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Welcome to this special Best of October issue of Talent Edge Weekly!

First, a shout-out to Shavit Bar-Nahum, Head of Talent Strategy & Development at Standard Industries, for referring new subscribers to Talent Edge Weekly. Thank you, Shavit, for your support of this newsletter!

🗞️ Not subscribed to Talent Edge Weekly? Subscribe now and immediately get 5 of my PDF cheat sheets!

PRESENTED BY TechWolf

Introducing TechWolf’s Work Intelligence Index 

How the world’s largest companies are navigating AI’s workforce impact, based on 2 billion data points

As organizations face mounting pressure to understand how AI is reshaping work, TechWolf’s Work Intelligence Index offers a first-of-its-kind look into how the 1,500 largest companies in the world are adapting.

The Index goes beyond skills; it provides a task-level view of work, helping leaders understand:

- What work is being done
- How it’s changing, and
- Where humans, AI, or both will perform it.

How it works

1. Tasks, not titles: TechWolf’s AI breaks down jobs into tasks, enabling a clear, consistent understanding of work across roles and industries.

2. AI-readiness lens: Each task is scored based on its potential to be automated, augmented, or remain human-led, across six dimensions, including empathy and complexity.

3. Decision-ready insight: This clarity helps leaders guide responsible workforce transformation, focusing on skills, role redesign, and change management.

“Solutions like this should be the very starting point for workforce transformation and for thoughtful work design. And it’s already here.”
— Diane Gherson, former CHRO, IBM & Board Director

THIS MONTH’S CONTENT

The Best of October includes 16 of the most popular resources from the October issues of Talent Edge Weekly. They're organized into two sections:  

1. 🤖 AI & The Future of Work. AI’s impact beyond knowledge-worker roles; determining when work should be automated, augmented through AI–human collaboration, or remain human-led; using AI to identify emerging skill gaps; shifting toward agentic human–AI collaboration models; and moving from AI experimentation to measurable business impact.

2. 🛠️ Talent Practices: Performance review calibration; strengthening talent reviews and identifying key talent segments; determining appropriate levels of succession transparency; aligning succession actions to scenario types; leadership effectiveness; embedding learning into the flow of work; adopting skills-first talent practices; reducing barriers to internal mobility; evaluating when roles should or should not be backfilled; and integrating scenario planning into workforce discussions.

This issue has many bonus resources.

✂️ Since this special issue contains a large amount of content, it may get cut off by some email providers. If you prefer, you can read it online.

🗓️ Lastly, for those of you who are part of my private community for internal HR practitioners, Talent Edge Circle, this is a reminder that Allan Church, Ph.D will be joining us this Wednesday, 11/5, for a 90-minute discussion on high-potential identification and succession planning. I’ll see you then!

AI’S IMPACT BEYOND KNOWLEDGE WORK

Jobs of Tomorrow: Technology and the Future of the World’s Largest Workforces | World Economic Forum

A 17-page white paper that explores how AI and other emerging technologies will reshape 80% of the global workforce—far beyond knowledge-worker jobs.

Discussions about AI have largely focused on its impact on white-collar and knowledge-worker jobs. But this new World Economic Forum paper shows how AI’s influence extends far beyond office roles. It explores how four technologies—AI, robotics, energy tech, and network and sensing technologies—will reshape seven job families that make up 80% of global employment: agriculture, manufacturing, construction, retail and wholesale trade, transport and logistics, business and management, and healthcare. The scale of opportunity, disruption, and workforce challenges varies widely across these sectors, reinforcing the need for tailored strategies. Agriculture—the world’s largest workforce, employing one-quarter of all workers—shows both promise and complexity. In South America, drones now transport harvested banana bunches from steep plantations, improving productivity and safety. Precision agriculture—powered by sensors, drones, and AI-driven analytics—enables real-time monitoring of soil, crops, and water use, reducing manual labor and creating roles such as drone operators, agritech technicians, and data analysts. In higher-income regions, autonomous tractors, robotic harvesters, and automated irrigation help address labor shortages. At the same time, most agricultural workers are smallholder farmers in low-income countries, where limited access to capital and digital infrastructure can constrain adoption. Without intentional efforts to expand access, technology could widen inequality and displace vulnerable workers. The paper provides many insights that are useful for informing workforce plans and technology investments.

Read paper

AI AND WORK TASKS

The Gen AI Playbook for Organizations | Harvard Business Review

Offers a framework for deciding which tasks are best automated, handled with AI–human collaboration, or kept human-led.

