AI is shattering many decades-old assumptions about how growth happens. The fundamentals of growth still matter, but the way companies create, capture, and scale growth with AI has fundamentally changed the game. It’s like what happened in basketball when the three-point shot was introduced: The foundations of the game didn’t change, but the strategies for how to win championships were transformed.人工智能正在打破许多延续数十年的增长假设。虽然增长的基本面依然重要，但企业利用人工智能创造、获取和扩大增长的方式已彻底改变了游戏规则。这就像篮球运动引入三分球一样：比赛的基础没有变，但赢得冠军的策略却发生了根本性的转变。
The race for growth has entered a new era. The companies pulling ahead recognize how deeply AI is changing the way they create demand, convert customers, and generate value. While every CEO wants to achieve above-market growth, early AI leaders are going bigger. They are achieving EBITDA uplifts of 20 percent or more by rewiring their commercial engines around AI, not simply deploying new technology.1增长竞赛已进入新时代。领先的企业深刻认识到，人工智能正在从根本上改变它们创造需求、转化客户和产生价值的方式。虽然每位首席执行官都希望实现超越市场的增长，但早期的人工智能领军者志向更为远大。他们通过围绕人工智能重构商业引擎，而非仅仅部署新技术，实现了息税折旧摊销前利润（EBITDA）20%或更高的增长。1“人工智能转型宣言”，《麦肯锡季刊》，2026年4月7日。
That value is not some theoretical aspiration. For example, Reckitt, a global leader in home and consumer health care, unlocked more than $500 million in new revenue with its ambitious growth transformation. The company used AI-powered simulations and scenario planning to improve commercial pricing, promotions, and assortment decisions as well as to guide field sales teams on which in-store merchandising activities to prioritize to create tangible value. In another case, DBS, the Singapore-based bank, has generated hundreds of millions of dollars in value by rewiring its business around AI.这种价值并非理论上的愿望。例如，全球家庭和消费者医疗保健领域的领导者利洁时（Reckitt），通过雄心勃勃的增长转型，释放了超过5亿美元的新收入。该公司利用人工智能驱动的模拟和情景规划，优化了商业定价、促销和品类决策，并指导一线销售团队优先开展哪些店内营销活动，从而创造了切实的价值。在另一个案例中，总部位于新加坡的星展银行（DBS）通过围绕人工智能重构业务，创造了数亿美元的价值。
Yet these companies remain the exceptions. Nearly 90 percent of companies regularly use AI in at least one business function, according to a global McKinsey survey, but just 37 percent of respondents attribute any level of EBIT impact to AI.2 Furthermore, the gap between leaders and laggards is growing. Our latest research shows that 60 percent of market leaders report double-digit revenue growth, compared with just 21 percent of laggards.3然而，这些企业依然是少数。根据麦肯锡的一项全球调查，近90%的企业在至少一个业务职能中定期使用人工智能，但只有37%的受访者认为人工智能对其息税前利润（EBIT）产生了影响。2“人工智能全球现状调查”，麦肯锡，2026年（即将发布）。此外，领先者与落后者之间的差距正在扩大。我们最新的研究显示，60%的市场领先者报告收入实现两位数增长，而落后者中仅有21%达到这一水平。3市场领先者是指市场份额同比增幅超过10%的企业；落后者是指同期市场份额下降超过5%的企业。调查结果来自麦肯锡2026年全球B2B脉搏报告，该报告收集了来自13个国家多个行业的近4,000名B2B决策者的见解。
To narrow the gap and turn AI into a force multiplier for growth, CEOs need to fundamentally rewire how they operate to move faster, more iteratively, and with clearer direction. That starts with rejecting the myths that can sink their growth aspirations.为了缩小差距并将人工智能转化为增长的倍增器，首席执行官们需要从根本上重塑运营方式，以更快的速度、更强的迭代能力和更清晰的方向开展工作。这首先要摒弃那些可能扼杀增长愿望的误区。
Myths and realities about AI and growth关于人工智能与增长的误区与现实
In our experience, we see seven common myths that can hamper business leaders’ ability to turn AI into value.根据我们的经验，有七个常见的误区可能会阻碍商业领袖将人工智能转化为价值。
Myths that limit ambition限制抱负的误区
