Amid this reality, the data available so far tells a more nuanced—and encouraging—story. A recent Ramp and Revelio report found that the companies that are adopting AI most aggressively are also growing their headcounts the fastest, while lower-adoption peers are flat. Specifically, these high-AI-adoption companies grew headcount by about 10% over the two years following adoption. Additionally, entry-level hiring at those companies grew even faster at around 12%.

When implemented well, AI adoption can make companies more efficient, and increased efficiency creates a knock-on effect: company ambition rises; output expectations go up; and more tasks and execution are required.

As such, the coordination required to hold all of this together across teams and systems (including software that now acts on its own) only becomes more important. This coordination work is HR’s to design, so the CHRO’s role in the AI era only becomes more important.

See also: Meet your AI agent: The new ‘chief of staff’ for HR professionals?

Four areas for CHROs to claim

More research from IBM validates this trend: the company’s 2026 CEO Study found that 59% of CEOs expect the CHRO’s influence to expand over the next three years—specifically because of AI.

Influence will follow ownership of the work. As more companies turn to AI-driven decision-making, ownership of this coordination falls into four areas that today’s CHRO must claim:

1. Workforce Architecture

Organizations are preparing themselves for a workforce that is part human and part digital, with AI agents executing tasks alongside human employees. One instinct has been to treat those agents as features inside the tools that HR already uses, but that’s a mistake.

A digital worker or agent needs similar structures and guidelines to those provided to a human employee on their first day on the job. They need a defined role, an owner/manager, and access that is scoped to the functions of their job. Designing that structure and workforce architecture should belong to HR. HR should define who does what, along with how the two halves of this new workforce architecture fit into a single operating model.

2. Agent vs. Human Decision Rights

Now that agents can act across an organization, someone will have to decide, deliberately, which choices or actions agents are allowed to make and which require human oversight and control. A good rule of thumb for today’s digital workforce is to give agents the routing and coordination work but keep the judgment calls with the people whose expertise should be relied upon over autonomous decisions.

Consider an organization that may take in thousands of job applications every year. An agent can pre-qualify applicants against the desired skills, but the ultimate decision-making around hiring is, of course, a human call. CHROs need to define these lines on purpose rather than let them get drawn elsewhere or by default.

3. Governance and Escalation Design

Governance and escalation are probably the most critical areas to own, yet they’re the ones that many companies get backward today. Effective governance can’t be bolted on after systems are already running, one monitoring dashboard or policy document at a time. It must be incorporated directly into a company’s workflows and processes that determine how AI agents operate, make decisions and escalate issues.

For example, policy checks should exist before an agent makes any consequential decisions. A second set of eyes should always be pulled in when policy requires it, and clear paths should exist for escalating exceptions to a person. Yet Gartner reports that only 13% of organizations believe they have the right governance in place for AI agents.

This indicates a real problem, as Gartner also projects that the average global Fortune 500 enterprise will be running more than 150,000 agents by 2028. If this prediction materializes, HR must close this governance gap and own the policies at the center of workforce orchestration between humans and agents.

4. Partnership With the CIO

To make this orchestration seamless, CHROs must create strong partnerships with their CIOs. While HR designs how the work should move and where human judgement applies, CIOs will be responsible for building and securing the orchestration layer that carries work across existing systems. Neither business function can deliver a governed, hybrid workforce on its own.

CHROs must co-own the workflows with their CIOs and agree on how they will be enforced and measured together. The responsibility can’t be left with IT to sort out. Like the tide, strong partnerships between CHROs and CIOs will lift both functions and the broader organization to realize AI’s potential.

Start with one process

What might seem daunting at first doesn’t require a multi-year transformation to begin. The most useful first step is to identify one process that already crosses multiple departments and establish a governed version of it. The employee onboarding process is one example, since it touches HR, IT, payroll, facilities and the hiring team.

Starting small accomplishes three things at once: It shows you where your coordination currently lacks; it gives you a measurable before-and-after framing; and it produces a documented record of how a real process operated in coordination with AI, including what systems it touched and precisely where humans were involved.

The auditable track record is crucial in demonstrating how work was orchestrated and completed. And once a CHRO establishes success with one process, they can rely on that proof point to replicate the approach across other areas of the business. By taking this approach, CHROs can move beyond simply managing workforce change and instead have a direct hand in transforming how the business operates with both human and digital workers.

