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Welcome to this special Best of September issue of Talent Edge Weekly.

First, a shout-out to Lindsey Peterson, Sr. Program Manager, People Team at Atlassian, for referring new subscribers to Talent Edge Weekly. Thank you, Lindsey, for your support of this newsletter!

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PRESENTED BY 365Talents 
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The Strategic Buyer Guide to Skills Intelligence

If skills are on your 2027 agenda, Q4 is when that agenda gets written. Budgets close, priorities lock, and the platform decision either happens now or slips another year.

The hard part isn't deciding to invest. It's telling platforms apart.

Every vendor says "skills-based," and most demos look alike: a taxonomy, a dashboard, a matching feature. The gap between a tool that stores skills and one that keeps them current usually shows up months after you've signed.

This guide helps you spot that gap before the decision is made. Inside:

• The 9 capabilities that separate real skills intelligence from a skills catalogue

• Questions to ask vendors for each one

• A weighted scoring grid to compare platforms and a final evaluation checklist

• How Alstom rolled out skills intelligence to 55,000 employees in 9 weeks

P.S. Already building your 2027 shortlist? See what live skills intelligence looks like with your own data. Book a demo

I. AI THROUGH THE LENS OF WORKFORCE STRATEGY AND LABOR MARKETS

Examines how AI is reshaping Chief HR Officer priorities, the labor force, the ROI and business value of AI investments, how AI is shifting the use of internal versus external expertise, and how these changes are influencing strategic workforce planning.

CHIEF HR OFFICER AND AI

Designing the Thinking Organization: 5 Focus Areas for CHROs as AI Reshapes Work | IBM Institute for Business Value

A new 52-page report identifies 10 capabilities and five priority areas CHROs can focus on to turn AI-created capacity into business growth.

As AI reshapes work, many Chief HR Officers continue to reassess how these changes affect the capabilities their organizations need to execute their business strategies. A new 52-page IBM Institute for Business Value report, drawing on surveys of 1,500 CHROs and other executives responsible for workforce strategy, as well as 8,800 employees, identifies 10 capabilities across five focus areas for turning AI-created capacity into business growth. While the full report explores the detailed implications for CHROs and their teams, page 47 summarizes the five areas and suggested actions for each. The one I want to zoom in on is advancing skill growth while preventing skill erosion. Although 80% of organizations surveyed have a reskilling plan for helping employees work with AI-based technology, those plans may overlook skill erosion: the gradual weakening of key skills employees already have, such as critical thinking, when AI reduces their opportunities to practice and develop them. It is a top concern for 46% of executives, and 60% of employees say they are experiencing it. This has implications for workforce planning and talent development. Where is AI taking on work that helps people build and maintain capabilities such as judgment? How can we redesign work and development so the skills underlying those capabilities continue to grow? These questions belong in our skills and reskilling strategies alongside plans to build new AI-related skills.

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AI AND THE LABOR FORCE

AI and the Labor Force: Scenarios for Stakeholders | The Conference Board

A new 20-page report outlines four scenarios for how AI could reshape the labor force, with talent implications for each. I zoom in on the risks of premature entry-level job cuts.

I have written extensively about scenario planning and its value in preparing for the different futures an organization might face, including the talent decisions involved. This new 20-page report from The Conference Board outlines four AI-driven scenarios and potential talent implications for each. While there are many insights and recommendations in the report, one implication I want to focus on is the future of early-career and entry-level roles. One recommendation for CEOs is to critically evaluate premature decisions to reduce these roles based on the assumption that AI will absorb the tasks they perform. The report warns that these decisions, coupled with senior departures, can "erode critical institutional knowledge." One of the many suggestions I have made in previous posts on this topic is that, rather than reactively reducing or eliminating these roles, organizations should reimagine their purpose in an AI-enabled environment. One recent example comes from Geotab, which shared how its student talent program puts this into practice: pairing interns with its internal AI assistant on real business challenges while teaching them to evaluate outputs critically and apply human oversight. Geotab has brought in more than 1,000 interns, with many going on to full-time roles after graduation. For organizations reimagining their entry-level roles while protecting their future leadership pipeline, I am resharing this June 2026 World Economic Forum report, which offers several tactics.

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AI AND ROI

The State of AI in 2026: On The Road to ROI | QuantumBlack AI by McKinsey

A new report examines several aspects of AI, from ROI on investments to the economics of scaling AI. I expand on the latter and why workforce plans need to account for the actual economics of AI capacity.