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Myth: Above-market growth goals are enough.误区：设定超越市场的增长目标就足够了。
Reality: AI raises the ceiling on how much growth is possible. While nearly 75 percent of companies set above-market growth targets, that’s not bold enough.4 Our research finds that leaders who set bold growth ambitions—underpinned by AI—can unlock nearly three times more value than they imagine.5现实：人工智能提高了增长潜力的上限。虽然近75%的企业设定了超越市场的增长目标，但这还不够大胆。4“打破陈规：领先增长转型者的五种行为”，麦肯锡，2024年12月16日。我们的研究发现，那些在人工智能支持下设定大胆增长抱负的领导者，所释放的价值几乎是他们预想的三倍。5“实现增长：将领导者的思维方式和行为付诸实践”，《麦肯锡季刊》，2025年1月13日。
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Myth: AI is primarily a productivity tool.误区：人工智能主要是一种生产力工具。
Reality: Some 80 percent of companies use AI to improve efficiency, but AI can do much more. The companies creating the greatest value are using AI to improve both efficiency and effectiveness.6 Greater effectiveness creates new demand, increases conversion, and accelerates growth and innovation.现实：约80%的企业利用人工智能提高效率，但人工智能的作用远不止于此。创造最大价值的企业正在利用人工智能同时提升效率和效能。6“2025年人工智能现状：智能体、创新与转型”，麦肯锡，2025年11月5日。更高的效能可以创造新需求、提高转化率，并加速增长与创新。
Myths that preserve old ways of working固守旧工作方式的误区
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Myth: Layering AI onto existing processes drives growth.误区：在现有流程上叠加人工智能就能推动增长。
Reality: Bolting AI onto existing processes—often resulting in a proliferation of isolated use cases—doesn’t lead to growth. Rather, it is activity without architecture. AI high performers are three times as likely to say their organizations have fundamentally redesigned individual workflows.7现实：将人工智能“硬塞”进现有流程（往往导致孤立用例的激增）并不能带来增长。这只是没有架构支撑的活动。人工智能表现卓越的企业中，有三倍以上的比例表示其组织已从根本上重塑了个人工作流。7“2025年人工智能现状：智能体、创新与转型”，麦肯锡，2025年11月5日。
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Myth: The chief technology officer (CTO) should lead the AI transformation.误区：首席技术官（CTO）应该领导人工智能转型。
Reality: The approach associated with growth is to undertake a business transformation enabled by technology, with people at the center. That’s why leaders at half of top-performing companies co-create strategy across technology and business to redesign new pathways to growth.8现实：与增长相关的成功路径是开展以技术为赋能手段、以人为核心的业务转型。这就是为什么半数表现顶尖的企业的领导者会跨技术和业务部门共同制定战略，以重塑通往增长的新路径。8“麦肯锡2026年全球技术议程”，麦肯锡，2026年2月9日。
Myths that slow down value capture延缓价值获取的误区
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Myth: Scaling AI is mostly a tech rollout.误区：扩展人工智能主要是一项技术推广工作。
Reality: Creating enterprise-wide growth involves more than new technology; it involves new ways of working, capabilities and skills, and governance models. Achieving this degree of change requires resources dedicated to change management. The rule of thumb is that for every $1 a company spends on an AI initiative, it needs another $3—and sometimes more—for change management.9现实：实现全企业的增长不仅仅涉及新技术，还涉及新的工作方式、能力与技能以及治理模式。实现这种程度的变革需要专门的资源来进行变革管理。经验法则是，企业在人工智能项目上每投入1美元，就需要投入另外3美元（有时更多）用于变革管理。9“告别生成式人工智能的蜜月期：首席信息官从试点到规模化必须面对的七个残酷事实”，麦肯锡，2024年5月13日。
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Myth: AI initiatives take a long time to create P&L value.误区：人工智能项目需要很长时间才能在损益表（P&L）上体现价值。
