德勤：人工智能在工作關系方面可能是一個巨大的未開發機會（英文版）（24頁）.pdf

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1、AI for work relationships may be a great untapped opportunityAI can do more than make work better for humansIt can help make better humans for work.A report by the Deloitte Center for Integrated Research About the Deloitte Center for Integrated ResearchThe Deloitte Center for Integrated Research(CIR

2、)offers rigorously researched and data-driven perspectives on critical issues affecting businesses today.We sit at the center of Deloittes industry and functional expertise,combining the leading insights from across our firm to help leaders confidently compete in todays ever-changing marketplace.To

3、learn more,please visit Strategy,Design,and TransitionDeloittes Organizational Strategy,Design,and Transition services help clients optimize their organizational structure and decision-making.Deloittes organization strategies method addresses several critical subject areas,such as organizational ass

4、essment,organization and role design,decision rights and governance,and workforce transition.Learn moreHuman Capital Deloittes Human Capital services leverage research,analytics,and industry insights to helpdesign and execute critical programs from business-driven HR to innovative talent,leadershipa

5、nd change programs.Learn moreWhat social aspects of work can AI improve?3Challenges confronting the social side of AI and potential solutions 9Getting started 12Endnotes 14Contents2YOURE FRUSTRATED.TWO functional leaders are pulling you into a nasty turf war when you need them to collaborate.Youre w

6、riting a frustrated reply,when a friend stops you.They recommend more appropriate wording,and that you ask the functional leaders to schedule a meeting to discuss conflicting priorities and come up with a solution.You take the recommendation and cool off.You would like to reach out and thank your fr

7、iend and confidante,but you cant because theyre an AI.With the help of current artificial intelligence(AI)technologies,thisand many other social capabilitiesmay already be possible with the tools that many organizations have access to.While 91%of business leaders surveyed in 2022 said they have an e

8、nterprisewide AI strategy,they are typically using AI in the workplace to generate insights,optimize processes,lower costs,improve collaboration across businesses,etc.1 Within the context of these applications,the potential for human-machine collaboration is well-established.2 However,the potential

9、of AI to improve human-to-human relationships among the workforce or with customers and potential recruitswhat we call the social side of workcan often be overlooked.By analyzing interactions and communications and generating personalized,data-driven recommendations,AI can do much more than just pro

10、moting email diplomacy.It could be a powerful tool for the workforce to nurture uniquely human capabilities.AI can help us prepare for key presentations,expand our professional networks,understand the personalities and feelings of customers,promote diversity and inclusion in everyday work,and even d

11、rive innovation and culture change across an organization.Of course,such capabilities come with adoption challenges.Skepticism for this kind of AI can run deep.But a careful,user-centric,opt-in/-out approach can help overcome resistance,and gradually introduce employees to AI.AI for work relationshi

12、ps may be a great untapped opportunity3Source:Deloitte analysis.Deloitte Insights| 1Select AI applications that can support the social side of workAmplifying emotional intelligence through AI simulations,personal upskilling,and networkingLeveraging“informal”networks to drive change management and in

13、novationFostering an inclusive work environmentUnderstanding customers better and providing superior customer serviceRecruiting a diverse workforce and building diverse project teamsWhat social aspects of work can AI improve?BEYOND THE TACTICAL knowledge,expertise,and skills needed to do ones job,th

14、ere are enduring human capabilities that are universally applicable and harder to develop,such as emotional intelligence,teaming,and empathy.3 These capabilities enable workers to build meaningful relationships with customers,leaders,peers,and potential recruits.The value of these human-to-human rel

15、ationships can be foundational and critical to organizational success.4 We surveyed 2,620 business leaders as part of Deloittes State of AI 2022 study.More than two-thirds of leaders noted that their organizations have either deployed or were developing AI applications for natural language processin

16、g(including sentiment detection and text summarization),computer vision,text chatbots,and voice agents.Additionally,less than a third were planning or exploring these technologies.5 Organizations are typically using these technologies to generate insights,optimize processes,lower costs,and improve c

17、ollaboration across businesses.In addition to these applications,AI technologies can analyze human interactions during and after an event to generate personalized,confidential recommendations at an individual and organizational level to help improve human interactions at work.There are multiple AI a

