如果你旁聽(tīng)任何企業(yè)的在線會(huì)議,無(wú)論是零售、電信還是金融行業(yè),你都很有可能聽(tīng)到:“我們要成為一家科技公司?!钡@意味著什么?
首先,你要清楚這不意味著以下這些事情:正如金錢(qián)買(mǎi)不到幸福一樣,這并不能把一家公司變成精通技術(shù)的明星企業(yè)。投資是必要的,但遠(yuǎn)遠(yuǎn)不夠:并非所有的技術(shù)項(xiàng)目都有回報(bào)。成為技術(shù)的早期采用者也沒(méi)有多大意義;事實(shí)上,如果投資僅限于科技領(lǐng)域,這可能會(huì)成為一個(gè)資金坑。
成為一家真正的科技公司意味著以提高利潤(rùn)和績(jī)效的方式部署技術(shù)。為了實(shí)現(xiàn)這一點(diǎn),組織通常需要搭建自己的技術(shù),而不只是做出正確的購(gòu)買(mǎi)決策。在競(jìng)爭(zhēng)優(yōu)勢(shì)受到威脅的領(lǐng)域,技術(shù)的最大好處在于能夠幫你打造獨(dú)特的能力。這可能意味著從頭開(kāi)始構(gòu)建某些東西,或者意味著把不同的組成部分整合起來(lái),形成真正能幫助到公司的特別的解決方案。這是麥肯錫近十年來(lái)研究了數(shù)百家真正的數(shù)字領(lǐng)袖企業(yè)后得出的結(jié)論。以下這個(gè)統(tǒng)計(jì)數(shù)據(jù)很有說(shuō)服力:70%的數(shù)字領(lǐng)袖企業(yè)推出了自己特有的軟件來(lái)推動(dòng)運(yùn)營(yíng)。
要想真正成為一家科技公司,還有四大原則尤為重要。
科技公司的四大配方
首先,也是最重要的,業(yè)務(wù)和技術(shù)團(tuán)隊(duì)需要很好地合作,將技術(shù)嵌入公司運(yùn)營(yíng)和文化中。現(xiàn)代數(shù)字/人工智能文化與20世紀(jì)的信息技術(shù)(IT)文化之間存在很大差異。這種差異可以用一個(gè)詞來(lái)概括:“需求”。
在IT文化中,業(yè)務(wù)團(tuán)隊(duì)將需求提交給技術(shù)團(tuán)隊(duì);業(yè)務(wù)和技術(shù)都很重要,但各自獨(dú)立運(yùn)作,技術(shù)人員被視為支持者。而在成功的數(shù)字/人工智能公司中,業(yè)務(wù)和技術(shù)團(tuán)隊(duì)被視為同等重要。大家不會(huì)為了一次性的項(xiàng)目來(lái)回傳遞需求文檔,而是持續(xù)地共同解決問(wèn)題。在我們2023年出版的《重新布線:麥肯錫在數(shù)字和人工智能時(shí)代的競(jìng)爭(zhēng)指南》(Rewired: The McKinsey guide to outcompeting in the age of digital and AI)一書(shū)中,我和我的合著者發(fā)現(xiàn),在我們基準(zhǔn)測(cè)試的50家銀行中,只有25%的銀行將數(shù)字投資轉(zhuǎn)化為重大價(jià)值。讓這些銀行與眾不同的不是他們花了多少錢(qián),也不在于他們選擇了何種技術(shù)架構(gòu),而在于業(yè)務(wù)和技術(shù)團(tuán)隊(duì)之間的緊密合作。
第二,速度很重要。正在成為科技公司的企業(yè)迭代速度越來(lái)越快。他們?cè)趲字軆?nèi)完成產(chǎn)品發(fā)布和更新周期,而不是花費(fèi)幾個(gè)月。他們以敏捷的短沖刺周期工作。為了實(shí)現(xiàn)這個(gè)目標(biāo),他們不只從硅谷招聘了很酷的年輕人和改變著裝要求,還非常重新培訓(xùn)和提高員工技能,以創(chuàng)造持久的影響力,通過(guò)為數(shù)字人才鋪設(shè)技術(shù)職業(yè)階梯,使技術(shù)人員可以向其他技術(shù)人員學(xué)習(xí)。至關(guān)重要的是,他們對(duì)外包的依賴程度較低。他們的技術(shù)主管是實(shí)干家,而不是供應(yīng)商經(jīng)理。