McKinsey’s new 31-page State of AI in 2026 report includes several useful findings from its global survey of 1,719 participants across 97 countries. One data point that caught my attention is the economics of scaling AI. About 20% of respondents say AI-related operating costs, including token costs (the units of data processed when using an AI model), have caused some organizations to limit how much they use AI. The issue becomes even more pronounced among AI high performers, which McKinsey defines as organizations that attribute at least 5% of EBIT to AI use and describe its impact as significant. This reinforces an important point: running AI at scale is not free or unlimited, and workforce plans need to account for the actual economics of scaling it. Another related data point: 39% of respondents expect AI to decrease their organization’s overall head count over the next year. From my standpoint, layering in the token and operating cost constraints noted above, the 39% headcount expectation raises an important second-order consideration: if headcount is reduced based partly on the assumption that AI will take on more of the workload, but AI use later becomes limited by cost or other constraints, workload demand could outpace the organization's capacity to deliver. AI’s capabilities are tremendous and will increasingly need to be part of workforce models. At the same time, we need to be intentional about these decisions so we can create ROI from AI’s benefits while reducing unintended consequences.

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AI & WORKFORCE PLANNING

The Vanishing Advantage of Specialization: AI, Knowledge Utilization, and the Boundary of the Firm | Draft for the Brookings Papers on Economic Activity

A new 25-page draft paper examines how AI may shift specialist work, such as consulting, from outside firms to in-house teams. I share questions to consider.

As AI reshapes work, where should organizations get specialized expertise: internally or from outside firms? In this 25-page Brookings draft paper, Luis Garicano of the London School of Economics examines the advantage behind specialization: firms such as law firms and consultancies can spread the cost of acquiring knowledge across many clients. But as AI lowers that cost, organizations may bring more frequent specialist work in-house while turning to outside experts for rarer, harder problems. For example, a company might use AI for market analyses once sent to consultants, while seeking outside help for an unfamiliar strategic challenge. Garicano’s analysis shows that, of the 337,000 jobs added by lawyers, accountants, software developers, and management analysts between 2022 and 2025, 99% were outside law firms, accounting firms, computer systems design firms, and consultancies, respectively. Stated differently, nearly all this growth occurred at employers other than those selling these services. The data do not show whether this reflects work moving in-house from outside firms or whether AI caused the shift. Even so, the findings raise questions that workforce planners can start to consider: Which specialist work should we build internally versus source externally? What criteria should guide those decisions? What risks would each choice create, and which are we willing to take on? What expertise will employees need to evaluate AI’s work and recognize when to bring in outside specialists?

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AI & WORKFORCE PLANNING

The AI Future: A Strategic Guide to AI In Workforce Planning | Deloitte

A new 13-page paper helps rethink how SWP continues to evolve, especially in the context of AI. I share where this fits alongside my own SWP resources and the practical approach I continue to reinforce.

I have written extensively on strategic workforce planning (SWP) over the years, including a book chapter in the SIOP Professional Practice Series, an article in People + Strategy, and numerous resources I’ve created and published on my website. One example is this two-page resource, which includes a sample business case slide for SWP and a one-page “good enough” plan to get started. And in my private community for internal HR practitioners, Talent Edge Circle, we also go deeper through practical discussions on SWP. One point I continue to reinforce across all of these channels is that a persistent gap in SWP often comes down to organizations waiting for perfect plans, technology, or data before they start. My view is the opposite: start where you are, build the foundation, get some quick wins, learn and adjust, and evolve from there. With that in mind, a new Deloitte paper on how SWP is evolving with AI caught my attention, particularly two slides. The first, on page 12, lays out a three-year roadmap from foundational data work to what Deloitte calls an always-on, AI-orchestrated process, helping organizations phase the capability rather than get stuck in “all or nothing” thinking. The second, on page 10, maps how six stakeholder groups, from business leaders to IT, need to show up differently as SWP evolves with AI. Frameworks like these can help simplify a complex topic and translate it into action. Hopefully, it helps you build some momentum.

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II. AI THROUGH THE LENS OF WORK REDESIGN, ORGANIZATION DESIGN, AND CAPABILITY BUILDING

Explores how AI is reshaping work and roles, organization structures and decision rights, human-AI collaboration, and the skills and capabilities needed to operate effectively in an AI-enabled environment.

