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Welcome to the new issue of Talent Edge Weekly!

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PRESENTED BY TalentNeuron

Workforce Design Is Entering a New Era

AI is reshaping work, but the economics of AI are shifting too. As the business landscape changes, workforce leaders face some difficult decisions:

• Where should you invest in people, and where should you put AI to work?

• Which skills and roles will matter as the pace of AI adoption changes?

• When should you build skills, hire new talent, or redesign the work itself?

Hear how leaders from BCG, Siemens, RTX and CodeSignal are answering them at TalentNeuron Live 26: The Future of Workforce Design on November 18 at 9 AM ET / 2 PM GMT / 3 PM CET. Half a day, virtual and free.

On the agenda:

• TalentNeuron CEO David Wilkins and Kyle Lagunas of Kyle & Co. on where workforce design goes next

• Sandra Nowak of Siemens and Bob Motion of RTX on the workforce decisions in front of them now

Plus, be among the first to see what TalentNeuron is launching in 2027.

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 Productivity Today, Workforce Capabilities Tomorrow: Can We Get the Best of Both Worlds? | Brian Heger | My one-pager on how AI can erode existing skills and limit future skill development, with questions to help HR practitioners address both risks.

• What R&D Looks Like for HR | Harvard Business Review | Max Blumberg and Dave Ulrich share four criteria for deciding when HR should invest in research to improve business decisions, plus my framework for using data to inform talent initiatives.

• Is HR Tech Undermining Your Strategy? | MIT Sloan Management Review | Sharna Wiblen and Alexis Fink offer a four-step framework for aligning HR technology with talent strategy, plus my one-pager with questions for evaluating HR tech investments.

• 8 Questions to Pressure-Test Your Hiring Process for AI-Enabled Candidate Misrepresentation and Fraud | Brian Heger | I share eight questions to assess hiring safeguards against AI-enabled candidate misrepresentation and fraud while avoiding unnecessary barriers for qualified candidates.

• The Reflection Penalty: When Managers Accept Leadership Coaching from A Human but Not an AI Agent | Human–Computer Interaction | An exploratory Harvard Graduate School of Education study examining how the perceived source of coaching influences managers' willingness to engage in self-reflection. I share recommendations for future research.

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. 

THIS WEEK'S EDGE 

AI’S IMPACT ON SKILLS

AI Productivity Today, Workforce Capabilities Tomorrow: Can We Get the Best of Both Worlds? | Brian Heger

My one-pager on how AI can erode existing skills and limit future skill development, with questions to help HR practitioners address both risks.

What if employees are becoming more productive with AI while losing, or failing to develop, skills their organizations will still need? I've previously explored this topic in posts I've written about two related risks. The first is skill erosion, discussed in BCG research, where AI reduces opportunities to practice existing skills (e.g., experienced analysts becoming less adept at independently identifying risks and challenging assumptions). The second is learning debt, discussed in an AIGW Policy Observatory report, where AI takes over developmental work without employees gaining the learning benefits (e.g., junior analysts using AI to build forecasts without developing modeling skills and judgment). While related, these concepts have different implications for skills strategy: skill erosion involves losing existing proficiency; learning debt involves missing opportunities to develop future skills. Both raise a productivity–capability dilemma: How do we capture AI's productivity benefits while maintaining and developing critical capabilities, especially when performance expectations and rewards emphasize near-term results? Not every skill needs preserving, so we must decide where to accept decline and where to protect or build capabilities. My one-page visual illustrates both risks and offers questions to guide these choices. For those of you who are part of my private community for internal HR practitioners, Talent Edge Circle, I look forward to our upcoming discussion on this topic, where we'll explore practical ways to address these risks and share approaches being considered across our organizations.

Access my one-pager

HR IMPACT AND ANALYTICS

What R&D Looks Like for HR | Harvard Business Review

Max Blumberg and Dave Ulrich share four criteria for deciding when HR should invest in research to improve business decisions, plus my framework for using data to inform talent initiatives.

HR collects extensive workforce data, but that doesn't mean it's always the right data for important decisions. In this new HBR article, Max Blumberg and Dave Ulrich suggest HR should treat human capability analytics more like R&D, starting with business questions and generating evidence when existing data falls short. They offer four criteria for when research is warranted: 1) Financial significance: Does the decision have meaningful financial consequences? 2) Evidence gaps: Can existing data answer the question? 3) Actionability: Could findings change what the organization does? 4) Feasibility: Can research be conducted reliably, ethically, and economically? One example in the article involves a company considering a $500,000 employee development program. Before investing, it could conduct a $40,000 study with a 20% chance of uncovering that the program would be ineffective. That translates into $100,000 in expected avoided costs, exceeding the cost of the research. It illustrates how investing in better evidence can help organizations avoid more costly decisions. The article also provides other examples of how this approach can be applied in practice. As a bonus, I'm resharing my framework for using data to support recommendations for talent initiatives and investments. Using workforce planning as an example, it connects Business Need, Supporting Data, Proposed Action, and Cost of Inaction. I particularly want to emphasize the cost of inaction, because clearly articulating the business impact of not taking action (when supported by data) can help convey the urgency and business value of a recommendation.

