充分发挥人工智能的潜力需要卓越的运营能力。
内容总结：
当前，人工智能已成为企业董事会、管理层会议及媒体热议的焦点。高盛数据显示，标普500指数中58%的企业在季度财报会议中提及AI。然而，MIT研究指出，仅5%的生成式AI试点项目实现了可量化的盈利，绝大多数投入未能产生实际回报。
调查显示，AI落地的核心障碍并非技术本身，而是企业运营流程的结构性缺失。近半数受访者指出，低效的流程文档化管理拖累了AI应用效率；超过60%的知识工作者认为企业AI战略与运营能力严重脱节。比尔·盖茨曾强调：“自动化只会放大现有运营模式的效率或缺陷”，这一观点在AI应用中得到验证。
企业普遍面临“最后一公里”难题——将AI技术融入实际业务场景的最终环节。尽管拥有先进算法，但若缺乏规范的流程文档（仅16%企业实现完善记录）和协同工具（40%受访者称时间不足，30%认为工具缺失），AI价值将难以释放。某财富500强企业的案例表明，陈旧的协作工具会直接阻碍AI效能发挥。
不同层级员工对AI战略的认知存在显著差异：61%高管认为策略周密，而基层员工仅36%认同。远程协作时代，数字化工具支持的协同空间成为刚需。调查中，37%的团队将文档协作列为首要需求，流程可视化（33%）与规范记录（34%）紧随其后。
实现AI价值的关键在于构建卓越运营体系。企业需聚焦基础能力建设：通过标准化流程、可视化协作和系统化知识管理，为AI应用铺平道路。唯有夯实运营根基，方能真正释放人工智能的变革潜力。
中文翻译：
赞助内容
释放人工智能的全部潜力需要卓越运营
企业要成功应用人工智能，领导者应关注体系构建而非推进速度
由Lucid提供
人工智能已成为无法回避的话题。无论是董事会、高管会议、企业团建还是媒体报道，这常常是核心议题。高盛数据显示，标普500指数成分公司在第二季度财报电话会议中提及AI的比例高达58%，创下历史纪录。
但知行合一并非易事。麻省理工学院近期研究显示，仅5%的生成式AI试点项目产生了可量化的盈亏影响。这意味着尽管投入大量关注与资金，95%的生成式AI试点项目尚未实现任何回报。
距离ChatGPT面世这一分水岭已近三年，绝大多数企业的AI应用仍停滞不前。问题出在哪里？Lucid的AI就绪度调查揭示了阻碍企业前进的绊脚石。幸运的是，对多数企业而言，解决这些问题无需斥资数亿美元招募顶尖AI人才。当企业加速推进AI落地时，领导者更需要建立严谨规范的运营流程。
运营体系是AI承诺与实际应用之间的鸿沟
我完全理解领导者全力推进AI应用的心情。在许多情况下，这关乎企业存亡和个人职业发展。AI在提升效率、降低成本、优化沟通方面展现的变革性潜力，确实让速度成为关键考量。
但在追求速度的同时，许多领导者忽略了技术落地必备的基础步骤。我们的调查显示，超过60%的知识工作者认为其企业的AI战略与运营能力匹配度欠佳。
AI能处理非结构化数据，但无序的组织架构只会让AI应用适得其反。正如比尔·盖茨所言：“企业应用技术的首要原则是：高效运营基础上的自动化将倍增效率；其次，低效运营基础上的自动化只会放大无效性。”
AI落地中的运营缺口何在？调查发现：49%的受访者指出文件缺失或临时流程时常影响效率，22%表示这种情况频繁发生。
AI转型的核心挑战不在技术本身，而在于融入日常工作的“最后一公里”。这好比物流行业的终极难题：无论前期流程多么高效，将货物送达客户手中始终是最关键的环节。
对AI而言，“最后一公里”意味着将技术嵌入实际业务场景。企业虽掌握强大模型，却难以将其与终端用户有效连接。若不能将AI融入业务流程，其潜力必将浪费，而这首先需要清晰规范的流程文档。
大规模知识获取、记录与传播是企业用好AI的关键。但调查中仅16%的受访者表示其工作流程有完善文档记录。阻碍文档建设的主因是时间不足（40%）和工具缺失（30%）。
某财富500强高管的经历生动展现了新旧流程融合之困：该公司一面大力推行AI增效，一面仍使用过时且不适合团队协作的工具。这正印证了我们的发现：若团队缺乏现代化协作与文档工具，再宏伟的AI计划也会举步维艰。
这种脱节表明AI应用超越技术范畴。要实现全企业成功落地，公司需要为团队提供集思广益、规划决策、记录沉淀的统一空间。技术成功落地的核心原则始终未变：需要合适的工具促进协作与知识沉淀，AI才能真正发挥作用。
协作与变革管理是AI落地的隐形障碍
不同职级员工对企业AI战略的感知存在显著差异：调查显示61%的最高管理层认为公司策略经过深思熟虑，而中层管理者与基层员工认同比例分别降至49%和36%。
与产品开发同理，构建成功的AI战略需要体系化方法。管理者和团队需要协同空间进行头脑风暴，甄选优质机会，规划清晰路径。随着混合办公模式普及，数字化协作工具的支持愈发重要。
我们近期运用AI处理管理层的战略难题，某产品负责人借此在极短时间内生成包含摘要、基准分析与建议的完备备忘录。
但AI生成文档仅是起点。我们仍需召开会议讨论细节、确定行动优先级、明确责任分工并正式记录决策与后续步骤。
调查中23%的受访者指出协作效率常成为复杂工作的瓶颈。员工乐于拥抱变革，但低效协作带来的摩擦会增加风险，削弱AI的潜在价值。
运营就绪度决定AI应用成熟度
无序的运营体系正阻碍众多企业实现AI价值。关于团队最需要的AI适配支持，受访者首选文档协作（37%）、流程记录（34%）和可视化工作流（33%）。
值得注意的是，这些需求均不涉及更复杂的AI技术。现有技术已足够强大，多数企业仅触及其潜能的冰山一角。团队真正需要的是夯实流程、文档与协作的基础建设。
AI为企业提升生产效能提供了重要机遇，但快速推进不等于成功。最有可能实现AI价值的企业，正是那些执着于打造卓越运营体系、精益求精直至最后一公里的组织。
本文由Lucid Software创作，未经《麻省理工科技评论》编辑团队撰写。
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Unlocking AI’s full potential requires operational excellence
For successful AI adoption, leaders need to focus on structure rather than speed.