18、pplications for the social side of work(figure 1).AI can do more than make work better for humansIt can help make better humans for work.41.Amplifying emotional intelligence through AI simulations,personal upskilling,and networking Simulations:Affective computing,also known as emotion AI,is a consta

19、ntly evolving application that understands human emotions in response to a situation and makes recommendations accordingly.Its real-world applications span across a few areas of communication.6 For example,before a meeting or presentation,leaders can practice interactions with AI avatars representin

20、g team members.7 Based on the narrative,AI would generate possible arguments,assess persuasiveness,and give feedback to make communication more effective.8 AI simulations can also be helpful when leaders are looking for inputs on early-stage thinking.For complex topics,leaders may first seek inputs

21、from AI,then review with peers and leaders at later stages when the thinking is more developed to save time.Upskilling at scale:Traditionally,coaching has largely been made available to select professionals in an organizationeither high performers or individuals experiencing performance issues that

22、require direct interaction and intervention.This leaves out much of the workforce.AI can enable learning experiences tailored to an individuals emotional intelligence needs and drive those learning experiences at scale.9 In one example,a coaching network uses AI and machine learning algorithms to ma

23、tch employees with coaches focused on different skills categories related to inclusive leadership,persuasive communication,etc.10 Networking:AI-enabled applications can connect professionals with other people who have similar interests and help them grow their professional networks within and outsid

24、e of their organization.Users provide information about their professional background,industry or sector specialization,areas of interest,etc.,to a model that can generate matches periodically,send introductory emails,and set up meetings.These interactions can drive virtual watercooler or coffee bar

25、 conversations in the hybrid work environment.On similar lines,AI-enabled platforms can also facilitate experience/expertise-based networking outside of the organization.11 2.Understanding customers better and providing superior customer serviceContact centers have been early adopters of automated v

26、oice systems to address higher call volumes,with labor shortages and lower IT budgets.12 However,the endless loops of automated responses can often lead to customer alienation,making this a much less popular and often-derided use of automation.AI can not only drive automation,but it can also make ea

27、ch customer touchpoint meaningfulwhile reducing the need for 24/7 human involvement.By analyzing data from past conversations,AI can help contact center representatives with insights to prepare a baseline customer profile before an interaction,enable them to perform well during the interaction,and h

28、elp them update the customer profile based on the interaction to generate recommendations for future use.Getting to know your customers before meeting them.Past customer interactions are a gold mine for deriving customer insights.AI tools can ingest basic customer data and previous conversations to

29、create their profile based on their communication style,personal priorities,responses in previous conversations,etc.Contact center representatives can review this profile before engaging with a customer and be better prepared to have a seamless conversation.13 AI can also identify the most appropria

30、te service representative for a customer based on similarities in personalities and communication styles.14In one example,Vodafone Italy combined customers profile data with a customized language generator algorithm,and developed personalized AI for work relationships may be a great untapped opportu

31、nity5promotional messages for each customer segment for plan upgrades,5G launch,etc.The effort resulted in increasing customer subscriptions by 40%in 2020.15Secondly,engaging effectively during a customer interaction.While engaging with customers,virtual agents or chatbots can conduct a real-time se

32、ntiment analysis of the conversation.The bot can then adjust its response based on the results of the sentiment analysis;for instance,if a customer interaction has a positive sentiment,the bot can pitch for cross-selling or upselling.And if the customer interaction has a negative sentiment,the bot c

33、an quickly transfer the call to a contact center representative with notes about the interaction,enabling the representative to take it forward.16Even when representatives are interacting with customers,AI programs can monitor the interaction in real-time and provide suggestions to the representativ

34、es(through text prompts)on how to respond.17 Humana Pharmacy uses voice analytics in its call centers.18 Voice signals can be analyzed to determine customer engagement and provide real-time feedback to contact center employees during the calls,allowing them to amend their approach accordingly.19 The

35、 conversational AI solution should be sophisticated enough and be able to combine language semantics with voice tonality to understand the customers emotion correctly.For instance,a user(in a stable and flat voice)says,“Im really surprised that you still havent managed to provide a resolution.”While

36、 the tone doesnt show anger or frustration,some words,such as really surprised or havent managed,when spoken with longer-than-normal pauses,could indicate a negative emotion.The application should be able to pick up these nuances to generate effective advice for customer service representatives.As c

