Meanwhile, the expectation to have expertise in the space feels overwhelming, and HR leaders want to ensure their organizations are prepared to get the most out of AI capabilities. Thus, they’re performing due diligence work to avoid the “garbage in, garbage out” problem. For example, a CHRO whose company has partnered with mine, Seramount, said they recently completed creating an AI governance structure. Now, they’re focused on understanding where AI can be deployed and what they can fully automate.

For CHROs, the challenge is clear: cutting through the noise to discern where AI can truly elevate the employee experience, workforce planning and organizational agility. My own reflections tell me this moment is less like a technology race and more like a leadership test. Are we willing to ask the harder questions—not just what AI can do, but what it should do?

Understanding The Hype Cycle

The AI transformation is well underway, but excitement peaked in 2023. Headlines declared the technology would revolutionize business and replace many, if not most, human workers. In just a year, however, sentiment shifted. In 2024, Indeed analysis indicated that there are no skills that an AI could fully take over.

The Hype Cycle, a model developed by Gartner to illustrate the stages of interest and adoption that new technologies typically undergo, serves as a helpful reference for understanding GenAI’s journey. There are five stages: innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment and plateau of productivity.

In the beginning, new technologies generate substantial excitement and unrealistic expectations. Over time, as real-world applications and limitations become apparent, this enthusiasm tends to wane. Finally, genuine advancements and practical applications emerge, but this outcome can take a long time and effort to reach.

Just as there wasn’t a single rollout of the internet, GenAI isn’t a single technology with a single adoption curve. It’s a series of technologies, so we should expect multiple AI hype cycles with each new advance.

Where Should CHROs Focus During Hype Cycle Swings?

Whether we’re riding a high or navigating a low, there’s real work for HR to do when it comes to GenAI. This moment calls for action on three levels: embedding AI into our own workflows, preparing our people for AI adoption and tracking broader shifts in the labor market.

1. Integrating AI Into Team Workflows

From talent acquisition to performance reviews, there are many ways HR teams can begin leveraging GenAI. But success isn’t about chasing the latest tech. It’s about solving real problems at the department level.

One example is Accenture’s “Feedback Coach” (registration required), a tool that drafts written feedback based on post-project assessments. Embedded in Microsoft Teams and Workday, the Feedback Coach has been used by staff more than 3 million times and has increased input by 89%.

When choosing GenAI tools, follow three key steps: start small, identify practical use cases, then build from there.

2. Organization-Wide AI Readiness

AI enablement can’t be IT’s job alone. According to 2024 Gallup research, few employees feel they have the skills needed to incorporate AI into their day-to-day work. HR leaders need to foster a culture of readiness and ensure employees have the necessary skills to integrate AI into their workflows.

Hearst presents a useful case study in the benefits of customized training around GenAI adoption. The major publisher implemented a function-specific GenAI training program. The initiative trained advertising operations employees on AI technology to streamline their sales and media planning processes. The resulting increase in efficiency and desired outcomes demonstrates that when organizations invest in relevant training, successful adoption is not only possible but attainable.

3. Macro Labor Market Changes

Beyond internal application and employee readiness, CHROs must keep an eye on AI’s impact on skill demand in the market. The real risk now is a skills mismatch. AI will disrupt and displace many jobs, requiring mass reskilling for workers considering industries with more resilient or new AI-driven jobs. As demand for AI-skilled talent continues to rise, HR teams should focus on fostering critical soft skills like leadership, problem-solving and interpersonal skills.

Where We Go From Here

As generative AI continues to evolve, the temptation is to chase the latest tools or mirror competitors moves. But true leadership means resisting the allure of hype and instead grounding decisions in clarity, ethics and long-term value. CHROs are uniquely positioned to lead this discernment—not as technologists, but as stewards of culture, talent and trust.

The choices made today will shape both how organizations leverage AI and how employees experience its impact on their work and well-being. The question is not whether generative AI will transform our world; we know it already is. The question is whether we will meet this moment with foresight, responsibility and a commitment to putting people at the center of progress.

The post CHROs’ Role In Separating Hype From Reality When It Comes To GenAI appeared first on Seramount.

According to McKinsey’s 2025 “State of AI” report, 78% of organizations document using AI, yet less than 1% consider themselves “mature” in its deployment. Even as adoption rates remain high, a 2025 MIT study found that 95% of GenAI pilots fail to produce meaningful ROI, largely because experimentation never evolves into enterprise-level change. The data is clear: AI adoption is accelerating, but organizational capability is not keeping pace.

HR’s role in AI implementation needs to go beyond calming anxieties that “robots will take our jobs.” The real risk is a growing divide between employees who have AI skills and those who don’t. For HR leaders, this moment presents a critical opportunity: Help employees see AI as a tool that enhances their impact, not one that undermines it.

