利用智能体AI转变订单至交付运营

Turning AI investment into real outcomes,including more sales, better margins, and strongercustomer loyalty Table of contents IntroductionWhy a new approach to O2D is essentialIntroducing agentic AI in O2DThe advantage over traditional automationMoving beyond the hype: How to succeed with agentic AIThe future of O2D is agenticAchieving impact and ROI at scale03040506071011 Introduction automation, and orchestration. AgenticAI builds on this groundwork, offeringsignificant productivity gains. But itsimpact goes far beyond efficiencyand a hands-free order managementprocess. As customer expectations continueto rise and supply chains grow morecomplex, the emergence of agenticAI signals a pivotal shift in howorganizations can orchestrate theiroperations more autonomously,particularly within order-to-delivery(O2D). This domain, often burdenedby repetitive, cross-functionaltasks, demands a level of agility andadaptability that traditional automationstruggles to deliver.Over the past decade, companieshave laid a solid foundation throughinvestments in process harmonization, When applied effectively andstrategically, agentic AI can becomea cornerstone of O2D excellence,uplifting sales, improving margins,enabling differentiated service levels,and strengthening customer loyalty –all while securing a needed return oninvestment (ROI) for the business. Why anew approachto O2D is essential Order-to-delivery operations are predominantly shaped byconventional automation frameworks layered on legacysystems, which limits responsiveness and adaptability indynamic supply chain environments. While rule-basedengines and tools focused on robotic process automation (RPA), advanced workflows, and orchestration platformsare useful for automating repetitive tasks and integratingsystems, they are less capable of managing the increasingcomplexity of modern supply chains. Some key limitationsare examined below. Time and resource intensiveness:Exception handling and failure resolution remain labor-intensive, particularly when data is fragmented across multiple systems. Rising customer expectations:Customers are demanding faster, more personalized, and seamless experiences, which traditional systems often fail to deliver. Escalating cost pressures:Cost-to-serve optimization is a strategic priority, with65 percent of executivesanticipating rising supply chain costs over the next two years. Limited customer segmentation and personalization:Most organizations have yet to achieve true hyper-personalization, resulting in overserved or underserved customer segments and inefficient resource allocation. Reactive communication and problem solving:Traditional end-to-end supply chain operations are often designed to respond to disruptions after they occur, rather than anticipating and preventing them. This reactive posture leads to delayed resolution times, increased operational costs,customer dissatisfaction, and limited scalability. Legacy infrastructure:Outdated systems hinder agility, scalability, and integration with modern technologies. These constraints highlight the need for a more intelligentand adaptive approach, which agentic AI is well Introducingagentic AIin O2D Unlike conventional automation, which relies on rule-based outputs, workflow engines, and predefined decisiontrees suited for structured and predictable tasks such asinventory checks, stock allocation rules, and routine statusupdates, agentic AI introduces goal-driven behavior with itsperceptive, autonomous, and real-time learning capabilities.This enables AI agents to plan, execute, and adapt O2Dworkflows based on outcomes rather than predefined tasks.Within O2D operations, agentic AI delivers distinct value indynamic scenarios that require balancing multiple variables,such as making intelligent decisions, handling exceptionsproactively, and making complex trade-offs around margin,speed, and sustainability based on real-time data and differentiated service levels. It enables better decision-making and proactively manages anomalies like orderfallouts, stockouts, and delivery delays. Its ability to learn and adapt makes it particularly effectivein navigating complexity and uncertainty. Crucially, agenticAI draws from a vast and diverse set of data sources,integrating structured and polystructured informationacross systems to inform decisions. This breadth ofinput allows it to autonomously resolve challenges thattraditionally required human judgment, driving smarter,faster, and more resilient operations. Theadvantageovertraditional automation Today’s top-performing enterprises already processover90 percent of their orderswithout human intervention. Theremaining orders typically require manual handling due tocomplex exceptions, such as pricing discrepancies, customer-specific terms, incomplete information on customerorders, stock allocation decisions, credit risk assessments,and compliance checks. These scenarios resist traditionalautomation because they de