Core Interpretation and Key Strategies of the 2025 Commercial AI Current Status Report

I. Current Status and Challenges of the Generative AI Gap

  1. Gap Between Technology and Application
    • Enterprises generally face the “high adoption, low transformation” dilemma: advanced technical parameters but difficulties in practical implementation, AI tools disconnected from business processes.
    • Key Issues: AI tools cannot continuously learn, fail to adapt to specific workflows, lack context memory capabilities, leading to employee resistance in adoption.
  2. Industry Differences
    • High Adoption Industries (e.g., sales, marketing): High budget investment, but limited actual effectiveness, relying on external outsourcing.
    • Low Transformation Industries (e.g., legal, procurement): Though not widely adopted, AI can significantly reduce costs (e.g., saving $10 million to $100 million annually).
  3. Data and Time Costs
    • Enterprises take an average of 18 months from demand to implementation, and switching costs are extremely high once integrated with AI tools (e.g., contract templates, approval rules need retraining).

II. Three Core Strategies to Cross the AI Gap

1. Prioritize Purchasing Over In-House Development

  • Reason: Success rate of in-house AI projects is 33%, while external collaboration projects have a 67% implementation rate.
  • Key Selection Criteria:
    • Customization Capability: Suppliers must adapt to enterprise workflows (e.g., approval processes, data formats).
    • Continuous Learning: Tools must improve based on feedback (e.g., model iteration).
  • Case: A financial company completed a risk assessment AI pilot in 2 months via an external supplier, saving $1 million annually in outsourcing costs.

2. Let Frontline Teams Lead AI Selection

  • Core Logic:
    • Frontline Teams (e.g., procurement managers, super users) better understand pain points and can rapidly validate tool effectiveness.
    • Central Labs often select “showy” tools, but employee adoption rates are low.
  • Data Support: Medium-sized enterprises can scale from pilot to full deployment in 90 days, while large enterprises with central leadership take over 9 months.
  • Practical Recommendations:
    • Let frontline managers lead selection, pilot first, then report to headquarters;
    • Prioritize testing “super users” (e.g., employees frequently using ChatGPT).

3. Focus on Learning Ability, Not Technical Parameters

  • Core Metrics:
    • Context Memory (63% of executives care): Tools must remember historical dialogues/data.
    • Feedback Improvement (66% care): Models must adjust based on user feedback.
    • Workflow Adaptation: Tools must seamlessly integrate with existing enterprise processes.
  • Avoid Pitfalls:
    • Avoid purchasing generic tools (e.g., only capable of content generation but unable to learn);
    • Choose AI with continuous memory functions (e.g., Microsoft, OpenAI’s latest tools).
  1. Technological Evolution Direction
    • From Single Tools to Agent Collaboration: Future AI will form an “Agent Network,” enabling cross-platform, cross-organization collaboration (e.g., procurement agents autonomously negotiate, customer service agents communicate across channels).
    • Key Protocols: Like MCP, A2A, similar to “AI internet protocols,” enabling interoperability between enterprise tools.
  2. Enterprise Competitive Barriers
    • First-Mover Advantage: The next 18 months is a critical window; enterprises must pre-emptively adopt AI tools with learning capabilities, or risk being locked into giant ecosystems.
    • Case: Microsoft and OpenAI have already embedded continuous memory functions; delayed adoption will lead to high switching costs.

IV. Action Recommendations and Summary

1. Take Immediate Action

  • Window Period: The next 18 months is the final opportunity to cross the AI gap; enterprises must make quick decisions.
  • Priorities:
    • Choose external suppliers with learning capabilities;
    • Let frontline teams lead selection and pilot;
    • Focus on backend departments (e.g., legal, procurement) automation potential.

2. Avoid Common Pitfalls

  • Pitfall 1: Blindly pursue technical parameters (e.g., model size), ignoring actual business adaptability.
  • Pitfall 2: Rely on IT departments to work in isolation, neglecting frontline needs.
  • Pitfall 3: Focus only on front-end sales/marketing, ignoring backend cost optimization.

3. Core Conclusion

  • The Generative AI Gap Is Not Permanent, but enterprises must make differentiated choices:
    • Choose tools that understand your business (not just more expensive models);
    • Rely on frontline teams (not technical teams) to drive transformation;
    • Solve real problems (not chase trends).

