思想领袖
AI 收养是上，但商业表现是？
在 Google 上的首选来源中添加 Unite.AI
公司正在努力将AI投资转化为有意义的回报。 使用率正在增长，但只有13%的组织报告AI有重大的转型价值。 这种差距应促使领导人退后一步，并问他们是在衡量活动还是有意义的变化。
在显示清晰的AI ROI的压力下，各组织往往转向容易报告的衡量标准，如工具使用率、活跃用户率或采用率等。 这些措施表明雇员是否与大赦国际有联系。 它们很少告诉我们工作正在改进还是投资正在产生价值。
例如，PwC发现，在过去12个月里，64%接受调查的工人在工作中使用人工智能，提高了10个百分点。 乍一看，这是一个有希望的增长迹象，但并没有对底线产生影响。 AI的被收养成为虚荣的衡量标准，当领导人将它视为转变的证明。 更强有力的衡量标准是AI如何帮助将以往项目的见解转化为框架、工具和加速器，以改善团队交付成果的方式。
隐藏问题：AI可以加速需要改变的工作
在增加AI之前，各组织需要了解工作流程的目的，确定不必要的步骤并重新考虑如何开展工作。 这一点很重要，因为实施AI可以加速一个效率低下的进程，将瓶颈转移到下游并创造出更多的审查或再造。
同样的理解应指导各组织衡量什么。 纵观整个工作流程，有助于各小组了解一项任务的变化如何影响随后的一切，以及明显的收益是实际减少努力还是只是将努力转移到别人身上。
AI可以加快单个任务，如起草内容，总结信息或更快地分析数据，而无需改进流程。 例如，更快地生成内容可能会为下游的审查者和批准者创造更多的工作。 瓶颈行动，即使个别雇员报告节省了时间。
生产力指标可在此成为虚荣指标。 更快地完成一项任务可能看起来是迅速取胜，但可能忽略了将产出转化为下游有益成果所需的努力。 重新设计工作流程应从预期结果入手，然后确定AI 新增值在哪里，需要改变哪些步骤。
任务生产率和工作流程生产率都很重要。 将它们放在一起衡量，需要各小组超越个人时间的节省来评估质量的再作和为取得成果而付出的全部人力努力。
我们从成为我们自己的AI客户端学到了什么
在Arionkoder，我们创建了苏格拉底，我们的内部改造计划，帮助我们成为一个AI本土组织。 将AI作为我们自己的“零案例”进行测试，可以发现个人任务生产率的提高如何掩盖整个工作流程的问题。 例如，在我们项目管理工作流程中，我们最初假定利用人工智能生成会议摘要、行动项目和项目更新将提高这些任务的效率。 然而，由于信息量增加，仍然需要各小组进行人工审查、确定优先次序并进行协调。 这转移了下游的工作量，而不是减少。 我们意识到，加快单个任务并不一定会提高整个工作流程的效率。 苏格拉底的目标依然相关，但方案需要帮助团队从加速孤立的任务转向重新设计端到端的工作流程。
这一挫折在我们审查所发生情况并将调查结果应用于我们下次尝试时变得有益。 它进一步表明，AI的转化不仅取决于引进技术。 这需要改变人们如何从实验中学习、作出决定并共同努力。 苏格拉底公司需要帮助员工将各自建设的东西与工作发生的情况联系起来。
订正办法使这项试验有了明显的进展。 一个雇员可以开始使用AI来解决一个小而真实的问题。 下一步是测试其他人能否使用他们建造的东西。 从那里，公司可以检查更广泛的工作流程，识别摩擦并与其他团队开发的能力相连接。 雇员之间的共同学习有助于评估一项改进是否仍然有用，超出了原先旨在支持的任务。
这种区别很重要。 建造对一个人有用的东西是起点。 创造组织价值需要了解他人如何使用它，当它成为更大进程的一部分时，会有什么变化。
因此，我们的重设始于预期的结果。 团队需要实现什么？哪些步骤有助于取得这一结果？哪些步骤是由于一个已经确立、但已不再符合其目的的进程而存在的？这些问题有助于将重点从增加人工智能转向个别任务，而转向围绕更明确的目标重新设计工作流程。
从那里，我们确定了适合每个工作流程的基线和标准。 我们审视了质量、再工作和雇员的努力，以及完成时间。 这些措施有助于我们评估整个进程的改进情况。
在整个苏格拉底，我们重新设计了20多条工作流程，将关于实际商业问题的实验转化为其他人可以使用的能力。 财务报告从4小时降至20分钟，而信息技术报告重复任务减少40%，答复时间减少60%。 这些结果显示了业务进展。 要了解这些衡量标准如何转化为业务价值，就需要评估整个工作流程中的努力是否减少，以及团队如何利用它们获得的能力。
对我们来说，教训是，要始终跟着改进。 个人节省时间很重要，但当他们帮助下一个人更好地工作并使团队更接近预期成果时，它们就成为组织价值。
首席执行官应如何衡量
在其业务影响得到改善之后，需要的不仅仅是跟踪AI的使用或节省的时间。 领导人需要将通过与改进工作方式及其成果联系起来。 进展情况应在三个不同的层面加以衡量：
- 收养：雇员是否使用人工智能？收养作为一种诊断措施仍然有用。 使用、参与试验和雇员的反馈可以揭示人们正在哪里建立信任，哪里需要支持。 这些信号帮助领导人了解该组织的准备状态，但他们并没有建立ROI. 高使用率是调查的起点，而不是转变的证据。
- 工作流程业绩：工作是否确实有所改善？成功标准应反映工作流程的预期目的。 视使用情况而定，改进可能意味着信息更准确，错误减少或解析更快。 将整个进程的业绩与基线相比较。 跟踪不必要的步骤被取消、重新工作、质量和员工的全部努力。 包括接受人工智能产出并采取行动的人。 如果一项任务在别处产生额外的审查或更正
- 业务影响：AI驱动的工作流程是否改善了业务成果？将工作流程与成果领导制的改进联系起来已经得到关注，例如客户保留、收入、服务质量或每个已解决的问题的费用。 例如，制作更多内容表明产出增加。 它的商业价值取决于它实现的内容。 跟着时间的节省到使用。 增加的能力是否改善了服务、支持增长或降低了业务费用？对照交付的全部成本评估价值，包括人力、人工智能消耗、整合、监督、培训和维护。
这些措施共同帮助领导人追踪AI的采用情况，从而改进工作流程和业务成果。 如果AI的使用增加而不减少人的努力，提高质量或提供商业价值，领导者需要在规模化之前重新评估方法。
领导人需要重新定义AI 进步
