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Welcome to this special Best of September issue of Talent Edge Weekly.

First, a shout-out to Lindsey Peterson, Sr. Program Manager, People Team at Atlassian, for referring new subscribers to Talent Edge Weekly. Thank you, Lindsey, for your support of this newsletter!

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PRESENTED BY 365Talents 
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The Strategic Buyer Guide to Skills Intelligence

If skills are on your 2027 agenda, Q4 is when that agenda gets written. Budgets close, priorities lock, and the platform decision either happens now or slips another year.

The hard part isn't deciding to invest. It's telling platforms apart.

Every vendor says "skills-based," and most demos look alike: a taxonomy, a dashboard, a matching feature. The gap between a tool that stores skills and one that keeps them current usually shows up months after you've signed.

This guide helps you spot that gap before the decision is made. Inside:

• The 9 capabilities that separate real skills intelligence from a skills catalogue

• Questions to ask vendors for each one

• A weighted scoring grid to compare platforms and a final evaluation checklist

• How Alstom rolled out skills intelligence to 55,000 employees in 9 weeks

P.S. Already building your 2027 shortlist? See what live skills intelligence looks like with your own data. Book a demo

My Private Community for Internal HR

Talent Edge Circle

We're now in October and the timeframe to make real progress on this year’s most critical talent priorities is shortening.

If you want to go deeper with me and other internal HR practitioners on talent topics tied to your most critical priorities, I invite you to learn more about my private community, Talent Edge Circle.

The goal is simple: to help you cut through the noise and complexity so you can accelerate the execution of your critical talent priorities.

With only 3 months left to make an impact in 2026, give yourself the edge now.

A few new spots will open on Nov 1. for a limited time, so apply now.

I. AI THROUGH THE LENS OF WORKFORCE STRATEGY AND LABOR MARKETS

Examines how AI is reshaping Chief HR Officer priorities, the labor force, the ROI and business value of AI investments, how AI is shifting the use of internal versus external expertise, and how these changes are influencing strategic workforce planning.

CHIEF HR OFFICER AND AI

Designing the Thinking Organization: 5 Focus Areas for CHROs as AI Reshapes Work | IBM Institute for Business Value

A new 52-page report identifies 10 capabilities and five priority areas CHROs can focus on to turn AI-created capacity into business growth.

As AI reshapes work, many Chief HR Officers continue to reassess how these changes affect the capabilities their organizations need to execute their business strategies. A new 52-page IBM Institute for Business Value report, drawing on surveys of 1,500 CHROs and other executives responsible for workforce strategy, as well as 8,800 employees, identifies 10 capabilities across five focus areas for turning AI-created capacity into business growth. While the full report explores the detailed implications for CHROs and their teams, page 47 summarizes the five areas and suggested actions for each. The one I want to zoom in on is advancing skill growth while preventing skill erosion. Although 80% of organizations surveyed have a reskilling plan for helping employees work with AI-based technology, those plans may overlook skill erosion: the gradual weakening of key skills employees already have, such as critical thinking, when AI reduces their opportunities to practice and develop them. It is a top concern for 46% of executives, and 60% of employees say they are experiencing it. This has implications for workforce planning and talent development. Where is AI taking on work that helps people build and maintain capabilities such as judgment? How can we redesign work and development so the skills underlying those capabilities continue to grow? These questions belong in our skills and reskilling strategies alongside plans to build new AI-related skills.

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AI AND THE LABOR FORCE

AI and the Labor Force: Scenarios for Stakeholders | The Conference Board

A new 20-page report outlines four scenarios for how AI could reshape the labor force, with talent implications for each. I zoom in on the risks of premature entry-level job cuts.