37、onversational AI continues to learn and improve over time,benefits can be significant.One study involving 445 businesses across industries using AI solutions for contact center service reported 2.2 times higher first-call resolution rates and 4.5 times greater service-level agreement(SLA)attainment

38、rates,compared to non-AI users.20Finally,deriving insights after a customer interaction for future use.AI applications can analyze interactions with customers to update customer profiles,enable service professionals to improve their pitches,and also reassess the pairing between customer service repr

39、esentatives and customers based on similarities in personalities and communication styles for future interactions.As the customer service use case shows,AI has the ability to automate processes(that have traditionally been done by humans)with a“human touch”and free up time for humans to take up high

40、er-quality work.This use case also illustrates that worker data can be used to not only draw meaningful insights but also create a better work experience and,as such,can be mutually beneficial for the organization and the workforce.3.Recruiting a diverse workforce and building diverse project teamsA

41、I and data-based algorithms can provide visibility into whether the organization is truly diverse.By analyzing the profile of the workforce,AI can assess diversity(race,gender,ethnicity,etc.)and monitor it in real-time across functions,career levels,and other criteria.AI can also help attract divers

42、e new talent in many ways,including:Blind hiring.One of the earliest results of blind hiring can be observed in orchestras:Female musicians in symphony orchestras in the United States comprised typically less than 5%of all performers in the 1970s.Gradually,AI can do more than make work better for hu

43、mansIt can help make better humans for work.6orchestras tweaked their audition process by introducing“blind auditions”adding partitions to shield the identity of those auditioning.The percentage of female musicians then increased from 5%in the 1970s to 2540%in the early 2000.21 In the workplace,AI c

44、an enable blind hiring by stripping away identifiable attributes from resumes that are typically not related to candidates skills,expertise,or experience.By removing attributes such as name,age,headshot,gender,race,or ethnicity from resumes before they reach hiring managers,AI can reduce human biase

45、s and help drive a more equitable recruitment process.Soft-skill assessments.Some companies use evaluative AI screeners(with neuroscience-based games embedded)to better understand candidates hard-to-assess competencies,such as risk-taking,perseverance,and emotional intelligence,along with traditiona

46、l traits such as logical reasoning and quantitative and verbal abilities.22 Interview panel design.After the initial screening,AI can also help hiring managers build diverse interview panels to minimize biases.Diverse team building from internal talent.Referred to as the“internal talent marketplace,

47、”AI can match peoples skills against project needs to build effective teams,while being intentional about bringing diverse professionals from outside of the core project team.23 In one example,IBM deployed its Opportunity Team Builder AI solution to identify the best candidates to join a sales team

48、based on their social skills and predict the impact each member would have on the sales teams overall performance.As team members are added to the team,the tool continuously calculates the gaps in skills needed until an optimal team is formed.24 Project teams may be more amenable to work assigned by

49、 AI compared to that assigned by their managers in some cases.Team members are likely to be more trusting toward AI when they are looking for quick and unbiased information,logic-driven solutions,confidential responses without the fear of scrutiny or retaliation,etc.25 By integrating AI in day-to-da

50、y workflows and allocations,managers can improve trust with their team members.4.Fostering an inclusive work environmentDiversity without inclusion is insufficient.AI can enable the workforce to drive respectful conversations and inclusive workflows that are criticalespecially in hybrid and remote w

51、ork environments.AI can drive inclusion and accessibility in meetings,including:Microaggression coaching.AI can detect microaggressions by analyzing written or verbal communications,suggest alternatives and provide feedback confidentially to the communicator to improve their sensitivity over time.Wh

52、en somebodys tone becomes disrespectful,a sophisticated AI application wouldnt scold or criticize them(a user may otherwise dismiss the coaching).Instead,the application would subtly suggest that their tone may have shifted toward the negative and nudge them to change their tonality.26 Encouraging t

53、urn-taking.Based on simple voice detection,AI can identify individuals and groups that take over a conversation,leaving no space for others to contribute to the discussion.27 Such in-the-moment analysis is helpful especially in hybrid/virtual settings to ensure everyone can speak and contribute to a