收养数据有助于公司了解谁是使用AI的，哪里需要更多的支持。 了解其价值需要倾听开展工作的人的意见并评估成果是否改善了更广泛的工作流程。 领导人需要让雇员有信心，采取不取得更佳成果而增加努力的做法，然后就这些调查结果采取行动。 改变或制止无效做法，是在学习改进下一项决定时取得的进展。
有意义的转变显示是消除不必要的工作，实现更好的结果并增强从事重要工作的能力。 这些是领导人应当用来判断AI进展并指导进一步投资的结果。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Thought Leaders
AI Adoption Is Up. But Is Business Performance?
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Companies are struggling to turn AI investment into meaningful returns. Usage is growing, yet only 13% of organizations report significant transformational value from AI. This gap should prompt leaders to take a step back and ask whether they are measuring activity or meaningful change.
Under pressure to demonstrate clear AI ROI, organizations often turn to easy-to-report metrics, such as tool usage, active users or adoption rates. These measures show whether employees are engaging with AI. They tell us little about whether the work is improving or the investment is delivering value.
PwC, for example, found that 64% of surveyed workers used AI at work in the past 12 months, up ten percentage points YoY. At first glance, this is a promising sign of growth, but it does not establish an impact on the bottom line. AI adoption becomes a vanity metric when leaders treat it as proof of transformation. The stronger measure is how AI helps transform insights from previous projects into frameworks, tools and accelerators that improve how teams deliver outcomes.
The Hidden Problem: AI Can Accelerate Work That Needs to Change
Before adding AI, organizations need to understand the workflow’s purpose, identify unnecessary steps and reconsider how the work should happen. This matters because implementing AI can accelerate an inefficient process, moving bottlenecks downstream and creating more review or rework.
That same understanding should guide what organizations measure. Looking across the full workflow helps teams see how changes in one task affect everything that follows and whether apparent gains actually reduce effort or simply shift it to someone else.
AI can accelerate individual tasks, such as drafting content, summarizing information or analyzing data faster, without improving processes. Faster content generation, for example, may create more work for reviewers and approvers downstream. The bottleneck moves, even as individual employees report time savings.
This is where productivity metrics can become vanity metrics. Faster turnaround on one task may look like a quick win, but it may overlook the effort required to turn the output into a useful result downstream. Workflow redesign should begin with the intended outcome, then determine where AI adds value and which steps need to change.
