The five AI value models driving business reinvention

大多数组织仍把 AI 当作一系列分散的用例来管理：这里做一个试点，那里改造一个工作流，某个职能里有个看起来有前途的工具。这种做法能带来局部胜利，但很少能改变企业创造价值的方式。

这有点像互联网初期做互动横幅和滴灌邮件，却忽视了电子商务带来的根本性变革。

真正领先的组织采用了另一种、更有野心的逻辑：它们不把 AI 当作若干互不相连的实验，而是当作一组“价值模型”的组合来管理。每种模型有自己的经济学、时间到价值和治理要求，而且每种模型都会让下一种更容易扩展。

因此，能从 AI 中获得最大收益的公司，不会是跑最多试点的公司，而是能理解该构建哪些价值模型、按什么顺序构建、并以怎样的基础来重塑自身业务的公司。

从试点到组合

企业中最清晰出现的五种 AI 价值模型各不相同，创造价值的方式各异，经济学、时间视角和治理需求也各有差别。而且每一种都能为下一种的规模化创造条件。

• 员工赋能会带来熟练度（fluency）。熟练度让治理变得可行。可行的治理促成更深层次的系统集成。集成使依赖关系管理成为可能。依赖关系管理又让由 agent 驱动的运营能更安全地实施。

组织正是通过这样的路径，从孤立的 AI 成果走向更广泛的业务重塑。战略问题不是选哪个模型，而是先从哪个模型开始、它将建立什么基础、并会打开什么后续机会。

1. 员工赋能（ CHATGPT ）

这是最容易启动、见效最快的价值模型。它把实用的 AI 能力铺到整个员工队伍里，既带来短期的生产力提升，也培养了更深层变革所需的熟练度。更大的收益并非只是更快地起草、综合或分析，而是组织层面的准备度：人力资源可以推动、法务可以制定治理、财务可以提供资金，业务团队则能在共同理解 AI 有效场景和安全使用方式的前提下协作。

衡量指标

• 不同岗位的重复使用率与熟练度
• 跨团队可复用的提示、工作流与资产
• 跨职能赋能的证据
• 新工作方式的出现

常见失败模式

• 两级化的员工队伍：少数高级用户遥遥领先，其他人停滞不前。

领导层应对

• 建立倡导者网络和入门级工作流，例如绩效评估、合同管理和采购到付款流程，让最佳实践既具体又有启发性。

1. AI 原生分发（ VERTICALS, APPS, ADS ）

这个模型至关重要，因为 AI 正以全新的互动深度改变客户发现、评估和选择产品或服务的方式。在 AI 原生渠道中，转化越来越多发生在对话内部。这把增长问题从“覆盖面”转向“信任”和“在关键意图时刻的存在感”。胜出者不会仅仅是最可见的，而是最有用、最可信、并且在用户做决策时出现在恰当时刻的。

衡量指标

• 合格意图以及用户承诺前的迭代次数
• 转化质量，包括留存、追加销售和生命周期价值
• 信任信号，如回访行为、重复互动和推荐
• 与业务相关的专用数据连接器或应用的激活情况

常见失败模式

• 把 AI 原生分发看作传统漏斗，追求规模而牺牲相关性和长期信任。

领导层应对

• 选定一个切面（如某个垂直体验、嵌入式应用或特定广告目标），先定义转化质量，再决定是否扩大投入。

1. 专家能力（ CO-SCIENTIST, SORA ）

该模型把专门化的 AI 能力嵌入研究、创意和领域密集型工作。短期内它能缓解专家瓶颈；长期看它会改变运营模式：团队从自行产出初稿转向指导、审阅并整合实时生成的高质量成果。价值在于扩大团队能审视、测试或产出的范围，使每一项洞见都能伴随可执行的计划和潜在 ROI，而不是仅凭直觉优先级排序。

衡量指标

• 缩短专家瓶颈环节的周期时间
• 质量提升，包括审阅评分、错误率和返工率
• 范围扩张，例如更多实验或更多创意变体被测试
• 因可行性假设而原本被排除的净新增收入流

常见失败模式

• 把专家能力当作演示品，而不是嵌入有明确责任的真实工作流中。

领导层应对

• 选定一个专家瓶颈，把价值主张聚焦于最终签批的决策者，并就把新概念转变为业务下一个构建块所需的证据达成明确共识。

1. 系统与依赖管理（ CODEX ）

当前最清晰的例子是代码化 agent，但更广泛的价值模型是确保互联工作系统能够安全升级。随着时间推移，组织会希望这种能力不仅作用于代码，还作用于标准作业程序（SOPs）、合同、政策文件、客户叙事、入职流程等随演进必须保持一致的文档。这里关注的不是生成，而是控制：更快的更新、更少的下游故障、更强的合规和更好的可审计性。

