Posted by Chris Havrilla and Matthew Shannon on December 12, 2019.
The realities of tomorrow’s workforce will require organizations to be more flexible in enabling the execution of work—wherever it needs to happen. Organizations are already working on their ability to build a distributed workforce, whether to tap into talent pools that live far from existing operations or to entice a population of nomadic workers who prefer to work with more flexibility. This need will intensify in 2020 in response to reduced budgets and geopolitical uncertainties that make it harder to move talent around the globe.

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What’s more, it will not be enough to just get work done. Organizations must be able to continually reskill their workforce and build their capabilities, no matter where they work: talent and population shortages, shifting demographics, and rapidly aging skills sets place a critical emphasis on learning and development.1 Companies must also deliver a compelling experience for their workforce, regardless of location. As a result, HR organizations will turn to emerging technologies to support the development and management of their distributed workforce.
Emerging Technologies Connect a Distributed WorkforceEmerging technologies tend to fall into one of two buckets: those that demand new skills from workers, such as robotic process automation and cognitive agents, and those that support the development of new skills and ways of working. The latter can help to connect individuals across physical spaces, provide tailored coaching and development recommendations, and create virtual learning experiences—thus enabling the performance and productivity of a distributed workforce.
Virtual Collaboration.Collaboration technology that can connect the distributed workforce is essential to workforce experience and productivity.2 In 2020, the number of self-employed workers in the United States is projected to reach 42 million—nearly triple what it was in 20183—and with this rise will come a higher expectation for remote work opportunities. As such, organizations will shift their focus to fostering teamwork and collaboration—and thus productivity and engagement, especially as it becomes easier and easier to find gig work. Beyond basic email and chat systems, collaboration and work management tools help ensure accountability across different workstreams and provide visibility into related automated tasks. We expect to see a rise in virtual conferencing tools—not just the video-conferencing options we have today, but in 3-D, via virtual- and augmented-reality technologies. The potential of these tools will catapult them past face-to-face meetings that require travel to become the preferred way to collaborate on ideation, design, and even modeling or prototyping—helping lead to better outcomes and better workforce experiences.
Virtual Coaching.Virtual coaching solutions help reduce the psychological distance between the learning opportunity (i.e., coaching) and an individual’s work.4 The ability to document and share immediate feedback on performance lets coaches offer real-time perspective on their colleagues’ performance. These conversations can directly help to overcome challenges and celebrate the successes of work. As such, they provide one example of how to embed learning in the flow of work.5 Emerging coaching technologies will target specific roles (e.g., executives or managers) and provide more scalable access to coaching for the entire workforce. In the case of the former, individuals will often connect with live coaches, either inside or outside their organization. Colleagues can schedule times to connect or correspond more casually with questions and tips for development. For greater scale and reach, automated chatbots will employ exploratory questioning and machine-learning capabilities to help surface new learning and development opportunities that match an individual’s capabilities, interests, development needs, and career aspirations—even ones not originally considered.
Virtual Learning.In addition to providing new ways of connecting and coaching, virtual tools have long enabled learning experiences that would otherwise not be possible. New technologies like VR headsets will help provide empathetic learning experiences from other perspectives or provide virtual environments that might be uncommon or unsafe in person (e.g., training retail employees how to respond during a holiday rush or in an emergency situation). While earlier versions of this type of learning technology have been around for some time, 2020 will mark a year of continued growth and new capabilities as more providers enable customizable learning experiences that help deliver unique learning environments not previously possible.
Forging Connections and Action
External factors will continue to drive organizations to leverage their distributed workforce. But just because workers’ locations are different does not mean their experience has to be. Organizations will look to emerging technologies to drive new ways of working and creating value—while fostering connection and development in the process. To do so, they will need to stay up to date on those technologies that can help them identify and respond to disruptions.6 As organizations continue to explore the different ways in which new technologies can support their distributed workforce, Bersin will be working with solution providers to help provide you with an up-to-date view of the available capabilities in the market.

Chris Havrilla is a vice president and the HR technology and solution provider strategy & research leader at Bersin, Deloitte Consulting LLP.
Matthew Shannon is a senior research analyst, Solution Provider Market, at Bersin, Deloitte Consulting LLP.

