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- Talent Edge Weekly - Issue #362 - Best of August 2026
Talent Edge Weekly - Issue #362 - Best of August 2026
The 15 most popular resources—plus many bonus resources—from the August issues of Talent Edge Weekly, spanning three categories: Future of Work, AI, and Labor Market; HR Impact and Value Creation; and Talent and HR Practices.
Brian Heger
August 30, 2026
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Welcome to this special Best of August issue of Talent Edge Weekly!
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THIS MONTH’S CONTENT
This Best of August issue includes the 15 most popular resources from the August issues of Talent Edge Weekly. They span three sections:
Future of Work, AI, and Labor Market. Covers how AI is shifting skills demand, including AI literacy; how AI is helping to uncover similarities in work tasks across occupations; broader AI-driven shifts in employment; how both AI and remote work is narrowing early-career opportunities; and the impact of AI on older workers' careers.
HR Impact and Value Creation. Explores how HR practitioners can continue to evolve their function to create even greater value for stakeholders; being more intentional about evaluating and selecting HR technology so that expected ROI can be achieved; and what it takes to build HR for the agentic era.
Talent and HR Practices. Addresses strategic workforce planning; freeing up workforce capacity from existing teams; AI-enabled coaching; assessing team readiness before rolling out a new strategy; executing more actionable talent reviews; hiring based on a candidate's potential; and enabling internal mobility.
Plus, bonus resources, along with separate sections on organization layoffs and Chief HR Officer movement that occurred in August.
Let’s dive in.
P.S. Are you an internal HR practitioner? Learn about my private community, Talent Edge Circle.
THIS MONTH’S EDGE
I. Future of Work, AI, and Labor Market
Covers how AI is shifting skills demand, including AI literacy; how AI is helping to uncover similarities in work tasks across occupations; broader AI-driven shifts in employment; how both AI and remote work is narrowing early-career opportunities; and the impact of AI on older workers' careers.
AI AND SKILLS
A new 60-page report examines how AI is reshaping skills demand, including AI literacy as a foundational capability.
HR leaders continue to help their organizations rethink how the skills of their workforce will need to shift as AI reshapes work. A new 60-page report from the International Labour Organization and five partner organizations helps shed light on this topic, with insights ranging from changing skills demand in AI-enabled organizations to AI hiring trends. One finding worth lifting up: AI literacy is becoming a foundational skill, not a nice-to-have. It frames AI literacy less as knowing how to use specific AI tools, and more as what preserves people's judgment as AI-augmented environments expand. It breaks AI literacy into three domains: technical literacy (understanding how AI systems function), analytical and data literacy (interpreting and judging AI outputs), and ethical and societal literacy (awareness of issues like data privacy, algorithmic bias, and accountability). According to the report, current AI literacy levels remain low, and training alone won't meet the growing demand, a gap that can leave the workforce unprepared. With that as the backdrop, I'm excited to have Tracy St.Dic, Global Head of Talent at Zapier, joining us soon for a discussion in my private community for internal HR practitioners, Talent Edge Circle. We'll dig into how her team built and rolled out an AI fluency initiative touching hiring, performance, and development, the rationale behind it, and lessons learned so far. If you want to be part of private discussions like these with a curated group of HR practitioners, learn more about Talent Edge Circle.
AI AND TALENT MOBILITY
A recent report analyzes 800,000+ workplace ChatGPT conversations to show how AI use overlaps across occupations, with implications for talent mobility across occupations.
As organizations look for ways to redeploy talent to where it is needed most, one underleveraged opportunity is moving people across occupational and functional boundaries. A recent report from OpenAI’s Economic Research team offers an interesting lens into where cross-occupational movement may be most feasible. The team analyzed more than 800,000 work-related ChatGPT conversations from US business users and introduced a concept they call task crossover, instances where someone uses AI to handle work that traditionally belonged to a different occupation. After removing generic tasks like writing and scheduling, they found that 43.5 percent of occupation-specific AI use crosses those traditional job lines. Customer experience, design, and HR workers showed the highest crossover rates. The pattern was strongest at smaller organizations, where workers often do not have a specialist readily available and instead handle the work themselves with AI's help. These insights carry real value for two areas in particular, internal mobility and skills-based talent strategy. They offer an early signal of where cross-discipline work is already happening, well before it would ever show up in a job posting, a title change, or a formal redeployment plan.
