Talent Edge Weekly - Issue #366

AI & the CHRO Agenda, work redesign, successor readiness by scenarios, AI and performance feedback, and how specialist work is shifting.

Author

Brian Heger
September 27, 2026

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Welcome to this new issue of Talent Edge Weekly!

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THIS WEEK'S CONTENT

Below are links and descriptions of the topics covered in this issue. If you're interested in my deep dive, you can read the full newsletter.

Also, check out my job cuts tracker & Chief HR Officer move of the week, which is an excerpt from my CHROs on the Go platform. 

Now let’s dive in.

Brian Heger
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THIS WEEK'S EDGE 

CHIEF HR OFFICER AND AI

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.

AI AND TALENT MANAGEMENT

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? I shared a post last week on an MIT CISR framework that considers ambiguity and risk in those decisions. Talent Edge Circle members can also watch the replay of our recent discussion on AI’s impact on organization design.

SUCCESSION PLANNING

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.

PERFORMANCE MANAGEMENT AND AI

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.

WORKFORCE PLANNING AND AI

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?

MOST POPULAR FROM LAST WEEK

PERFORMANCE MANAGEMENT

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.

BONUS RESOURCE

TALENT REVIEWS

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

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