Publicação
Seamless Customer Experience through AI-Driven CRM: A Framework and Adoption Accelerator for Enterprise Sales and Service Processes
| Resumo: | This study was motivated by the growing challenge enterprises face in translating Artificial Intelligence (AI) potential in Customer Relationship Management (CRM) into seamless, scalable, and measurable outcomes that deliver clear business value. Although AI is increasingly present in CRM, much of the existing discussion remains centered on isolated technical features, while the broader organizational, human, governance, and process conditions required for effective adoption are still less explored. For this reason, the main objective of this research was to understand how enterprises can apply AI in CRM in a more effective and scalable way, and to develop a practical framework that helps organizations identify what should be prioritized and what should be avoided during adoption. To achieve this objective, the study combined a systematic literature review with a Design Science Research approach focused on the design and evaluation of a conceptual artefact for Sales and Service CRM contexts. As a result, a framework was developed composed of a Six-layer AI-CRM Structural Foundation and a 1-2-3 Adoption Accelerator organized around the stages Align, Prepare, and Execute. The framework proposes that seamless AI-powered CRM depends not only on technology, but also on strategic value alignment, data and technology readiness, governance and trust, human enablement, embedded AI capabilities, and continuous validation and improvement. It was concluded from the expert evaluation that the proposed framework is relevant, useful, and logically consistent for enterprise applications, and that its core structure should be maintained. Simultaneously, the evaluation showed that its practical use can be further strengthened. |
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| Autores principais: | Monteiro, Luciano Martins |
| Assunto: | Customer Relationship Management (CRM) Artificial Intelligence (AI) Customer Experience (CX) Adoption CRM Business Processes Innovation Accelerator |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This study was motivated by the growing challenge enterprises face in translating Artificial Intelligence (AI) potential in Customer Relationship Management (CRM) into seamless, scalable, and measurable outcomes that deliver clear business value. Although AI is increasingly present in CRM, much of the existing discussion remains centered on isolated technical features, while the broader organizational, human, governance, and process conditions required for effective adoption are still less explored. For this reason, the main objective of this research was to understand how enterprises can apply AI in CRM in a more effective and scalable way, and to develop a practical framework that helps organizations identify what should be prioritized and what should be avoided during adoption. To achieve this objective, the study combined a systematic literature review with a Design Science Research approach focused on the design and evaluation of a conceptual artefact for Sales and Service CRM contexts. As a result, a framework was developed composed of a Six-layer AI-CRM Structural Foundation and a 1-2-3 Adoption Accelerator organized around the stages Align, Prepare, and Execute. The framework proposes that seamless AI-powered CRM depends not only on technology, but also on strategic value alignment, data and technology readiness, governance and trust, human enablement, embedded AI capabilities, and continuous validation and improvement. It was concluded from the expert evaluation that the proposed framework is relevant, useful, and logically consistent for enterprise applications, and that its core structure should be maintained. Simultaneously, the evaluation showed that its practical use can be further strengthened. |
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