The post The AI era for business is here. Here’s what CHROs must own appeared first on HR Executive.

“AI transformation across Southeast Asia follows the pace leaders set, not the pace at which tools are deployed,” she said.

In this region, employees look to senior leaders for permission, direction, and assurance that change is safe before moving forward.

Her perspective draws on Accenture’s Talent Reinventors research—the global study behind part one of this series—alongside the Wharton-Accenture Skills Index (WAsX), a labour-market benchmark published this January. The picture they describe is one where the same eagerness that should make the region a natural early adopter is being held back by what happens, or fails to happen, at the top.

In Singapore, Accenture’s research found that nearly half of employees (47%) cite a lack of leadership support as the single biggest barrier to upskilling with AI. For that reason, Lajmi argues, the top-down mandate is non-negotiable: senior leaders must own the redesign and set the vision for an innovation-led culture. But she frames it as a two-way effort—leadership commitment that empowers teams to redesign how they work from the bottom up. That bi-directional approach, in her account, is what separates genuine transformation from a simple technology upgrade.

See also: ‘Digital fluency’: What it is and why business leaders value it so much

Where the region diverges

What makes Southeast Asia distinct, Lajmi argues, is the proactive role governments are playing. She points to Singapore’s National AI Strategy 2.0, Malaysia’s ambition to become an “AI Nation” by 2030 and Indonesia’s National AI Strategy as signals that talent reinvention here is a shared enterprise between the public and private sectors. The practical implication for organisations, she says, is that they do not have to build AI talent pipelines from scratch—they can align with and leverage national movements already building that infrastructure.

The region’s demographics add to the case. Southeast Asia has a young, digitally native workforce, and the appetite to grow is high. In Singapore, Accenture’s research found that 79% of young employees prioritise skills development over their job titles, 85% expect to continuously renew their skills throughout their careers, and 81% say they are willing to be reskilled if their roles become automated. Two-thirds already use AI tools weekly.

Readiness and deployment, however, are not the same thing. The same research found that 46% of technology leaders have not begun redesigning job roles, and only 23% of employees trust their employer to act in their best interest when introducing AI. Without deliberate work redesign and visible leadership commitment, Lajmi warns, the region risks a “productivity trap”—where tools are deployed, but the work itself remains unchanged, leaving people unsupported.

Renegotiating the seniority contract

The harder structural problem, in Lajmi’s view, is that most HR systems across the region still reward tenure and role-based progression—and tenure here carries weight well beyond pay. It signals loyalty, confers authority, and underpins how many professionals read their own career security, particularly in Singapore, Malaysia and Indonesia. She describes it as a social contract around seniority, embedded not just in talent policies but in how teams operate and how decisions get made. Moving to a skills-based system, she says, asks organisations to renegotiate that contract—and “if you dismantle the old system before the new one is ready, you lose the people you need most.”

She argues that the goal is not to dismantle tenure but to refine it by clarifying which experience is actually worth more. This is where WAsX enters. The index found that the same skill can be worth markedly different amounts depending on the role: signalling strategic analysis is correlated with a validation lead’s salary—a technical execution role in pharmaceuticals—being over $10,000 lower, while a sales representative’s is nearly $8,000 higher. In this framing, the most valuable employee is not the one with the most experience but the one whose skills and experience best match the demands of a specific role.

Lajmi sets out three steps to make the transition workable. The first is diagnosis: using WAsX to identify which skills are in surplus, which are in deficit, and where real wage premiums sit at the role level, so that reskilling investment and pay can be aligned with the actual economics of skills.

The second is to reveal hidden pathways. She cites a Future Skills Pilot run by Accenture, Unilever, Walmart and the World Economic Forum, which found that when people self-report their skills, they identify an average of 11, but when AI analyses the same roles, the number rises to 34. The pilot also identified a 63% skills match between an inventory replenishment manager and an e-commerce manager—a transition, Lajmi notes, that most organisations would never have considered through traditional career planning, and one that could be bridged with reskilling in around six months. In a region where internal mobility is often underused, she argues that visibility is what gives mid-career professionals a credible future within the organisation.

The third is to make the value visible to employees—showing people what their skills are worth in dollar terms and where demand is growing. WAsX, she notes, points to rising value placed on judgment, coordination, compliance and domain expertise. “In this region,” she says, “delivery matters as much as strategy. People will move to the new system when leaders make the benefits visible and credible.”