AI & EMPLOYMENT
Updated research shares six findings on employment shifts since ChatGPT’s release. I expand on one related to early-career workers and include a recent Stanford webinar that goes deeper into the research.
Stanford's Digital Economy Lab recently released an updated 140-page research paper, Canaries in the Coal Mine? Six Facts About the Recent Employment Effects of Artificial Intelligence. Using ADP payroll data through mid-2026, the authors examine employment shifts since ChatGPT's release. One insight relates to a topic I’ve covered often in Talent Edge Weekly: AI's impact on entry-level and early-career roles. Employment among workers ages 22–25 in highly AI-exposed occupations is now about 19% below where it would be had it kept pace with similarly aged workers in less-exposed occupations, up from 15% a year ago. The decline appears driven primarily by reduced hiring rather than increased separations. The declines are concentrated among younger workers in roles relying more on “codified knowledge” AI can increasingly reproduce, while experienced workers in those same occupations show no comparable decline and rely more on tacit knowledge built through judgment and experience. These insights reinforce the need to answer this question: as AI takes on more entry-level tasks, what work should replace them so early-career employees can still build the judgment and experience needed for future roles and strengthen the talent pipeline? As a bonus, here is a one-hour Stanford webinar on the updated research and its Canaries Dashboard, which tracks AI's impact on hiring by occupation, age, and gender.
EARLY-CAREER AND REMOTE WORK
New research published in Administrative Science Quarterly, and repurposed for HBR, suggests remote work, not AI, is the stronger driver behind shrinking early-career opportunity.
Over the last few months, I have shared multiple resources on how AI is reshaping entry-level and early-career roles, from a section of Stanford's AI Index report on employment declines in AI-exposed junior positions to the World Economic Forum's report on safeguarding early-career pathways. New research published in Administrative Science Quarterly, and repurposed for this new Harvard Business Review article, introduces a different factor into this discussion: remote work. Researchers analyzed more than 50 million job postings across 28 European countries and ran a controlled experiment with 1,200 hiring managers, finding that remote roles required roughly 25 percent more skills, experience, and credentials than otherwise identical in-person roles, driven largely by the loss of informal, proximity-based mentoring that once let managers take a chance on potential over experience. When the authors tested AI exposure and remote work against each other directly, the remote work effect held while the AI effect largely disappeared. One implication for early-career strategy is that we should continue to widen the lens beyond AI and look at other factors, like remote work, shaping early-career outcomes.
AI & OLDER WORKERS
A recent analysis finds that workers 55 and older in highly AI-exposed jobs are leaving work earlier than expected, with the increase showing up mostly as unemployment rather than retirement.
Most of the current AI-and-jobs conversation, including my own recent coverage, has centered on entry-level role impact and the risk of failing to develop the next generation of leaders. But there are also impacts at the other end of the career tail: older workers ages 55 and older. A recent analysis from the Center for Retirement Research at Boston College finds that older, highly AI-exposed workers, such as programmers and accountants, are leaving work earlier than expected since ChatGPT's launch, with the increase showing up mostly as unemployment rather than retirement. For programmers, transitions out of work rose by more than 25 percent, compared with just 2 percent for painters, one of the lowest-exposure jobs studied. The author, Geoffrey Sanzenbacher, points to three drivers: automation directly displacing workers, employees choosing to exit rather than adapt, and, less commonly so far, AI-driven productivity gains that extend careers instead. The data so far suggests the first two are outweighing the third. A few practical questions: Do you have visibility into AI-driven exit patterns among workers 55 and older? Do your succession timelines account for earlier, involuntary exits? And are you equipping late-career workers in AI-exposed roles with a path to extend their careers, rather than exit? These and other questions have implications for workforce planning and talent strategy.
II. HR Impact and Value Creation
Explores how HR practitioners can continue to evolve their function to create even greater value for stakeholders; being more intentional about evaluating and selecting HR technology so that expected ROI can be achieved; and what it takes to build HR for the agentic era.