Reality: In our experience, AI leaders often see impact on leading indicators within weeks and create substantial economic value within three to six months. That happens when companies focus on high-value commercial workflows in priority domains and iterate rapidly.现实：根据我们的经验，人工智能领军者通常在几周内就能看到先行指标的影响，并在三到六个月内创造出可观的经济价值。这发生在企业专注于重点领域的高价值商业工作流并快速迭代的时候。
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Myth: Companies need perfect data in order to introduce AI.误区：企业需要完美的数据才能引入人工智能。
Reality: Good data matters, but in our experience, 80 percent of companies do not need to build a data lake or rebuild their entire data architecture to get value. “Good enough” data is sufficient to start capturing value quickly.现实：良好的数据固然重要，但根据我们的经验，80%的企业不需要为了获取价值而构建数据湖或重建整个数据架构。“足够好”的数据就足以快速开始获取价值。
McKinsey at Dreamforce 2026麦肯锡在2026年Dreamforce大会
Discover the shortest path from AI to ROI with us in San Francisco, September 15-179月15日至17日，欢迎在旧金山与我们共同探索从人工智能到投资回报率（ROI）的最短路径
Building an AI-driven commercial growth engine构建人工智能驱动的商业增长引擎
Business leaders who want to shake off the growth myths and build a different kind of commercial growth engine can do this by taking concerted steps (Exhibit 1). These actions are laid out sequentially, but to win the growth game, the best companies imagine them as a system that is continually running and optimizing in fast learning cycles.希望打破增长误区并构建新型商业增长引擎的商业领袖，可以通过采取协同步骤（见图表1）来实现。这些行动按顺序排列，但为了赢得增长竞赛，最优秀的企业会将它们视为一个在快速学习周期中持续运行和优化的系统。
Bold ambitions: Continuously hunt for growth大胆的抱负：持续寻找增长点
Companies have long searched for growth through periodic strategy reviews built on aggregated market data. AI changes the economics of growth by making growth discovery continuous instead of episodic. Companies can now spot emerging pockets of demand by analyzing specific customer behaviors, local market dynamics, or price sensitivities.长期以来，企业一直通过基于汇总市场数据的定期战略审查来寻求增长。人工智能改变了增长的经济学，使增长发现变得持续而非间歇。企业现在可以通过分析特定的客户行为、本地市场动态或价格敏感度来发现新兴的需求空间。
At the same time, AI dramatically lowers the cost of experimentation to pursue those opportunities. Companies can personalize engagement at scale, rapidly test offers, and enter niche segments or geographies that were previously too costly or complex to profitably pursue. They can leverage AI to set overall strategy.与此同时，人工智能极大地降低了追求这些机会的实验成本。企业可以大规模地进行个性化互动，快速测试报价，并进入以前因成本过高或过于复杂而无法盈利的利基市场或地理区域。他们可以利用人工智能来制定整体战略。
An industrial distributor used AI to transform how it finds and wins business, moving from transactional selling to predictive selling. Historically, sales teams relied heavily on relationship-based selling, local knowledge, and manual lead generation to identify projects that might require their products. Tight sales windows created even more variability.一家工业分销商利用人工智能改变了其寻找和赢得业务的方式，从交易型销售转向预测型销售。过去，销售团队严重依赖基于关系的销售、本地知识和人工线索生成来识别可能需要其产品的项目。紧迫的销售窗口期造成了更大的不确定性。
The distributor built an AI-driven engine that continuously scans signals such as building permits, project activity, customer history, and product data to identify where demand is likely to emerge. By scraping publicly available building permit approvals and surfacing opportunities precisely when customers are most likely to buy, the system recommends the next best sales action and generates personalized customer outreach with product recommendation and discount coupons.该分销商构建了一个人工智能驱动的引擎，持续扫描建筑许可证、项目活动、客户历史和产品数据等信号，以识别需求可能出现的位置。通过抓取公开的建筑许可证审批信息，并在客户最有可能购买时精准呈现机会，该系统会推荐下一个最佳销售行动，并生成包含产品推荐和折扣券的个性化客户外联信息。
