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生成式人工智能如何使无边界组织成为可能

Jack Azagury
2024-12-09

全行业对生成式人工智能技术的热情高涨。

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图片来源:Eugene Mymrin—Getty Images

全行业对生成式人工智能技术的热情高涨,掀起了试验浪潮。众多企业在其销售、市场营销、客户服务以及信息技术等领域的既有流程中嵌入独立用例。仅有少数企业能够利用生成式人工智能重塑整个流程,并且基于精确且切实可行的商业案例来指导其人工智能投资。

只有当领导者着手运用人工智能从根本上重塑端到端工作流程时,才能充分发挥生成式人工智能的优势。重新评估企业整体流程旨在实现三大目标:1)打造无缝的终端用户体验;2)弥合生产力差距,尤其是职能或部门之间的差距;3)根据业务成果更精准地追踪价值。

已有部分公司先行一步,走在了前列。以一家保险公司为例,它正着手全面重塑其承销流程。该公司将生成式人工智能融入流程的每一个环节,同时并未忽视承销人员的经验。通过将这一技术整合至整个价值链中,该公司极大地提升了服务客户的效率,进而吸引了更多客户,实现了收入的两位数增长。

无边界的价值

成功的数字化转型绝非单纯技术层面的革新,它要求企业必须重塑工作方式,并确保从高管到一线员工的所有成员都能深度参与到转型中。

要构建生成式人工智能驱动的端到端工作流程,就必须对人员和机器的组织架构进行根本性变革,并借鉴杰克借鉴杰克·韦尔奇(Jack Welch)于1990年在通用电气公司(General Electric)首次提出的经典理念的新版本:打破组织内部及外部的孤岛,实现无边界的运营模式。

我们就如何利用技术和数据推动业务变革对近乎全部(99%)高管进行了调查,他们表示,重塑跨职能能力是他们转型计划的重点。75%的高管在培养这些能力时,常常寻求行业外部的先进实践。那些能够以这种方式有效跨越内部与外部界限进行思考和行动的公司,其转型成功的几率可以提高50%。

尽管无边界运营被视为变革者的关键特质之一,但它也是最难实现的。调查显示,只有四分之一的高管认为他们所在的组织已经具备了支持战略重塑的正确运营模式。高达75%的高管坦言,他们的组织在跨部门协作方面效率低下。

那么,企业如何才能开发出一种有效的运营模式,从而进一步打破边界呢?生成式人工智能使这一需求变得更加迫切,同时也为企业提供了打破孤岛状态的终极利器。

生成式人工智能实现无边界组织的四种方式

1. 无边界数据 一切始于数据。大多数公司在这方面投资不足,因此在技术整合方面遭遇重重挑战。如今,生成式人工智能能够通过我们所称的“数字核心”基础架构,帮助连接繁杂多样的数据集和技术。例如,生成式人工智能能够自动整合来自多个遗留系统的结构化和非结构化数据,将繁杂多样的数据格式和模式转换为统一的数据格式和模式。

2. 无边界团队 企业一直在努力打破组织内部的孤岛,但领导层往往将结构变革视为失控。虽然许多信息技术部门已经采用了敏捷原则,但企业内的其他业务部门往往行动迟缓。如今,由企业不同部门组成的跨职能团队网络能够作为自我管理实体运营,并通过利用生成式人工智能系统来指导决策、解决冲突和提供更便捷的跨职能知识访问途径。

3.无边界技能 过去,需要持续教育来支持无边界技能发展,这似乎是一道难以逾越的鸿沟。如今,生成式人工智能能够近乎实时地分析劳动力技能,并识别出差距所在。学习已经能够融入日常工作流程中,人工智能会根据当前项目需求和长期职业发展目标推荐持续发展机会。平台而非教室能够提供完全个性化的培训模块。

4.无边界代理能力 代理架构是创建无边界组织的下一个重大飞跃。人工智能代理作为自主系统能够感知环境、理解意图和采取行动以实现目标,且仅需最少的人工干预。这些代理能够与人类和其他代理协同工作,处理复杂任务,并为用户提供全面的建议和洞察。与专注于单一任务的传统自动化不同,代理架构重塑了跨部门的全工作流程。例如,在贷款审批流程中,一个代理负责评估信用状况,另一个代理检测欺诈行为,第三个则负责客户沟通,所有这些代理都能与监督流程的员工实现无缝协作。

生成式人工智能具有重塑整个企业绩效的潜力,这是以往任何技术都无法比拟的。它能够助力企业真正实现无边界,并以全新的模式运营。在安全数据和灵活自主的团队基础上,辅以代理架构,生成式人工智能使得我们能够跨越生态系统和行业的界限,以前所未有的方式与机器实现大规模协同作业。(财富中文网)