54、 discussion.Receiving such recommendations could be uncomfortable for many people and they could simply reject them.Thus,its imperative that organizations build trust in AI systems and help the workforce appreciate the role of AI to enhance their emotional intelligence through fair and impartial fee

55、dback.Improving accessibility.AI can remove language barriers and improve accessibility in meetings and discussions.Meeting notes can be AI for work relationships may be a great untapped opportunity7immediately transcribed into multiple languages to improve participation from global teams.Accessibil

56、ity of content can be improved by providing lip-reading recognition for people with hearing impairment,facial or image recognition for people with visual impairment,and text summarization for professionals who arent comfortable with digesting large bodies of text in one sitting.5.Leveraging“informal

57、”networks to drive change management and innovationLeaders typically use a formal hierarchy and top-down communication to disseminate culture and values within the organization.However,they often face challenges in gaining acceptance through such channels.Not everyone who represents a box on an orga

58、nizational chart is more influential than those that flow below them.The truth is that influencers can fall all over an organizational chart,but we tend to prioritize hierarchy over influence.In reality though,workforce behaviors and culture change happen in the organizational network(figure 2).28By

59、 using technologies such as text mining,natural language processing,etc.,organizations can analyze who is connected to whom,the nature of their interactions and relationships,and informal influencers within the organization in a systematic and scientific way.Data-driven analysis of responses to surv

60、eys,focus group discussions,interviews,etc.,can highlight reasons for workforce hesitancy toward proposed changes and the degree of resistance.It can determine who is“on the fence”versus opposed to change.When leaders understand the reasons and degree of hesitance,theyre often better equipped to for

61、mulate potential actions to address that hesitance,and drive acceptance and change management with the help of influencers.29Informal influencers can also be helpful in driving innovation within the organization as they can mobilize individuals and groups to facilitate the flow of ideas and informat

62、ion within the organization.By analyzing the connections between employees,General Motors identified“influencers”from different teams and functions who could drive innovative ideas for product design and customer service.30 Then,they created an environment to develop the ideas by onboarding addition

63、al people interested in building the solutions and driving wider adoption across the organization.AI can do more than make work better for humansIt can help make better humans for work.8Organizational chartFIGURE 2Organizational charts are based on formal hierarchies and may not represent real-time

64、behaviors,while organizational networks represent an organizations actual interactions and relationshipsOrganizational networkSource:Deloitte analysis.Deloitte Insights| for work relationships may be a great untapped opportunity9Challenges confronting the social side of AI and potential solutionsAPP

65、LICATIONS OF SOCIAL AI will likely face many of the same challenges as other AI applicationsconcerns about lack of explainability in AI decisions and risks associated with data privacy,trust,reliability,etc.31 Source:Deloitte,State of AI:5th edition study,October 2022.FIGURE 3Ethical risks of AI tha

66、t organizations are most concerned about44%42%42%41%41%38%34%6%Lack of explainability and transparency in AI-derived decisionsInability to ensure data privacy or appropriately manage consentSafety concerns about AI-powered systemsUsing AI to manipulate peoples thinking and behaviorPotential bias or

67、low quality results of AI algorithmsElimination of jobs due to AI-driven automationUse of AI for surveillanceWe have no concerns about potential ethical risks of AIAI can do more than make work better for humansIt can help make better humans for work.10We discuss below some of the key elements that

68、organizations should consider integrating when developing and implementing social AI solutions.32 These elements can address some of the challenges and can help create better work for humans and better humans for work.1.Training the social AI model to generate impartial recommendations for building

69、workforce trustThe training dataset for the social AI algorithm should be chosen to ensure a fair representation of the population and mitigate biases resulting from human inputs.Also,its important to ensure that recommendations(for improvements in communication,workflows,etc.)are not influenced or

70、biased by career levels,e.g.,junior professionals need more training on inclusive communication.Further,the application should not only offer corrective action on communications and interactions but also convey appreciation and admiration when employees adapt their behavior to the recommendations an

71、d improve the quality of their interactions.2.Defining responsibility and accountability for the social AI solution and the workforceIn recent years,there has been much discussion about whether AI should be held to machine or human standardsboth ethically and legally.33 And,who should be held respon

72、sible and accountable for a decision:AI or the person who created or deployed it?Its important to establish responsibility and accountability in conversations and interactions among AI and human users.When doing so,social AI cannot be considered in isolation.Its a part of an organizations overall et

