全行業(yè)對生成式人工智能技術的熱情高漲,掀起了試驗浪潮。眾多企業(yè)在其銷售、市場營銷、客戶服務以及信息技術等領域的既有流程中嵌入獨立用例。僅有少數(shù)企業(yè)能夠利用生成式人工智能重塑整個流程,并且基于精確且切實可行的商業(yè)案例來指導其人工智能投資。
只有當領導者著手運用人工智能從根本上重塑端到端工作流程時,才能充分發(fā)揮生成式人工智能的優(yōu)勢。重新評估企業(yè)整體流程旨在實現(xiàn)三大目標:1)打造無縫的終端用戶體驗;2)彌合生產(chǎn)力差距,尤其是職能或部門之間的差距;3)根據(jù)業(yè)務成果更精準地追蹤價值。
已有部分公司先行一步,走在了前列。以一家保險公司為例,它正著手全面重塑其承銷流程。該公司將生成式人工智能融入流程的每一個環(huán)節(jié),同時并未忽視承銷人員的經(jīng)驗。通過將這一技術整合至整個價值鏈中,該公司極大地提升了服務客戶的效率,進而吸引了更多客戶,實現(xiàn)了收入的兩位數(shù)增長。
無邊界的價值
成功的數(shù)字化轉型絕非單純技術層面的革新,它要求企業(yè)必須重塑工作方式,并確保從高管到一線員工的所有成員都能深度參與到轉型中。
要構建生成式人工智能驅動的端到端工作流程,就必須對人員和機器的組織架構進行根本性變革,并借鑒杰克借鑒杰克·韋爾奇(Jack Welch)于1990年在通用電氣公司(General Electric)首次提出的經(jīng)典理念的新版本:打破組織內部及外部的孤島,實現(xiàn)無邊界的運營模式。
我們就如何利用技術和數(shù)據(jù)推動業(yè)務變革對近乎全部(99%)高管進行了調查,他們表示,重塑跨職能能力是他們轉型計劃的重點。75%的高管在培養(yǎng)這些能力時,常常尋求行業(yè)外部的先進實踐。那些能夠以這種方式有效跨越內部與外部界限進行思考和行動的公司,其轉型成功的幾率可以提高50%。
盡管無邊界運營被視為變革者的關鍵特質之一,但它也是最難實現(xiàn)的。調查顯示,只有四分之一的高管認為他們所在的組織已經(jīng)具備了支持戰(zhàn)略重塑的正確運營模式。高達75%的高管坦言,他們的組織在跨部門協(xié)作方面效率低下。
那么,企業(yè)如何才能開發(fā)出一種有效的運營模式,從而進一步打破邊界呢?生成式人工智能使這一需求變得更加迫切,同時也為企業(yè)提供了打破孤島狀態(tài)的終極利器。
生成式人工智能實現(xiàn)無邊界組織的四種方式
1. 無邊界數(shù)據(jù) 一切始于數(shù)據(jù)。大多數(shù)公司在這方面投資不足,因此在技術整合方面遭遇重重挑戰(zhàn)。如今,生成式人工智能能夠通過我們所稱的“數(shù)字核心”基礎架構,幫助連接繁雜多樣的數(shù)據(jù)集和技術。例如,生成式人工智能能夠自動整合來自多個遺留系統(tǒng)的結構化和非結構化數(shù)據(jù),將繁雜多樣的數(shù)據(jù)格式和模式轉換為統(tǒng)一的數(shù)據(jù)格式和模式。
2. 無邊界團隊 企業(yè)一直在努力打破組織內部的孤島,但領導層往往將結構變革視為失控。雖然許多信息技術部門已經(jīng)采用了敏捷原則,但企業(yè)內的其他業(yè)務部門往往行動遲緩。如今,由企業(yè)不同部門組成的跨職能團隊網(wǎng)絡能夠作為自我管理實體運營,并通過利用生成式人工智能系統(tǒng)來指導決策、解決沖突和提供更便捷的跨職能知識訪問途徑。