The fluency gap

For Gen Z and millennial professionals reading this, Lajmi acknowledges that the anxiety in the market is real—but argues that the path through it starts with recognising the gap between widespread AI use and genuine AI fluency.

While 48% of young Singaporeans describe themselves as confident in AI, and usage rates are high, more than 80% report a beginner-level or no understanding of prompt engineering, AI ethics, process design and interpretive analysis. The system, in her words, is producing enthusiastic users who lack applied fluency—and it is that gap, she argues, that separates the average jobseeker from an indispensable hire.

Her recommendation is to adopt a skills-first mindset and become genuinely AI-fluent, which she says means going beyond another introductory course toward structured, hands-on experience. She points to national programmes such as the Young Talent Programme for AI in Finance and courses under the TechSkills Accelerator (TeSA) as ready-made paths, and offers a test for evaluating any of them: will this help me develop a skill I can apply in a real-world context tomorrow? If the answer is no, she says, keep looking.

The window to differentiate on applied AI fluency, in Lajmi’s view, is the next 12 months. The professionals who equip themselves now, she says, “will be the ones who are hardest to catch by 2027.”

The post Why Western AI adoption strategies may fall short in Southeast Asia appeared first on HR Executive.

“We’re people-led, tech-powered,” MacIntyre said. “That’s who we are as an enterprise. That’s what we start from, and it’s how we lead every single conversation on this.”

Read more: What U.S. and UK workforce chiefs want HR to know about AI and hiring

Redeploying employees

An example is Sam’s Club Scan and Go tech, the app feature that lets members scan and pay for items on their phones and walk through an exit gate without a manual receipt check. Nawaz pressed MacIntyre on the worry that if a task disappears, does the headcount disappear with it?

“What we have is more people in other areas of the club,” MacIntyre said. The associate who once stood at the exit checking receipts item by item now stands there to thank members and ask if they found everything, while others are freed up to help members on the floor. “So that if you need some help and assistance, there are more people there.”

Sam’s Club has applied similar logic to a store-standards tool associates use on handheld devices. Associates photograph displays, such as a bakery case, and the tool flags whether products meet company standards. MacIntyre said the value is in the “human-to-human exchange” conversation it triggers between an associate and a manager about the work, not in the automated check.

Read more: Behind the ‘people-led, tech-powered’ Walmart

Building skills without judgment

Sam’s Club has also built an AI-powered interview coach for associates coming through Live Better You, the company’s tuition-free degree program for frontline workers. The tool conducts mock interviews and gives feedback on structure, pacing and delivery. MacIntyre pointed to the tool’s neutrality over a human, who inevitably forms an opinion of the answers.

“This coach is assessing. There’s zero judgment,” she said. “Nobody sees it. Nobody’s giving you a grade on it. But you get the rich, rich feedback in a safe environment that you can then take on to do the next thing.”

Sam’s Club has taken a similar approach to manager behavior, rolling out prompts on leadership devices that flag when a new hire is approaching a turnover risk point and suggest a specific conversation to have. MacIntyre said one club manager initially resisted the prompts before coming to rely on them. “When you rolled this out, I hated it,” the manager told her, before adding that the tool is now something he in fact needed but hadn’t been getting on his own.

The post Inside the Sam’s Club HR rule for adopting AI tools appeared first on HR Executive.

This article discusses the recent court decision addressing AI-related discrimination claims and what employers can do to mitigate these risks.

Key takeaways from recent court cases

• Courts have held that AI-powered hiring tools, like Workday’s AI recommendation system, can support federal and state discrimination claims.
• As part of the lawsuit against Workday, Workday was required to turn over a list of all companies that use its AI-powered hiring tools. Employers using Workday’s AI-powered HR tools since 2020 should take steps now to minimize risks.
• Both the owner of AI-powered HR tools, as well as their business customers, can be held liable under California’s Fair Employment and Housing Act.

Latest updates from Mobley v. Workday

In February 2023, a class action was filed against Workday (Mobley v. Workday, Inc.), alleging that Workday’s AI-driven HR tools, such as HiredScore AI and Candidate Skills Match, discriminate on the basis of race, age and disability in violation of several federal and California laws. The case alleges that Workday’s AI recommendation system, Candidate Skills Match, which scored and ranked individuals, produced discriminatory outcomes at scale across hundreds of employer-customers.

