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Welcome to this new issue of Talent Edge Weekly!

First, a shout-out to Brandon Hobby, VP Theatre HR Operations at Cinemark, for referring new subscribers to Talent Edge Weekly. Thank you, Brandon, for your support of this newsletter!

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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.

• Workforce Architecture for the AI Enterprise | AIGW Policy Observatory | A new 65-page report examines how nine firms are moving from AI adoption to workforce transformation. I focus on one concept, Learning Debt, how it differs from skill erosion, and why both are important.

• Assessing Durable Skills in the Age of AI: A Framework for Employers | Business Roundtable | A new paper provides a framework with four characteristics of a strong skill signal for evaluating durable skills like critical thinking. I expand on one.

• Avoiding “Shiny Objects” and Heeding “Red Flags”: A Sensemaking Framework for Emerging Technology Procurement | JAMIA Open | A new paper compares a successful and a failed technology purchase at one medical center. I explain why the lessons transfer to HR tech and reshare my questions for evaluating providers.

• Talent Development Systems: Expanding Access, Capability and Internal Mobility | FAIR360 | A 40-page toolkit explores how various talent practices connect. I highlight sections on talent reviews, internal mobility, and talent hoarding, while sharing three of my related resources.

• Why Great Teams Don’t Need to Like Each Other | HBR IdeaCast | A new 35-minute discussion where organizational psychologist Adam Grant challenges assumptions about building organizational culture, emphasizing shared purpose and constructive disagreement.

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. 

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THIS WEEK'S EDGE 

AI’S IMPACT ON SKILLS

Workforce Architecture for the AI Enterprise | AIGW Policy Observatory

A new 65-page report examines how nine firms are moving from AI adoption to workforce transformation. I focus on one concept, Learning Debt, how it differs from skill erosion, and why both are important.

This new 65-page report examines the shift from AI adoption (deploying and using AI in existing jobs) to AI workforce transformation (redesigning workflows, roles, and workforce practices around AI). Nine cases across firms such as Salesforce, PwC, and BNY cover role redesign, early-career development, reskilling, and AI agents. It offers a nine-domain diagnostic to identify workforce gaps (pp. 46–47) and an illustrative 12-month roadmap (pp. 48–50). Figure 6 (p. 24) caught my attention as it connects to my posts on skill erosion, based on IBM and BCG research. Skill erosion occurs when employees lose proficiency in existing skills as AI reduces opportunities to practice them (e.g., experienced analysts becoming less adept at questioning assumptions as AI interprets data for them). This new report from the AIGW Policy Observatory introduces Learning Debt, when AI removes developmental work without replacing its learning value (e.g., junior analysts missing foundational analysis that builds judgment). The “debt” is that AI productivity gains today may create skill and capability gaps tomorrow. Although these may sound interchangeable, I see a distinction beyond terminology: skill erosion is losing skills you already have, whereas Learning Debt is failing to build skills you and your organization will need in the future. That matters because each risk may require different tactics. Next week, I will share a one-pager with tactics for addressing both while capturing AI’s benefits.

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SKILLS AND HIRING

Assessing Durable Skills in the Age of AI: A Framework for Employers | Business Roundtable

A new paper provides a framework with four characteristics of a strong skill signal for evaluating durable skills like critical thinking. I expand on one.

As AI changes how work gets done, organizations are placing more weight on durable skills, such as judgment and problem-solving, that people can apply across different roles, situations, and challenges, even as the nature of their work changes. Yet these skills can be difficult to assess. With AI helping candidates polish resumes and prepare interview answers, responses may not fully reflect their own judgment or experience. A new Business Roundtable report identifies four characteristics of a strong skill signal: behavior-anchored, standardized, multi-source, and AI-resilient. While the report and accompanying image explain all four characteristics, I want to expand on AI resilience, particularly how to distinguish a person's own capabilities from AI-assisted output. Three interview questions I think can help: 1) “What other options did you consider, and why did you rule them out?” (explores trade-offs); 2) “When might this approach fail?” (tests awareness of limitations); and 3) “What would you change if the budget were cut in half?” (reveals how they adjust their approach, make trade-offs, and decide what to prioritize when resources are constrained). These questions can provide a stronger signal of actual skills when used consistently. Whether for hiring, promotion, internal mobility, or development, how well do your talent practices incorporate the four characteristics of a strong skill signal? Strengthening this aspect of talent decision-making can be an important part of building a more effective skills strategy for your organization.

