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Welcome to this new issue of Talent Edge Weekly!

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NEW! 2027 Skills Intelligence Report

Many HR teams can map their organizations’ workforce skills. Far fewer can name a business decision or outcome that their skills data actually influenced. That’s where skills strategies often stall: the taxonomy becomes the project, and the business problem goes untouched.

What fixes it isn’t a better taxonomy. It’s a C-suite mandate, current skills data and one plan HR and L&D share. With Docebo and Brandon Hall Group, 365Talents asked 1,000+ leaders across Europe and North America where their skills strategy stands.

Inside the 2027 Skills Intelligence Report:

• The 6 steps for getting a stalled strategy moving

• 3 enterprise rollouts end to end, including Alstom’s 55,000 employees in nine weeks

• 11 interviews with practitioners, analysts and academics

• 4 questions that tell you where you sit against your peers

Download the 42-page report now so you have the insights you need for your 2027 skills strategy and budget conversations.

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.

• AI Applications in Talent Management: Talent Acquisition and Employee Mobility | Cornell University, ILR School | A new 30-minute Cornell University webinar by Professor JR Keller reviews several studies on AI in recruiting and internal mobility. I highlight one finding and share a guide for evaluating AI-based employment tools.

• The People Premium: Human Capital as a Driver of Value Creation in Private Markets | World Economic Forum | A new 30-page report examines how private-market investors assess and strengthen human capital to create business value. I highlight implications for evaluating collective leadership capabilities and strengthening succession.

• The Adaptive Advantage: Who’s Winning with AI and How | Wharton Human-AI Research & GBI Collective | A new 102-page 4th annual study examines how organizations turn AI adoption into business value. I highlight a finding on performance management and share additional resources to help evaluate employee judgment when using AI.

• The 4:1 Change Management Return Most Organizations Are Leaving on The Table | Deloitte Insights | A new article examines how organizations can better manage change to achieve business outcomes. I focus on managing cumulative change capacity and share two of my one-pagers as practical resources.

• Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do | Soumitra Shukla, Economist at The Burning Glass Institute | A new working paper examines how tasks AI cannot yet perform or sufficiently accelerate may limit overall productivity gains if overlooked. I share five questions to consider.

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. 

My Private Community for Internal HR Practitioners, Talent Edge Circle 

Each week, Talent Edge Weekly brings you practical insights that help you cut through the noise and move your talent priorities forward. Talent Edge Circle is where you can sharpen that edge even further.

It's my private, paid community exclusively for internal HR practitioners who shape and execute talent strategy. Connect with peers facing similar challenges, pressure-test your ideas, exchange practical approaches, and gain perspectives that help you turn talent priorities into business value faster.

Applications are open for a few spots. Apply by November 1. Applications won’t reopen until December.

If you're looking for a trusted group of HR peers to challenge your thinking and help you make faster progress on your priorities, apply now.

THIS WEEK'S EDGE 

HR TECH & AI

AI Applications in Talent Management: Talent Acquisition and Employee Mobility | Cornell University, ILR School

A new 30-minute Cornell University webinar by Professor JR Keller reviews several studies on AI in recruiting and internal mobility. I highlight one finding and share a guide for evaluating AI-based employment tools.

I just returned from the 2026 HR Tech conference in Las Vegas, and it was great to see the latest innovations across HR technology. From AI-powered recruiting and talent mobility to workforce planning and digital coaching, it's encouraging to see how these technologies can help HR practitioners deliver greater value to stakeholders. (I'll share more of my HR Tech takeaways in an upcoming post). One thing I value about these conferences is the opportunity to pressure-test these technologies through live demonstrations and ask vendors questions about actual implementations, the ROI achieved, and potential shortcomings. I also value independent research that helps us evaluate these technologies and ask more informed questions of vendors. With this in mind, I'm sharing a new 30-minute Cornell University webinar by Professor JR Keller, who reviews several studies on AI's impact on recruiting and internal mobility. Topics range from AI-generated resumes and screening to automated interviews and internal matching. For example, one study published in the AAAI/ACM Conference on AI, Ethics, and Society found that AI models favored AI-generated resumes over human-written ones, even when candidates had comparable qualifications. A finding like this could prompt a question for vendors, such as: How do you test whether your algorithms assess candidates' qualifications rather than favoring AI-generated resumes? As a bonus, I'm resharing a 13-question guide by CHRO Association and SIOP Foundation on evaluating AI-based employment tools, including how vendors develop and validate them.

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HUMAN CAPITAL AND ROI

The People Premium: Human Capital as a Driver of Value Creation in Private Markets | World Economic Forum

A new 30-page report examines how private-market investors assess and strengthen human capital to create business value. I highlight implications for evaluating collective leadership capabilities and strengthening succession.