73、hics policy,and human users remain integral throughout the AI loop.Lets consider an example where a social AI application provides a recommendation about language choice to an employee or a suitable composition for a team to a project manager.In this case,the responsibility to generate the most appr

74、opriate recommendation lies with the developer teams;however,the accountability to act on that recommendation rests with the user,i.e.,the human workforce.Its imperative that this understanding of responsibility and accountability is documented and communicated to the developers as well as end users

75、.3.Defining the purpose of social AI clearly to drive data privacyDuring one of our research interviews,an AI specialist who focuses on AI/machine learning product management said,“The topmost challenge is privacy users freak out when they learn that their data is being collected they feel,I am bein

76、g monitored,and my behavior will be distributed where I dont have control.”34One way to alleviate privacy concerns is to ensure that the user data isnt used for evaluative purposes.In other words,dont use AI to rate your workforces emotional intelligence for performance reviews.Also,the application

77、should seek permission to use the workforce data for each purpose(analyzing team conversations,sales pitches,customer support calls,etc.),and there shouldnt be blanket consent from the workforce for the deployment of multiple social technologies.Depending on the AIs purpose,there may or may not be a

78、 need to store the data.In a simple example of turn-taking and analyzing airtime in a multiperson conversation,the data is useful in the moment to allow everyone to contribute to the discussion,and it can be deleted after the conversation.In other applications,such as AI for work relationships may b

79、e a great untapped opportunity11improving contact center conversations,data may need to be stored for future training and improvement purposes.Conversational AI should replicate the trust and discretion that is integral to human-to-human conversations.As we share information with other individuals,t

80、here is an unsaid understanding that the listener will exercise discretion when sharing that information with other individuals.Likewise,as social AI systems interact with other human users(say peers or customers)on behalf of the workforce,they must only share what the user is comfortable sharing wi

81、th other parties.For instance,an AI database has a users full date of birthbut when another human user or AI bot requests this information,the system uses discretion and only shares the day and month but not the year,thus,moving the conversation forward while keeping the data safe.4.Securing social

82、AI models by designThe European Union Agency for Cybersecurity(ENISA),the Federal Trade Commission(FTC)in the United States,and other organizations globally,have outlined cybersecurity frameworks to assess the exposure level of an AI model to cyberthreats.35 Organizations should test their social AI

83、 models against these security frameworks periodically to check for vulnerabilities to existing and emerging threats and deploy appropriate security controls.When developers are building the social AI training data,they could ingest harmful content into the training dataset,for example,malicious con

84、tent that tries to access and edit a users data or the complete dataset.This could help in training the algorithms to identify abnormal behaviors compared to normal user patterns and restrict further user activity,even leading up to denial of service as required.36 5.Deploying transparent social AI

85、models with explainable decision-makingThe workforce should be able to see how their data feeds into the social AI algorithm,how the algorithm makes decisions,and how it would benefit them.The algorithm should be open to inspection and corrections as required.For example,if AI recommends that someon

86、e modify their tone,it should also provide a decision tree explaining why something is appropriate or not based on organizational guidance on language nuances.IBM provides factsheets for each AI model that contain information about the creation and deployment of a model throughout the life cycle.End

87、 users can review the data captured and how it moves through the AI life cycle to determine the models decision-making process.37 Consumers trust food nutrition labels because it enables them to decide whether to purchase and consume an item.Social AI factsheets may drive transparency and trust with

88、 the workforce the way food labels do.6.Maintaining social AI models robustness and reliability over time When social AI systems can learn from users and each other,they can produce reliable results and,over time,build trust with users.Human intervention may be required to ensure the model is and st

89、ays robust.Teams need to identify the right people to provide human input.Have they received training on company guidelines and policies,and are they equipped to take on this responsibility?Its important to identify periodic refresher trainings on bias mitigation and ethics for those involved to kee

90、p the solution robust over time.AI can do more than make work better for humansIt can help make better humans for work.12Getting startedIN DELOITTES SURVEY of business leaders conducted in 2022,76%said they plan to increase or significantly increase their organizational spending on AI in the next ye

91、ar.38 In addition to the established uses of AI in the workplace for making internal processes more efficient and generating data insights,leaders have the untapped opportunity to leverage AI to enhance the social side of work.Here are some actions to consider to get started.Define social AI use cas

92、es and establish value metrics.Define what constitutes a social AI use or interaction,so you know how to set metrics and measure them.Identify value capture for each social AI application(increase in contact center resolution rates,higher employee engagement,improved acceptance of new processes,etc.