AI is accelerating and the stakes are rising. But culture—not code—is the secret weapon that keeps transformation on track. Managing AI responsibly means protecting the data that powers change and aligning people and purpose around how it’s used. That alignment now falls squarely to CHROs.

Three Reasons Why AI Efforts Stall

Despite significant investment, most organizations struggle to convert AI ambition into measurable impact. The challenge isn’t the technology—it’s alignment. Leaders can communicate AI’s potential, but employees need clear insight into how that potential affects what they do today, what will be expected tomorrow, and where they fit in the organization’s future. That alignment gap shows up across three persistent barriers.

1. Strategy Without Translation

AI strategies often look compelling on paper, yet employees still ask, “What does this mean for me?” Companies communicate transformation plans, launch new tools, and promote enterprise-wide vision, but employees rarely receive the practical translation they need to change behavior. Many hear about AI in broad terms but cannot see how it integrates into workflows or how their responsibilities will shift. Without that clarity, even well-designed strategies stall. Organizations invest heavily in messaging, yet employees often remain disconnected from the very changes they are expected to adopt.

2. Change Without Trust

Concerns about trust extend far beyond whether employees are using AI appropriately. Increasingly, the deeper question is whether employees trust their organization to use AI fairly, transparently, and in ways that support—not jeopardize—their opportunities. These internal concerns mirror a broader societal trend: Public skepticism in AI still circulates, shaped by constant headlines about biased outputs, misuse of data, and human replacement.

Inside organizations, those same issues show up in tangible ways. Research from the Algorithmic Justice League and Brookings finds that women, people of color, lower-wage earners, and later-career professionals are significantly less likely to receive AI training or be included in early pilots, clear signs of inequitable access. When employees see AI advancement as something happening around them rather than with them, skepticism grows, confidence erodes, and the cultural foundation needed for innovation becomes increasingly fragile.

3. Learning Without Context

Most employees don’t feel prepared for the future of work, not because they resist learning, but because the learning they receive lacks relevance. Here at Seramount, we found that only 23% of employees believe they have the skills they need to integrate AI into their workflow. Simultaneously, only one in five organizations has a defined AI adoption strategy. Managers, too, often lack the tools to model new behaviors or guide their teams through change. Without contextual learning anchored in real work, capability gaps widen and adoption stalls.

These barriers aren’t technical failures. They are cultural ones. And until organizations address the alignment gap between strategy and the human experience of change, AI will continue to advance faster than the culture needed to sustain it.

What Leading CHROs Do Differently: From Technical Rollout to Culture Redesign

AI transformation has become a test of leadership, and CHROs are now the linchpin. They are the only executives with line of sight across trust, culture, workflow friction, skills, manager capability, and workforce risk.

Organizations that break through treat AI adoption as a culture redesign, not a technical rollout. Leading CHROs are shifting their strategies in three ways:

1. They redesign workflows, not just introduce tools. Rather than pushing AI at employees, they co-design new processes that pair human judgment with machine intelligence. The goal isn’t automation—it’s collaboration.
2. They operationalize transparency. Rather than limiting the use of AI to specific pilot groups, they make implementation visible. They open discussion on where it’s used and governed, how decisions are made, and what resources employees have available to them to learn and experiment safely.
3. They build cultures that learn faster than change. Instead of one-off training, they invest in continuous, role-specific upskilling tied to actual workflows. They equip managers to model curiosity, normalize questions, and share what they learn.

Above all, effective HR leaders are partners in AI implementation. They start by listening to their employees—not to check a box, but to diagnose the friction beneath the surface. Scaled listening reveals where trust is fading, where skills gaps persist, and where employees have lost the thread of the “why” behind strategy. That clarity becomes a strategic accelerant: When people understand change and see their role in it, adoption, trust, and innovation rise together.

Bottom Line

AI doesn’t stall because the technology isn’t ready. It stalls because organizations haven’t aligned people, purpose, and culture with the strategy behind it. Employees resist change only when they can’t see where they fit, when the process feels inequitable, or when the “why” behind decisions remains unclear.

CHROs now sit at the center of this alignment challenge, responsible for connecting vision to behavior, strategy to capability, and innovation to trust. It’s a complex mandate that requires both foresight and a strong peer community to navigate what comes next.

Organizations that integrate these capabilities will build a workforce ready not just to adopt AI, but to thrive with it. Those without that alignment risk falling behind—not just in capability, but in credibility.

The post Why Your AI Strategy Needs HR More than Ever appeared first on Seramount.

As Inside Higher Ed recently reported, more law schools are embracing AI, reflecting a profession already reshaped by tools such as ChatGPT, CoCounsel, and Lexis+ AI. Nearly all executives responding to a 2024 LexisNexis survey of Am Law 200 firms indicated they expect investment in generative AI technologies to increase over the next five years, with nearly half currently exploring new lines of business or billable opportunities made possible by generative AI. For today’s students, the challenge isn’t just learning the law; it’s learning how to practice it effectively in a world where AI is part of the workflow.

