensures that AI investments align with strategic priorities while maintaining appropriate risk management. Leading organizations implement governance processes that evolve with technology capabilities, recognizing that static frameworks quickly become World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4428 obsolete in rapidly changing environments. The governance structure typically establishes appropriate escalation paths for edge cases that fall outside established parameters, ensuring timely resolution while maintaining oversight. Organizations implementing formalized governance frameworks report substantial improvements in AI implementation success rates compared to those pursuing ad-hoc approaches [11]. 6.2. Integration of Large Language Models within Enterprise Processes Large language models represent one of the most transformative AI technologies poised to reshape SAP environments. These sophisticated models integrate natural language understanding with contextual business knowledge, enabling entirely new interaction paradigms between users and enterprise systems. The implementation architecture typically leverages pre-trained foundation models enhanced with domain-specific data to ensure relevance within specialized business contexts. This approach combines the broad linguistic capabilities of general-purpose models with the precision required for enterprise applications. Technical implementations establish appropriate guardrails that ensure business-appropriate responses while leveraging the models' creative capabilities for scenarios including content generation, process recommendations, and knowledge management. Organizations implementing these technologies report significant improvements in system accessibility, particularly for occasional users who previously struggled with complex transaction codes and navigation paths. The integration with structured business processes enables sophisticated automation scenarios that combine unstructured information processing with formal workflow execution. This capability proves particularly valuable for document-centric processes including contract management, technical documentation, and regulatory filings [12]. 6.3. Edge Computing and Distributed Intelligence The expansion of AI capabilities to edge environments represents another significant evolution in the SAP technology landscape. This architectural approach extends intelligence beyond centralized data centers to operational endpoints including manufacturing equipment, retail locations, and field service environments. The implementation typically establishes a federated model that maintains centralized governance while enabling localized execution, balancing operational autonomy with enterprise consistency. Edge deployments prove particularly valuable for time-sensitive scenarios where latency impacts business outcomes, including quality control, predictive maintenance, and real-time decision support. The technical architecture implements sophisticated model optimization techniques that enable complex algorithms to operate within the constrained computing environments common at operational endpoints. Organizations implementing edge intelligence report substantial improvements in operational responsiveness compared to centralized approaches, particularly valuable in environments with connectivity limitations or bandwidth constraints. This distributed architecture creates new resilience capabilities where operational intelligence continues functioning even during central system disruptions, maintaining business continuity during challenging conditions [12]. Table 2 Emerging Technology Integration Roadmap [11, 12] Technology Domain Strategic Value Implementation Considerations Organizational Readiness Factors Large Language Models Enhanced natural language interaction and content generation Balance generalized capabilities with domain-specific knowledge Develop clear guidelines for appropriate use cases and limitations Edge Computing Real-time intelligence at operational endpoints Establish governance models for distributed model deployment Balance local autonomy with enterprise consistency Quantum-Ready Algorithms Preparation for breakthrough computational capabilities Identify high-value optimization problems suitable for quantum approaches Develop skills pipeline for emerging computational paradigms Federated Learning Privacy-preserving analytics across distributed data sources Design architectures that support learning without centralized data Address organizational and technical barriers to data collaboration 7. Conclusion The evolution of SAP ERP from a system of record to a system of intelligence represents a pivotal shift in how organizations manage their core operations. Through the strategic implementation of AI-driven analytics, enterprises can transform static business processes into dynamic, self-optimizing ecosystems that continuously adapt to changing World Journal of Advanced Research and Reviews, 2025, 26(02), 4421–4429 4429 market conditions. The convergence of predictive analytics, process automation, and cognitive computing within the SAP environment enables a future where supply chains self-correct, financial operations self-reconcile, and customer experiences self-personalize. Organizations that successfully navigate this transformation—addressing technical requirements, data governance, and workforce adaptation—will unlock substantial operational efficiencies while gaining unprecedented business insights. As SAP continues to advance its AI capabilities, forward-thinking enterprises must establish robust technical foundations and governance frameworks to fully capitalize on this new era of intelligent ERP, ensuring they remain competitive in an increasingly automated business landscape References [1] Ravi Kiran Puvvada, "The Impact of SAP S/4HANA Finance on Modern Business Processes: A Comprehensive Analysis," International Journal of Scientific Research in Computer Science Engineering and Information Technology, Vol. 11, No. 2, March 2025. [Online]. Available: https://www.researchgate.net/publication/389782032_The_Impact_of_SAP_S4HANA_Finance_on_Modern_Bus iness_Processes_A_Comprehensive_Analysis [2] Alex Hedenstrand, "The Intelligent Enterprise Resource Planning System from a Business Perspective," DiVA Portal. [Online]. Available: https://www.diva-portal.org/smash/get/diva2:1451142/FULLTEXT01.pdf [3] Smitha Banda et al., "SAP Business Technology Platform," SAP Press, Aug. 2024. [Online]. Available: https://s3eu-west-1.amazonaws.com/gxmedia.galileo-press.de/leseproben/5919/2617_reading_sample.pdf [4] Bernard Owusu Antwi and Eli Kofi Avickson, "Integrating SAP, AI, and Data Analytics for Advanced Enterprise Management," International Journal of Research Publication and Reviews, Vol. 5, No. 10, Oct. 2024. [Online]. Available: https://ijrpr.com/uploads/V5ISSUE10/IJRPR33869.pdf [5] Bipin Gajbhiye et al., "Intelligent Data Processing in SAP Environments," ResearchGate, Oct. 2024. [Online]. Available: https://www.researchgate.net/publication/390172075_Intelligent_Data_Processing_in_SAP_Environments [6] Oleh Tomashevskyy et al., "Machine Learning for Business Process Automation," CEUR Workshop Proceedings, April 2024. [Online]. Available: https://ceur-ws.org/Vol-3699/paper11.pdf [7] Surya Sai Ram Parimi, "AI-driven Financial Data Analytics for SAP ERP: Techniques and Applications," IJIRMPS, Vol. 12, No. 3, 2024. [Online]. Available: https://www.ijirmps.org/papers/2024/3/230800.pdf [8] Harish Narne, "AI and Machine Learning in Enterprise Resource Planning: Empowering Automation, Performance, and Insightful Analytics," IJRAR, March 2022. [Online]. Available: https://www.ijrar.org/papers/IJRAR22A2938.pdf [9] Mohammad I. Merhi, "An evaluation of the critical success factors impacting artificial intelligence implementation," International Journal of Information Management, Vol. 69, April 2023. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0268401222000792 [10] Leaseweb Insights, "What are the infrastructure requirements for Artificial Intelligence?" Leaseweb Blog, 4 July 2019. [Online]. Available: https://blog.leaseweb.com/2019/07/04/infrastructure-requirements-ai/ [11] Lara Abrash, "Strategic governance of AI: A roadmap for the future," Deloitte Center for Board Effectiveness, 2025. [Online]. Available: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/center-for-boardeffectiveness/ai-governance-roadmap.pdf [12] KU School of Business, "Emerging Technologies and Their Impact on Business," University of Kansas, 3 June 2024. [Online]. Available: https://onlinemba.ku.edu/experience-ku/mba-blog/emerging-technologies-and-theirimpact-on-business