The system was integrated into Salesforce CRM so the distributor’s sales reps could quickly review, edit, and send the emails directly to the customers. The result: an estimated $300 million to $400 million in incremental gross margin.该系统被集成到Salesforce CRM中，以便分销商的销售代表能够快速查看、编辑并直接将电子邮件发送给客户。结果：估计增加了3亿至4亿美元的增量毛利。
The broader lesson for CEOs is that AI turns growth from a periodic planning exercise into an always-on commercial capability.对首席执行官而言，更广泛的教训是，人工智能将增长从一种定期的规划演习转变为一种始终在线的商业能力。
Rewiring ways of working: Embed AI into all commercial operations重塑工作方式：将人工智能嵌入所有商业运营
The companies pulling ahead are compressing the distance between customer signal, decision, and action. AI enables commercial decisions to happen within the flow of work—continuously analyzing signals, recommending the next best action, and increasingly executing routine decisions with human oversight. As a result, a handful of people working alongside AI systems can deliver levels of personalization, experimentation, and optimization that once required entire departments.领先的企业正在压缩客户信号、决策和行动之间的距离。人工智能使商业决策能够在工作流中实时发生——持续分析信号、推荐下一个最佳行动，并越来越多地在人工监督下执行常规决策。因此，少数与人工智能系统协同工作的人员，即可实现曾经需要整个部门才能完成的个性化、实验和优化水平。
To make this shift, CEOs and chief commercial officers (CCOs) must focus less on deploying AI tools and more on redesigning how commercial decisions are made (Exhibit 2). AI high performers are nearly three times as likely as other companies to say their organizations fundamentally redesign individual workflows rather than simply automating existing processes.10 That redesign includes explicitly determining how commercial decision logic is integrated into ways of working.为了实现这一转变，首席执行官和首席商务官（CCO）必须减少对部署人工智能工具的关注，转而更多地关注如何重塑商业决策的制定方式（见图表2）。人工智能表现卓越的企业中，有近三倍的企业表示其组织从根本上重塑了个人工作流，而非仅仅自动化现有流程。10“2025年人工智能现状：智能体、创新与转型”，麦肯锡，2025年11月5日。这种重塑包括明确如何将商业决策逻辑整合到工作方式中。
The advantage is cumulative, as in the case of Reckitt’s RGM and smart execution transformation. Rather than relying on analysts to generate insights, pricing leaders use self-service platforms to test promotion calendars and dynamic price elasticity curves in real time. Planners simulate complex scenarios such as competitor moves, inflation shifts, and other market dynamics to find the optimal paths to maximize top-line revenue. Product teams prototype new packaging in seconds by using AI-generated consumer personas to gather simulated feedback. Similarly, in smart execution, sales leaders use AI-driven recommendations to prescribe to their teams what to execute at each point of sale. This combination helped increase average project sizes (measured in expected incremental net revenue) by 25 percent year on year.11这种优势是累积的，正如利洁时的收入增长管理（RGM）和智能执行转型案例所示。定价领导者不再依赖分析师来生成见解，而是使用自助服务平台实时测试促销日历和动态价格弹性曲线。规划人员模拟竞争对手举措、通胀变化和其他市场动态等复杂情景，以找到最大化顶线收入的最佳路径。产品团队利用人工智能生成的消费者画像来收集模拟反馈，在几秒钟内完成新包装的原型设计。同样，在智能执行方面，销售领导者使用人工智能驱动的建议，为团队规定在每个销售点应执行的操作。这种组合帮助平均项目规模（以预期增量净收入衡量）同比增长了25%。11“数字科学：赋能利洁时推动增长”，利洁时，2025年6月10日。