AI AND TALENT MANAGEMENT

How AI Is Changing Talent, Not Just Tasks: Rethinking Where Human Judgment Matters Most | HBR IdeaCast

A new 33-minute podcast conversation with Salesforce's Paula Goldman on how AI is rewriting the talent playbook. I zoom in on work redesign.

This new, 33-minute podcast episode is the first of a four-part series on how AI is changing leadership, with this episode focused on talent management. Host Adi Ignatius interviews Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer and author of Manage the Machine: How to Harness Human-AI Collaboration at Work. They discuss entry-level hiring, internal mobility, coaching, and performance evaluation. I want to highlight work redesign. Goldman describes how a division within Salesforce’s HR team uses AI with business units to map tasks to skills, identify changing work and where human capabilities are becoming more important, and redesign roles. From my standpoint, mapping tasks to skills can help identify where AI, people, or both can perform work most effectively. Mapping every task can become unwieldy, especially without the technology, data, and capacity to maintain that level of detail. It might be more practical to start at the activity level (a step in a process, such as processing a customer refund), rather than cataloging every task within it (a specific action, such as checking the purchase record against the refund policy), and go into more detail where needed. Another factor often overlooked is decision rights: even if AI checks whether a refund qualifies, who has authority to approve it?

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AI & WORK DESIGN

AI Transformation Requires Redesigning Work, Not Cutting Roles | Harvard Business Review

A new article shares evidence that AI-driven layoffs are often falling short of expected results and offers tactics for making better workforce decisions. I also share one of my bonus cheat sheets related to the topic.

Over the past few years, I have tracked organizational layoffs and job cuts through my layoff tracker, largely to monitor shifts in the labor and job market. One observation I have made over the past several months is an increase in the number of layoff announcements citing AI as a reason for workforce reductions. I have also shared in several posts how I believe many of these decisions are being made prematurely, before organizations fully understand how AI is actually changing the work. One example is the elimination or reduction of entry-level roles. A new HBR article puts numbers behind this broader concern, drawing on several data points. For example, it cites a survey of 600 HR leaders whose organizations made AI-driven layoffs, finding that only 8.4% said the restructuring delivered as promised, while one in three reported losing critical skills they had not anticipated losing. The authors argue the more effective approach starts with the work itself, not headcount targets: breaking roles into tasks and redesigning processes around the right mix of human and AI capabilities. They share several ideas for implementing this. As a bonus, I want to reshare one of my one-pagers that tackles another issue related to layoffs: overhiring, which can partly be caused by defaulting to a decision to backfill a recently vacant role without truly understanding whether it is needed. My one-page cheat sheet includes targeted questions to help leaders make that decision, reducing the costly cycle of overhiring, job cuts, and rehiring.

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AI & ORGANIZATION DESIGN

Why C-Suite Leaders Need to Rethink Organization Design for The Agentic Era | Deloitte

A new article explores how AI is reshaping organization design, from spans of control and layers to decision rights. I tie in insights from a recent Talent Edge Circle discussion we had on the topic.

A few weeks ago, members of my private community for internal HR practitioners, Talent Edge Circle, came together to discuss AI’s impact on organization design. The discussion ranged from spans of control, organizational layers, and decision rights to reimagining early-career roles and how work may flow differently with AI. For example, one part of the discussion focused on span of control and led us to question whether it should shift from a standard benchmark to a more contextual design choice. That could mean considering factors such as work complexity, process standardization, team maturity, and how much routine coordination AI is absorbing. The result may be wider spans in some areas and tighter spans in others. Within the one-hour discussion, we not only highlighted key issues across these topics but also came away with frameworks and working principles that can help inform these decisions. That is one reason this new Deloitte article caught my attention. It expands on many of these organization design topics. For example, on decision rights, the article extends the traditional RACI framework by adding an “O” for override authority, clarifying who can challenge, modify, or stop an AI agent’s output. As AI takes on more tasks and decisions, organizations need to be clear about who remains accountable for the outcome and who can intervene when needed. If you are a Talent Edge Circle member, you can catch the full replay of our discussion, along with some of the resources we developed following it, in the resource library on our private platform.

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AI AND ORGANIZATION DESIGN

Designing Decision Rights for AI | MIT Center for Information Systems Research

A practical framework for determining how humans and autonomous AI should share decision-making based on two factors: ambiguity and risk.