I have written extensively about scenario planning and its value in preparing for the different futures an organization might face, including the talent decisions involved. This new 20-page report from The Conference Board outlines four AI-driven scenarios and potential talent implications for each. While there are many insights and recommendations in the report, one implication I want to focus on is the future of early-career and entry-level roles. One recommendation for CEOs is to critically evaluate premature decisions to reduce these roles based on the assumption that AI will absorb the tasks they perform. The report warns that these decisions, coupled with senior departures, can "erode critical institutional knowledge." One of the many suggestions I have made in previous posts on this topic is that, rather than reactively reducing or eliminating these roles, organizations should reimagine their purpose in an AI-enabled environment. One recent example comes from Geotab, which shared how its student talent program puts this into practice: pairing interns with its internal AI assistant on real business challenges while teaching them to evaluate outputs critically and apply human oversight. Geotab has brought in more than 1,000 interns, with many going on to full-time roles after graduation. For organizations reimagining their entry-level roles while protecting their future leadership pipeline, I am resharing this June 2026 World Economic Forum report, which offers several tactics.

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AI AND ROI

The State of AI in 2026: On The Road to ROI | QuantumBlack AI by McKinsey

A new report examines several aspects of AI, from ROI on investments to the economics of scaling AI. I expand on the latter and why workforce plans need to account for the actual economics of AI capacity.

McKinsey’s new 31-page State of AI in 2026 report includes several useful findings from its global survey of 1,719 participants across 97 countries. One data point that caught my attention is the economics of scaling AI. About 20% of respondents say AI-related operating costs, including token costs (the units of data processed when using an AI model), have caused some organizations to limit how much they use AI. The issue becomes even more pronounced among AI high performers, which McKinsey defines as organizations that attribute at least 5% of EBIT to AI use and describe its impact as significant. This reinforces an important point: running AI at scale is not free or unlimited, and workforce plans need to account for the actual economics of scaling it. Another related data point: 39% of respondents expect AI to decrease their organization’s overall head count over the next year. From my standpoint, layering in the token and operating cost constraints noted above, the 39% headcount expectation raises an important second-order consideration: if headcount is reduced based partly on the assumption that AI will take on more of the workload, but AI use later becomes limited by cost or other constraints, workload demand could outpace the organization's capacity to deliver. AI’s capabilities are tremendous and will increasingly need to be part of workforce models. At the same time, we need to be intentional about these decisions so we can create ROI from AI’s benefits while reducing unintended consequences.

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AI & WORKFORCE PLANNING

The Vanishing Advantage of Specialization: AI, Knowledge Utilization, and the Boundary of the Firm | Draft for the Brookings Papers on Economic Activity

A new 25-page draft paper examines how AI may shift specialist work, such as consulting, from outside firms to in-house teams. I share questions to consider.

As AI reshapes work, where should organizations get specialized expertise: internally or from outside firms? In this 25-page Brookings draft paper, Luis Garicano of the London School of Economics examines the advantage behind specialization: firms such as law firms and consultancies can spread the cost of acquiring knowledge across many clients. But as AI lowers that cost, organizations may bring more frequent specialist work in-house while turning to outside experts for rarer, harder problems. For example, a company might use AI for market analyses once sent to consultants, while seeking outside help for an unfamiliar strategic challenge. Garicano’s analysis shows that, of the 337,000 jobs added by lawyers, accountants, software developers, and management analysts between 2022 and 2025, 99% were outside law firms, accounting firms, computer systems design firms, and consultancies, respectively. Stated differently, nearly all this growth occurred at employers other than those selling these services. The data do not show whether this reflects work moving in-house from outside firms or whether AI caused the shift. Even so, the findings raise questions that workforce planners can start to consider: Which specialist work should we build internally versus source externally? What criteria should guide those decisions? What risks would each choice create, and which are we willing to take on? What expertise will employees need to evaluate AI’s work and recognize when to bring in outside specialists?

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AI & WORKFORCE PLANNING

The AI Future: A Strategic Guide to AI In Workforce Planning | Deloitte

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.

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II. AI THROUGH THE LENS OF WORK REDESIGN, ORGANIZATION DESIGN, AND CAPABILITY BUILDING

Explores how AI is reshaping work and roles, organization structures and decision rights, human-AI collaboration, and the skills and capabilities needed to operate effectively in an AI-enabled environment.

AI AND TALENT MANAGEMENT

How AI Is Changing Talent, Not Just Tasks: Rethinking Where Human Judgment Matters Most | HBR IdeaCast

A new 33-minute podcast conversation with Salesforce's Paula Goldman on how AI is rewriting the talent playbook. I zoom in on work redesign.