Critically, Reckitt designed its AI capabilities backward from the tasks that commercial teams typically perform. The company realized that implementing a next-generation RGM system would require training commercial teams at the moment of action, such as when they are developing sales plans or shaping brand strategies. Practical, hands-on sessions using live market data helped prove the value of insights in real time, which accelerated buy-in and changing behavior at scale.至关重要的是，利洁时从商业团队通常执行的任务出发，逆向设计了其人工智能能力。该公司意识到，实施下一代RGM系统需要商业团队在行动的关键时刻（例如制定销售计划或塑造品牌战略时）进行培训。使用实时市场数据进行的实用、动手操作环节有助于实时证明见解的价值，从而加速了组织的认同并推动了大规模的行为改变。
This example shows the necessity of encoding AI into workflows, not just making individual tasks faster. The CEO and CCO roles increasingly focus on setting the operating rules, KPIs, funding thresholds, escalation paths, risk tolerances, and resource priorities that allow the system to move quickly without losing control.这个例子说明了将人工智能编码到工作流中的必要性，而不仅仅是让单个任务变得更快。首席执行官和首席商务官的角色日益侧重于设定运营规则、关键绩效指标（KPI）、资金门槛、升级路径、风险容忍度和资源优先级，使系统能够在不失控的情况下快速运行。
Accelerate the timeline: Scale value加速时间线：规模化价值
Growth comes from scaling what works across products, channels, geographies, and customer segments. Historically, that required large amounts of people, local coordination, and manual effort. AI changes those economics.增长源于在产品、渠道、地区和客户群中规模化推广行之有效的方法。过去，这需要大量的人力、本地协调和手动工作。人工智能改变了这种经济性。
The companies moving—and growing—fastest are those that standardize the underlying models that drive growth decisions. They create reusable components (for example, customer propensity models, pricing logic, offer generation systems, sales agents, experimentation environments, and performance signals) that can be redeployed across the business without increasing head count or organizational complexity.那些行动最快、增长最快的企业，是那些标准化了驱动增长决策底层模型的企业。他们创建了可重用的组件（例如客户倾向模型、定价逻辑、报价生成系统、智能销售代理、实验环境和绩效信号），这些组件可以在不增加人员编制或组织复杂性的情况下在整个业务中重新部署。
The key decision for CEOs and CCOs is how to strike a balance between standardization and localization. Leaders increasingly centralize core data, models, governance, and decision systems while decentralizing experimentation, customer engagement, and market adaptation. This allows successful strategies to spread quickly across the organization while still being adapted to local market conditions.首席执行官和首席商务官的关键决策在于如何在标准化和本地化之间取得平衡。领导者越来越多地集中核心数据、模型、治理和决策系统，同时分散实验、客户互动和市场适应工作。这使得成功的策略能够迅速在整个组织中传播，同时仍能根据当地市场条件进行调整。
Reckitt’s approach to rolling out its RGM solution reflects this understanding. For more mature, data-rich regions, Reckitt provides an advanced version of the solution, while other countries have access to a simpler version that helps them develop better data hygiene and transition teams to fact-based models before giving them access to more advanced features.12利洁时推广其RGM解决方案的方法反映了这种理解。对于更成熟、数据丰富的地区，利洁时提供该解决方案的高级版本；而其他国家则可以使用较简单的版本，帮助它们在获得更高级功能之前，建立更好的数据卫生习惯并使团队过渡到基于事实的模型。12“利洁时的宏伟抱负：利用人工智能重定义收入增长管理”，麦肯锡，2026年1月15日。