第三,科技公司積極率先采用、并規(guī)?;瘧?yīng)用新技術(shù)。他們深入研究了如何轉(zhuǎn)變業(yè)務(wù)領(lǐng)域。認(rèn)真思考哪些激勵(lì)措施能鼓勵(lì)業(yè)務(wù)團(tuán)隊(duì)共同開(kāi)展技術(shù)項(xiàng)目。如果公司以正確的心態(tài)對(duì)待,他們可以在最傳統(tǒng)的行業(yè)和一些最不顯眼的地方打造出優(yōu)異的技術(shù)團(tuán)隊(duì)。
最后,從高層至下的領(lǐng)導(dǎo)力至關(guān)重要。首席執(zhí)行官們不僅需要了解技術(shù)能如何重塑他們的業(yè)務(wù),還要了解如何改變公司,以便更好地利用技術(shù)。他們要抵制“一次結(jié)束所有任務(wù)”的誘惑,專注于最重要的領(lǐng)域。要想像一家真正的科技公司那樣運(yùn)作,領(lǐng)導(dǎo)者就要去提出難以回答的問(wèn)題:哪些業(yè)務(wù)領(lǐng)域最適合技術(shù)轉(zhuǎn)型?公司如何吸引所需的人才?技術(shù)人才路線圖是否與技術(shù)擴(kuò)展路線圖一樣詳細(xì)?有多少高層領(lǐng)導(dǎo)自認(rèn)為有技術(shù)能力?公司如何利用其數(shù)據(jù)創(chuàng)造競(jìng)爭(zhēng)優(yōu)勢(shì)?
Gen AI帶來(lái)新的壓力
隨著生成式人工智能(gen-AI)出現(xiàn),成為一家科技公司變得更加緊迫和復(fù)雜。生成式人工智能之所以如此困難,是因?yàn)樗雌饋?lái)如此簡(jiǎn)單。幾乎任何人都能借助人工智能成為一個(gè)優(yōu)秀飛行員,但把這件事變成一個(gè)強(qiáng)大的、可重復(fù)的、安全的和可擴(kuò)展的業(yè)務(wù)要困難得多。
大多數(shù)公司都明白這一點(diǎn)。超過(guò)半數(shù)的人表示,他們計(jì)劃通過(guò)提高技能、重新培訓(xùn)和重新部署人才,在內(nèi)部打造新一代人工智能能力。2023年,盡管科技投資整體下降,但對(duì)人工智能的支出增長(zhǎng)了七倍。然而在今年早些時(shí)候針對(duì)近900家公司的一項(xiàng)調(diào)查中,只有不到5%的公司表示,人工智能對(duì)其組織息稅前利潤(rùn)的貢獻(xiàn)超過(guò)10%。
雖然第二代人工智能是全新而且令人興奮的,但我們不能忘掉早期的轉(zhuǎn)型教訓(xùn):采用新技術(shù)這件事本身并不能創(chuàng)造價(jià)值。競(jìng)爭(zhēng)優(yōu)勢(shì)來(lái)自于建立獨(dú)特的組織能力,使公司能夠大規(guī)模創(chuàng)新、部署和改進(jìn)技術(shù)解決方案。僅僅采用現(xiàn)成的人工智能工具可能還不夠,因?yàn)楫吘垢?jìng)爭(zhēng)對(duì)手也可以這樣做。
簡(jiǎn)而言之,真正的技術(shù)領(lǐng)袖打造自己的專業(yè)知識(shí),并在這個(gè)過(guò)程中確保公司會(huì)進(jìn)行技術(shù)投資。
這篇評(píng)論文章來(lái)自財(cái)富全球論壇知識(shí)合作伙伴之一——麥肯錫公司。羅德尼·澤梅爾(Rodney Zemmel)是麥肯錫高級(jí)合伙人。(財(cái)富中文網(wǎng))
譯者:樂(lè)云起
如果你旁聽(tīng)任何企業(yè)的在線會(huì)議,無(wú)論是零售、電信還是金融行業(yè),你都很有可能聽(tīng)到:“我們要成為一家科技公司?!钡@意味著什么?