衡量指标

• 在连接文档间实现安全变更所需时间与版本冲突解决情况
• 审计准备度，包括编辑、审批和证据的可追溯性
• 下游文档、系统与工作流的一致性
• 在海量相互依赖流程中保证可靠性的能力

常见失败模式

• 内容或代码生成的规模扩张跑得比治理快，导致日后需要费时修复的体系性债务。

领导层应对

• 从一个高依赖域入手，先绘制依赖图、审批路径与证据要求，再用 AI 控制层自动化变更。

1. 流程再造（ AGENTS ）

这是最慢也常常是最具颠覆性的模型。在这里，agent 协调跨职能的端到端工作流：从采购到付款、理赔、制造变更控制、临床运作等。潜在收益呈指数级，但前提是基础设施真实可靠：身份与访问控制、清晰的数据集及子组件权限、可观测性、大规模异常处理的置信指示器，以及明确的归属。没有这些，自动化带来的风险往往比价值增长得更快。

回报同样远超单纯的效率提升。重塑工作流会迫使组织重新审视流程的目的、判断权应在何处，以及可以在哪些环节创造新价值。这正是商业模式变革的入口。

衡量指标

• 端到端周期时间
• 异常率与解决时间
• 合规与审计结果
• 创新产出，例如发现的新机会或验证的新假设

常见失败模式

• 在权限、控制与责任尚未成熟前就试图自动化端到端流程。

领导层应对

• 选一个工作流，开展关于身份、授权、工具集成、日志记录、异常处理与归属的成熟度评估。

为何这些价值模型会相互叠加

AI 战略的失败点不仅在于只做孤立试点，还在于把转型当作一场赌注：现在投钱、长期等待、希望未来能成规模地产生价值。更强的做法是既自律又更有野心：通过连续的 ROI 序列把价值复利放大。

这条序列以广泛的赋能开始——这是其他所有价值模型的前提。组织内部的熟练度如同一片森林，才能孕育出高价值的用例。当更多人理解 AI 如何工作、在哪里创造价值以及如何安全使用时，更好的机会会更快浮现。治理更可行，集成更可达成，且更高价值的系统会作为灯塔式的示例被跨职能共享并变得有韧性。

这就是组织如何从“更好”走向“不同”的商业模式：先是改进任务，然后重设工作流，接着改变控制层与运营模式，最终重塑商业模式。零售并不是通过让实体店更高效而变成电子商务的；真正的变化是当领导者学会构建绕开实体店、将营销和物流在一次以用户为中心的动作中连接起来的全新价值主张。 AI 会遵循相同的路径。

举几个例子

• 零售商先从全员采用开始，然后优化 AI 原生的发现与会话式商业，最终创造出个性化销售的新渠道。
• 制药公司先在研发与临床运作上建立员工熟练度和专家能力，再构建有治理的研究工作流，从而发现新的适应证，重塑后期审批的管线经济学。
• 制造企业先在各职能部署协同助手（copilots），再把 AI 应用于变更控制、SOP 与质量工作流，直到运营成为可适应的系统，重新定义市场经济学。
• 保险公司先从理赔辅助工具起步，再构建有治理的专家审查与工作流编排，最终围绕更快决策、更少异常和更好客户结果重塑理赔处理。

下一步该怎么做：实用的排序手册

如果你今天在负责 AI 战略，把路线简化为三个阶段即可。

阶段一：建立熟练度与信任

• 通过基于角色的工作流和倡导者网络，赋能广大员工。
• 建立治理基础：什么被允许、什么需要复核、什么要记录、谁负责推动采纳。
• 衡量重复使用、熟练度、可复用工作流与跨职能赋能。

阶段二：捕捉价值并抬高上限

• 选择少数高价值动作：一个分发玩法、一个专家瓶颈和一个可见 ROI 的工作流。
• 用业务指标衡量价值：转化质量、周期缩短、质量提升、风险降低和新营收潜力。
• 将这些胜利再投资到下一层基础设施：数据质量、身份、集成、可观测性与控制。

