Automation of real estate deal closing using salesforce AI
Bus Sys Analyst Sr Staff, Move.Inc, Westlake Village, California, USA.
Research Article
World Journal of Advanced Research and Reviews, 2023, 17(01), 1363-1379
Article DOI: 10.30574/wjarr.2023.17.1.1440
Publication history:
Received on 17 November 2022; revised on 20 January 2023; accepted on 24 January 2023
Abstract:
The real estate industry transforms through automation as a solution to solve the complex operations it faces. Real estate deals experience their most important transformation during this crucial resource-intensive time-consuming phase known as the deal-closing stage. The document classification necessitates accurate data extraction and strict compliance checks throughout deal-closing procedures that currently require enormous manual intervention with rising probability of human errors and inefficiencies. The integration of Salesforce AI with Doc AI serves as a solution to develop innovative deal-closing operations through automated document processing and precise data management and workflow scalability. The research details how manual processing continues to cause problems by producing delayed results while data errors occur frequently together with compliance risks. The paper demonstrates how AI creates operational efficiencies while decreasing human involvement to achieve scalable operations. The main study results show that automated systems yield superior outcomes because they decrease errors during processing and provide dependable data-based decisions while boosting operational efficiency and obtaining better customer satisfaction. This research expands understandings about AI adoption in real estate through systematic analysis describing practical applications that improve complex process management in the industry.
Keywords:
Real Estate Automation; Salesforce AI; Doc AI; Deal Closing Efficiency; Document Processing; AI-Powered Workflows; Data Extraction; Regulatory Compliance; Scalability in Real Estate; Customer Satisfaction
Full text article in PDF:
Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0