93、).Measure value both in terms of breadth and depth.Breadth can be assessed by looking at how far-reaching the impact of the social AI solution is.Is it compartmentalized to select functions within the organization or across the organization?Is the impact within the organization or outside as well wi

94、th external stakeholders such as customers,potential recruits,etc.?Depth can be assessed by looking at whether the social AI application is simply improving existing processes or establishing new trustworthy processes,thereby reinventing work practices.Make the workforce comfortable with social AI.I

95、t is a huge shift for the workforce to trust a machine sociallypeople have to get comfortable holding a mirror up to their development areas.Leaders and managers have the responsibility to enroll the workforce with the idea that the use of their data is mutually beneficial for them and the organizat

96、ion.It often starts with letting the workforce know how their data will be used,giving them a“trial period”to evaluate the application,and an opt-in/-out ability at any point in time.Also,professionals tend to prefer to take“recommendations”from AInot instructions.As such,its important to make it cl

97、ear in the social AI user interface that the application is playing the role of a coach or buddy and not that of a gatekeeper or enforcer.Identify how the workforce would like to engage with social AI considering cross-cultural differences.Begin by identifying workforce needs for teaming,relationshi

98、p-building,networking,etc.,and assess where AI solutions can be implemented to address current problems or uncover value-creation opportunities.There may be cross-cultural differences in social AI deployments for a globally dispersed workforce.For instance,based on a survey of 1,015 respondents from

99、 48 countries,respondents from East Asia are more likely to have a trusting attitude toward emotion AI compared to respondents from western countries.This could require leaders to develop location-specific strategies for their global teams.39 Build a custom solution suited to your organizations soci

100、al nuances.When implementing a solution,its important to work closely(as a partner)with the AI solution provider.Since every organization is different in terms of its processes,communication styles,work dynamics,etc.,its important to deploy a solution that is customized to the needs of the organizat

101、ion and the unique needs of different functions within the organization(sales,customer support,human resources,learning and AI for work relationships may be a great untapped opportunity13development,etc.).Also,its important to have the right training dataset to train AI models;some of the training d

102、atasets should come from the organizations actual data to keep the model close to reality and ensure that the model keeps adapting to incoming data.Pilot the social AI solution for internal conversations,incorporate feedback,then scale to external applications.Pilot the solution with conversations a

103、nd interactions within the organization(among the workforce)and build feedback loops from the workforce before scaling the solution to external interactions(with potential recruits,customers,etc.).While scaling the solution,a transfer-learning approach may be helpful.For example,when a team is devel

104、oping a microaggression detector algorithm,they will have to train the model on hours of audio inputs,which would be time-and cost-expensive.Instead,the development team can use pretrained models(used elsewhere in the organization)or external open-source models and adapt them to their needs.When usi

105、ng an external open-source dataset,make sure to check that it is diverse to train your model well.Time is shortseize the opportunity.There is a confluence of cost and performance improvements in enabling technologies(such as cloud,network speeds,computer vision,and language recognition)that could ma

106、ke it opportune for organizations to implement social AI now.40 AI is a powerful tool in leaders arsenals.With it,they can drive efficiency by creating leaner and simpler organizations and enhance unique human capabilities for long-term organizational success.By driving greater trust and transparenc

107、y in hybrid operations,AI can improve the quality of work,increase employee engagement,and reduce attrition.As such,organizations adopting a wait-and-watch approach may run the risk of losing competitive advantage in the current race for talent.AI can do more than make work better for humansIt can h

108、elp make better humans for work.141.Deloitte,The State of AI 5th edition,October 2022.2.Jim Guszcza and Jeff Schwartz,Superminds,not substitutes,Deloitte Insights,July 31,2020;Jeff Schwartz et al.,Superteams:Putting AI in the group,Deloitte Insights,May 15,2020.3.John Hagel III,John Seely Brown,and

109、Maggie Wooll,Skills change,but capabilities endure,Deloitte Insights,August 30,2019.4.Tina Hovsepian,“Business and people:Why relationships are essential for a successful business,”Forbes,July 20,2018;Indeed,“What are human relations in the workplace?(With steps for practicing them),”August 16,2022.