1 2019 Global Human Capital Trends: Leading the social enterprise—Reinvent with a human focus, Deloitte Consulting LLP and Deloitte Insights, 2019.2 Interactive Workforce Experience Framework, Bersin, Deloitte Consulting LLP / Christina Rasieleski and Matthew Deruntz, 2019.3 “Be Your Own Boss, Be Happy,” AARP.org / Austin O’Connor, February 27, 2018, https://www.aarp.org/work/small-business/info-2018/self-employed-numbers-fd.html.4 Placing Meaningful Tools and Information in the Flow of Work, Bersin, Deloitte Consulting LLP / Julie Hiipakka, 2019.5 Four Practices to Embed Learning in the Flow of Work, Bersin, Deloitte Consulting LLP / Julie Hiipakka and Chelsey Taylor, 2019.6 “Staying Ahead of Disruption with Workforce Sensing,” Workforce.com / Daniel Roddy and Chris Havrilla, 2019, http://download.workforce.com/staying-ahead-of-disruption-with-workforce-sensing.
The post Prediction: Organizations will use virtual work and workforce development to improve the performance and productivity of people and teams appeared first on Capital H Blog.
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Author: hrtimesblog
Date/time: 13th December 2019, 00:02


When you are falling in love with something, you will give your everything to get and achieve that thing. The same applies to the work-related matter as well. When you are in love with your role as a human resources professional, you will be willing to go the extra mile in order to be in […]
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Date: 12th December 2019 at 15:02
Author: hrinasia – Renny


Starbucks has just announced a partnership with the Employment and Employability Institute (e2i), an organization established by the National Trades Union Congress (NTUC) to create better jobs and lives for workers. Starbucks is enhancing its existing training programs to equip at least 2,000 Starbucks partners – comprising store managers, district managers and baristas – with […]
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Date: 12th December 2019 at 12:01
Author: hrinasia – HR ASIA


Posted by Zachary Toof, Nehal Nangia, and Janet Clarey on December 11, 2019.
Bias is everywhere. Nearly two-thirds of respondents in Deloitte’s 2019 report on the state of inclusion reported experiencing bias in the workplace last year. And the sobering statistics continue from there: respondents reported that bias had negative impacts on productivity (68 percent), engagement (70 percent), and on happiness, confidence, and wellbeing (84 percent).1

Related links

2019 Global Deloitte Human Capital Trends.Reinvention starts here.

Bersin
Learn more.