3.無邊界技能 過去,需要持續(xù)教育來支持無邊界技能發(fā)展,這似乎是一道難以逾越的鴻溝。如今,生成式人工智能能夠近乎實時地分析勞動力技能,并識別出差距所在。學習已經(jīng)能夠融入日常工作流程中,人工智能會根據(jù)當前項目需求和長期職業(yè)發(fā)展目標推薦持續(xù)發(fā)展機會。平臺而非教室能夠提供完全個性化的培訓模塊。
4.無邊界代理能力 代理架構是創(chuàng)建無邊界組織的下一個重大飛躍。人工智能代理作為自主系統(tǒng)能夠感知環(huán)境、理解意圖和采取行動以實現(xiàn)目標,且僅需最少的人工干預。這些代理能夠與人類和其他代理協(xié)同工作,處理復雜任務,并為用戶提供全面的建議和洞察。與專注于單一任務的傳統(tǒng)自動化不同,代理架構重塑了跨部門的全工作流程。例如,在貸款審批流程中,一個代理負責評估信用狀況,另一個代理檢測欺詐行為,第三個則負責客戶溝通,所有這些代理都能與監(jiān)督流程的員工實現(xiàn)無縫協(xié)作。
生成式人工智能具有重塑整個企業(yè)績效的潛力,這是以往任何技術都無法比擬的。它能夠助力企業(yè)真正實現(xiàn)無邊界,并以全新的模式運營。在安全數(shù)據(jù)和靈活自主的團隊基礎上,輔以代理架構,生成式人工智能使得我們能夠跨越生態(tài)系統(tǒng)和行業(yè)的界限,以前所未有的方式與機器實現(xiàn)大規(guī)模協(xié)同作業(yè)。(財富中文網(wǎng))
本評論由《財富》分析、《財富》人工智能頭腦風暴大會和《財富》聚焦人工智能的贊助商埃森哲(Accenture)提供。杰克·阿扎古里(Jack Azagury)擔任埃森哲咨詢集團首席執(zhí)行官。
譯者:中慧言-王芳
全行業(yè)對生成式人工智能技術的熱情高漲,掀起了試驗浪潮。眾多企業(yè)在其銷售、市場營銷、客戶服務以及信息技術等領域的既有流程中嵌入獨立用例。僅有少數(shù)企業(yè)能夠利用生成式人工智能重塑整個流程,并且基于精確且切實可行的商業(yè)案例來指導其人工智能投資。
只有當領導者著手運用人工智能從根本上重塑端到端工作流程時,才能充分發(fā)揮生成式人工智能的優(yōu)勢。重新評估企業(yè)整體流程旨在實現(xiàn)三大目標:1)打造無縫的終端用戶體驗;2)彌合生產(chǎn)力差距,尤其是職能或部門之間的差距;3)根據(jù)業(yè)務成果更精準地追蹤價值。
已有部分公司先行一步,走在了前列。以一家保險公司為例,它正著手全面重塑其承銷流程。該公司將生成式人工智能融入流程的每一個環(huán)節(jié),同時并未忽視承銷人員的經(jīng)驗。通過將這一技術整合至整個價值鏈中,該公司極大地提升了服務客戶的效率,進而吸引了更多客戶,實現(xiàn)了收入的兩位數(shù)增長。
無邊界的價值
成功的數(shù)字化轉型絕非單純技術層面的革新,它要求企業(yè)必須重塑工作方式,并確保從高管到一線員工的所有成員都能深度參與到轉型中。
要構建生成式人工智能驅動的端到端工作流程,就必須對人員和機器的組織架構進行根本性變革,并借鑒杰克借鑒杰克·韋爾奇(Jack Welch)于1990年在通用電氣公司(General Electric)首次提出的經(jīng)典理念的新版本:打破組織內部及外部的孤島,實現(xiàn)無邊界的運營模式。
我們就如何利用技術和數(shù)據(jù)推動業(yè)務變革對近乎全部(99%)高管進行了調查,他們表示,重塑跨職能能力是他們轉型計劃的重點。75%的高管在培養(yǎng)這些能力時,常常尋求行業(yè)外部的先進實踐。那些能夠以這種方式有效跨越內部與外部界限進行思考和行動的公司,其轉型成功的幾率可以提高50%。
