内容来源：https://www.technologyreview.com/2025/12/05/1128730/harnessing-human-ai-collaboration-for-an-ai-roadmap-that-moves-beyond-pilots/

内容总结：

企业AI应用步入深水区：专家共议人机协同新路径

近期一场行业网络研讨会上，来自Concentrix、Everest Group及Valmont的多位高管指出，当前企业人工智能应用正面临关键转折。尽管AI投资持续高涨，但约四分之三的企业仍停留在试验阶段，难以实现规模化部署与业务价值转化。

Everest Group合伙人Shirley Hung指出，许多企业正受困于“流程、技术、技能与数据”的综合挑战——僵化割裂的工作流程、互不兼容的技术系统、深陷低效任务的员工以及缺乏整合的海量数据，共同阻碍了AI效能的释放。

与会专家一致认为，破局关键在于重构“人、流程与技术”的协同关系。无论是客户服务还是农业设备领域，传统的集中决策、碎片化工作模式已无法适应“智能体AI”时代的要求。企业必须系统性地重塑决策流程、工作方式，并明确人类在其中的独特价值。

Concentrix执行副总裁Ryan Peterson强调：“人类持续对AI生成内容进行核验至关重要，这将是未来投入的重点方向。” 讨论聚焦于如何将人机协作真正“运营化”——AI不应被简单视为独立工具或“虚拟员工”，而应作为增强人类判断、加速执行、端到端重构工作流的系统级能力。

Valmont北美售后副总裁Heidi Hough从实践角度建议：“企业需将数据安全与治理置于首位，为AI商业化应用奠定可信基础。” 先行者的经验表明，从低风险场景起步、构建高整合度数据模块、将治理嵌入日常决策，并让业务端主导价值挖掘，正成为AI成熟度提升的新蓝图。

正如Shirley Hung所言：“优化是把现有事情做得更好，而重新想象则是发现值得做的新事物。” 这场讨论揭示，企业AI的成功已超越技术试点本身，更取决于组织能否以人机协同为核心，推动系统性运营变革。

中文翻译：

赞助内容
驾驭人机协作：制定超越试点阶段的AI路线图
在本场独家网络研讨会中，Concentrix的Ryan Peterson、Everest Group的Shirley Hung以及Valmont的Heidi Hough共同探讨如何将AI愿景转化为运营优势。

与Concentrix联合呈现
过去一年成为企业AI讨论的转折点。经历一段热切的试验期后，各组织正面临更复杂的现实：尽管AI投资达到历史高点，从试点到规模化应用的道路依然模糊。四分之三的企业仍困在试验模式中，尽管将早期测试转化为运营效益的压力日益增大。

Everest Group合伙人Shirley Hung指出：“多数组织可能受困于所谓的‘PTSD’——流程、技术、技能与数据挑战。他们拥有僵化割裂的工作流程、难以协同的技术系统、深陷低价值任务而非创造高影响力的人才，以及淹没在无尽信息流中却缺乏统一架构整合数据的困境。”

因此，核心挑战在于重新思考人员、流程与技术如何协同工作。

从客户服务到农业设备等截然不同的行业，正浮现出相同模式：传统的组织结构——集中化决策、碎片化工作流、数据散落在互不兼容的系统中——已被证明过于僵化，难以支撑自主化AI。要释放价值，领导者必须重塑决策机制、工作执行方式以及人类独有的贡献维度。

Concentrix执行副总裁兼首席产品官Ryan Peterson强调：“人类持续验证AI生成内容至关重要，这将是未来重点投入能量的领域。”

讨论焦点集中于被称为“下一个关键突破口”的议题：实现人机协作的运营化。这种方法不再将AI视为独立工具或“虚拟员工”，而是将其重构为系统级能力，用以增强人类判断、加速执行并端到端重塑工作模式。这种转变要求企业明确价值创造目标，设计融合人类监督与AI驱动自动化的工作流，并建立可信赖的数据、治理与安全基础。

Valmont北美售后市场副总裁Heidi Hough建议：“务必预留缓冲时间以确保数据安全。在推进AI商业化或运营化过程中，若从零开始并将治理置于首位，将显著提升成果质量。”

先行者已展现出实践路径：从低风险运营场景切入，将数据整合为严密可控的模块，把治理嵌入日常决策，并赋能业务领导者（而非仅技术人员）识别AI可创造显著价值的领域。最终，一套基于现代企业运营重构的AI成熟度新蓝图正在形成。

Hung总结道：“优化重在改进现有事务，而重构旨在发现值得探索的全新领域。”

本网络研讨会由Concentrix合作呈现。
此内容由《麻省理工科技评论》定制内容团队Insights制作，并非编辑部撰写。内容由人类作者、编辑、分析师与插画师完成调研、设计与撰写，包括问卷编写与数据收集。可能使用的AI工具仅限经过严格人工审核的辅助生产流程。

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英文来源：

Sponsored
Harnessing human-AI collaboration for an AI roadmap that moves beyond pilots
In this exclusive webcast, Concentrix’s Ryan Peterson, Everest Group’s Shirley Hung, and Valmont’s Heidi Hough discuss turning AI ambitions into operational advantages.
In partnership withConcentrix
The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.
“Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don't adapt well to change, technology systems that don't speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”
The central challenge, then, lies in rethinking how people, processes, and technology work together.
Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.
"It is very important that humans continue to verify the content. And that is where you're going to see more energy being put into," Ryan Peterson, EVP and chief product officer at Concentrix.
Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.
"My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes."
Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.
"Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing," says Hung.
This webcast is produced in partnership with Concentrix.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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