To unlock the value of AI in the workplace, a critical step is determining which work tasks should be automated, supported through AI–human collaboration, or remain human-led. Given the scale and complexity of this effort, frameworks can help leaders prioritize where AI creates value without introducing unacceptable risk. A new article offers a practical framework that evaluates tasks across two dimensions: 1) cost of errors—ranging from low impact (e.g., a missed nuance in a draft) to high impact (e.g., legal liability, reputational damage, or flawed medical guidance), and 2) type of knowledge required—whether the task relies on explicit, structured data or tacit knowledge such as empathy, ethical reasoning, intuition, or contextual judgment. These dimensions form a 2×2 matrix that groups tasks into four categories: Creative Catalyst (AI generates options, humans refine), Human-First (humans lead and AI assists due to higher risk and judgment), Quality Control (AI drafts and humans verify), and No Regrets (AI handles low-risk, data-heavy tasks). The article also highlights in the visual “Why Don’t Gen AI Gains Show Up in My P&L?” six ways AI-driven productivity gains are often missed and not articulated. Both frameworks provide useful tools for organizing work tasks and ensuring that projected gains and ROI from AI are adequately captured.

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AI AND SKILLS

Workforce Intelligence: AI and the Future of Work | MIT Sloan School of Management

A 25-page special issue with a collection of five articles on AI in the workplace, including using AI to identify and close skills gaps.

This special 25-page issue from MIT Sloan School of Management compiles five previously published articles exploring how AI is transforming work and workforce strategy. The pieces cover: 1) the human capabilities that complement AI’s shortcomings, 2) five factors to consider as AI reshapes work, 3) how to use generative AI to augment your workforce, 4) the risks of letting junior professionals teach AI to senior colleagues, and 5) how companies can use AI to identify and close skills gaps. While each article offers useful insights, I want to draw attention to the one that begins on page 21. It highlights how Johnson & Johnson (J&J) used AI-driven skills inference to gain a precise, forward-looking view of workforce capabilities through a three-step process: creating a skills taxonomy to define 41 future-ready skills, gathering skills evidence from multiple systems (HRIS, recruiting, learning, and project management platforms), and conducting a skills assessment using a large language model to measure proficiency and compare results with self-ratings. This approach provided both individual and organizational insights—helping employees identify development priorities and enabling leaders to make targeted investments in upskilling and workforce planning. For a deeper dive into the J&J case study, I’m sharing an additional 12-page open-access article published in the Information Systems Journal, The Deployment of AI to Infer Employee Skills: Insights from Johnson & Johnson’s Digital-First Workforce Initiative.

Read the MIT issue

AGENTIC AI

The Agentic Organization: Contours of The Next Paradigm for The AI Era | McKinsey

Presents the case for how the true AI advantage comes from redesigning the enterprise into an “agentic organization,” where humans and AI agents work together—reshaping roles, skills, and core talent practices.

As more organizations adopt AI at scale, a recent McKinsey article argues that competitive advantage will come not from using individual AI tools but from redesigning the enterprise around an “agentic organization,” where humans and AI agents create value together. This model requires rethinking five pillars: 1) Business model (AI-native channels, hyperpersonalization, proprietary data), 2) Operating model (AI-first workflows and agent teams), 3) Governance (real-time decisions and controls by humans and AI), 4) Workforce, people, and culture (how roles, skills, and mindsets evolve as humans orchestrate AI), and 5) Technology and data (platforms that enable AI agents at scale). Focusing on the workforce pillar—employees shift from performing tasks to orchestrating outcomes, supervising AI agents, setting goals, and managing trade-offs. Humans move “above the loop,” overseeing workflows instead of completing every step. These shifts introduce new roles, including supervisors who direct AI agents, specialists who redesign workflows and manage exceptions, and AI-augmented frontline workers. A key implication for HR is how this transformation will reshape core talent practices—such as workforce planning (accounting for humans and AI agents), performance management (evaluating how well employees guide AI to create value), and learning and development (expanding beyond AI literacy to systems thinking, judgment, and decision-making with AI).

Read article

AI AGENTS AND LARGE LANGUAGE MODELS

AI Workforce– from Hype to Hard Truths: What It Takes to Deliver AI Value | KPMG

A 27-page report on turning AI tech investments into measurable impact, highlighting how capabilities—like agentic AI and LLMs—are redefining roles, workflows, and the skills needed for success.