首先,你要清楚這不意味著以下這些事情:正如金錢(qián)買(mǎi)不到幸福一樣,這并不能把一家公司變成精通技術(shù)的明星企業(yè)。投資是必要的,但遠(yuǎn)遠(yuǎn)不夠:并非所有的技術(shù)項(xiàng)目都有回報(bào)。成為技術(shù)的早期采用者也沒(méi)有多大意義;事實(shí)上,如果投資僅限于科技領(lǐng)域,這可能會(huì)成為一個(gè)資金坑。
成為一家真正的科技公司意味著以提高利潤(rùn)和績(jī)效的方式部署技術(shù)。為了實(shí)現(xiàn)這一點(diǎn),組織通常需要搭建自己的技術(shù),而不只是做出正確的購(gòu)買(mǎi)決策。在競(jìng)爭(zhēng)優(yōu)勢(shì)受到威脅的領(lǐng)域,技術(shù)的最大好處在于能夠幫你打造獨(dú)特的能力。這可能意味著從頭開(kāi)始構(gòu)建某些東西,或者意味著把不同的組成部分整合起來(lái),形成真正能幫助到公司的特別的解決方案。這是麥肯錫近十年來(lái)研究了數(shù)百家真正的數(shù)字領(lǐng)袖企業(yè)后得出的結(jié)論。以下這個(gè)統(tǒng)計(jì)數(shù)據(jù)很有說(shuō)服力:70%的數(shù)字領(lǐng)袖企業(yè)推出了自己特有的軟件來(lái)推動(dòng)運(yùn)營(yíng)。
要想真正成為一家科技公司,還有四大原則尤為重要。
科技公司的四大配方
首先,也是最重要的,業(yè)務(wù)和技術(shù)團(tuán)隊(duì)需要很好地合作,將技術(shù)嵌入公司運(yùn)營(yíng)和文化中。現(xiàn)代數(shù)字/人工智能文化與20世紀(jì)的信息技術(shù)(IT)文化之間存在很大差異。這種差異可以用一個(gè)詞來(lái)概括:“需求”。
在IT文化中,業(yè)務(wù)團(tuán)隊(duì)將需求提交給技術(shù)團(tuán)隊(duì);業(yè)務(wù)和技術(shù)都很重要,但各自獨(dú)立運(yùn)作,技術(shù)人員被視為支持者。而在成功的數(shù)字/人工智能公司中,業(yè)務(wù)和技術(shù)團(tuán)隊(duì)被視為同等重要。大家不會(huì)為了一次性的項(xiàng)目來(lái)回傳遞需求文檔,而是持續(xù)地共同解決問(wèn)題。在我們2023年出版的《重新布線:麥肯錫在數(shù)字和人工智能時(shí)代的競(jìng)爭(zhēng)指南》(Rewired: The McKinsey guide to outcompeting in the age of digital and AI)一書(shū)中,我和我的合著者發(fā)現(xiàn),在我們基準(zhǔn)測(cè)試的50家銀行中,只有25%的銀行將數(shù)字投資轉(zhuǎn)化為重大價(jià)值。讓這些銀行與眾不同的不是他們花了多少錢(qián),也不在于他們選擇了何種技術(shù)架構(gòu),而在于業(yè)務(wù)和技術(shù)團(tuán)隊(duì)之間的緊密合作。
第二,速度很重要。正在成為科技公司的企業(yè)迭代速度越來(lái)越快。他們?cè)趲字軆?nèi)完成產(chǎn)品發(fā)布和更新周期,而不是花費(fèi)幾個(gè)月。他們以敏捷的短沖刺周期工作。為了實(shí)現(xiàn)這個(gè)目標(biāo),他們不只從硅谷招聘了很酷的年輕人和改變著裝要求,還非常重新培訓(xùn)和提高員工技能,以創(chuàng)造持久的影響力,通過(guò)為數(shù)字人才鋪設(shè)技術(shù)職業(yè)階梯,使技術(shù)人員可以向其他技術(shù)人員學(xué)習(xí)。至關(guān)重要的是,他們對(duì)外包的依賴程度較低。他們的技術(shù)主管是實(shí)干家,而不是供應(yīng)商經(jīng)理。
