Talent Edge Weekly - Issue #105

Articulating the business implications of HR metrics is essential to the CHRO role and HR roles in general. And given the various changes in work, the workplace, and the workforce over the past two years, now is a good time for HR leaders and their teams to reevaluate which HR and talent metrics are most important to their business. This 15-page paper provides twelve HR metrics, ranging from Predicted Resignation Count, Succession Coverage Ratio, and Applicant-to-Hire Conversion Ratio, to name a few. Each metric offers a definition, example, and summary of why the metric is important. One interesting measure is Manager Instability Ratio, which calculates the ratio of employees with more than one direct supervisor within the preceding twelve-month period. Given the disproportionate impact (e.g., retention, engagement, etc.) managers have on their direct reports, understanding the manager instability ratio can provide important talent insights. As HR teams determine which metrics are most important, I recommend starting with the question—not the metric—that can help answer firms’ most important talent challenges. Once the right question is determined, teams can identify the underlying metric(s). With that in mind, I am resharing this 16-page SAP reference, which includes 100 people analytics questions that span eight talent categories—from recruiting to employee experience.

As workers rethink the role they want work to play in their lives, firms seek to understand how workers’ motivations and preferences continue to shift. This 76-page report is based on a survey of 20,000 workers across ten countries representing 65% of global GDP. It explores five themes that are reshaping work today, one of which is worker preferences. Figure 1.5 on p. 13 shows how respondents from this global survey rank various job/employment attributes. While workers rank compensation first over any other job attribute, it’s the top priority for just one in five workers—reinforcing variance in worker preferences. As shown on p.20, the authors identified six worker archetypes, each providing a prism for understanding worker preferences. For example, Pioneers and Givers often value working in organizations with a clear social mission. Strivers want to make something of themselves and are motivated by status and compensation. Artisans find purpose in mastery, and Explorers are likely to find it by gaining a breadth of experiences. Operators often view work as “a means to an end” and are more likely to find purpose outside their work lives. Although there is no shortage of worker archetypes and personas, this resource might provide additional ideas for managers as they understand how individuals on their teams find a sense of purpose at work. Other topics are discussed.

Last week, I posted how workers ages 65 and over in the US will account for more than 60% of projected labor-force growth from 2020 to 30. I mentioned how firms could partly address their talent shortages by attracting these workers to their companies with an employee value proposition aligned with this talent segment’s priorities. Organizations can tap into another overlooked worker segment for their talent needs: those without a college degree. These workers often have the skills and desire to perform a job, but do not meet academic degree requirements for specific roles. This 30-page report shares findings from an analysis of over 51 million job postings in the US. The goal was to understand the extent to which firms are removing degree requirements from job descriptions. Results show that companies are moving away from degree requirements and toward skills-based hiring, especially in middle-skill jobs. This shift can open up 1.4 million jobs over the next five years for Americans without a college education. However, overall, 37% of middle-skilled jobs do not show a reduction in requiring a college degree, effectively stripping 15.7 million people out of firms’ candidate pool. Where are there opportunities for your organization to shift from narrow academic requirements in job descriptions to more skills-based hiring? For another resource on “hidden workers,” check out this 74-page report by Harvard Business School, Hidden Workers: Untapped Talent.

I’ve made several posts espousing the responsible use of AI in human resources and talent management. And while I am a proponent of these platforms’, I have shared many posts that bring awareness to the risks. A few of the resources include 1) How to Address Racial Biases and Algorithmic Discrimination in HR? 2) Ethical AI: A New Strategic Imperative for Recruiting and Staffing, 3) What AI Regulations From the EU and FTC Mean for HR Practitioners and HR Technology, and 4) How to Tap the Talent Automated HR Platforms Miss. This new article points out the risk of developing a “reliance” on AI to decide for us rather than augmenting human judgment. It argues that when we employ judgment, our decisions take into account the social and historical context and different possible outcomes. Judgment relies not only on reasoning but also, and importantly so, on capacities such as imagination, reflection, examination, valuation, and empathy. Algorithmic systems, in contrast, output decisions after processing data through an accumulation of calculus, computation, and rule-driven rationality. While I share these concerns, the opportunity before us is to figure out how we can tap the potential of AI for HR and talent management while minimizing the risks. One tactic is to develop the capabilities of users of these platforms to critically evaluate the output produced by AI platforms and apply human judgment when making decisions. Just as we have reduced the risks associated with flying a plane or driving a car through various safety tactics and protocols, we need to apply critical thinking and innovative solutions that mitigate unintended consequences associated with AI.

Many organizations use “coaching” as a tactic for developing their workers. Despite its effectiveness, coaching limits how many workers you can affect simultaneously, usually because of resource and budget constraints. But as pointed out in this article, a segment of firms is combining behavioral-science insights with the latest AI technologies to create a more scalable, tailored coaching experience for their employees. Using AI technology, organizations can send a nudge — a reminder sent by text, email, or digital application— that is tailored and timed to prompt people toward small behavioral changes. For example, an employee might get a nudge encouraging her to speak up in the first 10 minutes of a meeting. These nudges provide personalized, real-time coaching—where workers can get the right advice at just the right moment. The article offers examples of how firms use nudging for both employees and managers. These AI-based coaching platforms are not intended to replace traditional in-person performance management and coaching; instead, they augment human-based coaching. If you missed it, I recommend checking out two other reports on this topic. 1) Coaching at Scale: AI Democratizes Leadership Development by Josh Bersin. This 15-page paper explores the benefits of AI-based coaching, when AI-based coaching is most appropriate, and case studies on how firms use AI-based coaching. 2) Coaching Tech: The Humans and the Robots by Redthreadresearch. This 56-page report looks at the coaching models of 45 coaching tech vendors.