One of the reasons I value frameworks, and spend time developing many of my own, is that they help organize and simplify complex topics. That simplification is critical because it builds shared understanding, fosters better discussion, and enables more informed and faster decision-making. One topic where frameworks can be particularly helpful is how organizations redesign work around AI, including decision rights: Where should AI have authority to decide or act? Where should humans remain involved? Who ultimately owns the outcome? These were among the questions that surfaced in a recent discussion in my private community for HR practitioners, Talent Edge Circle, on AI and organization design. Against this backdrop, I am sharing an MIT CISR framework for determining how humans and autonomous AI should participate in decisions based on two factors: ambiguity and risk. Ambiguity reflects how clearly the available information points to an answer, while risk reflects the consequences of being wrong. The result is a 2×2 framework with four types of decisions (routine, consequential, exploratory, and strategic), helping clarify the appropriate role for AI, where human judgment is most important, and who remains accountable. For HR, the opportunity is twofold: help the organization work through these decision-rights questions, while also applying the same discipline within HR itself.

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AI AND WORK TASKS

Human–AI Collaboration at Scale: Task Criticality, Agency, and Friction Across 250,000 Conversations | Stanford SALT Lab

A new Stanford study analyzes 250,000 real Claude conversations to reveal how people actually delegate work to AI and where the collaboration breaks down.

Most of what we know about how employees actually use AI at work comes from surveys and self-reports. However, a new Stanford study offers a rigorous behavioral look instead, and it is the first of three studies from a new Anthropic pilot giving outside research teams (Stanford's SALT Lab, Oxford's Human Information Processing Lab, and METR) privacy preserving access to real Claude usage data. Each team received access to roughly 250,000 real Claude conversations. Here are just two of the many findings from Stanford's report (the first to be released) along with some of the practical implications. 1) 56% of the tasks people brought to AI were classified as consequential or high stakes, meaning work that is hard to reverse and carries real professional or financial weight, concentrated in advisory and professional domains. Implication: this challenges the assumption that employees mostly hand AI low stakes work, and suggests governance should focus on the majority of work that actually carries higher stakes. 2) friction, meaning moments where the human-AI collaboration broke down or required correction, showed up in nearly half of all conversations, but researchers classified much of it as productive, since working through it tended to improve the output or deepen understanding of the issue. Implication: organizations can frame this kind of friction as a normal, even necessary, part of working with AI rather than a sign the tool has failed.

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AI AND SKILLS

The Value of Prompting Skills | IZA Institute of Labor Economics

A new study with German hiring managers measures the actual hiring and pay premium for AI prompting skills, and finds they raise the bar for a role rather than replacing core expertise.

Last week, I covered a new International Labour Organization report arguing that AI literacy is becoming a foundational expected skill rather than a nice to have. A new IZA Institute of Labor Economics study drills into one subset of that literacy, prompting skills: the ability to formulate instructions, evaluate outputs, and deploy workflows. Researchers had decision makers from 992 German firms repeatedly choose between hypothetical applicants varying in prompting skills, skills gaps, and social skills, yielding 15,660 hiring choices. Applicants with stronger prompting skills had a 4 to 5 percent higher selection probability, and employers paid roughly 2 percent more for those skills. At larger and AI adopting firms, that advantage nearly doubled to about 9 percent. While prompting skills mattered, it was not the most important skill. Employers valued job fit and social skills far more, and prompting did not offset gaps in occupation specific knowledge, suggesting it is becoming an added expectation rather than a substitute for expertise. There are limitations to the study: it was a hypothetical experiment, not real hiring decisions, conducted in Germany amid rapid AI adoption, and not yet peer reviewed, though it still offers valuable insight. From my standpoint, as various aspects of AI fluency become part of hiring decisions, we need real ways to evaluate and develop it, not just recruit for it.

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AI FLUENCY

The AI Literacy Development Canvas: Assessing and Building AI Literacy in Organizations | Business Horizons

A research paper introduces practical frameworks for assessing and building AI fluency and includes a real case study. I also share my bonus slides for connecting talent initiatives to business priorities.