阶段三：有信心地规模化并重塑

• 只有在权限、可审计性和异常处理成熟时，才把 AI 扩展到高依赖系统和端到端工作流。
• 用这些基础来重设计运营模式，而不仅仅是加速旧有模式。
• 询问 AI 能在哪些方面创造全新的价值，而不仅仅是更便宜的执行。

行动号召不应仅限于 AI 在旧模式中能提供怎样的帮助，而应问先构建哪个价值模型、它将建立什么基础、并会解锁哪些后续机会。起步要足够广以培养熟练度；每一步都要有纪律性以捕捉价值；然后以足够的信心扩展，从对当下的更好版本迈向完全不同的未来。

Most organizations still manage AI as a series of use cases: a pilot here, a workflow there, a promising tool inside one function. That approach can generate local wins but it rarely transforms how a business creates value.

It is akin to creating interactive banners and drip email campaigns with the arrival of the internet, and missing the point of the eCommerce revolution.

The organizations pulling ahead use a different, and more ambitious logic. They treat AI not as a collection of disconnected experiments, but as a portfolio of value models. Each has its own economics, time-to-value, and governance requirements, and each makes the next one easier to scale.

This is why the companies that get the most from AI will not be the ones running the most pilots. They will be the ones that understand which value models to build, in what sequence, and with what foundations to reinvent their own business.

From pilots to portfolios

There are five AI value models emerging most clearly in the enterprise. Each creates value differently. Each has its own economics, time horizon, and governance. And each can create the conditions for the next to scale.

Workforce empowerment builds fluency. Fluency makes governance workable. Governance enables deeper system integration. Integration makes dependency management possible. Dependency management makes agent-led operations safe.

This is how organizations move from isolated AI wins to broader business reinvention. The strategic question is not which model to choose. It is which one to start with, what foundation it builds, and what it unlocks next.

This is the fastest value model to activate. It spreads practical AI capability across the workforce, creating near-term productivity gains while building the fluency required for deeper transformation. The larger benefit is not faster drafting, synthesis, or analysis but organizational readiness. HR can enable, Legal can govern, Finance can fund, and business teams can collaborate with a shared understanding of where AI works and how to use it safely.

What to measure

• Repeated use by role, and proficiency level
• Reusable prompts, workflows, and assets across teams
• Evidence of cross-functional enablement
• Emergence of new ways of working

Common failure mode

A two-tier workforce: a small group of power users moves ahead while the rest of the organization stalls.

Leadership move

Build a champions network and starter workflows, such as performance evaluation, contract management and procure to pay, that make best practices relatable and inspiring.

This model matters because AI is changing how customers discover, evaluate, and choose products and services with an entirely new level of engagement. In AI-native channels, conversion increasingly happens inside a conversation. That shifts the growth question from reach to trust and presence at moments of intent. The winners will not simply be the most visible. They will be the most useful, credible, and well-timed when a decision is being made.

What to measure

• Qualified intent, and number of iterations before user commitment
• Conversion quality, including retention, upsell, and lifetime value
• Trust signals such as return behavior, repeat engagement, and referral
• Activation of dedicated data connectors or apps related to your business

Common failure mode

Treating AI-native distribution like a legacy demand funnel and optimizing for volume at the expense of relevance and durable trust.

Leadership move

Pick one surface such as a vertical experience, an embedded app, or a specific ad objective, and define conversion quality before scaling your investment.

This model inserts specialized AI capability into research, creative, and domain-heavy work. Near term, it compresses expert bottlenecks. Over time, it changes the operating model: teams shift from producing first drafts themselves to directing, reviewing, and integrating high-quality outputs generated in real-time. The value comes from expanding what the team can examine, test, or produce in an environment that enables every insight to be investigated with action plans and ROI potential instead of prioritizing upstream on intuition alone.

What to measure

• Cycle-time reduction on expert bottlenecks
• Quality lift, including reviewer scores, error rates, and rework
• Expansion of scope, such as more experiments run or more creative variants tested
• Net new revenue streams that would have been excluded on feasibility assumptions

Common failure mode

Treating expert capability like a demo rather than embedding it in a real workflow with clear accountability.

Leadership move

Choose one expert bottleneck and focus the value proposition on the decision makers who sign off, with a clear agreement on what evidence is required to turn a new concept into the next building block of your business.

4. Systems and dependency management (Codex)

Coding agents are the clearest current example, but the larger value model is safe upgrades across interconnected systems of work. Over time, organizations will want the same capability applied not just to code, but to SOPs, contracts, policy documents, customer narratives, onboarding flows, and other artifacts that must stay consistent as they evolve. This is less about generation than control: faster updates, fewer downstream breakages, stronger compliance, and better auditability.