This 27-page report by KPMG explores key lessons from the past year on AI integration in the workplace. One of the most important: technology alone doesn’t drive transformation—it’s how people adopt, adapt, and reimagine how work gets done. While the report offers too many insights to summarize fully, a section beginning on page 14 highlights two defining innovations reshaping how we work: 1) the emergence of agentic AI, digital collaborators capable of planning, acting, and adjusting with limited human input; and 2) the advancement of large language models (LLMs), which now integrate memory, personalization, and visual reasoning to drive smarter insights and outcomes. Newer generations of these systems can retain prior interactions, adapt to individual user preferences, and sustain greater consistency across tasks. As the report notes, “your AI collaborator isn’t just answering the question in front of it—it’s increasingly able to recognize your style, goals, and needs over time.” This evolution means users can shape LLM behavior by defining what “good” looks like, helping models better align outputs with expectations and context. In this new environment, feedback becomes a critical skill, where employees must learn to coach their LLMs, just as managers develop their teams. More ideas are discussed in the report, and as a bonus, I’m re-sharing five resources on AI agents in the workplace.

Read KPMG report

PERFORMANCE MANAGEMENT

Performance Calibration Discussions: 10 Questions to Help Managers Reflect on Employees’ Year-End Performance | Brian Heger

My cheat with questions to help managers prepare for performance calibration discussions and drive more objective, consistent evaluations.

I recently shared my one-page sheet to help managers identify six risk factors that may derail team goals, so they can prioritize actions and make timely course corrections. And while there are 60 days left in 2025 to influence performance and outcomes, many organizations are also preparing for—or already conducting—performance calibration discussions. These are sessions where managers come together to ensure greater consistency and objectivity in performance evaluations. They aim to promote fairness by addressing discrepancies between “tough graders,” who hold employees to exceptionally high standards, and “easy graders,” who offer overly generous evaluations. When done well, calibration discussions lead to more accurate and credible performance differentiation. To help managers prepare, I’ve created a one-pager featuring 10 sample questions. They range from Goal Achievement—“What were the employee’s most significant accomplishments during the performance period?”—to Obstacle Navigation—“What were the most significant obstacles this employee faced, and how did they overcome them?” and Impact on Team Performance—“In what ways has the employee positively influenced the performance of others?” Reflecting on questions like these can help managers approach evaluations more thoughtfully and objectively. Employees can also use them to reflect on their performance and contributions.

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TALENT MANAGEMENT

How Smart Companies Overcome Today’s Talent Paradoxes | BCG

Highlights how organizations can leverage four talent paradoxes into competitive advantages, including using scenario-based workforce planning.

As talent challenges become increasingly complex, they continue to create a number of paradoxes in talent management—situations with seemingly contradictory qualities that organizations must now learn to manage, rather than avoid. In this BCG article, the authors outline how organizations can overcome four major talent paradoxes: 1) predictability (how to plan for the future when talent needs evolve faster than skills can be built), 2) scarcity (why companies face talent shortages even amid abundant labor pools), 3) skills (how to keep pace when needed capabilities change faster than traditional learning can adapt), and 4) motivation (how to engage individuals whose needs, preferences, and motivations differ widely). For example, to address the predictability paradox, one approach is through scenario-based strategic workforce planning (SWP), where organizations anticipate multiple “what if” scenarios rather than reacting after the fact. While many companies use scenario planning for finance and operations, few apply it to SWP. Although scenario planning varies widely in sophistication—often enabled by technology and analytics—simply discussing scenarios and possible responses to gain directional insights is a valuable starting point. With this as the backdrop, I’m resharing my one-page cheat sheet, which includes questions that help translate “what if” business scenarios into directionally correct talent actions.

Read article

TALENT REVIEWS

Talent Reviews: Key Questions, HiPo Metrics, and Regrettable Loss Segments | Brian Heger

My one-page sheet consolidates three elements from my past posts on talent reviews: key discussion questions, talent segments to retain, and sample HiPo metrics.

As I continue to receive requests for resources to support talent review discussions, I’ve created a one-page cheat sheet that brings together three elements of my previous posts on the topic. It includes eight questions to help structure and guide the discussion, nine employee segments that organizations may consider regrettable losses if they exit, and nine metrics related to high-potential (HiPo) talent—a key focus of many talent review practices. For example, questions like “What are our organization’s top priorities for the next 18–36 months?” help ground the discussion in strategic goals and identify where leadership and skills are most critical. When prioritizing efforts to retain key talent—another focus of many talent reviews—the regrettable loss segments highlight employees who are not only HiPos and key successors but also culture carriers, employees with rare cross-functional expertise, and those with specialized or scarce skills. And when measuring progress and impact within the HiPo segment, metrics such as HiPo performance consistency—the stability of performance ratings over time, especially as individuals take on new roles—can help ensure they continue to perform at a high level despite new challenges. Use the cheat sheet as a starting point for your own purposes.

Access cheat sheet