如何避免AI的‘绝望之谷'?
Amdahl的法律规定，改善系统单一部分只能加速整个系统。 即使一个进程的一个步骤以指数速度加快，其他步骤也规定了上限，除非它们也加快速度。
这一概念可以解释一些公司在人工智能方面所经历的失望。 一个AI助手可以数秒后起草一个提案，但随后却坐了数天等待审查，定价和批准。 同样，一个编码工具可以在几分钟内生成软件，但测试，安全和部署仍然需要几天或几周的时间。
公司在不重新设计环绕它们的系统的情况下加快了具体的任务。 人们称之为“绝望之谷 ”。
经过一段时间的大力采纳和试验，管理人员现在面临不断增长的成本和有限证明底线影响的问题。 分析公司Forrester Research Inc.最近发现，只有15%的大赦国际决策者报告说，在过去12个月里，在利息、税收、折旧和摊销之前收入有所增加。 不到三分之一的人可以清楚地将AI的值映射回底线变化。
这种怀疑态度是健康的；AI投资应该像任何其他资本支出一样面对同样的审查。 领导人需要明确预期收益、时间表和里程碑，表明其投资是否带来可衡量的业务成果。
生产力悖论
AI确实有业务转变的潜力，虽然让雇员获得通用AI工具可以改进离散任务，提高技术的流畅度，但这种价值是分散的，与决定业务业绩的衡量标准脱节。 这是Amdahl的定律
更成熟的方法需要重新评估和重新设计企业的运作方式，应用AI来创造全新的经营过程。 自动化询问如何更快地完成现有任务，而重新设计则询问该任务是否应该存在。 各组织往往试图使它们已经做的事情自动化，而不是对其进行革新。
节省的时间不会自动成为收入或利润，一般而言，各组织应在以下一个或多个领域实现积极、可衡量的变化：收入、效率或风险。 一个可量化的业务目标可能是减少客户人数、缩短提交建议书周期、提高预测准确性或更快地发现和管理欺诈。
企业应当在大约6个月内合理期待确定ROI数据。 虽然可以迅速提供情景测试和试点方案等主要指标，但大规模、立即的回报是不现实的。 深思熟虑的业务流程重新设计需要90至120天的时间来设计和实施，然后进行验收测试，关键是进行重点突出的改革管理。
当对商业成果负责的人负责提供具体成果，而不是简单地实现技术启用时，这些举措最有可能取得成功。
成本也应得到更复杂的处理，因为各种代理工艺的成本可能因使用复杂案件、模型调用和背景等因素而大相径庭。 公司应衡量和跟踪每项结果的成本分配情况，而不是依赖部署前的平均数。
这是一个重要的细微差别：早期的采用者发现，监测每个AI处理的案件成本和花费多长时间，然后完善其将两个数字都保持在可接受的范围内的战略，对于AI成功重新设计业务流程至关重要。
避免`绝望之谷 '
将AI视为一系列有针对性的业务转型，而不仅仅是技术部署。 这意味着将明确的结果与有纪律的处决结合起来。
认真研究业务流程。 审查组织内工作的实际情况，而不仅仅是程序“应该”的情况。 确定瓶颈、交割、批准、例外和重新工作来源，然后决定在何处适用大赦国际。 期望在有文件记载的进程与如何实际完成工作之间找到实质性差异。
将每一项倡议与明确的结果挂钩。 说明AI倡议旨在影响的收入、效率或风险衡量标准，确定基线，使企业领导人对结果拥有自主权，并为影响数据确定一个硬时间表。
为生产设计飞行员。 AI飞行员应测试持续使用的途径，而不仅仅是在受控制的条件下显示令人印象深刻的能力。 从一开始就包括业务数据、整合、治理、安全和改革管理要求。
衡量经济学，而不是活动。 跟踪衡量标准，如周期时间、质量、收养、每项结果的成本和财务影响，而不是席位、提示或演示。 就代理系统而言，监测运行成本的范围和平均值，因为复杂的案件可以消耗的资源远远多于简单的案件。
定期审查和调整。 定期审查，以决定是前进，缩小范围，重新设计工作流程还是停止。 探索性工作并非总能成功
大赦国际的“绝望之谷”并非不可避免的。 了解投资时间和资本的价值以重新考虑和重新设计其业务流程的公司，可将AI支出转化为实际结果。
耶利米·斯通是SnapLogic公司的首席技术官。 他为SiliconANGLE撰写了这一意见文章。
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How to avoid AI’s ‘valley of despair’
Amdahl’s Law states that improving a single part of a system can only speed up the whole system so much. Even if one step of a process becomes exponentially faster, the other steps impose a ceiling unless they, too, are accelerated.