本评论由《财富》分析、《财富》人工智能头脑风暴大会和《财富》聚焦人工智能的赞助商埃森哲(Accenture)提供。杰克·阿扎古里(Jack Azagury)担任埃森哲咨询集团首席执行官。

译者:中慧言-王芳

全行业对生成式人工智能技术的热情高涨,掀起了试验浪潮。众多企业在其销售、市场营销、客户服务以及信息技术等领域的既有流程中嵌入独立用例。仅有少数企业能够利用生成式人工智能重塑整个流程,并且基于精确且切实可行的商业案例来指导其人工智能投资。

只有当领导者着手运用人工智能从根本上重塑端到端工作流程时,才能充分发挥生成式人工智能的优势。重新评估企业整体流程旨在实现三大目标:1)打造无缝的终端用户体验;2)弥合生产力差距,尤其是职能或部门之间的差距;3)根据业务成果更精准地追踪价值。

已有部分公司先行一步,走在了前列。以一家保险公司为例,它正着手全面重塑其承销流程。该公司将生成式人工智能融入流程的每一个环节,同时并未忽视承销人员的经验。通过将这一技术整合至整个价值链中,该公司极大地提升了服务客户的效率,进而吸引了更多客户,实现了收入的两位数增长。

无边界的价值

成功的数字化转型绝非单纯技术层面的革新,它要求企业必须重塑工作方式,并确保从高管到一线员工的所有成员都能深度参与到转型中。

要构建生成式人工智能驱动的端到端工作流程,就必须对人员和机器的组织架构进行根本性变革,并借鉴杰克借鉴杰克·韦尔奇(Jack Welch)于1990年在通用电气公司(General Electric)首次提出的经典理念的新版本:打破组织内部及外部的孤岛,实现无边界的运营模式。

我们就如何利用技术和数据推动业务变革对近乎全部(99%)高管进行了调查,他们表示,重塑跨职能能力是他们转型计划的重点。75%的高管在培养这些能力时,常常寻求行业外部的先进实践。那些能够以这种方式有效跨越内部与外部界限进行思考和行动的公司,其转型成功的几率可以提高50%。

尽管无边界运营被视为变革者的关键特质之一,但它也是最难实现的。调查显示,只有四分之一的高管认为他们所在的组织已经具备了支持战略重塑的正确运营模式。高达75%的高管坦言,他们的组织在跨部门协作方面效率低下。

那么,企业如何才能开发出一种有效的运营模式,从而进一步打破边界呢?生成式人工智能使这一需求变得更加迫切,同时也为企业提供了打破孤岛状态的终极利器。

生成式人工智能实现无边界组织的四种方式

1. 无边界数据 一切始于数据。大多数公司在这方面投资不足,因此在技术整合方面遭遇重重挑战。如今,生成式人工智能能够通过我们所称的“数字核心”基础架构,帮助连接繁杂多样的数据集和技术。例如,生成式人工智能能够自动整合来自多个遗留系统的结构化和非结构化数据,将繁杂多样的数据格式和模式转换为统一的数据格式和模式。

2. 无边界团队 企业一直在努力打破组织内部的孤岛,但领导层往往将结构变革视为失控。虽然许多信息技术部门已经采用了敏捷原则,但企业内的其他业务部门往往行动迟缓。如今,由企业不同部门组成的跨职能团队网络能够作为自我管理实体运营,并通过利用生成式人工智能系统来指导决策、解决冲突和提供更便捷的跨职能知识访问途径。

3.无边界技能 过去,需要持续教育来支持无边界技能发展,这似乎是一道难以逾越的鸿沟。如今,生成式人工智能能够近乎实时地分析劳动力技能,并识别出差距所在。学习已经能够融入日常工作流程中,人工智能会根据当前项目需求和长期职业发展目标推荐持续发展机会。平台而非教室能够提供完全个性化的培训模块。

4.无边界代理能力 代理架构是创建无边界组织的下一个重大飞跃。人工智能代理作为自主系统能够感知环境、理解意图和采取行动以实现目标,且仅需最少的人工干预。这些代理能够与人类和其他代理协同工作,处理复杂任务,并为用户提供全面的建议和洞察。与专注于单一任务的传统自动化不同,代理架构重塑了跨部门的全工作流程。例如,在贷款审批流程中,一个代理负责评估信用状况,另一个代理检测欺诈行为,第三个则负责客户沟通,所有这些代理都能与监督流程的员工实现无缝协作。

生成式人工智能具有重塑整个企业绩效的潜力,这是以往任何技术都无法比拟的。它能够助力企业真正实现无边界,并以全新的模式运营。在安全数据和灵活自主的团队基础上,辅以代理架构,生成式人工智能使得我们能够跨越生态系统和行业的界限,以前所未有的方式与机器实现大规模协同作业。(财富中文网)

本评论由《财富》分析、《财富》人工智能头脑风暴大会和《财富》聚焦人工智能的赞助商埃森哲(Accenture)提供。杰克·阿扎古里(Jack Azagury)担任埃森哲咨询集团首席执行官。

译者:中慧言-王芳

Eugene Mymrin—Getty Images

The broad enthusiasm about generative AI (gen AI) has led to a burst of experimentation. Most companies are implementing standalone use cases on top of existing processes in areas such as sales, marketing, customer service, and IT. Too few are reinventing the entirety of their processes with gen AI and running their gen AI investments with a precise and actionable business case.