The court previously conditionally certified an Age Discrimination in Employment Act (ADEA) class of persons aged 40 and older whose applications were processed by Workday’s AI recommendation system from September 2020 to the present. Earlier this year, the court also allowed a claim against Workday’s AI recommendation system to proceed under California’s Fair Employment and Housing Act (FEHA). In addition to letting the FEHA claim proceed, the court also held that the FEHA claim can extend to Workday’s AI-powered hiring tools used by non-California employers to evaluate non-California applicants.

FEHA’s application to California-based AI vendors

The court’s most significant holding is that FEHA reaches Workday’s conduct as a California-based agent engaging in allegedly discriminatory screening on behalf of employer-customers nationwide. The court rejected three principal arguments for dismissal:

• Direct vs. derivative liability. Workday argued its liability as an “agent” turns on its employer-customers’ liability. The court held this is “at odds with the FEHA’s concept of agency liability”. Workday is directly liable for its “own engagement in FEHA-regulated activities on the employer’s behalf” independent of employer-customer liability.
• Extraterritoriality. Workday argued FEHA cannot be applied based solely on California operations. The court disagreed and held that because Workday designs, develops and operates the tools from California, plaintiffs adequately alleged tortious discriminatory conduct occurring in California.
• Products-liability analogy. The court rejected Workday’s analogy to choice-of-law analysis in products-liability cases, reasoning that such cases do not illuminate FEHA’s text, structure or remedial intent to “prevent and deter unlawful employment practices.” The court declined to reach Workday’s constitutional arguments, leaving them for a fuller factual record.

Key legal implications

• Nationwide reach: A California-domiciled AI hiring-tool vendor may face FEHA liability for discriminatory outcomes nationwide, independent of whether any employer-customer would itself be subject to FEHA.
• FEHA “agent” liability: The court held that Workday, operating as an “agent” of its customers, could still be held liable and “share liability” with its customers.
• Product-suite sweep: Liability theories and collective definitions may encompass a vendor’s entire AI feature set, including acquired products running on third-party systems, under a “unified policy” theory.

How employers can mitigate risks

To mitigate risks from potential AI-related lawsuits, employers and HR departments should take certain mitigation measures, including:

• Develop clear company policies and guidelines regarding use of AI tools in the workplace, especially with HR-related functions such as recruitment, promotions, benefits, disciplinary actions, etc.
• Before contracting with an AI vendor, request more information from the vendor to better understand the legal implications of the vendor’s AI tools. Companies may want to create a list of standard questions posed to an AI vendor.
• When contracting with an AI vendor, HR and legal departments should consider the allocation of responsibilities and risks in key employment compliance areas, including:

1. 1. compliance with federal, state and local anti‑discrimination and employment laws;
   2. ADA and accessibility compliance in the screening process;
   3. data sources and accuracy of data;
   4. bias testing, impact assessments and audit rights (including access to information needed to evaluate adverse impact);
   5. data security and retention obligations;
   6. compliance with applicable laws regarding required notice and applicant consent; and
   7. allocation of liability and indemnification for claims arising from the AI tool or application.

• Before deploying an AI tool, ensure that all necessary disclosures are made to those affected by the AI tool (e.g., job applicants), and obtain any legally required consent. In general, the more transparent a company is, the more insulated the company is from legal risks.
• Once an AI tool has been deployed, maintain adequate human oversight of any AI tools and document such oversight. In the HR context, human oversight should look for indicia of bias, disparate impact, inaccuracy of information or lack of transparency.

The post Why Mobley v. Workday has far-reaching legal impacts for HR leaders appeared first on HR Executive.

Analysts and reporters have started calling the pattern the “AI boomerang,” and Ford isn’t an isolated case. Robert Half research shows that 3 in 10 employers eliminated positions after implementing AI, then later added those roles back. A related breakdown of the survey put the figure at nearly a third (32%) of hiring managers, with finance, tech and HR reporting the highest rates.

Visier’s analysis of 2.4 million employees across 142 companies found the overall rehire rate after layoffs sits at about 5.3%, a pattern the firm says has shown up for years.