Final Goal: Use AI to reduce external expenses (e.g., outsourcing, consulting), liberate employee productivity, and achieve a leap from “repetitive labor” to “value creation.”

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Action Recommendation: Immediately assess the current AI application status of your enterprise, and prioritize the layout of learning tools and frontline collaboration models.

Translation

《2025年商业AI现状报告》核心解读与关键策略

一、生成式AI鸿沟的现状与挑战

  1. 技术与应用的差距
    • 企业普遍面临“高采用、低转型”的困境：技术参数先进但实际落地困难，AI工具与业务流程脱节。
    • 关键问题：AI工具无法持续学习、无法适配具体工作流、缺乏上下文记忆能力，导致员工抵触使用。
  2. 行业差异
    • 高采用行业（如销售、营销）：预算投入大，但实际效果有限，依赖外部外包。
    • 低转型行业（如法务、采购）：虽未大规模应用，但AI能显著降低成本（如节省100-1000万美元/年）。
  3. 数据与时间成本
    • 企业从需求到落地平均耗时18个月，且一旦对接AI工具，切换成本极高（如合同模板、审批规则需重新训练）。

二、跨过AI鸿沟的三大核心策略

1. 优先购买而非自建

  • 原因：自建AI项目成功率为33%，而外部合作项目落地率高达67%。
  • 关键选择：
    • 定制化能力：供应商需能适配企业工作流（如审批流程、数据格式）。
    • 持续学习：工具需具备从反馈中改进的能力（如模型迭代）。
  • 案例：某金融企业通过外部供应商2个月完成风险评估AI试点，年省100万美元外包成本。

2. 由业务前线主导AI选型

  • 核心逻辑：
    • 前线团队（如采购经理、超级用户）更了解痛点，能快速验证工具有效性。
    • 中央实验室常选“炫技”工具，但员工实际使用率低。
  • 数据支持：中型企业试点到全量部署仅需90天，而大企业中央主导需9个月以上。
  • 实践建议：
    • 让一线经理主导选型，试点后上报总部；
    • 优先测试已使用个人AI工具的“超级用户”（如常使用ChatGPT的员工）。

3. 关注学习能力，而非技术参数

  • 核心指标：
    • 上下文记忆（63%高管关注）：工具需记住历史对话/数据。
    • 反馈改进（66%关注）：模型需根据用户反馈调整。
    • 工作流适配：工具需无缝集成企业现有流程。
  • 避坑指南：
    • 避免购买通用工具（如仅能生成内容但无法学习）；
    • 选择具备持续记忆功能的AI（如微软、OpenAI最新工具）。

三、未来趋势：智能体网络（Agentic Web）

  1. 技术演进方向
    • 从单体工具到智能体协作：未来AI将形成“智能体网络”，跨平台、跨组织协作（如采购智能体自主谈判、客户服务智能体跨渠道沟通）。
    • 关键协议：如MCP、A2A等，类似“AI互联网协议”，实现不同企业工具的互联互通。
  2. 企业竞争壁垒
    • 先发优势：未来18个月是关键窗口期，企业需提前布局具备学习能力的AI工具，否则将被巨头生态锁定。
    • 案例：微软、OpenAI已开始嵌入持续记忆功能，企业若延迟布局，将面临高切换成本。

四、行动建议与总结

1. 立即行动

  • 窗口期：未来18个月是跨过AI鸿沟的最后机会，企业需快速决策。
  • 优先级：
    • 选择具备学习能力的外部供应商；
    • 让前线团队主导选型与试点；
    • 重点关注后台部门（如法务、采购）的自动化潜力。

2. 避免常见误区

  • 误区1：盲目追求技术参数（如模型参数量），忽视实际业务适配性。
  • 误区2：依赖IT部门闭门造车，忽略一线需求。
  • 误区3：仅关注前端销售/营销，忽视后台成本优化。

3. 核心结论

  • 生成式AI鸿沟并非永久，但需企业做出差异化选择：
    • 选更懂你的工具（而非更贵的模型）；
    • 靠前线团队（而非技术团队）推动转型；
    • 解决真问题（而非追风口）。

最终目标：通过AI减少外部支出（如外包、咨询），解放员工生产力，实现从“重复劳动”到“价值创造”的跃迁。

参考链接

[报告原文链接]（需用户自行查找）
行动建议：立即评估企业当前AI应用现状，优先布局学习型工具与前线协作模式。

Reference:

https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

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