Architecting Agentic CRM: A Reference Architecture for AI-Driven Enterprise Customer Relationship Management
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Abstract
The metamorphosis of enterprise Customer Relationship Management (CRM) where rule-based automation is autonomous and AI-driven must be on a scalable, secure, cross-architectural framework. The proposed paper proposes an agentic CRM reference architecture that entails the incorporation of intelligent AI agents with business information, business operations, and customer-interaction levels. The design is implemented using Salesforce Agentforce, Data 360 (previously known as Data Cloud) and MuleSoft integration services, in the context and an already developed Salesforce development platform, including Apex and Flow. It has made a distinction between agentic capabilities (contextual reasoning, dynamic task planning and tool selection) and conventional deterministic automation. The framework suggested by virtue of this project breaks down the system into inter-connecting system layers to manage customer data, coordinate agents, integrate the enterprise, execute deterministic actions, and governance. Data 360 and Customer 360 services give access to contextual customer data, whereas MuleSoft helps to communicate with diverse enterprise systems. The Agent-to-System Model Context Protocol (MCP) and Agent-to- Agent (A2A) patterns of communication offer standard agent-system communication and cor-ordinated workflows of a multi-agent system. They comprise security and authorization measures, policy control, auditable and human-in-the-loop oversight in mitigating operational risks and accountability. The architecture is tested conceptually on interoperability, scalability, reliability, security and governance tests. The conceptual framework provides a systematic foundation to establish trusted agentic CRM ecosystems to deliver superior services to customers, individualize their interactions, ease the process of entails amidst enterprises and support the regulated decision-making procedure of AI.
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[1] Gaczek, P., et al. (2023). Collaboration with machines in B2B marketing: Overcoming managers' aversion to AI-CRM with explainability. Industrial Marketing Management, 115, 127–142. https://doi.org/10.1016/j.indmarman.2023.09.007
[2] Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013
[3] Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: An empirical study of Indian organizations. The Bottom Line, 33(4), 359–375. https://doi.org/10.1108/BL-08-2020-0057
[4] Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kotterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56.
[5] Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marques, D. (2021). Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research. Industrial Marketing Management, 98, 161–178.
[6] Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
[7] de Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
[8] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of big data—Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2018.01.021
[9] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
[10] Puranam, P. (2021). Human–AI collaborative decision-making as an organization design problem. Journal of Organization Design, 10(2), 75–80. https://doi.org/10.1007/s41469-021-00095-2
[11] Arrieta, A. B., Díaz-Rodríguez, N., del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
[12] Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245–317.
[13] Cabiddu, F., Moi, L., Patriotta, G., & Allen, D. G. (2022). Why do users trust algorithms? A review and conceptualization of initial trust and trust over time. European Management Journal, 40(5), 685–706.
[14] Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, Article 102312. https://doi.org/10.1016/j.technovation.2021.102312
[15] Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021). Human-machine collaboration in managerial decision making. Computers in Human Behavior, 119, Article 106730. https://doi.org/10.1016/j.chb.2021.106730
[16] Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, Article 120420.