Read more | Nvidia’s Huang and Google DeepMind’s Hassabis call out ‘lazy’ AI layoff logic

AI restructuring with an ‘easy button’

Peter D. Banko, president and CEO of Baystate Health and author of The Necessary Goodbye: How Great Leaders Fire with Clarity, Confidence and Compassion, has spent nearly two decades running large health systems. HR Executive asked him why experienced employees, the ones with institutional knowledge, tend to be first out the door in an AI restructuring.

“The ‘easy button’ in restructuring is to target high-salary line-items to drastically cut costs,” Banko says. “The ‘real button’ to push here is to use this as an opportunity to address the team members who don’t perform (bad attitudes, behaviors and relationships) and don’t deliver the necessary results.”

That distinction reframes what went wrong at companies now rehiring, since a miscalibrated bet on what AI can do shouldn’t serve as a shortcut for an evaluation of who was actually adding value.

CHRO Daniela Seabrook, whose Adecco Group surveyed 2,000 C-suite executives across 13 countries earlier this year, sees the same shortcut from the workforce-planning side. Her research found that only 36% of leaders say their talent strategy clearly demonstrates how AI creates opportunity for employees, and just 39% are involving employees directly in redesigning their own jobs. Only 22% feel confident their organization is building future-ready skills at all.

“The best way to avoid rehiring as a correction mechanism is to slow down the decision before the cut,” Seabrook told HR Executive. She recommends leaders ask a short set of questions before a role is eliminated: which tasks are genuinely automatable, what happens to the rest of the role if only part of it is, what expertise disappears if the role goes entirely and whether the role could be redesigned or redeployed instead of removed.

Read more | The AI layoff trap: Why half will be rehired

What convinces someone to boomerang?

Research on the trend suggests most companies are rehiring for a vacated role rather than winning back the specific person who held it. But what would make someone who was let go say yes to coming back?

Seabrook points to transparency as the entry price. “Employees will likely need evidence that their employer has learned from past decisions and has identified where AI is best suited as well as where human capabilities remain critical,” she says. She ties this to Adecco’s Global Workforce of the Future research, which found that 99% of workers who feel a strong sense of purpose day to day plan to stay with their employer over the next 12 months, compared with 53% of those who never feel it.

Banko locates the same trust question earlier in the process, at the moment of departure rather than the moment of return. “How an individual leaves an organization is more important than the hiring and onboarding process,” he says. “The team members remaining watch, listen and heavily scrutinize the goodbye more than hello. You rebuild hiring credibility with goodbyes that are kind, empathetic, supportive, communicated with crystal clarity.”

That point lands with particular weight given what Ford itself has said about the return of its veteran engineers. Charles Poon, Ford’s vice president of vehicle hardware engineering, told Bloomberg the company had let go of its most experienced people before their knowledge could be used to train the AI systems replacing them. The people watching that decision, and the ones who eventually got the call to come back, are the same audience Banko and Seabrook are describing.

Read more | Meta’s Zuckerberg: ‘We’ve made mistakes’ in AI transformation

Discipline before layoffs

Forrester’s research forecasts that roughly half of AI‑attributed layoffs will be rehired. Banko was asked what warning signs precede that kind of regret, and his answer centers on ego rather than analysis. “Cutting roles is the most important decision a leader will make in their careers,” he says. “The warning signs about ‘being right’ are prioritizing your ego, defending your beliefs, winning debates and maintaining immovable positions. Getting it right requires placing the organization first, listening, learning, adapting and, yes, even changing your mind and direction.”

Seabrook argues leaders need to shift from headcount planning to skill-based planning, building internal mobility into how roles change as AI is adopted rather than treating headcount reduction as the default response to any automation gain. Companies like IKEA franchisee Ingka Group offer a version of this already in practice. Rather than cutting call center jobs after its Billie chatbot began resolving 47% of inbound inquiries, Ingka reskilled 8,500 agents into remote interior design consultants, a channel that had already reached 1.3 billion euros in sales, about 3.3% of total revenue, with a stated goal of growing that share to 10%.

Who gets rehired?

Visier’s data also points to who gets rehired most often. It found that high performers return at a 120% higher rate than mid and low performers; managers return at a 68% higher rate than individual contributors; and employees with 10 to 15 years of tenure return at a 42% higher rate than other tenure groups, the same institutional-knowledge profile Banko and Poon both described losing.