Practicing the Lawyer’s Craft

Legal education has long wrestled with the gap between theory and practice. Now, that divide is widening as routine legal tasks—such as document review or research—become more automated, shifting a lawyer’s focus to client communication and strategic analysis. Students need more than just exposure to these tools; they will need hands-on experience as clients begin to expect the incorporation of AI into a firm’s offered services.

Through Forage simulations from leading firms such as White & Case, Kilpatrick Townsend, and Latham & Watkins, students step into the shoes of associates to practice real responsibilities: drafting privileged correspondence, conducting due diligence, preparing litigation letters, and even pitching strategies to clients.

By collaborating with the top law student resource in BARBRI, Forage virtual simulations gain a wider exposure to students globally as they utilize these services in preparation for SQEs or U.S. bar exams. These collaborations bring experiential learning to thousands of aspiring lawyers, enhancing their skills as part of their journey to workplace readiness. With Forage simulations appearing alongside essential study materials, employers know that students are receiving the most modern legal training.

These simulations aren’t classroom hypotheticals. They mirror the work that junior lawyers can expect to take on from day one, building confidence in the skills that matter most.

Strengthening Skills AI Can’t Replace

Generative AI may draft contracts or surface relevant case law, but it can’t replace human judgment, communication, or empathy. Forage simulations give students the chance to practice and hone valuable client-facing interactions, supporting their transition of theoretical knowledge into practical application. In White & Case’s Intellectual Property simulation, candidates draft client memos and respond to “cease and desist” letters. In Kilpatrick Townsend’s Corporate Law experience, they conduct due diligence while executing redlines in a contract negotiation. Through the Latham & Watkins Antitrust module, students conduct internal antitrust investigations as part of witness preparation.

Equally important, law students and recent grads sharpen the skills needed to check AI’s work, such as spotting inaccuracies, evaluating sources, thinking critically, and writing with clarity. These abilities are exactly what law firms emphasize when hiring: AI fluency paired with ethical human judgment.

Outcomes That Matter

For law firms, corporate counsel, and legal organizations competing for top talent, virtual career experiences such as those offered by Forage bring measurable advantages. Being integrated with a globally recognized legal exam prep such as BARBRI guarantees a firm’s brand visibility in an overcrowded market. Hiring managers recognize that graduates who have completed Forage simulations often signal the combination of ability and engagement that is highly desirable in junior associates. The outcomes are evident. Eighty-seven percent of Forage learners reported gaining new, practical skills. Students who complete a Forage job simulation are 2× more likely to get an interview and 3× more likely to receive a job offer. Beyond the marketing impact, participation in Forage simulations translates into more efficient onboarding and quicker contributions from new hires. Firms that focus on building AI fluency alongside real-world training aren’t just winning talent today; they are future-proofing their workforce.

The Path Forward

Firms that focus on building AI fluency alongside real-world training aren’t just winning talent today—they’re shaping a generation of lawyers ready to practice with confidence.

See how Forage helps forward-thinking organizations build a workforce ready to thrive in an AI-enabled future.

The post The Future of Legal Talent: Why AI + Human Skills Will Define Success appeared first on Seramount.

Below are some key takeaways and themes from the conversation.

Employees Will Keep Using Outside AI Unless Internal Tools Improve

Leaders acknowledged that staff are already using tools like ChatGPT on personal devices, often because they feel easier and more responsive than company-provided platforms. Several CHROs said the real risk is not curiosity itself, but that unsanctioned use of these tools may expose organizations to data security and IP risks. Leaders agreed that internal tools must be safe and user-friendly; otherwise, employees will continue to bypass them.

Recruiting is Where AI Has Gained the Most Traction

AI has gained the most traction in recruiting, likely because the business case is straightforward. Leaders described how AI tools are being used to guide candidates toward relevant roles, automate scheduling, and reduce the amount of administrative time recruiters spend on each search. One example shared was from a large professional services firm that built an internal assistant to support candidate engagement. The tool streamlines interactions and connects applicants to opportunities but deliberately avoids delivering rejections, a choice made for both legal and cultural reasons. Participants agreed that this type of human-centered approach, where AI handles repetitive tasks while people retain responsibility for judgment and empathy, is the basis for adoption moving forward.

Rolling Out AI is a Cultural Choice

Leaders noted that decisions about when and how to deploy AI carry cultural weight. Some described holding back on certain applications because the timing didn’t feel right for their organization. Others pointed out that transparency around data, fairness, and legal guardrails matters just as much as efficiency gains.

