OpenAI发布了《企业AI》战略报告，详细介绍了领先企业如何在其工作流程中整合AI。该报告基于与摩根士丹利、Indeed、Klarna、Lowe's、BBVA、Mercado Libre和OpenAI自身等公司的合作，概述了基于七个核心经验教训的AI规模化应用框架。报告强调技术性和方法论，而非快速成功，并强调了持续迭代、深度定制以及与现有业务系统的紧密集成对于企业AI应用的重要性。

✅ 评估先行：摩根士丹利通过“评估”框架来基准测试AI模型输出，验证性能和安全性。这种结构化方法使得该公司能够扩大AI的使用范围，98%的顾问每天使用OpenAI工具，文档访问量从20%增加到80%。

💡 核心产品嵌入AI：Indeed将GPT-4o mini集成到其职位推荐引擎中，为候选人匹配职位提供上下文解释。这种透明度提高了20%的申请量和13%的雇主参与度。定制微调模型后来将token使用量降低了60%。

💰 早期投资的重要性：Klarna的早期AI投资带来了可衡量的改进。他们的AI助手现在处理了三分之二的客户支持互动，将解决时间从11分钟缩短到2分钟。90%的员工定期使用AI，加速了内部创新，并实现了4000万美元的预计利润改善。

⚙️ 针对特定用例进行微调：Lowe's通过在其专有产品数据上微调GPT-3.5，增强了其电子商务搜索引擎。这提高了20%的产品标签准确性和60%的错误检测率。OpenAI强调，微调对于领域适应至关重要，使模型能够反映内部语言、格式和行业细微差别。

👨‍💼 赋能专家：BBVA授权员工构建自定义GPT应用程序，而不是集中化AI开发。在五个月内，创建了超过2900个自定义GPT，以简化法律、合规、客户服务和信用风险流程。这种方法缩短了实现价值的时间，并确保AI应用于最需要的地方。

🛠️ 支持开发人员：Mercado Libre构建了Verdi，一个由GPT-4o驱动的内部平台，解决了开发人员瓶颈问题。它允许团队通过自然语言开发AI驱动的应用程序，同时保持安全性和逻辑护栏。用例包括欺诈检测（99%的准确性）、多语言产品描述和库存优化，表明AI工具可以扩展开发人员的能力。

🎯 尽早设定自动化目标：OpenAI内部对自动化的使用展示了设定大胆目标的影响。一个与Gmail集成的自定义自动化层帮助团队撰写回复、检索数据和启动工作流程。每月有数十万个任务现在自主处理，使团队能够专注于更具战略性的工作。

OpenAI has published a strategic report, AI in the Enterprise, detailing how leading organizations have integrated AI into their workflows. Drawing on partnerships with companies like Morgan Stanley, Indeed, Klarna, Lowe’s, BBVA, Mercado Libre, and OpenAI itself, the guide outlines a framework built on seven core lessons for adopting AI at scale.

Unlike traditional IT deployments, enterprise AI adoption demands continuous iteration, deep customization, and tight integration with existing business systems. This blog summarizes the report’s key takeaways, emphasizing a technical and methodical approach over quick wins.

1. Begin with Structured Evaluation

Morgan Stanley’s deployment began with “evals”—rigorous frameworks to benchmark AI model outputs. These evaluations assessed translation, summarization, and domain expert comparison to validate performance and safety. This structured approach enabled the firm to scale its AI usage: 98% of advisors now use OpenAI tools daily, and document access rose from 20% to 80%.

2. Embed AI in Core Product Experiences

Indeed integrated GPT-4o mini into its job recommendation engine, allowing it to generate contextual explanations for why a job matched a candidate. This added transparency led to a 20% increase in applications and a 13% improvement in employer engagement. A custom fine-tuned model later reduced token usage by 60%, illustrating how thoughtful integration and optimization can scale impact efficiently.

3. Invest Early to Capture Compounding Benefits

Klarna’s early AI investments have led to measurable improvements. Their AI assistant now handles two-thirds of support interactions, cutting resolution times from 11 minutes to 2. With 90% of employees using AI regularly, the organization has accelerated internal innovation and achieved $40M in projected profit improvements.

4. Fine-Tune for Specific Use Cases

Lowe’s enhanced its e-commerce search engine by fine-tuning GPT-3.5 on proprietary product data. This improved product tagging accuracy by 20% and error detection by 60%. OpenAI emphasizes that fine-tuning is essential for domain adaptation, enabling models to reflect internal language, formats, and industry nuances.

5. Put AI in the Hands of Experts

Rather than centralizing AI development, BBVA empowered employees to build custom GPT applications. In five months, over 2,900 custom GPTs were created to streamline processes in legal, compliance, customer service, and credit risk. This approach reduced time-to-value and ensured AI was applied where it was most needed.

6. Support Developers with Scalable Tooling

Mercado Libre tackled developer bottlenecks by building Verdi, an internal platform powered by GPT-4o. It allows teams to develop AI-powered apps through natural language while maintaining security and logic guardrails. Use cases include fraud detection (99% accuracy), multilingual product descriptions, and inventory optimization—demonstrating how AI tooling can expand developer capacity.

7. Set Automation Targets Early

OpenAI’s internal use of automation showcases the impact of setting bold goals. A custom automation layer integrated with Gmail helps teams craft responses, retrieve data, and initiate workflows. Hundreds of thousands of tasks are now handled autonomously each month, freeing teams for more strategic work.

Conclusion

The AI in the Enterprise report makes a compelling case for structured, iterative AI integration grounded in real-world use. Rather than rushing adoption, OpenAI advises starting small, investing early, fine-tuning for relevance, and scaling from high-impact use cases.

Across all seven examples, a common thread emerges: effective enterprise AI is built on disciplined experimentation, robust tooling, and empowering the people closest to the problems. For technical and business leaders, OpenAI’s playbook offers a clear and actionable blueprint for long-term AI success.

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