The full benefits of gen AI may only be realized if leaders start using it to fundamentally reinvent end-to-end workflows. Re-examining processes across the enterprise serves three purposes: 1) creating a seamless end-user experience; 2) addressing productivity gaps, particularly at the seams between functions or departments; and 3) tracking value more effectively against business outcomes.

There are companies already leading from the front. One insurer, for example, is reinventing the entirety of its underwriting capabilities. For each step, the insurer embedded gen AI, never losing sight of the underwriter’s experience. Taking account of this across the value chain enabled a step-change in how quickly—and therefore how many—customers could be served, driving double-digit revenue increases.

The value of becoming boundaryless

It’s never just about technology when it comes to successful digital transformations. Companies must reinvent how they work and ensure that employees—from the C-suite to the frontline—are fully engaged in the journey.

Building gen AI-enabled end-to-end workflows requires a radical change in how people—and machines—are organized, drawing on a new version of an old idea first coined by Jack Welch at General Electric in 1990: a boundaryless operating model that breaks down silos across the organization and beyond.

Almost all (99%) executives we surveyed about how they are using technology and data to change their business say reinventing cross-functional capabilities is the focus of their transformation programs. And 75% frequently look outside their industry for leading practices when developing those capabilities. Companies that are effective in thinking and acting beyond internal and external boundaries in this way increase their odds of reinvention success by 50%.

While operating boundaryless is a key characteristic of reinventors, it is often the hardest to achieve. Only one in four executives are confident their organizations have the right operating model to support their reinvention strategy, with 75% saying their organizations are ineffective in working across silos.

How then can companies develop an effective operating model that pushes the boundaries even further? While gen AI has made this need more urgent, it can also give organizations the tools to finally dismantle their silos.

Four ways gen AI enables a boundaryless organization

1. Boundaryless data It all starts with data. Most companies have underinvested in this area and thus experienced challenges with technology integration. Now, gen AI can help connect what was a disparate collection of data sets and technologies through a foundation we call a “digital core.” For example, gen AI can automatically integrate data—both structured and unstructured—from multiple legacy systems, translating different data formats and schemas into a unified one.

2. Boundaryless teams Companies have struggled to break down organizational silos, with leadership often equating a loss of structure with a loss of control. While many IT departments embrace agile principles, the business side of enterprises often lags behind. Now, networks of cross-functional teams from across the enterprise can operate as self-managing entities, empowered by gen AI systems that guide decision-making, resolve conflicts, and provide easier access to cross-functional knowledge.

3. Boundaryless skills Before, the need for ongoing education to support boundaryless skill development seemed too overwhelming to overcome. Now, gen AI can analyze workforce skills and identify gaps in near real-time. Learning can be integrated into daily workflows, with AI recommending continuous development opportunities that align with immediate project needs and long-term career development goals. And platforms—rather than classrooms—can deliver personalized training modules that are fully individualized.

4. Boundaryless agentic capabilities Agentic architecture represents the next leap in creating a boundaryless organization. AI agents are autonomous systems that perceive their environment, understand intent, and take action to achieve goals with minimal human intervention. These agents can collaborate with humans and other agents to solve complex tasks, providing users with comprehensive recommendations and insights. Unlike traditional automation, which focuses on individual tasks, agentic architecture reinvents entire workflows that span departments. For example, in a loan approval process, one agent assesses creditworthiness, another detects fraud, and a third manages customer communication, all working seamlessly together with employees who oversee the process.

Gen AI has the potential to redefine performance across the enterprise like no technology before. It can enable organizations to become truly boundaryless and operate in a radically different way. Built on secured data, agile, autonomous teams, and augmented by agentic architectures, gen AI will allow us to collaborate at scale with machines, across ecosystems and industries, in ways we haven’t seen before.

This commentary is from Accenture, a sponsor of Fortune Analytics, Fortune Brainstorm AI and Fortune Eye on AI. Jack Azagury is group chief executive–consulting at Accenture.

财富中文网所刊载内容之知识产权为财富媒体知识产权有限公司及/或相关权利人专属所有或持有。未经许可,禁止进行转载、摘编、复制及建立镜像等任何使用。
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