Access HBR article

HR TECH STRATEGY

Is HR Tech Undermining Your Strategy? | MIT Sloan Management Review

Sharna Wiblen and Alexis Fink offer a four-step framework for aligning HR technology with talent strategy, plus my one-pager with questions for evaluating HR tech investments.

Most HR technology purchases may make sense when evaluated individually. However, an organization's HR tech stack, accumulated over time, may not advance its talent strategy and can detract from it if not managed intentionally. In this new article, Sharna Wiblen and Alexis Fink offer four steps to assess whether an organization's HR technology supports its talent strategy: 1) Blank-Page Exercise: envision an ideal tech ecosystem without constraints; 2) Ecosystem Inventory: map tools and owners; 3) Accumulation Audit: examine purpose, data, decision logic, and value to find duplication and limitations; and 4) Strategy Test: assess whether embedded criteria and processes support or constrain talent priorities. In my experience, each step adds incremental value, and even the HR Tech Ecosystem Inventory alone can be revealing. Viewing platforms, investments, and owners together exposes duplication and gaps. As a bonus, I'm resharing my one-page HR tech evaluation sheet, which extends these considerations to new investments. Under business needs: What problems are we solving or opportunities unlocking? How will this technology help us do things more effectively than today? These anchor decisions in business needs and test whether new capabilities improve current practices. Under cost and ROI: Will ROI be realized in the near term or over a longer horizon, and what value will be captured in years one, two, and beyond? Presenting ROI from this angle helps more accurately convey the value HR tech investments can deliver over time, especially since much of their potential is realized gradually rather than immediately.

Access MIT article

AI AND HIRING

8 Questions to Pressure-Test Your Hiring Process for AI-Enabled Candidate Misrepresentation and Fraud | Brian Heger

I share eight questions to assess hiring safeguards against AI-enabled candidate misrepresentation and fraud while avoiding unnecessary barriers for qualified candidates.

AI is creating new opportunities to improve hiring and talent acquisition, from helping organizations identify candidates and assess skills to making recruitment processes more efficient. At the same time, these advances are creating new challenges for verifying candidate qualifications, assessing demonstrated capabilities, and maintaining hiring integrity. For example, recent research supported by funding from Yale University's Cowles Foundation found that when an AI cover-letter tool became available on a large online job platform, letters became more tailored to job postings but 51% less useful in predicting which applicants employers would contact. This suggests AI-generated content can make it harder for employers to distinguish among candidates based on their applications. A new Chartered Management Institute survey found that 58% of UK hiring managers have noticed or suspected candidates using AI during virtual interviews. Academic research analyzing nearly 200,000 résumés found that about 1% contained hidden text aimed at AI screening tools, such as skills in white font that software reads but recruiters cannot see. And a new framework in Human Resource Management Review examines one of the most serious risks, the “phantom candidate,” an AI-created persona that uses deepfake video, AI-generated credentials, and fabricated identities to secure remote jobs. These examples point to a broader question: Are our hiring processes equipped to verify qualifications, assess demonstrated capabilities, and identify misrepresentation? To help, here are eight questions for pressure-testing hiring processes, each paired with an explanation of what it helps uncover. The goal is to strengthen hiring decisions without discouraging legitimate AI use or creating unnecessary barriers for qualified candidates.

Access questions

AI-ENABLED COACHING

The Reflection Penalty: When Managers Accept Leadership Coaching from A Human but Not an AI Agent | Human–Computer Interaction

An exploratory Harvard Graduate School of Education study examining how the perceived source of coaching influences managers' willingness to engage in self-reflection. I share recommendations for future research.

AI-enabled coaching is helping organizations deliver more accessible, in-the-moment coaching at scale to strengthen employee development and performance. I've previously shared several resources on this topic, including a randomized study examining career-coaching outcomes and a 191-page open-access volume exploring AI coaching's opportunities and challenges. A new exploratory study by Harvard Graduate School of Education researchers adds an interesting perspective. Eight consulting-firm managers experienced human coaching by video and AI coaching by text, both emphasizing reflective questioning rather than direct answers. All eight preferred human coaching, while six expected AI to provide answers and resisted its reflective questions. Researchers call this the reflection penalty, where individuals resist reflective questioning from AI even when they value that same approach from a human coach. The findings raise questions about how people perceive and engage with AI coaching. However, the small sample and different coaching formats limit what we can conclude. One area I believe warrants further research is whether resistance reflects expectations about AI or the quality and relevance of the questions themselves. For example, would questions tailored more precisely to a manager's situation, challenges, and previous responses generate greater engagement? Could different delivery formats or combinations of human and AI coaching change the experience? These are hypotheses worth testing as organizations continue developing AI-enabled coaching capabilities.

Access paper

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