Talent Edge Weekly - Issue #368

Redesigning organizations to capture the value of AI, guarding against skill erosion as AI use increases, evaluating succession depth, skills-based talent practices, and questions to ask HR tech vendors about what happens if they merge or are acquired.

Author

Brian Heger
October 11, 2026

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Welcome to this issue of Talent Edge Weekly!

First, a shout-out to Allison McCaffrey, Director of Talent, Learning, & Experience at TruGreen, for referring new subscribers to Talent Edge Weekly. Thank you, Allison, for your support of this newsletter!

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A new chapter for TechWolf, and a Skills Journey Conversation worth joining

Last week we signed a definitive agreement for SAP to acquire TechWolf, subject to customary closing conditions. Our purpose stays the same: to help people flourish at work. We will continue to serve all of our customers, whether they are SAP customers or not.

Meanwhile, the work continues. On Tuesday, October 27 at 11:00 AM ET, Tomislav Vujec, Director of Learning at Red Hat, shares how his team put skill supply and demand side by side to make the skills gap measurable. He also explains why the next step belongs to business leaders who can ask an agent directly.

Register (it’s free) to join TechWolf’s first webinar after the big news!

P.S. Click here to read CEO Andreas De Neve’s letter on the announcement.

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. 

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Now let’s dive in.

Brian Heger
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THIS WEEK'S EDGE 

AI AND WORKFORCE PLANNING

A new article on redesigning organizations to capture the value of AI. I expand on the choices for how AI-freed workforce capacity can be used.

AI continues to create opportunities to free up workforce capacity by taking on parts of people’s work. But translating that freed capacity into organizational value requires deliberate choices about how it will be used. With this in mind, this new McKinsey article, which outlines four shifts for redesigning organizations around AI, offers a few ideas. The authors name five places that released capacity can go, and Exhibit 1 illustrates the choice through a customer-service role. Here is how I would describe them to help frame their application beyond that example: 1) Greater output: use released capacity to handle more existing work with the same workforce (e.g., serve more customers without adding staff). 2) Different work: redirect time toward other priorities or responsibilities (e.g., shift from routine inquiries to proactive customer outreach). 3) More sustainable workloads: use time savings to reduce pressure while maintaining needed results (e.g., reduce overtime while maintaining service levels). 4) Lower costs: reduce the resources required to deliver the work (e.g., maintain service levels with less reliance on temporary staffing). 5) A combination: allocate released capacity across multiple objectives (e.g., increase customer outreach while reducing overtime). If you have identified where AI can free up capacity in your business, I would ask: Which of these options are you deliberately choosing, and how will you know whether that use of capacity creates value?

AI AND SKILL EROSION

A new study finds that people who form their own view before seeing AI advice are less likely to follow it when it is wrong. I connect it to my earlier post on skill erosion.

In a recent post, I shared a 52-page report from IBM Institute for Business Value on five priority areas Chief HR Officers and their teams can focus on to turn AI-created capacity into business growth. One of the areas I expanded on was preventing skill erosion, which is the gradual weakening of skills people already have when AI reduces their opportunities to practice them. One example is an analyst who relies on AI to summarize data and gradually loses the habit of asking what is missing or misleading. A new study provides some insight. In a randomized experiment with 240 preservice teachers, some participants made their own judgment before using an AI recommendation, while others saw the AI recommendation before deciding. When the AI advice was wrong, participants who had made their own judgment first chose the answer experts judged correct 53.5% of the time. Participants who saw the AI recommendation first chose it only 37.3% of the time, a statistically significant difference. Despite the limitations of the study (e.g., participants were student teachers responding to hypothetical scenarios, and a follow-up test showed no lasting effect), it suggests that people who form their own view before consulting AI are less likely to follow misleading advice. For practical strategies to reduce skill erosion while still gaining the benefits of AI, I am resharing my post on the BCG article When Everyone Uses AI, Companies Risk Losing Critical Skills.

SUCCESSION PLANNING

My one-page reference sheet helps organizations test whether the names on their succession plans are realistic successors, using questions organized into 5 themes.

Organizations spend a lot of time and resources identifying and developing successor for critical roles. Yet many of these plans can become names on a page that never turn into successors. When this happens, we get a false sense of succession depth, meaning the plan shows more successors than we really have. We may have kept people on a plan long after their readiness stopped progressing, listed the same person on several plans without deliberately developing them for any of those roles, added names based on opinion without validation, or never confirmed that the successor is even interested in the role. Part of addressing this is challenging ourselves and the decision makers who are part of the discussions to critically evaluate those named on a succession plan. For example, when someone has been on a plan for multiple cycles with no change in readiness, at what point do they get removed from the succession plan? If a person is on several plans and those roles open at the same time, which one would we most likely place them in? To help, I created a one-page reference sheet with questions organized into five themes. Four of them test the names already on the succession plan, and the fifth looks for hidden candidates who are not on the plan but should be considered. Each theme includes questions to ask, insights HR practitioners can bring to the discussions, and a possible action. While no approach is perfect, asking these questions helps close the gap between the number of names on the plan and the number of realistic successors.

SKILLS-BASED TALENT PRACTICES

A new 21-page toolkit for measuring whether skills-based talent strategies are working. I expand on removing academic degree requirements from job postings.

As more organizations claim that they are adopting skills-based talent practices, a common challenge is showing whether those practices are making a difference in the outcomes that matter most. This new 21-page toolkit, written for state governments but relevant to any employer, prioritizes nine metrics for measuring skills-based practices and outlines the data fields, calculation steps, and common data issues for each one. While the toolkit includes many useful insights, the one I want to focus on relates to academic degree requirements in job postings, since removing unnecessary ones is a common step toward skills-based hiring. The toolkit includes two separate measures here. The first is the share of postings that do not require a degree, which helps confirm whether a policy change has reached the job language candidates see. The second is the share of postings that offer multiple ways to signal competency, such as certifications, apprenticeships, assessment results, and military or other equivalent experience. This one matters because it shows whether jobs are becoming more skills-based, not just less degree-based, which are two separate things. As a bonus, here is my one-pager with nine questions to help evaluate whether an academic degree is essential in the first place for a given role.

HR TECH

My one-pager includes 10 questions to ask HR tech vendors you are renewing with or considering bringing on about what happens if you sign and the vendor merges with or is acquired by another organization.

Next week, I am looking forward to attending HR Tech 2026 in Las Vegas and seeing the latest HR technology capabilities from a variety of providers. It is also a time of the year when many HR teams are evaluating and getting demos from HR tech vendors, as well as considering renewal of their current platforms for the upcoming year. As teams make these decisions, one good practice is to ask questions about what happens if the platform you are bringing on merges with another company or is acquired. This becomes increasingly important as there continue to be opportunities for different platforms to combine their capabilities and consolidate in the future. To help, here is my one-page reference sheet with 10 questions you can use in your vendor discussions, each paired with why it is worth asking. The questions touch on areas such as existing contracts, the product roadmap, integrations, pricing and renewal terms, customer support, employee data, and exit rights. The 10 questions are just examples, so you can modify them as you see fit. I have also included an editable text box at the very end of the one-pager where you can add your own questions. For those of you who will be at next week’s 2026 HR Tech, I am looking forward to meeting many of you. If you spot me, please make it a point to say hello, as I always love to meet Talent Edge Weekly readers in person!

MOST POPULAR FROM LAST ISSUE

AI & WORKFORCE PLANNING

A 13-page paper helps rethink how SWP continues to evolve, especially in the context of AI. I share where this fits alongside my own SWP resources and the practical approach I continue to reinforce.

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