第三,科技公司積極率先采用、并規(guī)?;瘧?yīng)用新技術(shù)。他們深入研究了如何轉(zhuǎn)變業(yè)務(wù)領(lǐng)域。認(rèn)真思考哪些激勵(lì)措施能鼓勵(lì)業(yè)務(wù)團(tuán)隊(duì)共同開(kāi)展技術(shù)項(xiàng)目。如果公司以正確的心態(tài)對(duì)待,他們可以在最傳統(tǒng)的行業(yè)和一些最不顯眼的地方打造出優(yōu)異的技術(shù)團(tuán)隊(duì)。
最后,從高層至下的領(lǐng)導(dǎo)力至關(guān)重要。首席執(zhí)行官們不僅需要了解技術(shù)能如何重塑他們的業(yè)務(wù),還要了解如何改變公司,以便更好地利用技術(shù)。他們要抵制“一次結(jié)束所有任務(wù)”的誘惑,專注于最重要的領(lǐng)域。要想像一家真正的科技公司那樣運(yùn)作,領(lǐng)導(dǎo)者就要去提出難以回答的問(wèn)題:哪些業(yè)務(wù)領(lǐng)域最適合技術(shù)轉(zhuǎn)型?公司如何吸引所需的人才?技術(shù)人才路線圖是否與技術(shù)擴(kuò)展路線圖一樣詳細(xì)?有多少高層領(lǐng)導(dǎo)自認(rèn)為有技術(shù)能力?公司如何利用其數(shù)據(jù)創(chuàng)造競(jìng)爭(zhēng)優(yōu)勢(shì)?
Gen AI帶來(lái)新的壓力
隨著生成式人工智能(gen-AI)出現(xiàn),成為一家科技公司變得更加緊迫和復(fù)雜。生成式人工智能之所以如此困難,是因?yàn)樗雌饋?lái)如此簡(jiǎn)單。幾乎任何人都能借助人工智能成為一個(gè)優(yōu)秀飛行員,但把這件事變成一個(gè)強(qiáng)大的、可重復(fù)的、安全的和可擴(kuò)展的業(yè)務(wù)要困難得多。
大多數(shù)公司都明白這一點(diǎn)。超過(guò)半數(shù)的人表示,他們計(jì)劃通過(guò)提高技能、重新培訓(xùn)和重新部署人才,在內(nèi)部打造新一代人工智能能力。2023年,盡管科技投資整體下降,但對(duì)人工智能的支出增長(zhǎng)了七倍。然而在今年早些時(shí)候針對(duì)近900家公司的一項(xiàng)調(diào)查中,只有不到5%的公司表示,人工智能對(duì)其組織息稅前利潤(rùn)的貢獻(xiàn)超過(guò)10%。
雖然第二代人工智能是全新而且令人興奮的,但我們不能忘掉早期的轉(zhuǎn)型教訓(xùn):采用新技術(shù)這件事本身并不能創(chuàng)造價(jià)值。競(jìng)爭(zhēng)優(yōu)勢(shì)來(lái)自于建立獨(dú)特的組織能力,使公司能夠大規(guī)模創(chuàng)新、部署和改進(jìn)技術(shù)解決方案。僅僅采用現(xiàn)成的人工智能工具可能還不夠,因?yàn)楫吘垢?jìng)爭(zhēng)對(duì)手也可以這樣做。
簡(jiǎn)而言之,真正的技術(shù)領(lǐng)袖打造自己的專業(yè)知識(shí),并在這個(gè)過(guò)程中確保公司會(huì)進(jìn)行技術(shù)投資。
這篇評(píng)論文章來(lái)自財(cái)富全球論壇知識(shí)合作伙伴之一——麥肯錫公司。羅德尼·澤梅爾(Rodney Zemmel)是麥肯錫高級(jí)合伙人。(財(cái)富中文網(wǎng))
譯者:樂(lè)云起
Listen in to any corporate conference call, whether the company is in retail or telecoms or finance, and there’s a good chance you will hear: “We need to be a tech company.” But what does that mean?