Salesforce demonstrates the importance of building AI as an integrated system. When the company launched Agentforce, a platform for building, deploying, and governing AI agents, it made sure the agents were tied into its data cloud, Data 360. This allows agents to reason based on trusted business context rather than disconnected data sources. Salesforce recently reported that Data 360 ingested 112 trillion records in fiscal year 2026, up 114 percent year over year, reflecting how strong data structures can enable agentic performance.13Salesforce展示了将人工智能构建为集成系统的重要性。当该公司推出用于构建、部署和治理人工智能智能体的平台Agentforce时，它确保了智能体与其数据云Data 360相连。这使得智能体能够基于可信的业务背景而非断开连接的数据源进行推理。Salesforce最近报告称，Data 360在2026财年摄入了112万亿条记录，同比增长114%，这反映了强大的数据结构如何能够实现智能体性能。13“Salesforce公布2026财年第四季度及全年业绩”，Salesforce新闻稿，2026年2月25日。
Rather than deploying AI as a stand-alone capability, Salesforce embedded agents into the core sales, service, marketing, and commerce workflows that customers already used. This means agents operate in the same systems as the people they work alongside, governed by the same trusted processes, permissions, and controls. This focus, along with flexible pricing that lowered the barrier to entry and helped customers manage their AI spend, increased adoption among existing customers. To help customers track and manage performance, Salesforce also introduced Agentic Work Units, a standardized measure of the work that agents perform across the platform. For the first quarter of fiscal year 2027, Salesforce reported that Agentforce’s annual recurring revenue (ARR) reached $1.2 billion, up 205 percent year over year, with more than 50 percent of bookings coming from existing customers.14Salesforce没有将人工智能作为独立功能部署，而是将智能体嵌入客户已经在使用的核心销售、服务、营销和商务工作流中。这意味着智能体与他们并肩工作的人员在相同的系统中运行，并受到相同的可信流程、权限和控制的治理。这种专注，加上降低准入门槛并帮助客户管理人工智能支出的灵活定价，提高了现有客户的采用率。为了帮助客户跟踪和管理绩效，Salesforce还引入了“智能体工作单元”（Agentic Work Units），这是一种衡量智能体在整个平台上执行工作的标准化指标。对于2027财年第一季度，Salesforce报告称Agentforce的年度经常性收入（ARR）达到12亿美元，同比增长205%，其中超过50%的预订来自现有客户。14“Salesforce公布2027财年第一季度业绩”，Salesforce新闻稿，2026年5月27日。
The value of an integrated AI approach becomes clear in business workflows like sales coaching, where AI can shift frontline managers’ focus from monitoring activity to improving outcomes. Historically, frontline managers have spent much of their time interpreting dashboards, checking CRM activity, and preparing for one-on-ones with limited context. AI can now equip sellers with superpowers, coaching them in the moment to improve performance.集成人工智能方法的价值在销售辅导等业务工作流中变得显而易见，人工智能可以将一线经理的重心从监控活动转移到改善结果上。过去，一线经理花费大量时间解读仪表板、检查CRM活动并为缺乏背景信息的一对一会议做准备。现在，人工智能可以为销售人员配备“超能力”，在关键时刻进行辅导以提升绩效。
AI does this by synthesizing CRM data, account history, pipeline movement, seller activity, customer interactions, and peer benchmarks to identify the highest-impact opportunities for improvement. Instead of simply flagging that a rep is behind plan, AI could, for example, identify that the rep is selling too narrow of a product set, discounting too early, or failing to tailor offers.人工智能通过综合CRM数据、账户历史、管道变动、销售人员活动、客户互动和同行基准来识别最具影响力的改进机会。人工智能不仅可以简单地标记销售代表落后于计划，还可以识别出该代表销售的产品范围太窄、折扣过早或未能根据客户需求定制报价等问题。
That feedback loop can now happen almost immediately. AI-enabled tools can analyze recorded sales calls, meeting transcripts, and customer notes after an interaction, then give the seller direct feedback. Managers can see patterns across reps and teams and intervene where coaching will matter most to drive growth. Such a focus on performance drives results. For example, a program of AI-enabled coaching as part of a broader commercial effort enabled one company in the built-environment space to increase conversion by more than 20 percent in just nine weeks.这种反馈循环现在几乎可以立即发生。人工智能赋能的工具可以在互动后分析录制的销售电话、会议记录和客户笔记，然后直接向销售人员提供反馈。经理们可以看到跨代表和团队的模式，并在辅导最能发挥作用的地方进行干预以推动增长。这种对绩效的关注带来了成果。例如，作为更广泛商业努力的一部分，一项人工智能赋能的辅导计划使建筑环境领域的一家公司在短短九周内将转化率提高了20%以上。
