企业AI代理的未来

The Future ofEnterprise AI Agents Unlocking Autonomous Transformation in 2025 Table of Contents INTRODUCTION Enterprise workflows are leveling up in 2025. Thanks to advancesin generative AI (GenAI), large language models (LLMs), and naturallanguage processing (NLP), agentic AI is transforming the wayenterprises approach automation and decision-making, impactingeverything from customer interactions to business operations. AI agents are autonomous software systems that canreason, plan, and act on behalf of users. Many considerAI agents to be the next evolution of chatbots. Both useAI to receive input and take action based on that input;however, chatbots follow a predetermined workflowand are limited to handling finite scenarios and userinputs. AI agents—whether model-based, goal-basedor multi-system—are more interactive and capable ofhandling complex tasks by applying reason anddetermining the best course of action autonomously. To explore how organizations are investing in agenticAI,Clouderasurveyed 1,484 enterprise IT leadersacross 14 countries. This report, fielded in February2025, dives deep into adoption patterns, use cases,and sentiments around AI agents–including industry-specific insights across finance, retail, healthcare,manufacturing, and telecommunications—and looksat how enterprises are using AI agents to level-up theirenterprise workflows in 2025. When implemented correctly, agentic AI offerstremendous benefits such as increased efficiency,reduced costs, improved customer experience, andsmarter (real-time, data-driven) decision-making. ForCIOs and CTOs looking to drive innovation, agentic AIcan accelerate their efforts. AI Agents: Ushering in the Futureof Enterprise Technology 83% The last two years have seen an adoption boom ofagentic AI. A majority of respondents (57%) startedimplementing AI agents within the last two years, and21% did so in the last year alone. This adoption ratereflects how quickly agentic AI has moved fromconcept to reality, likely catalyzed by advancementsin AI technologies between 2023 and 2024. of organizations believe it’s important to investin agents to maintain a competitive edge withintheir industry. 2025 is a pivotal window for AI agent adoption asmany companies move from experimentationto execution. This rapid rate of adoption shows that organizationsview agentic AI as essential to their businesscompetitiveness and hope to capitalize on theirreturn on investment (ROI) sooner rather than later. Consequently, expansion plans for agentic AI arenearly universal. An overwhelming 96% ofrespondents plan to expand their use of AI agents inthe next 12 months, with half aiming for significant,organization-wide expansion. Performance optimization bots may take the form ofan IT infrastructure bot that can dynamically adjustcloud resource allocation, database configurations,and server loads to optimize performance in real time.Security monitor agents can continuously analyzenetwork activity, detect anomalies andautonomously respond to potential cyber threats.Development assistants can take the form of botsthat generate, execute and refine test cases basedon real-time code changes. As enterprises move agentic AI from pilot stage tobroad deployment, they’re exploring specificapplications, with respondents most interested inperformance optimization bots (66%), securitymonitoring agents (63%), and developmentassistants (62%). IT leaders are looking for these improvements in the AI agents they use: Improved Interoperability Key Business Functions:Where AI Agents Add Value 81% The synergy between AI agents and GenAI paves theway for investments in agentic AI to deliver immediateROI. 98% of surveyed organizations are either alreadyusing agentic AI to orchestrate GenAI use cases or planto do so in the near future. In fact, 85% say their priorinvestments in GenAI have prepared them well toimplement AI agents. Enterprises should view agenticAI as a natural next step to capitalize on their GenAIinvestments, especially as agentic AI vendors increasethe availability of out-of-box API integrations. of enterprises are leveraging agentic AI toenhance their existing GenAI models. Early deployments of AI agents tend to focus on IT andcustomer-facing operations, according torespondents. When it comes to business functions, AIagents are most embedded in IT operations (61%), butcustomer support (18%) and marketing (6%) are alsokey areas of adoption. In terms of concreteapplications, AI agents are most used for customersupport (78%), process automation (71%), andpredictive analytics (57%). For enterprise companiesthat have embedded agents into IT operations, theyare most likely to also use agents for customer support,operations, and marketing, respectively. AI agents are most commonly used for: 78%Customer Support 71% Process Automation These top use cases show that many companies startadoption in well-defined, ROI-driven domains, andinternal functions, such as IT helpde