What to measure

• Time to safe change across connected artifacts and version conflict resolutions
• Audit readiness, including traceability of edits, approvals, and evidence
• Consistency across downstream documents, systems, and workflows
• Reliability across vast ecosystems of interdependent processes

Common failure mode

Scaling content or code generation faster than governance, creating systemic debt that will need painstaking resolution down the line.

Leadership move

Start with one high-dependency domain and define the dependency graph, approval path, and evidence requirements before automating changes with an AI control layer.

5. Process re-engineering (Agents)

This is the slowest model to scale and often the most transformative. Here, agents orchestrate end-to-end workflows within and across functions: procure-to-pay, claims, manufacturing change control, clinical operations, and more. The upside is exponential, but only when the foundations are real: identity and access controls, clean permissions on datasets and sub-components, observability at scale, exception handling with confidence indicators, and clear ownership. Without them, automation creates risk faster than value.

The payoff is once again much larger than mere efficiency. Re-engineering a workflow forces your organization to revisit what the process is for, where judgment belongs, and where new value can be created. This is the hidden door where business-model change begins.

What to measure

• End-to-end cycle time
• Exception rate and resolution time
• Compliance and audit outcomes
• Innovation output, such as new opportunities surfaced or new hypotheses tested

Common failure mode

Trying to automate end-to-end workflows before permissions, controls, and accountability are mature.

Leadership move

Pick one workflow and run a readiness assessment across identity, entitlements, tool integration, logging, exception handling, and ownership.

Why and how the value models compound

The failure point in AI strategy is not just isolated pilots but also treating transformation as a leap of faith: invest now, wait a long time, and hope value appears later at scale. The stronger approach is more disciplined and more ambitious. It compounds value in a continuous ROI sequence.

That sequence starts with broad empowerment which is the enabling condition for all other value models. The forest of fluency across the organization creates the trees of high-value use cases. When more people understand how AI works, where it creates value, and how to use it safely, better opportunities surface faster. Governance becomes more practical. Integration becomes more feasible. And higher-value systems become resilient and shared across functions as lighthouse examples and identity markers.

This is how organizations move from better to different business models. AI first improves tasks. Then it redesigns workflows. Then it changes control layers, operating models, and eventually business models. Retail did not become eCommerce by making stores slightly more efficient. It changed when leaders learned to build an entirely new value proposition bypassing stores entirely and connecting marketing with logistics in a single, user-centric motion. AI will follow the same pattern.

A few examples:

• A retailer starts with broad employee adoption, then improves AI-native discovery and conversational commerce, and eventually creates a new channel for personalized selling.
• A pharmaceutical company starts with workforce fluency and expert capability in R&D and clinical operations, then builds governed research workflows that surface new indications for late-stage approvals and reshape pipeline economics.
• A manufacturer starts with copilots across functions, then applies AI to change control, SOPs, and quality workflows until operations can be managed as an adaptive system redefining market economics rather than a static one.
• An insurer starts with claim-assistance tools, then builds governed expert review and workflow orchestration, and eventually redesigns claims handling around faster decisions, fewer exceptions, and better customer outcomes.

What to do next: a practical sequencing playbook

If you are leading an AI strategy today, keep it simple with three stages.

Phase 1: Build fluency and trust

• Empower the broad workforce with role-based workflows and a champions network.
• Establish the governance basics: what is allowed, what is reviewed, what is logged, and who owns adoption.
• Measure repeated use, proficiency, reusable workflows, and cross-functional enablement.

Phase 2: Capture value and raise the ceiling

• Pick a small number of high-value motions: one distribution play, one expert bottleneck, and one workflow with visible ROI.
• Measure value in business terms: conversion quality, cycle-time reduction, quality lift, risk reduction, and new revenue potential.
• Reinvest those wins into the next layer of foundations: data quality, identity, integration, observability, and control.

Phase 3: Scale with confidence and reinvent

• Extend AI into high-dependency systems and end-to-end workflows only when permissions, auditability, and exception handling are real.
• Use those foundations to redesign the operating model, not just accelerate the old one.
• Ask where AI can create entirely new value, not just cheaper execution.

The call to action doesn't need to be where AI can help in the legacy model. Ask which value model to build first, what foundation it creates, and what it unlocks next. Start broad enough to create fluency. Be disciplined enough to capture value at every step. Then scale with enough confidence to move from a better version of the present to a different future altogether.

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