Many organizations are focusing on building AI fluency to tap the potential of AI investments in their workforce. To support this effort, I recently made a post about a report by the International Labour Organization that expanded on the topic, including framing AI fluency into three categories. To build on that resource, I wanted to share a paper offering two practical frameworks: the AI Literacy Assessment Matrix, which evaluates AI skills across job levels, and the AI Literacy Development Canvas, a strategic planning tool for investing in AI training. The paper's appendix also includes prompting questions to help walk through the Canvas. What I find particularly useful is that the paper also grounds these frameworks in a real case study. PharmaCo, a global pharmaceutical company, used both tools to identify literacy gaps across executives, middle managers, and frontline staff, then built a phased rollout from pilot to company-wide adoption, including clear metrics of success tied to business priorities. This reinforces something I often emphasize: talent initiatives resonate most when anchored in the real business problem being solved, with a clear and "good enough" plan to start and build momentum. With this as the backdrop, I am resharing my two-slide resource on framing a talent initiative around business needs while jumpstarting ideas for phased execution. As it relates to AI fluency, I am looking forward to having Tracy St. Dic, VP of Global Talent at Zapier, join Talent Edge Circle for an upcoming discussion on how Zapier is building AI fluency in its organization.

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III. TALENT PRACTICES: PERFORMANCE, CAREERS, MOBILITY, AND SUCCESSION

Covers approaches for strengthening performance management and goal execution, employee reactions to human, AI, and hybrid performance feedback, career growth beyond promotion, accelerating internal talent movement, and assessing leadership and successor readiness.

PERFORMANCE MANAGEMENT

3 Performance Management Tools to Accelerate Execution Through Year-End | Brian Heger

Three of my one-pagers to help leaders strengthen execution by recalibrating goals and capacity, improving ways of working, and reallocating resources to critical priorities.

As organizations quickly approach the final quarter of the calendar year, there is still time to influence critical business objectives. I am resharing three of my performance management tools that can help leaders accelerate execution by focusing on three areas. 1) Recalibrate goals, priorities, and capacity: As new work gets added, this tool helps determine what should change, stop, or receive additional resources so teams do not simply absorb more work without making trade-offs. 2) Examine ways of working: Even well-defined goals can be undermined by slow decisions, unclear ownership, excessive approvals, or other operating barriers. This tool helps identify where the way work gets done may be constraining performance. 3) Reallocate resources to priority work: Strategy execution ultimately depends on whether critical priorities have enough talent, budget, and capacity behind them. This tool helps leaders question whether resources are still aligned with what matters most. Taken together, the three tools reinforce a simple point: performance management is not only about setting goals and evaluating results. It is also about continually removing the barriers that keep people and teams from delivering them. As you enter the next quarter, where is there an opportunity to use one or more of these levers to accelerate execution on your most critical objectives?

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PERFORMANCE MANAGEMENT AND AI

Source Matters: Comparing Employee Reactions to Human, AI, and Hybrid Performance Feedback | Frontiers in Psychology

A new study compares anticipated reactions to identical performance feedback described as manager-written, AI-generated, or a combination of both, raising questions about how managers use AI in performance reviews.

As more managers begin using AI to help write performance reviews, I think we need to pay attention to how employees experience the feedback they receive. A new study in Frontiers in Psychology offers an interesting example. Researchers gave 192 employed adults the same excerpt from a hypothetical performance review, but varied what they were told about how it was produced: 1) written by the manager, 2) generated by AI, 3) generated by AI and reviewed by the manager, or 4) written by the manager and refined with AI. Compared with feedback described as manager-written, feedback described as fully AI-generated produced significantly less favorable anticipated reactions on eight of nine measures. One result relates to the employee’s willingness to ask the manager for help. On a seven-point scale, it averaged 3.43 for the AI-generated feedback versus 5.31 for the manager-written feedback, a statistically significant difference. Feedback described as manager-written and AI-refined did not differ significantly from fully manager-written feedback on the measured outcomes. The study has limitations, including its hypothetical setting and relatively small sample from one country. Still, as year-end reviews soon approach for many, it raises a practical question: Are we providing guidance to managers on how AI can be of benefit and where it can lead to unintended consequences? That guidance could shape how employees receive feedback and whether they feel comfortable seeking their manager’s help and support afterward.

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CAREER DEVELOPMENT

Most Careers Stall at Mid-Level, No Matter How Long People Work | Harvard Business School Working Knowledge

A new analysis of 150 million resumes finds that most careers plateau well below senior leadership. I share why career growth needs to extend beyond promotion.