HR VALUE CREATION
Dave outlines how HR can continue to evolve to create even greater value for stakeholders. I tie it into a recent discussion with Dave in my private community for internal HR practitioners, Talent Edge Circle.
As HR practitioners explore new ways to create stakeholder value, I was excited to have Dave Ulrich, often called the Father of Modern HR, join my private community for internal HR practitioners, Talent Edge Circle, last month as a guest. We spent 90 minutes with Dave, also of The RBL Group, on the next evolution of stakeholder value through HR. This article by Dave lays out the shifts defining this moment for HR, built around the same four pivots we discussed. One point ties to his first pivot: pivoting from describing what HR does to why it matters to stakeholders. During our discussion, Dave shared the "so that" value chain, a thoughtful and practical exercise for connecting HR's impact to a stakeholder. For example: I want to build better leadership, so that our strategy happens, so that a customer buys more product, so that an investor invests more money. The "so that" value chain pushes past the first link ("build better leaders") until it answers what every stakeholder is really asking: so why does it matter to me? Try it: run an initiative you're pursuing through the "so that" value chain. If it breaks down before reaching stakeholder value, reevaluate it or think it through further. If you're part of my Talent Edge Circle, you can catch the replay of our discussion with Dave in our private online platform. Also, learn more about one of the premier programs that Dave and his colleagues at RBL run, The HR Learning Partnership (HRLP), a 5-day immersive program (with additional virtual modules) where a company sends a team of five to transform their HR function.
HR VALUE THROUGH HR TECH
My editable worksheet with starter questions to help assess HR tech solutions, covering areas such as business needs, vendor evaluation, cost and ROI, and more.
I am looking forward to attending the HR Tech Conference in October, where I'll see the latest advancements in HR technology and how they can unlock stakeholder value within organizations. As HR teams evaluate various HR tech solutions, here's my one-pager with questions across four areas, including Business Needs (e.g., What problems are we solving, and how will the technology help us do things more effectively than today?) and Vendor Evaluation (e.g., What percentage of the vendor's customers use the platform for the functionality we need, and with what results?). A third area, Cost and ROI, gets at things such as What is the total cost of ownership, including implementation and ongoing maintenance? How will we measure the ROI? Will the ROI be delivered in the near term or over a longer period of time? What value will be captured in year one, year two, year three, and beyond? This part of the business case is often overlooked, yet critical, since value from tech solutions tends to be unlocked gradually rather than immediately. On that note, a special thanks to Patti Phillips, Ph.D., CEO of ROI Institute, who recently joined my Talent Edge Circle as a guest speaker for a great discussion on ROI for talent initiatives and human capital. For those of you ready to demonstrate the impact and ROI of a specific initiative, consider joining the other 21,000+ practitioners who have participated in the ROI Institute’s ROI Certification program. And if you're part of my Talent Edge Circle community, RSVP in our private platform for my 11/5/26 session where I'll share my insights and key takeaways from the HR Tech conference.
AI & HR FUNCTION
A new article on building HR for the agentic era includes a framework mapping the automation potential of specific HR processes into three levels.
As many HR leaders and their teams continue to think through the impacts of AI on HR's work, a new McKinsey article offers a few useful ideas. The one part I want to highlight is Exhibit 1, which maps the automation potential of specific HR processes, from candidate screening to payroll to benefits administration, into three levels: 1) fully automated (an agent completes the work end to end, no human involvement), like generating a verification letter; 2) fully automated in delivery (an agent runs the workflow, but a human owns policy and exceptions), like payroll; and 3) augmented (a human leads, an agent supports), like a business partner using agent-assembled analytics in a succession review while still owning the judgment call. A visual like this can help facilitate conversations within HR teams about where AI may reshape their work, and how new ways of delivering that work will require changes in the HR skill set. As HR work moves toward augmented, more consultative, judgment-based work, HR practitioners will need to continue to build capabilities to match. Against this backdrop, you should check out a four-part series by Deloitte, HR Reimagined 2.0., along with downloadable slides, which helps explain how AI will continue to shape the HR function, unlocking more opportunities for HR to create stakeholder value.
III. Talent and HR Practices
Addresses strategic workforce planning; freeing up workforce capacity from existing teams; AI-enabled coaching; assessing team readiness before rolling out a new strategy; executing more actionable talent reviews; hiring based on a candidate's potential; and enabling internal mobility.