As humans, we can hold a variety of unconscious biases. Many are necessary to daily life, almost intuitive; some are less productive and are holdovers from the past, no longer relevant. We tend to favor people most like ourselves (similarity bias). We often prefer information that confirms our beliefs and are prone to discount information that contradicts them (confirmation bias). We also can put greater emphasis on things that have just happened (recency bias). These and other types of biases can unconsciously influence our decision-making: we may inadvertently hire or promote those most like us, make talent selections that align with our preconceived notions, and base our performance evaluations on what we expect to see or have seen most recently.
Organizations are increasingly recognizing that humans are biologically hardwired to operate on instinct and habit and are seeking nonhuman solutions to mitigate outmoded and problematic biases. For instance, the use of artificial intelligence (AI) in recruitment alone is expected to increase threefold over the next two years.2
Using AI to Help Reduce Bias across HR
AI is not new, but it has been making increasingly interesting strides into talent acquisition, internal mobility, learning and development, and performance management. Some common use cases of AI include:
Revising job postings to use gender-neutral language
Anonymizing resume information (e.g., names, photos, gender, schools, ZIP codes, graduation dates) to reduce reviewer bias
Using gamification to assess abilities beyond resume text and match applicants to their best-suited roles
Providing real-time performance metrics to nudge more frequent feedback, transparency, and learning recommendations
However, AI is not without its own challenges. The algorithms that drive AI (including the parameters for machine learning applications) are created by humans—and humans have unconscious biases. Until we reach the technology singularity, at which point AI will program itself (we’ll save that prediction for a future year), this means that AI is also subject to bias.
For example, if your company is currently made up of mostly Caucasian males over 40 years of age, and the talent acquisition AI tool is establishing correlations using only this data set to bring in more high performers, then it should be no surprise that the result will be more Caucasian males over 40. Clearly, a more thoughtful approach to “programming” the AI is required in order to identify and bring on a more diverse talent pool.
Many organizations are aware of AI’s flaws and are taking steps to address them. For example, several leading technology companies have announced their use of open-source software tools that can be used to examine bias and fairness in AI models.3 Furthermore, there is a growing number of AI auditing firms emerging to help address these issues.
Combining AI with Behavioral Science
AI can provide humans with powerful tools to reduce unconscious bias, but in turn, humans need to design AI with fairness standards in mind and routinely monitor and test algorithms to ensure they do not favor or disadvantage any particular group. In this way we can use human judgment, aided by AI, to reduce both our unconscious biases and inadvertent machine-learning biases.
Of course, even when work is augmented by AI, many decisions will still fall to humans—who are prone to cognitive shortcuts. But we can take this another step forward: behavioral science can help create environments and offer choices that encourage better decision-making.
For example, a hiring manager or recruiter may show similarity bias in reviewing a resume. A resume-masking AI tool could be used to anonymize demographic details in order to reduce bias and nudge the resume reviewer to focus on the most critical job-related aspects. The intent is not to rely on biased shortcuts or “trick” people into one decision or another but rather to nudge them to consider the most pertinent factors.
Considerations for Mitigating Bias in Your Organization
To get started:
Examine your end-to-end talent life cycle to identify the areas most prone to bias (e.g., decisions on resume screenings, interviewing, selection, performance management, or internal mobility).
Explore AI/data science solutions while designing for fairness to reduce the bulk areas of potential bias (e.g., identifying processes or tasks that can be automated).
Determine behavioral science opportunities to nudge decision-makers at the right times with the right information to inform decisions (e.g., examining a full review period rather than only recent actions when measuring performance, evaluating ability test results to supplement resumes when selecting candidates for interviews, or showing candidate details as a group instead of one by one to compare to the desired fit).
Keep in mind that, for humans, a bias issue can be seen as a learning issue: Think, for example, how we all learned to drive. We start the learning process at an “unconscious incompetence” level and move on to “conscious incompetence,” then to “conscious competence,” and finally, through learning and practice, arrive at “unconscious competence.” Similarly, we can think of the journey from bias to inclusion in the same way, starting with “unconscious bias,” moving to “conscious bias” (uncomfortable), then through learning to “conscious inclusion,” and finally through practice and more learning to “unconscious inclusion” and new business-as-usual inclusive behaviors. AI, nudging, and behavioral science tools can help us get there.
The combination of AI and behavioral science will be on the rise in 2020. An increased number of AI tools will continue to emerge, and organizations will become more familiar with behavioral science tools and nudges to help their people make better and more informed talent decisions.
Bersin will continue to explore the topics of bias and the impact of AI and behavioral science through 2020 with research in areas such as nudging and AI for inclusion, people analytics for the individual, the diversity and inclusion solution provider market, and our next High-Impact People Analytics study.

Zachary Toof is a research manager, People Analytics, at BersinTM, Deloitte Consulting LLP.
Nehal Nangia is a research manager, Talent and Workforce Performance, at BersinTM, Deloitte Consulting LLP.
Janet Clarey is lead advisor, Technology, Analytics & Diversity & Inclusion, at BersinTM, Deloitte Consulting LLP.

1The bias barrier: Allyships, inclusion, and everyday behaviors, Deloitte Development LLP, 2019, https://www2.deloitte.com/content/dam/Deloitte/us/Documents/about-deloitte/us-inclusion-survey-research-the-bias-barrier.pdf.2The 2019 State of Artificial Intelligence in Talent Acquisition, HR Research Institute, 2019, https://www.oracle.com/a/ocom/docs/artificial-intelligence-in-talent-acquisition.pdf?elqTrackId=1279a8827f3d4548ae3f966beeeef458&elqaid=83148&elqat=2.3“Artificial Intelligence Can Reinforce Bias, Cloud Giants Announce Tools For AI Fairness,” Forbes.com / Paul Teich, September 24, 2018, https://www.forbes.com/sites/paulteich/2018/09/24/artificial-intelligence-can-reinforce-bias-cloud-giants-announce-tools-for-ai-fairness/#332c72fd9d21.
The post Prediction: Organizations will use AI and behavioral nudges to reduce bias across the workplace appeared first on Capital H Blog.
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Author: hrtimesblog
Date/time: 12th December 2019, 00:01


Life without a problem is just an illusion. Everyone will always have to deal with various kinds of problems in their everyday life, be it major or minor such as being unable to open a jam can. In a workplace setting, problems, difficult situations, and challenges can occur more frequently because the workplace is the […]
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Date: 11th December 2019 at 18:02
Author: hrinasia – Renny