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HR TECH AND DECISION-MAKING

Avoiding “Shiny Objects” and Heeding “Red Flags”: A Sensemaking Framework for Emerging Technology Procurement | JAMIA Open

A new paper compares a successful and a failed technology purchase at one medical center. I explain why the lessons transfer to HR tech and reshare my questions for evaluating providers.

I am excited to attend HR Tech 2026 in Las Vegas this week and see the different capabilities that many HR Tech providers will be unveiling and sharing. Many HR practitioners will gain an advantage by seeing those capabilities firsthand and ultimately bringing in some vendors for more detailed demos within their organizations. As we consider adding any new HR technology, it is important to do a critical evaluation to ensure that we gain the intended benefits the technology can offer while flagging any potential risks. This new research paper sheds light on why this matters. While the research is not about HR technology in particular, it provides insights into technology purchases that I feel are transferable. Comparing two technology purchases at one medical center, it found that in the successful one, the organization did more due diligence, such as seeking input from comparable health systems, raising its concerns directly with the vendor, and testing with a small group of clinicians before making decisions. Against this backdrop, I am resharing my one-pager with sample questions internal HR teams can ask and align on, starting with the business need, expected value, and ROI. Some of the questions can then be asked of providers. While not all-inclusive, they provide a good starting point and can increase the likelihood of selecting HR tech solutions that enable the intended business outcomes. As a bonus, here is my one-pager from last week with 10 example questions to ask HR tech providers about what happens to your contract and more if they merge or are acquired.

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TALENT STRATEGY

Talent Development Systems: Expanding Access, Capability and Internal Mobility | FAIR360

A 40-page toolkit explores how various talent practices connect. I highlight sections on talent reviews, internal mobility, and talent hoarding, while sharing three of my related resources.

Effective talent strategies draw on various levers to support business outcomes. Yet these levers, ranging from succession planning and talent development to performance management, skills strategy, and internal mobility, are sometimes approached as independent initiatives rather than interconnected parts of a broader talent management ecosystem. The more intentionally we connect them, the better positioned we are to develop and execute a holistic talent strategy. This 40-page toolkit explores those connections. Here are a few sections to highlight along with links to related resources I have created and shared in Talent Edge Weekly: 1) Talent reviews (p. 31): Identify development actions, owners, and follow-up. Relates to my eight talent review questions, including those that create accountability for developmental actions. 2) Internal mobility (p. 29): Examine barriers such as manager approval and compensation rules that discourage lateral moves. Relates to my one-pager on nine internal mobility barriers and tactics to address them. 3) Talent hoarding (p. 20): Address managers’ reluctance to release strong performers to other opportunities in the organization. Relates to my 10-statement manager diagnostic that helps managers gain self-awareness of behaviors that block internal talent movement. You can explore other sections of the toolkit that relate to your areas of interest.

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CULTURE & TEAM PERFORMANCE

Why Great Teams Don’t Need to Like Each Other | HBR IdeaCast

A new 35-minute discussion where organizational psychologist Adam Grant challenges assumptions about building organizational culture, emphasizing shared purpose and constructive disagreement.

As HR practitioners, we spend considerable time developing talent strategies, practices, programs, and technology to support business priorities. We also know that helping leaders build a culture that supports the execution of those priorities is central to our work. As such, it's worth periodically reassessing whether our culture-building efforts are reinforcing the behaviors and outcomes we intend or unintentionally producing different results. That's why this new 35-minute HBR IdeaCast discussion interested me. Adam Grant joins host Adi Ignatius to explore what leaders can get wrong about culture. A few points: 1) Shared purpose vs. personal connections: Alignment around common goals matters more than friendships at work. Are we focused enough on shared outcomes, or too much on getting people to like one another? 2) Productive disagreement: Grant distinguishes disagreements about work from personal conflict and describes the “meeting after the meeting,” where people appear to agree during a discussion but later express concerns they weren't comfortable raising. If this happens regularly in our organizations, what might it tell us about how comfortable people feel challenging ideas or decisions? 3) Agreeable takers vs. disagreeable givers: Some seem cooperative but serve themselves; others challenge colleagues to help the team. Do we give enough credit to people who challenge our thinking, even when they're difficult to work with? These ideas don't dismiss positive relationships. They help pressure-test our culture-building efforts. Access the full podcast to get a more complete view of the discussion.

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