110、5.Deloitte,The State of AI 5th edition.6.Tamara Cibenko,Amelia Dunlop,and Nelson Kunkel,Human experience platforms,Deloitte Insights,January 15,2020;Meredith Somers,“Emotion AI,explained,”MIT Sloan,March 8,2019.7.Alelo,“Home,”accessed September 23,2022.8.Ibid.9.Jun Wu,“How AI can help companies thri

111、ve in post-pandemic uncertainty,”Forbes,March 1,2020;Sarah Fister Gale,“AI brings coaching to the masses,”Reworked,September 8,2021.10.Business Wire,“BetterUp unveils new features to its leadership development platform to drive improved business outcomes and employee transformation,”October 1,2019.1

112、1.Shephali Bhat,“Professional networking in times of COVID-19,”Economic Times,September 9,2020.12.Deloitte AI Institute,“The AI Dossier,”accessed September 23,2022.13.Daniel Limon and Bryan Plaster,“Can AI teach us how to become more emotionally intelligent?,”Harvard Business Review,January 25,2022.

113、14.Limon and Plaster,“Can AI teach us how to become more emotionally intelligent?”;Gong.io,“Home,”accessed September 23,2022.15.Bloomberg,“Vodafone Italy drives new accounts with personalized,AI-powered messaging,”June 29,2020;Jason Heller and Vipul Vyas,“How AI is helping companies make deeper huma

114、n connections,”Harvard Business Review,November 11,2020.16.Aberdeen,The ROI of intelligent virtual assistants in customer experience programs,accessed September 23,2022.17.Cresta,“Home,”accessed September 23,2022;Limon and Plaster,“Can AI teach us how to become more emotionally intelligent?”18.Cogit

115、o,“How Humana Pharmacy leverages AI to enhance member experience,”accessed September 23,2022.19.Ibid.20.Michael Parker,“Why contact center AI is a big deal for customer-focused brands,”Cresta,August 11,2021;Aberdeen,The ROI of intelligent virtual assistants in customer experience programs.21.Claudia

116、 Goldin and Cecilia Rouse,“Orchestrating impartiality:The impact of“blind”auditions on female musicians,”NBER,January 1997.EndnotesAI for work relationships may be a great untapped opportunity1522.Monika Mahto et al.,A rising tide lifts all boats,Deloitte Insights,January 18,2022;Bernard Marr,“Artif

117、icial intelligence in the workplace:How AI is transforming your employee experience,”Forbes,May 29,2019.23.Ina Gantcheva et al.,Activating the internal talent marketplace,Deloitte Insights,September 18,2020.24.Oznur Alkan,Elizabeth Daly,and Inge Vejsbjerg,“Opportunity team builder for sales teams,pr

118、oceedings of the 2018 International Conference on Intelligent User Interfaces,”ACM,March 2018.25.Michael Schneider,“64 percent of employees trust AI over managers because robots give unbiased information,”Inc.,accessed September 23,2022;Oracle,“New study:64%of people trust a robot more than their ma

119、nager,”press release,October 15,2019.26.Based on an interview with an AI specialist focusing on human-machine interaction,conducted on April 15,2022.27.Ibid.28.Siri Anderson,Making the invisible visible,Deloitte Insights,February 27,2019.29.Elizabeth J.Altman,David Kiron,Robin Jones,Jeff Schwartz,Or

120、chestrating Workforce Ecosystems:Strategically managing work across and beyond organizational boundaries,MIT Sloan Management Review,May 17,2022.30.Michael Arena et al.,“How to catalyze innovation in your organization,”MIT Sloan Management Review(2017);David Green,“The role of organisational network

121、 analysis in people analytics,”LinkedIn,May 23,2018.31.Deloitte Insights,Becoming an AI-fueled organizationState of AI in the enterprise,4th edition,accessed September 23,2022.32.Deloitte,“Deloitte introduces trustworthy AI framework to guide organizations in ethical application of technology in the