Task productivity and workflow productivity both matter. Measuring them together requires teams to look beyond individual time savings to assess quality rework and total human effort involved in reaching the outcome.
What We Learned by Becoming Our Own AI Client
At Arionkoder, we created Socrates, our internal transformation program, to help us become an AI-native organization. Testing AI as our own “case zero” revealed how gains in individual task productivity could mask problems across the entire workflow. For example, in our project management workflows, we initially assumed using AI to generate meeting summaries, action items and project updates would make those tasks more efficient. However, the increased volume of information still required manual reviews, prioritization and coordination across teams. This shifted the workload downstream, rather than reducing it. We realized that making individual tasks faster doesn’t necessarily make the overall workflow more efficient. Socrates’ goal remained relevant, but the program needed to help teams move from accelerating isolated tasks toward redesigning end-to-end workflows.
This setback became useful when we examined what happened and applied the findings to our next attempt. It reinforced that AI transformation depends on more than just introducing technology. It requires changing how people learn from experiments, make decisions and work together. Socrates needed to help employees connect what they built individually with how work happened.
The revised approach gives that experimentation a clear progression. An employee could begin using AI to solve one small, real problem. The next step would be to test whether someone else could use what they built. From there, the company could examine the wider workflow, identify friction and connect work with capabilities developed by other teams. Shared learning between employees helps assess whether an improvement remains useful beyond the task it was originally designed to support.
That distinction is important. Building something that works for one person is the starting point. Creating organizational value requires understanding how others use it and what changes when it becomes part of a larger process.
Our reset, therefore, began with the intended outcome. What did the team need to achieve? Which steps contributed to that result, and which existed because of an established process that no longer served its purpose? Those questions helped shift the focus from adding AI to individual tasks toward redesigning the workflow around a clearer goal.
From there, we established baselines and criteria suited to each workflow. We looked at quality, rework and employee effort, alongside completion time. These measures helped us assess improvement across the whole process.
Across Socrates, we redesigned more than 20 workflows, turning experiments around real business problems into capabilities others could use. Finance reporting fell from four hours to 20 minutes, while IT reported 40% fewer repetitive tasks and 60% reduction in response times. Those results showed operational progress. Understanding how these metrics translate into business value requires assessing whether effort decreased across the workflow and how teams used the capacity they gained.
For us, the lesson was to follow improvements all the way through. Individual time savings matter, but they become organizational value when they help the next person do better work and bring the team closer to the intended outcome.
What CIOs Should Measure Instead
Following an improvement through to its business impact requires more than tracking AI usage or time saved. Leaders need to connect adoption with improvements in how work gets done and the outcomes it delivers. Progress should be measured at three distinct levels:
- Adoption: Are employees using AI? Adoption remains useful as a diagnostic measure. Usage, participation in experiments and employee feedback can reveal where people are building confidence and where they need support. These signals help leaders understand the organization’s readiness, but they do not establish ROI. High usage is a starting point for investigation, not proof of transformation.
- Workflow performance: Is the work actually improving? Success criteria should reflect the workflow’s intended purpose. Depending on the use case, improvement might mean more accurate information, fewer errors or faster resolution. Compare performance against a baseline across the full process. Track unnecessary steps eliminated, rework, quality and total employee effort. Include the people receiving and acting on AI-generated output. A task hasn’t necessarily become more efficient if it creates additional review or correction elsewhere.
- Business impact: Is the AI-enabled workflow improving business outcomes? Connect workflow improvements to outcomes leadership already cares about, such as customer retention, revenue, service quality or cost per resolved issue. Producing more content, for example, demonstrates increased output. Its business value depends on the content it achieves. Follow time savings through to their use. Did the additional capacity improve service, support growth or reduce operating costs? Assess the value against the full cost of delivering it, including human effort, AI consumption, integration, oversight, training and maintenance.
Together, these measures help leaders trace AI adoption through to better workflows and business outcomes. If AI usage increases without reducing human effort, improving quality or delivering business value, leaders need to reassess the approach before scaling.
Leaders Need to Redefine AI Progress
Adoption data helps companies see who is using AI and where more support is needed. Understanding its value requires listening to the people doing the work and assessing whether gains improve the wider workflow. Leaders need to give employees the confidence to flag approaches that add effort without better results, then act on those findings. Changing or stopping an ineffective approach is progress when the learning improves the next decision.
Meaningful transformation shows up as eliminating unnecessary work, realizing better outcomes and having greater capacity for the work that matters. Those are the results that leaders should use to judge AI progress and guide further investment.
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