This concept may explain some of the disappointment companies are experiencing with artificial intelligence. An AI assistant can draft a proposal in seconds, but it then sits for days waiting on reviews, pricing and approval. Similarly, a coding tool can produce software in minutes, but testing, security and deployment still take days or weeks.
Companies have accelerated specific tasks without reengineering the systems that surround them. This gap between inflated expectations and modest returns is what some are calling AI’s “valley of despair.”
After a period of vigorous adoption and experimentation, executives are now being confronted with ever-growing costs and limited proof of bottom-line impact. Analyst firm Forrester Research Inc. recently found that only 15% of AI decision-makers reported an increase in earnings before interest, taxes, depreciation and amortization over the past 12 months. Fewer than one-third could clearly map the value of AI back to bottom-line changes.
This skepticism is healthy; AI investments should face the same scrutiny as any other capital expense. Leaders need to be clear on expected returns, timelines and milestones that show whether their investment delivers measurable business results.
The productivity paradox
AI does have business-transforming potential, but small, localized productivity improvements aren’t game changers. Though giving employees access to general-purpose AI tools improves discrete tasks and builds fluency in the technology, this value is diffuse and disconnected from the metrics that determine business performance. This is Amdahl’s Law playing out at scale.
A more mature approach requires reassessing and reengineering how the business operates, applying AI to create fundamentally new operating processes. Whereas automation asks how existing tasks can be performed faster, reengineering asks whether that task should even exist. Organizations often seek to automate what they already do rather than reinvent it.
Time saved doesn’t automatically become revenue or margin, so what does AI ROI actually look like? Generally speaking, organizations should target positive, measurable change in one or more of the following areas: revenue, efficiency or risk. A quantifiable business goal might be to reduce customer churn, shorten the lead-to-proposal cycle, improve forecast accuracy or detect and manage fraud faster.
Enterprises should reasonably expect firm ROI data within about six months. Though leading indicators such as scenario testing and pilot programs can be delivered quickly, large-scale, immediate returns are unrealistic. A thoughtful business process redesign can take 90 to 120 days to design and implement, followed by acceptance testing and, crucially, focused change management.
These initiatives are most likely to succeed when people accountable for business results are charged with delivering a specific outcome rather than simply achieving a technology go-live.
Costs also deserve more sophisticated treatment, since the expense of various agentic processes can vary drastically based on factors like use case complexity, model calls and context. Companies should measure and track the distribution of cost per outcome rather than relying on a pre-deployment average.
This is an important nuance: Early adopters find that monitoring how much each AI-handled case costs and how long it takes, and then refining their strategy to keep both numbers within an acceptable range, is crucial for successfully redesigning business processes with AI.
Avoiding the ‘valley of despair’
Treat AI as a series of targeted business transformations, not solely a technology deployment. That means pairing clear outcomes with disciplined execution.
Take a closer look at business processes. Examine how work actually happens within the organization, not just how a procedure “should” happen. Identify bottlenecks, handoffs, approvals, exceptions and rework sources before deciding where to apply AI. Expect to find substantial differences between documented processes and how work actually gets done.
Tie every initiative to a clear outcome. Name the revenue, efficiency or risk metric the AI initiative is intended to impact, establish a baseline, give a business leader ownership of the result and set a hard timeline for impact data.
Design pilots for production. AI pilots should test the path to sustained use, not merely demonstrate impressive capabilities under controlled conditions. Include operational data, integration, governance, security and change-management requirements from the beginning.
Measure economics, not activity. Track metrics such as cycle time, quality, adoption, cost per outcome and financial impact rather than seats, prompts or demos. For agentic systems, monitor both the range and the average of operating costs, since complex cases can consume far more resources than simpler ones.
Review and adjust regularly. Review regularly to decide whether to move forward, narrow the scope, redesign the workflow or stop. Exploratory work won’t always succeed, but it should continually produce evidence that improves the next capital allocation.
AI’s “valley of despair” isn’t inevitable. Companies that understand the value of investing time and capital to rethink and reengineer their business processes can turn AI spend into tangible outcomes.
Jeremiah Stone is chief technology officer at SnapLogic Inc. He wrote this opinion article for SiliconANGLE.
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