As internal mobility and career development remain top priorities, I am always interested in data on how careers progress. A new Harvard Business School analysis looks at one dimension: upward advancement. Based on this working paper, Professor Paul Gompers and coauthors analyzed 150 million resumes to build a seniority measure, defined as the typical number of years it takes someone to reach a given job title within their industry and firm size. Using this measure, more than half of those with 10 years of experience had kept pace, reaching seniority level 10 or higher, but only 19% of those with 20 years had kept pace to reach level 20. What this reinforces for me is that career growth cannot be defined only by upward advancement. Deepening expertise, expanding scope, lateral moves into higher-value work, and broader business exposure can all be meaningful forms of progression. The challenge is making sure employees see them that way, a matter of positioning and communication. One tactic I like for creating these opportunities is project-based work, or short-term work tied to a real business need outside someone's regular responsibilities. It can give employees new skills, experiences, and connections while helping the organization move talent to priority work. Internal talent marketplaces, often enabled by technology, can make these opportunities more visible and accessible. With this in mind, I'm resharing my one-pager with eight questions to help identify potential projects for an organization's internal talent marketplace.

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INTERNAL MOBILITY

Managing Internal Mobility: How Social and Human Capital Shape Movers' Integration Over Time | Journal of Applied Psychology

A new study examines what helps employees succeed after an internal move. I translate one finding into practical ideas for strengthening the transition.

With internal mobility (IM) being a key talent strategy, I’ve shared many resources on IM, from reducing talent hoarding and restrictive policies to using technology to enable internal movement. While many IM strategies focus on surfacing opportunities, matching people to roles, and making internal movement easier, other important aspects receive less attention, such as how we set employees up for success once they move into a new internal opportunity. A new Journal of Applied Psychology paper offers useful insights on this question. While the full paper requires journal access, one finding that stands out is that prior relationships with members of the team they are moving into can accelerate the transition and help set the employee up for success. For me, this is useful because organizations can influence it through how internal moves are structured. For example, if talent processes such as talent reviews and succession planning identify people for potential future roles, we can create early exposure through project work or cross-functional assignments with those teams. This can build relationships and credibility while giving the future team a view of the employee’s performance and capabilities. Internal mobility success is not just about matching people to opportunities, but setting them up for success once they get there. And that work can often begin before a full-time opportunity becomes available. If you’re conducting talent reviews, one question to ask is: How are we building early relationships and exposure into the talent actions that follow?

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SUCCESSION PLANNING

Leadership Readiness as a Measurable Construct: An Agency-Grounded, Three-Layer Architecture for the Leadership Transition | BlackRidge Institute

A conceptual paper introduces developmental readiness as another lens for successor readiness. I connect it to additional ways of making readiness discussions more practical.

One focus of succession planning is accelerating a successor’s readiness to perform effectively in a role they do not yet occupy. However, we can improve how we define readiness by going beyond general labels such as ready now, ready later, and future potential, often tied to arbitrary timeframes (e.g., 18 to 24 months). While these labels can provide useful directional guidance, by themselves they lack the specificity needed for more meaningful succession planning. I’ve shared other ways to think about readiness, such as the number of development moves away a successor is from being ready. Another is asking ready for what likely scenario?, since a turnaround, rapid growth, or transformation can influence readiness for the situation a successor may inherit. A recent conceptual paper adds another dimension: developmental readiness, or how ready someone is to benefit from development. Two successors may have similar development gaps but differ based on motivation, career stage, life circumstances, or whether they actually want the role. This is one reason transparency matters: listing someone who has little interest in the role or cannot pursue the required development may overstate bench strength and create succession risk. Where could you improve how you define and accelerate readiness to better account for business needs, successor aspirations, and what each successor is willing and able to do to become ready? These are the types of practical discussions we have in my private community, Talent Edge Circle.

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SUCCESSION PLANNING

Succession Planning: Successor Readiness Considerations Across 8 Scenarios | Brian Heger

An expanded version of my recent one-pager on eight succession scenarios, with examples and questions to guide readiness and development discussions.

