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How Behavioral Analytics Can Help Decide When to Go Back to the Office

虽然“恢复正常”仍然是大多数公司的遥远的愿景,但许多领导人已经开始指导员工群体回到办公室与混合方法,就像一些在家工作的员工,而其他人则在办公室。

既有人和遥控作品是否可以对公司有优势?答案是肯定的,并且精心设计的混合策略是前进的方向。管理混合劳动力是艰难的,但采用数据驱动方法的公司定制到其独特的文化,将自己与包装分开。

Artificial intelligence and network analysis can help leaders identify the ideal environment for each employee and each team, while also mitigating the communication difficulties of distributed teams.

How AI Can Determine the Best Work Environment for Each Employee

保罗格雷厄姆着名分类员工分为两组:“经理”和“制造商”。虽然管理人员需要持续的上下文切换和更频繁的会议,但制造商(例如程序员)需要更多的下降工作时间,几乎没有分心。

锁定期间遥控工作的兴起增加了复杂性。

领导人努力了解谁在家里做得很好,谁需要办公室茁壮成长,可以依靠ai来帮助做出这些艰难的决定。员工可以通过分析关于使它们最有效的行为数据进行分析到工作风格中。

An effective algorithm combines objective and subjective data: measures of how frequently employees attend meetings and how much time they spend doing heads-down work, with a survey of employee preferences and input from managers. This allows us to go a level deeper than Graham’s binary approach.

For example, employees whom the algorithm identifies as “concentrators”, doing predominantly solo work and reporting good remote work conditions are prime candidates to continue working from home, at least part of the time. But that’s an easy case. It’s the hard decisions where the data-driven approach is critical.

例如,员工可能会说他们富有成效,从家工作,但行为数据显示它们是“合作者”,并且在与办公室的同事无缝通信时可能更有效。在这些情况下,最好的解决方案通常是一个混合工作 - 从家庭,工作到办公时间表。

Additionally, one must consider those in hybrid roles among the collaborators. Network analysis identifies such individuals by asking employees to identify the types of information or help they get from key influencers in their network.

球员教练

例如,中级管理人员经常充当“玩家 - 教练”,要求各个贡献者的功能专门知识与传统管理和领导职务相结合。

Player-coaches face a more complex burden — and a higher risk of burnout — than employees who occupy only a single role, because their multifaceted work often shifts between collaboration and concentration. Remote work can exacerbate this challenge, as can a haphazard return to the office. This will require careful planning to ensure they receive the appropriate support to avoid burnout.

将新员工纳入此模型

另一个常见的棘手问题是新员工,他们受益于建立网络的早期阶段的面对面互动。最近的Dojo Cohort分析报告称,平均而言,在2020年第三季度(锁定期间)的组织平均新的雇用社交网络比2020 Q1(在锁定前)小于19%。

A recommended course of action is to have new hires meet their peers face-to-face early on, even if they go remote thereafter. By spending their first weeks coming into the office, they can build up that foundation of trust and collegiality that can be so conspicuously missing in our remote-only world.

AI Can Prevent Silos Emerging Among Remote Teams

强大的团队通过增加预定的呼叫和签到的数量,自然而然地回应了自发咖啡聊天和非正式会议。

But, in recent projects comparing organizations’ communication networksthe lockdown, we’ve noticed something unusual. The over-communication happening之内团队不会泄漏到合作之间teams.

Teams that become so consumed by the processes of remote work risk turning into silos that communicate intensely之内他们的行列,但与组织的其他部分隔离。

没有自由流动的合作,交叉部门线,组织往往会失去竞争优势,并随着时间的推移发展“创新债务”。这些组织还倾向于开发一个“影响者”的子集,以弥合筒仓之间的差距 - 一种风险的命题,即倦怠或对单个恒星的过度依赖性的配方。

使用AI,组织可以在开发之前识别这些孤岛。

例如,上面的网络图显示了快速增长的技术组织的通信网络,并且基于来自会议,电子邮件,共享文档和共享代码存储库的Meta-Data,而在90天段。

Although the two blue-colored clusters belong to the same Sales division, by 2020 Q3, they have splintered off into two functional silos, representing the ‘deal sourcing’ and ‘closing’ teams. The handful of employees in between mediate much of the interaction.

Since a split like this was not particularly welcome news to leadership, this analysis was an impetus for action. Early monitoring and benchmarking enable leaders within an organization to intervene with smarter management, better communication practices or informal digital gatherings before problems get out of hand, and innovation debt begins to mount.

Data and AI for Navigating Our “New Normal”

Managing the hybrid workspace will be an ongoing challenge for organizations for the foreseeable future. Leveraging network analysis and AI is an important step towards effectively building and adapting a plan. Using this approach can help leaders maintain productivity, tailor employee environments and experience, and sustain effective collaboration between teams.

对于我们所知道的,我们了解劳动力未来的规划,事实上导航这些挑战可能是我们的“新正常”。

维克多·贝尔根

Mcchrystal Analytics负责人

Victor Bilgen是一支团队的麦克里斯特分析的负责人麦克里斯塔群由数据科学家和工业心理学家组成,他们分析和衡量组织绩效和人类互动网络。

Dan Goldstern

Dojo的联合创始人

Dan Goldstern is co-founder ofdojo.是一个帮助公司设计使人们更有效的更加有效的更加效率的AI平台。

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