122、 Age of With,”press release,accessed September 23,2022.33.Jeremy Elman and Abel Castilla,“Artificial intelligence and the law,”Tech Crunch,January 28,2017;Council of Europe,Responsibility and AI,accessed September 23,2022.34.Based on an interview conducted on April 15,2022.35.ENISA,Securing machine

123、learning algorithms,December 14,2021;Alex Engler,“The EU and US are starting to align on AI regulation,”Brookings,February 1,2022.36.Andrew Marshall et al.,“Securing the future of artificial intelligence and machine learning at Microsoft,”Microsoft Ignite,accessed September 23,2022.37.Pearl Chen,“IB

124、Ms open source strategy champions AI trust and transparency,”IBM,September 10,2020.38.Deloitte,The State of AI 5th edition.39.Peter Mantello,“Bosses without a heart:socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace,”AI&Society(2021).40.Deloitte Insigh

125、ts,The Internet of Things:A technical primer,February 8,2018.AI can do more than make work better for humansIt can help make better humans for work.16The authors would like to thank Steve Hatfield,Cindy Skirvin,Kate Schmidt,Sue Cantrell,Yuki Iwase,and Daniel Githanga for their valuable contributions

126、 to the development and review of the report.They would also like to extend their thanks to Negina Rood,Siri Anderson,Abha Kulkarni,Iram Parveen,and Aditya Narayan for their contributions to the research and analysis.Further,the authors would also like to thank Andy Bayiates,Aditi Rao,and Junko Kaji

127、 for their editorial inputs and Andrew Ashenfelter,Saurabh Rijhwani,and Charlean Parks for their marketing support.AcknowledgmentsMonika Mahto|Monika Mahto is the India research lead for the Deloitte Center for Integrated Research.Mahto has close to 15 years of experience in research focused on adva

128、nced manufacturing,smart factory,future of work,Industry 4.0,IoT,and other advanced technologies.Her research is cited in prominent platforms including MIT Sloan Management Review,The Wall Street Journal,and Thrive Global.She collaborates with other thought leaders,industry executives,and academicia

129、ns to deliver insights into the strategic and organizational implications of advanced technologies.Don Miller|Don Miller leads Deloitte Consulting LLPs organizational design practice,empowering global clients to design their organization structures based on their best human impulses and aspirations

130、to future-proof their businesses.He has more than 15 years of experience in bringing together diverse leaders to cocreate new organization governance and decision rights models that enable them to quickly foster their teams accountabilityto organize,operate,and behave differently,and to stay resilie

131、nt in a fast-paced world.While his organizational design industry experience spans all sectors and functions,Miller is also one of the leaders of Deloittes Human Capital Media,Entertainment,and Sports practice.About the authorsAI for work relationships may be a great untapped opportunity17Maya Bodan

132、|Maya Bodan brings 17+years of consulting experience advising global clients in the designing and implementation of large-scale transformations.Her expertise is in managing all people aspects of transformations,including organization design and restructures,M&A,offshoring,organizational culture chan

133、ge,employee engagement,talent management,and career development.She helps her clients prepare for the Future of Work through flexible organization structure and talent practices.Brenna Sniderman|Brenna Sniderman is the executive director of the Center for Integrated Research,Deloitte Services LP,whe

134、re she oversees a cross-industry research team focused on global shifts in technology,mobility,climate,and workforcedeveloping work that ultimately helps guide how organizations can operate and strategize in an age of rapid technology and cultural change.Snidermans work is cited in prominent media p

135、latforms such as Forbes,GE Digital,and Wired,and she frequently gives keynote speeches at industry and client conferences.AI can do more than make work better for humansIt can help make better humans for work.18Contact usOur insights can help you take advantage of change.If youre looking for fresh i

136、deas to address your challenges,we should talk.Industry leadershipSteve Hatfield Global Future of Work leader|Principal|Deloitte Consulting LLP+1 212 618 4046|Steve Hatfield is a principal with Deloitte Consulting and serves as the global leader for Future of Work for the firm.Center for Integrated

137、Research Brenna Sniderman Executive director|Deloitte Services LP+1 929 251 2690| Brenna Sniderman leads the Deloitte Center for Integrated Research.Monika MahtoExecutive manager|Deloitte Services India Pvt.Ltd.+91 9930 425 494| Monika Mahto is the India research lead for the Deloitte Center for Int

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