Boomerang hires also come back more expensive, at an average 5% pay increase versus 2% for employees who stayed, which Visier estimates cost the finance industry alone about $19 million in 2024. “The biggest risk for organizations today is not moving too slowly on AI; it is moving too quickly without a people strategy in place,” Seabrook says.

The post Ford’s rehire wave has HR leaders rethinking how AI layoffs get decided appeared first on HR Executive.

The deal significantly expands an engineering cadre already built around the acquisition of Tomoro, which OpenAI announced at the Deployment Company’s May launch.

What is a forward-deployed developer?

As HR Executive reported in May, the OpenAI Deployment Company (and its Frontier Alliances) is OpenAI’s new services arm that sends engineers into customer organizations to help redesign workflows and get AI into day-to-day use, which means it can affect hiring, performance, workforce planning and the technology HR teams use every day. Forward-deployed engineers are tech specialists who sit alongside employees, learn how work actually gets done, and then adjust HR and other systems so that new AI tools support workers.

In July, Amazon Web Services launched its own forward-deployed engineer organization, funding it directly rather than raising an outside investment vehicle the way OpenAI and Anthropic did for their deployment ventures, per Plank’s reporting.

The model is also spreading outside the tech sector. The Department of Defense launched a program called War Force on June 30 to recruit forward-deployed software and AI engineers into two-year tours, according to Plank’s FDE Brief. That puts the federal government in direct competition with OpenAI, Anthropic and AWS for the same talent pool, a dynamic that HR and talent acquisition teams may start to feel in their own hiring pipelines for technical and AI-adjacent roles.

High-paying tech roles

Compensation for these roles has kept pace with demand. Plank reported that postings for forward-deployed engineer positions increased more than 1,000% year over year, with senior roles at AI labs ranging from $560,000 to $785,000 and Palantir’s median forward-deployed salary near $215,000. Recruiting and total rewards teams benchmarking technical talent this year should treat that figure as a moving target and confirm current ranges before using them in planning.

The buildout lines up with how consulting firms are now describing the shift. KPMG advisors Swami Chandrasekaran and Matteo Colombo wrote that enterprise systems such as ERP, CRM and HCM platforms will keep operating as systems of record, while an AI-powered work surface sits above them and coordinates work across those systems.

The KPMG authors also cautioned that this kind of orchestration only creates value with clear process ownership, defined decision rights and controls for when AI can act versus when a person has to step in.

The post AWS joins OpenAI and Anthropic in race to embed engineers inside enterprises appeared first on HR Executive.

Infrastructure or replacing work?

The research firm tracked 156,975 tech job cuts globally through mid-July, with nearly 60% linked to AI-driven restructuring. Oracle cut 25,254 roles this year while reporting a 95% surge in net income to $6.13 billion, redirecting the savings toward AI infrastructure and data center investment. Amazon cut more than 17,000 jobs to simplify its organizational structure, even as 2025 revenue reached $716.9 billion and the company plans up to $200 billion in AI-related capital expenditures this year. Neither company has said AI is fully replacing the work of the employees it laid off; instead, both are using the cuts to support large AI buildouts that are still being scaled.

Cloudflare and Block represent a different pattern. Cloudflare cut more than 1,100 roles, about 20% of its global workforce, tied to what it calls an agentic AI-first operating model. The company said internal AI tool usage rose more than 600% in three months, and it expects the restructuring to cost up to $150 million, mostly in severance. Block cut 4,000 roles, and its leadership said AI could now handle a meaningful share of the work those employees had done.

Read more: Employers face a ticking clock under California’s AI layoff order

Layoff communication

TradingPlatforms’ research notes that relatively few companies have AI systems capable of taking over significant workloads at scale, meaning many of this year’s cuts are pre-emptive cost-cutting to finance infrastructure rather than direct evidence of automation replacing jobs.

That gap has direct implications for how HR teams communicate layoffs internally. Employees, works councils and, in some jurisdictions, regulators may expect different disclosures depending on the stated rationale. Mixing those explanations can create expectations that do not match what is actually happening inside the business.

