Talent Edge Weekly - Issue #363

Workforce planning, succession planning, redesigning work for the AI-era, how workers are engaging with AI on work tasks, and AI prompting as a part of AI fluency.

Author

Brian Heger
September 06, 2026

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Welcome to the new 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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PRESENTED BY TalentNeuron

There is no playbook for AI-led workforce transformation. So, we went looking for one in the hiring data of organizations investing aggressively in AI: Salesforce, Klarna, Wells Fargo, Google, Microsoft, Citi, and BT Group.

What we found wasn't a single workforce strategy. It was several different playbooks, running in parallel, according to each organization's goals and needs.

In this new report, learn how these organizations are making fundamentally different workforce choices—spanning workforce size, organizational structure, talent location, skills, hiring, and development—as AI investment moves from experimentation to execution.

P.S. To see how TalentNeuron's workforce intelligence can shape your own workforce strategy, request a demo.

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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.

If you’re an internal HR practitioner who wants to go deeper with me and other HR practitioners to help accelerate your talent priorities, apply to my private community, Talent Edge Circle.

Now let’s dive in.

Brian Heger
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THIS WEEK'S EDGE 

AI & WORKFORCE PLANNING

A new 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.

I have written extensively on strategic workforce planning (SWP) over the years, including a book chapter in the SIOP Professional Practice Series, an article in People + Strategy, and numerous resources I’ve created and published on my website. One example is this two-page resource, which includes a sample business case slide for SWP and a one-page “good enough” plan to get started. And in my private community for internal HR practitioners, Talent Edge Circle, we also go deeper through practical discussions on SWP. One point I continue to reinforce across all of these channels is that a persistent gap in SWP often comes down to organizations waiting for perfect plans, technology, or data before they start. My view is the opposite: start where you are, build the foundation, get some quick wins, learn and adjust, and evolve from there. With that in mind, a new Deloitte paper on how SWP is evolving with AI caught my attention, particularly two slides. The first, on page 12, lays out a three-year roadmap from foundational data work to what Deloitte calls an always-on, AI-orchestrated process, helping organizations phase the capability rather than get stuck in “all or nothing” thinking. The second, on page 10, maps how six stakeholder groups, from business leaders to IT, need to show up differently as SWP evolves with AI. Frameworks like these can help simplify a complex topic and translate it into action. Hopefully, it helps you build some momentum.

SUCCESSION PLANNING

A recent study with implications for how the scenario a successor inherits shapes their readiness for the role. I expand on how succession planning can account for this.

Much of succession planning focuses on defining future role profiles, identifying required skills, and closing readiness gaps. These are all important. But I believe there is even greater potential in making readiness more contextual by asking: Ready for what? The same role may require a very different leader depending on whether the organization is entering a turnaround, rapid growth, major transformation, stabilization, culture reset, or a period where strong performance simply needs to be sustained. A recent study published in Journal of Applied Psychology reinforces why this distinction matters. Researchers followed leadership transitions across 113 U.S. elementary schools and found that one specific leadership approach, hands-on coaching, improved engagement and performance when employees believed change was needed, but produced declines when they did not. While the research focused on elementary schools, I believe the idea extends well beyond education: context shapes which leadership behaviors are most effective. A successor who is well prepared to lead a turnaround, for example, may not be the strongest choice for a business that needs stability and continuity. How are you factoring different scenarios, ranging from a turnaround to a culture reset, into how you assess and develop successors? We'll go deeper on this in Talent Edge Circle, my private community for internal HR practitioners.

AI & WORK DESIGN

A new article shares evidence that AI-driven layoffs are often falling short of expected results and offers tactics for making better workforce decisions. I also share one of my bonus cheat sheets related to the topic.