WORKFORCE PLANNING
My one-pager helps leaders determine whether additional hiring is needed or if other talent options can address the need.
Strategic workforce planning (SWP) is consistently ranked as a top priority by HR leaders. Yet it has one of the largest gaps between ranked importance and organizational capability. One costly result of ineffective SWP is a familiar cycle: organizations hire aggressively based on optimistic or insufficiently tested assumptions, lay people off when workforce supply exceeds demand, and then rehire when demand returns or essential capabilities prove harder to replace than expected. Not every workforce reduction is evidence of overhiring. But when organizations repeatedly hire, cut, and rehire for similar work, it is a signal to examine whether demand assumptions, work design, capacity planning, and talent alternatives are being sufficiently tested before costly workforce decisions are made. To help leaders identify where this risk may be building, I created a simple “Before You Add Headcount” diagnostic covering demand signals, work and capacity signals, and talent-alternative signals. Instead of asking only, “Do we need more people?” leaders can ask: “What work needs to be done, what is driving the need, and what is the best way to meet that work demand?” Testing these three signal areas before hiring enables leaders to make better and more informed talent decisions.
ORGANIZATIONAL CAPACITY
My one-pager shares 9 ways organizational ways of working can quietly diminish workforce capacity, with warning signs to watch for and one practical next step to address each one.
One objective of workforce planning is ensuring that an organization has the workforce capacity to execute its critical goals and priorities. While acquiring more people (e.g., employees, contractors, etc.) is one way to increase workforce capacity, another way is to unlock capacity of an organization's existing workforce through improved ways of working. Picture a team losing 30% of its time to low-value meetings, prolonged decision-making processes, and work duplicated because no one is clearly accountable for it. By streamlining meetings, speeding up decision-making, and assigning clear ownership for shared work, that same team can free up significant capacity. For example, Shopify reclaimed 76,500+ meeting hours by cutting recurring meetings and completed 25% more projects. AT&T saved 3.6 million hours and over $230 million in three years by eliminating outdated processes, tools, and policies hindering effective ways of working. Where is there trapped capacity on your team and organization? My one-pager covers 9 barriers that quietly limit workforce capacity, from priority misalignment to goal creep (where goals gradually expand without recalibrating priorities and resources) to meeting overload, with the warning signs to watch for and one practical next step for each. As a bonus, since goal creep is one of the barriers covered, I am resharing my other one-pager, to help teams recalibrate goals intentionally.
AI-ENABLED COACHING
A recently published 191-page open-access ebook spans 17 chapters on AI-enabled coaching, from leadership development applications to ethical standards.
Technology continues to create new opportunities for organizations to expand coaching at scale, prompting HR practitioners to determine where AI-enabled coaching fits within their broader coaching strategies. In fact, given growing Talent Edge Weekly reader interest in this topic, I recently invited Dr. Anna Tavis, Clinical Professor and Chair of the Human Capital Management Department at NYU and author of The Digital Coaching Revolution, to join my private community, Talent Edge Circle, as a guest speaker on the topic. We covered topics ranging from the democratization of coaching and the emergence of fully AI-powered coaches to privacy and data ownership. For those looking to explore the topic further, another useful resource is the recently released, open-access ebook, Coaching in the Age of AI, edited by Robert Wegener, Tamara Garcia, Nicky Terblanche, and Till Grossrieder. This 191-page volume spans 17 chapters covering everything from AI coaching for leadership development to ethical standards for AI coaching platforms and the future of the coaching profession. A physical book version is also available for purchase. And if you are part of Talent Edge Circle, you can also access the 90-minute replay of our recent discussion on AI-enabled coaching in our private member platform.
CHANGE READINESS & CAPACITY
A new article offers a framework for increasing organizational readiness before launching a new strategy, including three conditions leaders should assess and tactics for strengthening each one.