Cloudflare’s case adds a reputational layer. The company had publicly promoted expanded internship hiring and argued AI should augment workers rather than replace them, according to the TradingPlatforms report. Months later, it announced cuts of more than 1,100 roles tied to a shift toward what it calls an agentic AI-first operating model. The timing raises a question CHROs may want to ask before making similar public commitments about how quickly a stated AI-and-talent philosophy can be overtaken by the next strategy shift.

TradingPlatforms based its analysis on data from TrueUp, TechCrunch, Layoffs.fyi and multiple U.S. WARN databases, with country and sector totals compiled through mid-July 2026.

The post Four big-name AI layoffs, four different explanations appeared first on HR Executive.

Date & Time: Thursday, August 20, 2026 at 2:00 pm ET

Speaker:

Karisa Primini, Principal Value & Strategy, Cornerstone

Description:

This event is part of AI Week, sponsored by Cornerstone OnDemand.

AI is reshaping what work looks like faster than most workforces can adapt. For HR leaders, the question is no longer whether readiness matters, it’s if they can honestly explain where their organization stands today.

Join Cornerstone workforce readiness experts and HR practitioners as they share first-hand experience building adaptive workforces inside real organizations:

• How to assess your current readiness posture
• Where most companies get stuck
• The practical moves that separate organizations that keep pace with change from those that fall behind

You’ll walk away with a clear framework for evaluating your own workforce readiness and an action plan for what to do next, whether you’re just getting started or ready to scale.

Register Now

The post AI Week | Workforce Readiness in the Age of AI: Where Do You Stand — and What Do You Do Next? appeared first on HR Executive.

Date & Time: Tuesday, August 18, 2026 at 2:00 pm ET

Speakers:

Kelly Carmody, Principal Product Marketing Manager, Cornerstone

Mike Siano, Director, Strategy and Value Services, Cornerstone

Description:

This event is part of AI Week, sponsored by Cornerstone OnDemand.

Your people don’t have time to stop and learn, and your business isn’t slowing down for them. In this session, Cornerstone’s Kelly Carmody and Mike Siano explore invisible learning and how to move beyond traditional training to build skills continuously, in the flow of work.

From embedded learning experiences to AI-powered guidance to AI learning agents, learning is becoming something people do as they work, not something they step away to complete. This isn’t learning as you know it. It’s how skills are built, applied, and adapted in real time to keep pace with change.

You’ll learn five ways to embrace invisible learning: 

1. AI-driven adaptive learning
2. Generative and conversational learning
3. Social and collaborative learning
4. Mentorships, gigs, and job shadowing
5. Social and collaborative learning

Join to understand how leading organizations are putting invisible learning into practice – and starting to measure what really matters, going beyond traditional metrics like course completion rates.

Register Now

The post AI Week | Invisible Learning: Building Skills at the Pace of Work appeared first on HR Executive.

Apple sued OpenAI, the maker of ChatGPT, earlier this month, alleging the latter poached employees and encouraged them to share trade secrets.

In an email to The Guardian, an Apple spokesperson said that recently “significant evidence has emerged suggesting individuals employed by OpenAI wrongfully took Apple’s secret and confidential information regarding our unreleased technologies, processes and products.” The suit claims more than 400 former Apple employees now work at OpenAI.

See also: HR tech CEO and former OpenAI exec discuss AI’s workforce implications

Apple suit specifically names two former employees, now at OpenAI

Specifically, the federal suit, filed in the Northern District of California, names OpenAI Chief Hardware Officer Tang Yew Tan—a former vice president of product design at Apple—whom the firm contends shared supplier information and urged candidates coming from Apple to share confidential information during job interviews. According to the court ruling, Tan instructed some candidates to bring “actual parts” to “show and tell” at their interview.

Also named in the suit is former Apple employee Chang Liu, whom Apple alleges kept a company-issued laptop after he left and downloaded “confidential hardware-related files.”

“OpenAI’s nascent hardware business now rests on the shakiest of foundations,” the filing states, “rotten to its core by its illegal reliance on misappropriated trade secrets.”

In a statement to The Wall Street Journal, an OpenAI spokesperson said the company has “no interest in other companies’ trade secrets. We remain focused on building innovative technology that empowers people everywhere.”

However, the complaint suggests that the outlined allegations are just the “tip of the iceberg.”

“Discovery will expose that the misappropriation has been occurring on a scale many times greater” than what is contained in the filing.

The post What’s behind Apple’s explosive suit against OpenAI? appeared first on HR Executive.