A few weeks ago, I shared my one-page resource on eight scenarios that can shape successor readiness, including a turnaround, rapid growth, culture change, and major transformation. The idea was to add another question to succession discussions: Beyond whether someone is ready for a role, are they ready for the situation they are likely to inherit, which can vary over time? Given the popularity of that resource, I expanded it into a more detailed guide for Talent Edge Circle, my private community for internal HR practitioners. I’m sharing a few slides from that guide here to help you and your teams apply this thinking. For each scenario, the slides describe what it is, gives business examples, explains when it is most relevant, and suggests selection criteria to consider. It also includes four questions for succession and talent review discussions, such as: If the business shifts to a different scenario than the one we are currently planning for, how would this successor’s fit change? Assessing readiness against a role’s requirements remains important, and considering the situation a successor may inherit helps expand how we evaluate readiness and prepare people to succeed. I hope these sample slides from Talent Edge Circle help you bring that dimension into your succession planning and development discussions.

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JOB CUTS TRACKER

Here is my tracker, which includes announcements from a segment of organizations that have announced job cuts and layoffs since the start of 2023.

A few announcements from September:

• Campbell's Company (NASDAQ: CPB). The packaged food company reduced its salaried workforce by approximately 13% through voluntary early retirements and layoffs, alongside the closure of two snack plants. The cuts are part of a new $500 million cost-savings program targeting fiscal 2030, disclosed during the company's fourth-quarter earnings call.

• CarMax (NYSE: KMX). The used-car retailer cut approximately 145 corporate roles, about 4% of its corporate staff, to operate with a leaner corporate workforce. It is the company's third round of cuts in less than a year and the first under CEO Keith Barr.

• Jaguar Land Rover. The luxury automaker plans to cut about 4,000 jobs globally over the next two years as part of a £1.7 billion cost-savings push. The company cited intense competition from Chinese EV makers, US tariffs, and the fallout from a recent cyberattack.

• Uber Technologies (NYSE: UBER). The ride-hailing company plans to lay off about 3,300 employees, or 10% of its global workforce, in its largest cuts since the pandemic. The restructuring will flatten management layers and redirect savings toward autonomous-vehicle investment.

• The Walt Disney Company (NYSE: DIS). The entertainment company laid off a few hundred employees, mostly in human resources and technology. It is the third round of cuts this year under CEO Josh D'Amaro.

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CHIEF HR OFFICER MOVEMENT

In September, I tracked 73 hires, promotions, and resignations in the Chief HR Officer (CHRO) role through CHROs on the Go, my subscription-based digital platform that monitors movement in and out of the CHRO role.

A few headlines from September:

• A.P. Moller - Maersk (COPENHAGEN, DENMARK) [OTCMKTS: AMKBY] — an integrated logistics company — announced that Roberta Duarte will join as Chief People Officer, succeeding Susana Elvira. Duarte previously spent a decade with Maersk from 2014 to 2024, before joining Werfen, where she currently serves as Chief People Officer.

• Exemplar Luxury Group​ (NEW YORK) — formerly Saks Global, the luxury collective uniting Neiman Marcus, Saks Fifth Avenue and Bergdorf Goodman— announced the appointment of​ Gretchen Koback Pursel​ as Chief People Officer, effective September 28. Koback Pursel succeeds​ Sarah Garber​, who is leaving the organization after 15 years of leadership and service. Koback Pursel joins from Equinox, where she served as Chief People Officer.

• JPMorgan Chase (NEW YORK, NEW YORK) [NYSE: JPM] — a financial services firm with operations worldwide — named Mark O'Donovan (no LinkedIn found) its next Head of HR, effective January 2027, succeeding the retiring Robin Leopold, who joined JPMorgan Chase in 2010 and has led HR since 2018. O'Donovan, a 30-year JPMorgan veteran, is currently CEO of International Consumer Banking. O'Donovan and Leopold will work together on the transition during the first quarter of 2027. 

To access all 73 detailed announcements from September and +5,000 archived announcements, join CHROs on the Go. It’s the easiest way to stay informed about movement in and out of the Chief HR Officer role. Monthly + annual subscriptions available.

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My Private Community for Internal HR

Talent Edge Circle

We're now in October and the timeframe to make real progress on this year’s most critical talent priorities is shortening.

If you want to go deeper with me and other internal HR practitioners on talent topics tied to your most critical priorities, I invite you to learn more about my private community, Talent Edge Circle.

The goal is simple: to help you cut through the noise and complexity so you can accelerate the execution of your critical talent priorities.

With only 3 months left to make an impact in 2026, give yourself the edge now.

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