2025年生成式人工智能展望：面向企业管理者与高管

Insightsfor BusinessLeaders&Executives Randall Hunt,CTOGuille Ojeda, Cloud Software ArchitectAnat Fraenkel, GenAl Program LeaderMelissa Leide, Lead Experience Designer Table of Contents Introduction03 Key Developments of 202404 Focus Areas for 202505 Core Trends and Predictions06 Agentic Architectures06 Optimization for Cost, Performance, and Security07 Multimodal Al and Data Processing08 Evolving Search and Discovery Technologies60 Decentralized Al and Federated Learning11 Al Governance & LLMOps12 Generative AI UI/UX14 Should You Build or Buy Generative Al Solutions?16 Case Studies17 Multi-Agent Systems in Enterprise Workflows17 Agentic Al-Powered Voice Service18 Looking Forward19 Let's Get Started20 Introduction The generative Al landscape has fundamentallyshifted from experimental technology to enterprise-ready solutions that drive measurable businessvalue. Modern generative Al systems demonstratesophisticated capabilities in orchestrating complextasks, processing multiple tvpes of data, andadapting to specific business contexts whilemaintaining robust security and governanceframeworks. report significant operational improvements, withsome saving 70% of analysts' time by automatingmanual data retrieval. This whitepaper provides enterprise leaderswith a practical framework for implementinggenerative Al technologies, focusing on costoptimization, operational excellence, andresponsible development. Drawing from real-world implementations and industry research, weexamine key trends, technical requirements, andstrategic considerations that will shape successfuldeployments in 2025. Enterprise adoption of these technologies continuesto accelerate, with Gartner projecting that 33% ofenterprise software anplications_willincoroorateagentic Al capabilities by 2028, up from less than 1%in 2024. Organizations implementing these systems Key Developments of 2024 This past year built the foundation for enterprise generative Al adoption. Architectural Advancement The practical implementation of agent-based systems moved from theoretical frameworks to productiondeployments. Major cloud providers like AwS introduced robust orchestration frameworks, enablingorganizations to implement complex multi-agent workflows while maintaining security and governancecontrols. A notable example is Amazon Bedirock's Multi-Agent Qrchestration, which was announced in previewin December 2024. This service provides enterprises with a scalable framework for implementing agenticworkflows. PerformanceOptimization Siqnificant progress in model distillation and inference optimization addressed the computational anceconomic challenges of large-scale deployments, These advances make enterprise-scale deployment morepractical, with distilled models in Amazon Bedrack being.up to 5nn% faster. MultimodalIntegration practical, This advancement enables more natural interactions between Al systems and users while expanding2024, represent a significant step towards more cost-efficient multimodal processing. FocusAreasfor2025 The transformation of generative Al from experimental technology to enterprise cornerstone brings bothopportunities and challenges. Organizations that understand these dynamics and prepare accordingly willbe best positioned to leverage Al's capabilities for a competitive advantage while maintaining operationalexcellence and ethical standards. These focus areas are: 01.CostandPerformanceOptimization Large models can be powerful but expensive - at times more powerful than is needed, Organizations shouldbalance the capabilities of large models with the practicality of specialized, task-specific implementationsthat can operate within reasonable computational and economic constraints. This requires careful attentionto model selection, model distillation, deployment architecture, and ongoing optimization strategies. Industrybenchmarks are helpful when evaluating models, but organizations should evaluate models and optimizationoptions based on their business needs. 02.OperationalExcellence Deploying generative Al systems requires robust operational frameworks that ensure reliability, security, andgovernance, These frameworks must implement: Comprehensive monitoring and observability systems that track both technical performance and businessoutcomes • Clear accountability structures that define roles and responsibilities across technical and business teams•Effective risk management protocols that ensure responsible deployment 03.Human-AlCollaboration The evolving capabilities of Al systems are redefining how humans interact with technology. necessitatingnew frameworks for intuitive, transparent, and adaptable collaboration. Success in 2025 will require thoughtfulinteraction design that ensures Al enhances-rather than complicates-user experiences. This meansestablishing clear protocols for decision-making authority, user control, and explainability, ensuring Al-drivensystems are both trustworthy and seaml