This new, 33-minute podcast episode is the first of a four-part series on how AI is changing leadership, with this episode focused on talent management. Host Adi Ignatius interviews Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer and author of Manage the Machine: How to Harness Human-AI Collaboration at Work. They discuss entry-level hiring, internal mobility, coaching, and performance evaluation. I want to highlight work redesign. Goldman describes how a division within Salesforce’s HR team uses AI with business units to map tasks to skills, identify changing work and where human capabilities are becoming more important, and redesign roles. From my standpoint, mapping tasks to skills can help identify where AI, people, or both can perform work most effectively. Mapping every task can become unwieldy, especially without the technology, data, and capacity to maintain that level of detail. It might be more practical to start at the activity level (a step in a process, such as processing a customer refund), rather than cataloging every task within it (a specific action, such as checking the purchase record against the refund policy), and go into more detail where needed. Another factor often overlooked is decision rights: even if AI checks whether a refund qualifies, who has authority to approve it?

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AI & WORK DESIGN

AI Transformation Requires Redesigning Work, Not Cutting Roles | Harvard Business Review

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.

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AI & ORGANIZATION DESIGN

Why C-Suite Leaders Need to Rethink Organization Design for The Agentic Era | Deloitte

A new article explores how AI is reshaping organization design, from spans of control and layers to decision rights. I tie in insights from a recent Talent Edge Circle discussion we had on the topic.

A few weeks ago, members of my private community for internal HR practitioners, Talent Edge Circle, came together to discuss AI’s impact on organization design. The discussion ranged from spans of control, organizational layers, and decision rights to reimagining early-career roles and how work may flow differently with AI. For example, one part of the discussion focused on span of control and led us to question whether it should shift from a standard benchmark to a more contextual design choice. That could mean considering factors such as work complexity, process standardization, team maturity, and how much routine coordination AI is absorbing. The result may be wider spans in some areas and tighter spans in others. Within the one-hour discussion, we not only highlighted key issues across these topics but also came away with frameworks and working principles that can help inform these decisions. That is one reason this new Deloitte article caught my attention. It expands on many of these organization design topics. For example, on decision rights, the article extends the traditional RACI framework by adding an “O” for override authority, clarifying who can challenge, modify, or stop an AI agent’s output. As AI takes on more tasks and decisions, organizations need to be clear about who remains accountable for the outcome and who can intervene when needed. If you are a Talent Edge Circle member, you can catch the full replay of our discussion, along with some of the resources we developed following it, in the resource library on our private platform.

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AI AND ORGANIZATION DESIGN

Designing Decision Rights for AI | MIT Center for Information Systems Research

A practical framework for determining how humans and autonomous AI should share decision-making based on two factors: ambiguity and risk.

One of the reasons I value frameworks, and spend time developing many of my own, is that they help organize and simplify complex topics. That simplification is critical because it builds shared understanding, fosters better discussion, and enables more informed and faster decision-making. One topic where frameworks can be particularly helpful is how organizations redesign work around AI, including decision rights: Where should AI have authority to decide or act? Where should humans remain involved? Who ultimately owns the outcome? These were among the questions that surfaced in a recent discussion in my private community for HR practitioners, Talent Edge Circle, on AI and organization design. Against this backdrop, I am sharing an MIT CISR framework for determining how humans and autonomous AI should participate in decisions based on two factors: ambiguity and risk. Ambiguity reflects how clearly the available information points to an answer, while risk reflects the consequences of being wrong. The result is a 2×2 framework with four types of decisions (routine, consequential, exploratory, and strategic), helping clarify the appropriate role for AI, where human judgment is most important, and who remains accountable. For HR, the opportunity is twofold: help the organization work through these decision-rights questions, while also applying the same discipline within HR itself.

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🔓️ There are 9 more featured resources (and many bonus resources) in this issue, on areas such as succession planning, AI fluency, performance management, internal mobility, and more but you must be subscribed to view it. Subscribe Now (it’s free) or sign in if you are already a subscriber!