For a start, here is what it does not mean. Just as money cannot buy happiness, it cannot turn a company into a tech-savvy star. Investment is necessary but far from sufficient: Not all tech initiatives pay off. Nor does being an early adopter mean much; indeed, that can become a money pit if the investments are in tech alone.
Here is what becoming a true tech company does mean: deploying tech in a way that boosts profits and performance. For that to happen, organizations often need to build their own technology, not just make good choices about what to buy. In domains where competitive advantage is at stake, the greatest benefit comes from building unique capabilities. This can mean creating something from scratch or assembling something new from components that work specifically for the company. That is the conclusion of nearly a decade of McKinsey research into hundreds of companies that are true digital leaders. Here is one telling statistic: 70% of the digital leaders created their own software to drive operations.
What else matters in terms of truly becoming a tech company? Four principles stand out.
The 4 ingredients to a tech company
First, and most important, business and technology teams need to work well together, with technology embedded in company operations and culture. There is a big difference between a modern digital/AI culture versus the 20th century information technology (IT) culture. That difference can be summed up in one word: “requirements.”
In an IT culture, the business team hands off requirements to the technology team; both are important but operate separately, and the technologists are seen as support. At successful digital/AI companies, on the other hand, business and technology teams are seen as equal in importance. Rather than passing requirements documents back and forth for one-off projects, they own problems together and on an ongoing basis. In our 2023 book, Rewired: The McKinsey guide to outcompeting in the age of digital and AI, my co-authors and I found that of the 50 banks we benchmarked, only 25% had turned their digital investments into significant value. What set these banks apart was not how much they spent or what technology architecture they chose, but how well business and technology teams worked together.
Second, speed counts. Companies that are on the way to becoming tech companies iterate faster and faster. They complete the product release and update cycle in weeks, rather than months. They work in agile, short-sprint cycles. To make this happen, they don’t just hire cool kids from Silicon Valley and change the dress code. Instead, they reskill and upskill their workforces to create lasting impact, creating technology career ladders for their digital talent, so that technologists can learn from other technologists. Critically, they are less dependent on outsourcing. Their technology executives are doers, not vendor managers.
Third, tech companies adopt and scale well. They dig into how a business domain can be transformed. They think hard about what incentives can encourage business teams to co-own tech initiatives. Companies can build amazing technology teams in the most traditional industries and some of the least obvious locations if they approach it with the right mindset.
Finally, leadership is critical and starts from the top. CEOs need to understand not only how technology could reinvent their business, but how to change their company to harness technology. They resist the “everything all at once” temptation to focus on the most important domains. Determining whether their organization is functioning as a true tech company requires leaders to ask difficult questions. Which business domains are best positioned for technological transformation? How can the company attract the talent it needs? Is the tech talent road map as detailed as the road map for scaling technology? How many senior leaders would self-identify as tech capable? How can the company use its data to create competitive advantage?
Gen AI adds new pressure
With the advent of generative artificial intelligence (gen AI), becoming a tech company is even more urgent—and complicated. What makes gen AI so hard is that it can look so easy. Almost anyone can fire up an impressive looking pilot, but turning that into a robust, repeatable, safe, and scaled business impact is much more difficult.
Most companies understand that. More than half say they plan to build their gen AI capabilities internally, through upskilling, reskilling, and redeploying talent. And spending on gen AI rose sevenfold in 2023, even though tech investment as a whole fell. Nevertheless, in a survey of almost 900 companies done earlier this year, fewer than 5% said that gen AI was contributing more than 10% of their organizations’ EBIT.
While gen AI is new and exciting, the lesson from earlier transformations remains relevant: technology adoption for its own sake doesn’t create value. Competitive advantage comes from building unique organizational capabilities that enable companies to innovate, deploy, and improve technological solutions at scale. Simply adopting off-the-shelf gen AI tools will likely not be enough: after all, the competition can do the same.
In short, true tech leaders build their own expertise, and in doing so, ensure that their tech investments show up on the bottom line.
This commentary is from McKinsey & Company, a Fortune Global Forum Knowledge Partner. Rodney Zemmel is a senior partner.