What CEOs can do now首席执行官现在可以做什么
Developing an AI-driven growth engine is an investment of time and resources. But our experience has shown that companies can make progress quickly, especially if they tackle three actions first:开发人工智能驱动的增长引擎需要时间和资源的投入。但我们的经验表明，企业可以快速取得进展，特别是如果他们首先采取以下三项行动：
- See the full set of AI-enabled growth opportunities in days, not months. Don’t start with the one or two use cases already on the table. Using advanced AI tools, first- and third-party data, and industry benchmarks, companies should develop a strong self-assessment that helps them identify where AI can drive profitable growth (new demand pools, underserved segments, pricing, sales). The CEO’s job is to demand that full view quickly. The goal is to concentrate efforts on the one to three domain-level opportunities with the most value.在几天而非几个月内看到人工智能赋能的全部增长机会。不要从现有的那一两个用例开始。利用先进的人工智能工具、第一方和第三方数据以及行业基准，企业应进行深入的自我评估，帮助识别人工智能可以在哪些方面推动盈利增长（新需求池、未充分服务的细分市场、定价、销售）。首席执行官的工作是要求快速获得全景视图。目标是将精力集中在最具价值的一到三个领域级机会上。
- Align on specific outcomes and commit to bold goals. Turn the opportunity into a short set of specific, measurable outcomes (for example, market share growth, improved margin) on a clearly stated timeline. Those outcomes should be bold enough to force leadership to reimagine and redesign how growth happens. It is important to have strong benchmarks from across industries and geographies to help teams pressure-test their ambition, define what “good” really looks like, and translate it into concrete targets the organization can rally behind. The CEO’s job is to push their team to develop truly aspirational goals and commit the organization to achieving them.就具体成果达成一致并承诺实现宏伟目标。将机会转化为一套简短、具体、可衡量的成果（例如市场份额增长、利润率改善），并设定明确的时间表。这些成果应该足够大胆，足以迫使领导层重新构想和重塑增长的发生方式。拥有来自不同行业和地区的强大基准非常重要，这有助于团队对他们的抱负进行压力测试，定义什么是真正的“好”，并将其转化为组织可以支持的具体目标。首席执行官的工作是推动团队制定真正志向远大的目标，并承诺组织实现这些目标。
- Mobilize cross-functional teams and commit the resources to fuel them. Instead of expecting a central AI or technology function to deliver the outcomes, organizations should make use of small, cross-functional teams that combine commercial, technical, data, and operational talent. The CEO works with growth leaders to stand these teams up, point them at the highest-value workflows, and reallocate capital, talent, and attention to support them. Given the speed of change, the CEO needs to put a premium on fast adaptation and learning from mistakes.动员跨职能团队并投入资源来支持他们。组织不应期望中央人工智能或技术部门来交付成果，而应利用结合了商业、技术、数据和运营人才的小型跨职能团队。首席执行官与增长领导者合作建立这些团队，将他们指向最高价值的工作流，并重新分配资本、人才和注意力来支持他们。鉴于变革的速度，首席执行官需要高度重视快速适应和从错误中学习。
The companies that turn AI into growth aren’t those with the flashiest models. They are the ones building repeatable systems that help their sellers create sustained commercial advantage.将人工智能转化为增长的企业，并非那些拥有最华丽模型的公司，而是那些构建了可重复系统，帮助销售人员创造持久商业优势的公司。
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