IDC FutureScape：全球智能 ERP 预测，2025

Group 1 - The core viewpoint emphasizes the necessity for companies to accurately grasp technological trends and adopt flexible decision-making and efficient operational models to navigate market challenges [1] - IDC's global smart ERP forecast outlines key development directions for ERP and enterprise application technologies, aiding companies in their digital transformation [1] Group 2 - The report highlights that with the deep integration of AI technology, companies will leverage intelligent tools to quickly process vast amounts of data, generating insightful analysis [2] - By the end of 2026, 65% of companies are expected to utilize AI-driven technology assistants to optimize decision-making processes and enhance operational efficiency [2] - These technologies will not only automate daily tasks but also continuously optimize algorithms through deep learning, enabling rapid responses to market changes [2] Group 3 - Companies will achieve efficient resource allocation and rapid skill enhancement of employees by introducing digital workers and intelligent collaboration tools [3] - By the end of 2025, 35% of companies are projected to utilize digital workers to improve resource collaboration and achieve more valuable outcomes [3] - The integration of AI into processes aligned with employee workflows is expected to boost overall operational efficiency by 45% while ensuring financial stability during scale expansion [3] Group 4 - In response to increasingly stringent regulatory environments and ESG requirements, companies need to integrate sustainability concepts into daily operations [4] - By mid-2027, 55% of companies globally are anticipated to use ERP systems as the main hub for handling ESG-related tasks, ensuring accurate data collection and transparent analysis [4] - By 2028, 70% of companies are expected to adopt unified electronic invoicing and compliance-as-a-service to address evolving tax regulations and reduce compliance risks [4] Group 5 - The shift towards automation is becoming a critical development area, focusing on automating tasks that require human judgment and decision-making [7] - Careful implementation is essential, necessitating meticulous management of data quality, governance, and storage to maintain the accuracy of automation tools [7] Group 6 - The current phase of AI technology is transitioning from experimentation to monetization, with a focus on proving that "realized AI" can generate tangible business impacts [8] - Technology buyers are initially concentrating on efficiency and automation scenarios, but the long-term goal is to leverage AI to initiate new business models and revenue streams [8] - Companies must consider ethical and data privacy risks associated with AI while identifying optimal scenarios for effective implementation [8]