I recently shared a post on organizational capacity to absorb change, drawing on research showing employees can realistically take on only one or two major changes per year, even though leaders are planning three or four by 2027. That post focused on the volume of change across an organization. This HBR article picks up a different thread: even when the volume of change is reasonable, a single initiative can still fail if the right conditions were never built before launch. The author calls this the Readiness Triad: appetite (are people genuinely committed to the outcome and do they see it as theirs to shape), capacity (does the organization have the bandwidth, focus, and resilience to sustain the work), and skillset (the degree to which current capabilities match the actual demands of the work). McKinsey and BCG research cited in the article finds roughly 30% of large-scale change programs deliver expected value, with execution failures tracing to one of these gaps, not a flawed strategy. A few tactics proposed for each: for appetite, explicitly connect the work to something people value. For skillset, create low-stakes practice before the real stakes get high. For capacity, postpone one initiative so another can succeed, or shift resources to what matters most. On that last point, I am resharing my one-page template that helps leaders see all organizational changes in one view, so they can make strategic choices about resource allocation, timing, pacing, and other decisions that determine the success of change efforts.
TALENT REVIEWS
My cheat sheet on the three interrelated tactics that determine whether a talent review leads to action or just conversation.
Many organizations conduct some version of a talent review. While the specific topics covered will vary by organization, themes range from identifying employees who can advance into larger, more complex roles, to managing unwanted retention risk, to ensuring succession depth for critical roles. One thing I have learned over the years of running many talent reviews across different industries is the real differentiator is strong discipline on what happens after the talent review, not just during it. And that discipline is informed by answering three interrelated questions: 1) Action plan clarity: what specific action are we agreeing to take as a result of this review? 2) Business leader accountability: who outside of HR owns this action? 3) Follow up: What is the timing and expectation for reporting back on the progress and outcomes of the actions identified, and what is the success metric for each action? These three are not independent checkboxes but rather are interconnected. For example, a clear and owned action with no check in date rarely happens on time, and an action with a deadline but no named owner rarely gets prioritized. While these three areas sound easy and fundamental, they are not consistently executed and sometimes are missed completely. To help put this into practice, here is my cheat sheet with a few ideas that can help identify opportunities for enabling more impactful talent reviews.
HIRING FOR POTENTIAL
A new article argues for hiring based on potential, not pedigree, to uncover overlooked talent. I also reshare my hidden talent infographic.
One persistent challenge in hiring is the gap between how objective organizations believe their process is and how objective it actually is. A new article by Emilio J. Castilla, professor at MIT Sloan and author of The Meritocracy Paradox, examines this challenge. His research has found that the more confident a hiring team feels its process is fair and merit-based, the less vigilant it becomes about the bias that shapes who gets hired, which can result in hiring criteria that unintentionally exclude capable candidates from less traditional backgrounds. His alternative is what he calls an opportunity mindset: hire people who are good enough to succeed, then build the onboarding, mentorship, and development structures that help good people become great. He illustrates the shift with a simple example. Say an engineering team has an open entry-level role. The traditional approach narrows the pipeline to candidates with a four-year computer science degree from a highly ranked university. Under an opportunity mindset, the team instead asks what skills the role actually requires, then widens the pool to include community college graduates, career switchers, and parents returning to the workforce. As a bonus, I am resharing my post and infographic on eight segments of “hidden workers” and the red flags that may indicate your hiring process is overlooking them.
INTERNAL MOBILITY RESEARCH
My one-pager highlights seven research studies on internal mobility, translating each into a key finding and practical implication for talent leaders.
With many organizations increasingly focused on moving internal talent to opportunities, from full-time roles and lateral moves to short-term projects and gigs, we’ve been spending time in my private community, Talent Edge Circle, discussing ways to strengthen internal mobility. Aside from sharing practices and lessons learned from implementations within our own organizations, we also draw from academic and scientific research to help shape and test different strategies. With that in mind, I pulled together seven studies that can help others stay current on the research. They cover different factors impacting internal mobility, including talent hoarding, how development breadth affects lateral movement, how certain jobs shape future career options, the role managers play in enabling mobility, how internal talent markets balance employee preferences with business needs, and the career value of lateral moves. The studies were published in journals such as the American Economic Review, Academy of Management Journal, Quarterly Journal of Economics, and Management Science. Since some full articles require a subscription, my one-pager summarizes what each study examined, a key finding, and one potential implication for talent practice, with links to the original research so you can explore further.
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