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Find new opportunities with the Business Reality Check

To move forward in today’s business environment, you need to first know the real barriers. From finding and maintaining the right talent to ensuring the security of customer data, business leaders have to look at many potential barriers, but in some cases, a different focus may unlock opportunity.

How a conservative thinks about climate change: An insider’s account

To understand how conservative leaders think about climate change, I had an intriguing conversation with John Gummer, or perhaps better known as Lord Deben of the UK's House of Lords. Lord Deben is a blue-blooded conservative from the beginning. As he says it, “I believe firmly in the ability of the marketplace to solve most problems, given the proper foundation in economic and scientific truth. And we have this obligation to our children and subsequent generations.”

Making the Most of Machine Learning: How will AI affect business process outsourcing?

Watch our video above to learn how AI will affect business processing and oursourcing from:

-Stanton Jones, Director and Principle Analyst, ISG

Making the Most of Machine Learning: How will AI change organisational culture and structure?

Watch our video above to learn how AI will change organisational culture and structure from:

-Stanton Jones, Director and Principle Analyst, ISG
-Suhir Jha, Head of Product Management Strategy, Infosys 
-Cliff Justice, Principle, Innovation & Enterprise Solutions, KPMG

Making the Most of Machine Learning: What are the advantages for business?

Watch our video above to learn about the advantages Machine Learning can offer your business from:

-Cliff Justice, Principle, Innovation & Enterprise Solutions, KPMG
-Suhir Jha, Head of Product Management Strategy, Infosys 

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Making the Most of Machine Learning: 5 Lessons from Fast Learners

Research conducted by the Economist Intelligence Unit (EIU) and written in discussion with SAP shows that many organizations are moving ahead now, some aggressively, to integrate ML into their operations. For example, the survey of 360 organizations shows that on average 68% use ML to at least some extent today to enhance their business processes.

Making the Most of Machine Learning: 5 Lessons from Fast Learners

ML is not just a technology; it is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models—think Amazon, Uber, and Airbnb. Fast Learner organizations get that. 

Fewer Fast Learners than other organizations suffer from a lack of strategic clarity about ML. And fewer are plagued by organizational resistance to change. The reason may be that ML is viewed as more than a tactical tool for simply automating away costs and people. 

驕傲與偏見:變革的動力

2016年,經濟學人集團發起了“驕傲與偏 見:商業和經濟中男同性戀、女同性戀、雙性戀和跨性別者(LGBT)的多元化和包容性問題”計劃的第一階段。作為計劃的一部分,經濟學人智庫(The Economist Intelligence Unit/The EIU)開展了一項研究, 基於廣泛的全球調查和深入的案頭研究探討 LGBT人群在工作場所的地位。現在,在計劃 開展的第二年,這項研究涵蓋了最能為工作 場所的LGBT人群推動積極變革的群體:公司領導、年輕人和女性。 

為了找出這些變革的動力,我們首先要了解企業中存在的偏見程度。儘管有一些正面的發現,例如支持一般意義上的LGBT權利的高管人員所佔比例很高,但對LGBT的敵意仍然非常普遍;每十位高管中就有一位意識到辦公場所存在對LGBT人群的某種歧視。LGBT群體通常會在工作中隱藏自己的身份,這種低調致使人們認為這並不是一個需要係統化解決的問題,專門製定計劃來不斷推進問題解決的動力少之又少。 

驕傲與偏見:關於工作場所中LGBT包容性的態度和意見

為了揭示當前職場中對LGBT人群的看法和態度,經濟學人智庫(The Economist Intelligence Unit/EIU)於2015年10月到11月 期間針對全球1,021名意見領袖在網上進行了「驕傲與偏見」基準調查。從他們的觀點中,我們深刻了解到了在為LGBT群體權利謀 求更多平等權利時需解決的問題。 

大體上,那些調查對像看上去並沒有表現出 對LGBT人群的歧視態度。在很多時候,基於性取向和性別認同(SOGI)多樣性的進步根本還沒有成為熱門話題。這要是由於LGBT一直被視為「隱性」的少數群體。過去有研究表明,很多LGBT人士在某種程度上都會在工作中隱瞞自己的性取向,在美國這一比例高達53%。根深蒂固的規範阻止員工談論這個話題,迫使他們不得不隱瞞身份;而這方面的空白反過來又塑造了規範,迫使更多的人隱藏自己的秘密。在北美和西歐以外的寬容度不太明顯的社會中,未出櫃員工的比例很可能要比這高得多。 

骄傲与偏见:变革的动力

2016年,经济学人集团发起了“骄傲与偏 见:商业和经济中男同性恋、女同性恋、 双性恋和跨性别者(LGBT)的多元化和 包容性问题”计划的第一阶段。作为计划的一部分,经济学人智库(The Economist Intelligence Unit/The EIU)开展了一项研究,基于广泛的全球调查和深入的案头研究探讨LGBT人群在工作场所的地位。现在, 在计划开展的第二年,这项研究涵盖了最能 为工作场所的LGBT人群推动积极变革的群 体:公司领导、年轻人和女性。 

为了找出这些变革的动力,我们首先要了解企业中存在的偏见程度。尽管有一些正面的发现,例如支持一般意义上的LGBT权利的高管人员所占比例很高,但对LGBT的敌意仍然非常普遍;每十位高管中就有一位意识到办公场所存在对LGBT人群的某种歧视。LGBT 群体通常会在工作中隐藏自己的身份,这种低调致使人们认为这并不是一个需要系统化解决的问题,专门制定计划来不断推进问题解决的动力少之又少。 

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