Artificial Intelligence Embedded Customer Relationship Management for Business-to-Business Firms using Improved Long Short-Term Memory

The influence of adopting an Artificial Intelligence-embedded Customer Relationship Management (AI-CRM) system for Business-to-Business (B2B) firm’s Relationship Management (RM) is crucial. However, in the existing models, there are some limitations such as inadequate data and error in prediction which affects decision making of firm, causing defects in RM. To overcome this, an Improved Long Short-Term Memory in automatic decision-making model that reduces error and enhances prediction, positively impacting the relationship of firms is proposed. Initially, a questionnaire-based data is acquired from14 firms in B2B context with 312 responses. The proposed decision making model in AI-embedded CRM system has a substantial positive impact on relationship satisfaction and hence the firm’s performance and B2B. Also, the negative impact of technology turbulence moderator on the relationships of ADM is highlighted with the satisfaction result of Operational Efficiency (OE) with B2B relationship, as mentioned in this research. The positive impact of leadership support moderator on the firm’s performance and B2B relationship satisfaction is also increased by decision making in the proposed conceptual model. In future, advanced deep neural network will be implemented to enhance CRM with AI integration in B2B firms.