Provided byLucid
Talk of AI is inescapable. It’s often the main topic of discussion at board and executive meetings, at corporate retreats, and in the media. A record 58% of S&P 500 companies mentioned AI in their second-quarter earnings calls, according to Goldman Sachs.
But it’s difficult to walk the talk. Just 5% of generative AI pilots are driving measurable profit-and-loss impact, according to a recent MIT study. That means 95% of generative AI pilots are realizing zero return, despite significant attention and investment.
Although we’re nearly three years past the watershed moment of ChatGPT’s public release, the vast majority of organizations are stalling out in AI. Something is broken. What is it?
Date from Lucid’s AI readiness survey sheds some light on the tripwires that are making organizations stumble. Fortunately, solving these problems doesn’t require recruiting top AI talent worth hundreds of millions of dollars, at least for most companies. Instead, as they race to implement AI quickly and successfully, leaders need to bring greater rigor and structure to their operational processes.
Operations are the gap between AI's promise and practical adoption
I can’t fault any leader for moving as fast as possible with their implementation of AI. In many cases, the existential survival of their company—and their own employment—depends on it. The promised benefits to improve productivity, reduce costs, and enhance communication are transformational, which is why speed is paramount.
But while moving quickly, leaders are skipping foundational steps required for any technology implementation to be successful. Our survey research found that more than 60% of knowledge workers believe their organization’s AI strategy is only somewhat to not at all well aligned with operational capabilities.
AI can process unstructured data, but AI will only create more headaches for unstructured organizations. As Bill Gates said, “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”
Where are the operations gaps in AI implementations? Our survey found that approximately half of respondents (49%) cite undocumented or ad-hoc processes impacting efficiency sometimes; 22% say this happens often or always.
The primary challenge of AI transformation lies not in the technology itself, but in the final step of integrating it into daily workflows. We can compare this to the "last mile problem" in logistics: The most difficult part of a delivery is getting the product to the customer, no matter how efficient the rest of the process is.
In AI, the "last mile" is the crucial task of embedding AI into real-world business operations. Organizations have access to powerful models but struggle to connect them to the people who need to use them. The power of AI is wasted if it's not effectively integrated into business operations, and that requires clear documentation of those operations.
Capturing, documenting, and distributing knowledge at scale is critical to organizational success with AI. Yet our survey showed only 16% of respondents say their workflows are extremely well-documented. The top barriers to proper documentation are a lack of time, cited by 40% of respondents, and a lack of tools, cited by 30%.
The challenge of integrating new technology with old processes was perfectly illustrated in a recent meeting I had with a Fortune 500 executive. The company is pushing for significant productivity gains with AI, but it still relies on an outdated collaboration tool that was never designed for teamwork. This situation highlights the very challenge our survey uncovered: Powerful AI initiatives can stall if teams lack modern collaboration and documentation tools.
This disconnect shows that AI adoption is about more than just the technology itself. For it to truly succeed enterprise-wide, companies need to provide a unified space for teams to brainstorm, plan, document, and make decisions. The fundamentals of successful technology adoption still hold true: You need the right tools to enable collaboration and documentation for AI to truly make an impact.
Collaboration and change management are hidden blockers to AI implementation
A company's approach to AI is perceived very differently depending on an employee's role. While 61% of C-suite executives believe their company's strategy is well-considered, that number drops to 49% for managers and just 36% for entry-level employees, as our survey found.
Just like with product development, building a successful AI strategy requires a structured approach. Leaders and teams need a collaborative space to come together, brainstorm, prioritize the most promising opportunities, and map out a clear path forward. As many companies have embraced hybrid or distributed work, supporting remote collaboration with digital tools becomes even more important.
We recently used AI to streamline a strategic challenge for our executive team. A product leader used it to generate a comprehensive preparatory memo in a fraction of the typical time, complete with summaries, benchmarks, and recommendations.
Despite this efficiency, the AI-generated document was merely the foundation. We still had to meet to debate the specifics, prioritize actions, assign ownership, and formally document our decisions and next steps.
According to our survey, 23% of respondents reported that collaboration is frequently a bottleneck in complex work. Employees are willing to embrace change, but friction from poor collaboration adds risk and reduces the potential impact of AI.
Operational readiness enhances your AI readiness
Operations lacking structure are preventing many organizations from implementing AI successfully. We asked teams about their top needs to help them adapt to AI. At the top of their lists were document collaboration (cited by 37% of respondents), process documentation (34%), and visual workflows (33%).
Notice that none of these requests are for more sophisticated AI. The technology is plenty capable already, and most organizations are still just scratching the surface of its full potential. Instead, what teams want most is ensuring the fundamentals around processes, documentation, and collaboration are covered.
AI offers a significant opportunity for organizations to gain a competitive edge in productivity and efficiency. But moving fast isn’t a guarantee of success. The companies best positioned for successful AI adoption are those that invest in operational excellence, down to the last mile.
This content was produced by Lucid Software. It was not written by MIT Technology Review’s editorial staff.
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