In my 2016 article, Identifying Leadership Capabilities That Drive Business Performance, I outlined four steps for identifying the collective leadership capabilities needed to execute an organization’s business strategy. That article came to mind as I reviewed a new 30-page World Economic Forum report on how private-market investors, including private equity and venture capital firms, pension and sovereign wealth funds, and family offices, assess human capital pre-deal and strengthen it post-deal to create business value. Topics range from leadership assessment to internal mobility across portfolio companies. A Bain Capital example starting on page 11 connects to my article's emphasis on collective leadership capabilities. Its Team Acceleration Framework combines individual executive assessments into a team-level profile, recognizing that results depend not just on individual strengths for specific roles but also on leaders' ability to work together to execute strategy. Bain Capital then evaluates the profile against the two or three highest-priority business objectives to assess whether the team's collective capabilities align with what the business needs to accomplish. This also applies to succession planning. Individual readiness to step into a role matters, but organizations should also assess how successors would function collectively as a future leadership team, especially as AI continues to transform how organizations operate. Identifying potential gaps early allows us to take targeted actions now, not only to reduce talent risk but to convert it into a talent advantage.

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AI & PERFORMANCE MANAGEMENT

The Adaptive Advantage: Who’s Winning with AI and How | Wharton Human-AI Research & GBI Collective

A new 102-page 4th annual study examines how organizations turn AI adoption into business value. I highlight a finding on performance management and share additional resources to help evaluate employee judgment when using AI.

Now in its fourth year, this 102-page Wharton and GBK Collective study examines how organizations turn AI adoption into business value. Topics include AI returns, workflow redesign, agentic AI, productivity, hiring, skills, employee readiness, leadership, and governance. The study's self-reported data from 1,416 U.S. leaders and employees offer useful signals. One finding concerns AI in performance management. Managers and above reporting strong departmental AI returns were more likely to report AI usage in performance evaluations (87% vs. 65%, statistically significant at 95% confidence). Beyond AI usage and speed, evaluations should consider employee judgment, decision quality, and outcomes to differentiate performance. To help translate this into practice, I'm sharing a recent paper in Academy of Management Perspectives proposing two measures: override rate and override accuracy. Override rate measures how often employees reject or change AI recommendations, while override accuracy measures whether those decisions were correct. Together, they assess calibrated judgment: when to rely on AI and when to intervene. As organizations consider these measures, I suggest four questions managers can use with their direct reports: 1) What did AI recommend, and what did you accept, modify, or reject? 2) What alternatives did you consider, and what trade-offs influenced your choice? 3) How did you verify AI's output? 4) Looking back, was relying on or overriding AI the right call?

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CHANGE READINESS & FATIGUE

The 4:1 Change Management Return Most Organizations Are Leaving on The Table | Deloitte Insights

A new article examines how organizations can better manage change to achieve business outcomes. I focus on managing cumulative change capacity and share two of my one-pagers as practical resources.

Organizations continue to navigate significant change, both in the number of initiatives and their impact on employees. A new Deloitte article helps quantify the challenge. In a survey of 1,000 change leaders, 83% report multiple large-scale initiatives underway simultaneously, yet many struggle to achieve intended outcomes. Of Deloitte's five recommendations, I'll expand on one of them: managing change capacity across the enterprise. Last year, I shared my one-page template to help leaders gain a holistic view of current and proposed changes, their timing, and their cumulative impact across different employee groups. Without this visibility, decisions about new changes may overlook the cumulative demands already placed on employees. To draw an analogy, consider how we use personal credit cards. An individual purchase may seem reasonable at the time, but when our monthly statement shows the total number of purchases throughout the month, we may reconsider our spending decisions. The same applies to organizational change: individual initiatives may make sense, but their combined demands can tell a different story. There's also goal creep: adding work without reassessing priorities, capacity, or resources. My one-page cheat sheet offers six questions to address it. What can leaders do to capture the value of change without overloading employees or putting other priorities at risk? As organizations set objectives for 2027, it's worth considering both the changes being introduced and employees' collective capacity to execute them.

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AI & WORK REDESIGN

Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do | Soumitra Shukla, Economist at The Burning Glass Institute

A new working paper examines how tasks AI cannot yet perform or sufficiently accelerate may limit overall productivity gains if overlooked. I share five questions to consider.

Much has been shared about what AI can automate or augment and its impact on jobs and skills. A new working paper by The Burning Glass Institute economist Soumitra Shukla examines the other side: tasks AI cannot yet perform or sufficiently accelerate. Shukla notes that as AI speeds up some tasks, others that still require significant human effort can become bottlenecks, limiting overall productivity gains. Using 288 million U.S. job postings, the paper identifies which occupations may face the greatest productivity constraints from tasks that AI cannot yet perform or sufficiently accelerate. Consider my recruiting example: AI may accelerate sourcing, initial screening, and scheduling. Yet human-dependent work, such as assessing candidates against business needs and making hiring decisions, can limit gains in hiring quality and time to fill. In other words, AI can make individual tasks significantly faster without necessarily improving the speed or effectiveness of the end-to-end process. Five questions HR teams can use to help their organizations identify and address potential bottlenecks: 1) Which tasks is AI making faster? 2) Which remaining tasks aren't keeping pace? 3) How are these slower tasks affecting the overall process and its outcomes? 4) What capabilities, processes, or organizational changes could address the bottlenecks? 5) How will we know whether these changes improve end-to-end performance? To make this manageable, start with one process where your organization sees the greatest opportunity for AI to improve outcomes. Use these questions to identify potential bottlenecks, guide decisions on where to focus attention, and assess whether expected benefits materialize.

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MOST POPULAR FROM LAST ISSUE

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.

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