盡管無邊界運營被視為變革者的關鍵特質之一,但它也是最難實現(xiàn)的。調查顯示,只有四分之一的高管認為他們所在的組織已經(jīng)具備了支持戰(zhàn)略重塑的正確運營模式。高達75%的高管坦言,他們的組織在跨部門協(xié)作方面效率低下。
那么,企業(yè)如何才能開發(fā)出一種有效的運營模式,從而進一步打破邊界呢?生成式人工智能使這一需求變得更加迫切,同時也為企業(yè)提供了打破孤島狀態(tài)的終極利器。
生成式人工智能實現(xiàn)無邊界組織的四種方式
1. 無邊界數(shù)據(jù) 一切始于數(shù)據(jù)。大多數(shù)公司在這方面投資不足,因此在技術整合方面遭遇重重挑戰(zhàn)。如今,生成式人工智能能夠通過我們所稱的“數(shù)字核心”基礎架構,幫助連接繁雜多樣的數(shù)據(jù)集和技術。例如,生成式人工智能能夠自動整合來自多個遺留系統(tǒng)的結構化和非結構化數(shù)據(jù),將繁雜多樣的數(shù)據(jù)格式和模式轉換為統(tǒng)一的數(shù)據(jù)格式和模式。
2. 無邊界團隊 企業(yè)一直在努力打破組織內部的孤島,但領導層往往將結構變革視為失控。雖然許多信息技術部門已經(jīng)采用了敏捷原則,但企業(yè)內的其他業(yè)務部門往往行動遲緩。如今,由企業(yè)不同部門組成的跨職能團隊網(wǎng)絡能夠作為自我管理實體運營,并通過利用生成式人工智能系統(tǒng)來指導決策、解決沖突和提供更便捷的跨職能知識訪問途徑。
3.無邊界技能 過去,需要持續(xù)教育來支持無邊界技能發(fā)展,這似乎是一道難以逾越的鴻溝。如今,生成式人工智能能夠近乎實時地分析勞動力技能,并識別出差距所在。學習已經(jīng)能夠融入日常工作流程中,人工智能會根據(jù)當前項目需求和長期職業(yè)發(fā)展目標推薦持續(xù)發(fā)展機會。平臺而非教室能夠提供完全個性化的培訓模塊。
4.無邊界代理能力 代理架構是創(chuàng)建無邊界組織的下一個重大飛躍。人工智能代理作為自主系統(tǒng)能夠感知環(huán)境、理解意圖和采取行動以實現(xiàn)目標,且僅需最少的人工干預。這些代理能夠與人類和其他代理協(xié)同工作,處理復雜任務,并為用戶提供全面的建議和洞察。與專注于單一任務的傳統(tǒng)自動化不同,代理架構重塑了跨部門的全工作流程。例如,在貸款審批流程中,一個代理負責評估信用狀況,另一個代理檢測欺詐行為,第三個則負責客戶溝通,所有這些代理都能與監(jiān)督流程的員工實現(xiàn)無縫協(xié)作。
生成式人工智能具有重塑整個企業(yè)績效的潛力,這是以往任何技術都無法比擬的。它能夠助力企業(yè)真正實現(xiàn)無邊界,并以全新的模式運營。在安全數(shù)據(jù)和靈活自主的團隊基礎上,輔以代理架構,生成式人工智能使得我們能夠跨越生態(tài)系統(tǒng)和行業(yè)的界限,以前所未有的方式與機器實現(xiàn)大規(guī)模協(xié)同作業(yè)。(財富中文網(wǎng))
本評論由《財富》分析、《財富》人工智能頭腦風暴大會和《財富》聚焦人工智能的贊助商埃森哲(Accenture)提供。杰克·阿扎古里(Jack Azagury)擔任埃森哲咨詢集團首席執(zhí)行官。
譯者: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.