Over the past few years, I have tracked organizational layoffs and job cuts through my layoff tracker, largely to monitor shifts in the labor and job market. One observation I have made over the past several months is an increase in the number of layoff announcements citing AI as a reason for workforce reductions. I have also shared in several posts how I believe many of these decisions are being made prematurely, before organizations fully understand how AI is actually changing the work. One example is the elimination or reduction of entry-level roles. A new HBR article puts numbers behind this broader concern, drawing on several data points. For example, it cites a survey of 600 HR leaders whose organizations made AI-driven layoffs, finding that only 8.4% said the restructuring delivered as promised, while one in three reported losing critical skills they had not anticipated losing. The authors argue the more effective approach starts with the work itself, not headcount targets: breaking roles into tasks and redesigning processes around the right mix of human and AI capabilities. They share several ideas for implementing this. As a bonus, I want to reshare one of my one-pagers that tackles another issue related to layoffs: overhiring, which can partly be caused by defaulting to a decision to backfill a recently vacant role without truly understanding whether it is needed. My one-page cheat sheet includes targeted questions to help leaders make that decision, reducing the costly cycle of overhiring, job cuts, and rehiring.

AI AND WORK TASKS

A new Stanford study analyzes 250,000 real Claude conversations to reveal how people actually delegate work to AI and where the collaboration breaks down.

Most of what we know about how employees actually use AI at work comes from surveys and self-reports. However, a new Stanford study offers a rigorous behavioral look instead, and it is the first of three studies from a new Anthropic pilot giving outside research teams (Stanford's SALT Lab, Oxford's Human Information Processing Lab, and METR) privacy preserving access to real Claude usage data. Each team received access to roughly 250,000 real Claude conversations. Here are just two of the many findings from Stanford's report (the first to be released) along with some of the practical implications. 1) 56% of the tasks people brought to AI were classified as consequential or high stakes, meaning work that is hard to reverse and carries real professional or financial weight, concentrated in advisory and professional domains. Implication: this challenges the assumption that employees mostly hand AI low stakes work, and suggests governance should focus on the majority of work that actually carries higher stakes. 2) friction, meaning moments where the human-AI collaboration broke down or required correction, showed up in nearly half of all conversations, but researchers classified much of it as productive, since working through it tended to improve the output or deepen understanding of the issue. Implication: organizations can frame this kind of friction as a normal, even necessary, part of working with AI rather than a sign the tool has failed.

AI AND SKILLS

A new study with German hiring managers measures the actual hiring and pay premium for AI prompting skills, and finds they raise the bar for a role rather than replacing core expertise.

Last week, I covered a new International Labour Organization report arguing that AI literacy is becoming a foundational expected skill rather than a nice to have. A new IZA Institute of Labor Economics study drills into one subset of that literacy, prompting skills: the ability to formulate instructions, evaluate outputs, and deploy workflows. Researchers had decision makers from 992 German firms repeatedly choose between hypothetical applicants varying in prompting skills, skills gaps, and social skills, yielding 15,660 hiring choices. Applicants with stronger prompting skills had a 4 to 5 percent higher selection probability, and employers paid roughly 2 percent more for those skills. At larger and AI adopting firms, that advantage nearly doubled to about 9 percent. While prompting skills mattered, it was not the most important skill. Employers valued job fit and social skills far more, and prompting did not offset gaps in occupation specific knowledge, suggesting it is becoming an added expectation rather than a substitute for expertise. There are limitations to the study: it was a hypothetical experiment, not real hiring decisions, conducted in Germany amid rapid AI adoption, and not yet peer reviewed, though it still offers valuable insight. From my standpoint, as various aspects of AI fluency become part of hiring decisions, we need real ways to evaluate and develop it, not just recruit for it. With that backdrop, I am excited for our upcoming conversation with Tracy St. Dic, Zapier's VP of Global Talent, in Talent Edge Circle, my private community for internal HR practitioners, on Zapier’s AI fluency initiative.

MOST POPULAR FROM LAST WEEK

WORKFORCE PLANNING

My one-pager helps leaders determine whether additional hiring is needed or if other talent options can address the need.

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