Leadership Development is a Missing Piece

Technical capability alone is not enough. Several HR executives pointed out that leadership development was “decimated” during COVID, leaving many managers without the skills to support employees effectively. Today, those same managers are being asked to juggle hybrid work dynamics, employee burnout, and the uncertainty brought by AI, often without the tools they need. Leaders agreed that organizations must reinvest in leadership development and coaching to rebuild empathy, adaptability, and change management so managers are prepared to guide their teams through this period of disruption.

Employee Burnout and Culture Remain Top Concerns

Breakout sessions reinforced that well-being and culture are still pressing concerns. Leaders in healthcare and other sectors described employees facing constant new demands, leading to burnout and exhaustion. Several noted that hybrid work policies have created tensions and perceptions of unfairness between different groups of employees. Others highlighted the importance of reducing stigma by positioning mental health within broader wellness conversations. Across these perspectives, participants agreed that organizations must stay focused on supporting employees holistically, even as they explore new technologies.

Join our growing community of HR Leaders transforming the future of work—together.

The post 40 HR Executives Gathered to Discuss Today’s Biggest Challenges: Here is What They Said appeared first on Seramount.

The benefits of AI are striking, but they come with complex trade-offs. Bias persists in subtle forms, employees experience stress and disengagement, and organizations face heightened governance challenges.

To address these risks, this article offers a six-step accountability framework that equips leaders to balance innovation with inclusion, responsibility, and employee well-being. To understand why such a framework is necessary, it is helpful to first consider how quickly AI has spread and what early adoption is revealing.

AI Adoption Trends: Some Insights

The speed of adoption is staggering. Morgan Stanley reports that AI adoption in financial services climbed from 66% to 73% in 2025.

Companies that adopt AI early often gain competitive traction. In financial services, a Bain report finds that on average, productivity is improved by 20% as a result of generative AI deployments. Law firms report dramatic gains: up to 80% faster contract review and significant time savings in legal research. Pharmaceutical firms investing in AI for drug modeling are slashing development timelines and costs while complying with FDA initiatives to reduce animal testing. Among early adopters, 74% are already reporting ROI, with 86% seeing revenue growth of 6% or more.

But rapid adoption at the organizational level does not always translate into coordinated or secure use at the employee level. Surveys by Microsoft and LinkedIn Work Trend Index show that while 75% of employees already use AI at work, nearly 80% of that is “bring-your-own AI,” with 53% not informing their employers, thus posing high data security risks for companies. This cautionary note points to an urgent need for organizations to articulate clear AI strategies and guidelines for employees. Successful generative AI adoption hinges not only on the infrastructure and tools but also on how it is rolled out by organizations and the ways that people think, adapt, and collaborate with AI.

But There’s a Catch: Covert Bias and Employee Well‑Being Risks

As AI accelerates and offers new ways to work, so do the hidden pitfalls. A Stanford HAI study reveals a worrisome pattern: While language models may no longer generate overtly racist content, they continue to produce language that have negative associations with certain groups, especially against African American English (AAE) speakers. Models label these voices as “dirty,” “lazy,” or “stupid,” and when asked to make dialect-based decisions, they assign AAE speakers lower-status jobs or recommend harsher sentencing in criminal justice systems.

In addition to bias, AI at work can take a toll on workers’ mental health. A Harvard Business Review article finds that AI usage is linked to increased loneliness, insomnia, and unhealthy coping behaviors. Productivity may rise, but motivation and engagement can drop, especially when tasks without AI feel mundane or less meaningful.

Another study points to a “productivity paradox,” where employees might experience a temporary decline in performance after AI introduction, followed by stronger growth in output, revenue, and employment. The temporary instability in workflow and output for those employees facing a sharp learning curve has to be accounted for in performance reviews and feedback functions.

Finally, disparities in adoption cannot be overlooked. A study from the Haas School of Business, reported in The Wall Street Journal, found that women are 20–22% less likely than men to use generative AI tools, due largely to occupational distribution and workplace dynamics. Without intentional oversight, such gaps risk compounding existing disparities. Inclusive adoption strategies are therefore critical if AI is to benefit the full workforce rather than exacerbate divides.

Bridging Promise and Responsibility: The Six-Step Accountability Playbook, Roles, and Responsibilities

To harness AI’s exciting potential without amplifying harm, organizations must embed accountability at every layer while incorporating its tools into daily operations. Effective management of AI in the workplace requires a shared responsibility model. No single group can oversee all the ethical, technical, and organizational implications of AI. Stakeholders such as senior leaders, HR, tech teams, compliance officers, and Employee Resource Groups (ERGs) must work together to ensure AI is implemented responsibly.

ERGs play a critical role in ensuring that AI adoption is inclusive, responsible, and reflective of diverse employee perspectives. While functional leaders bring strategic, technical, and operational expertise, ERGs provide the lived experiences and insights that help organizations identify risks, close representation gaps, and build trust. Integrating ERGs into AI accountability structures ensures that AI systems reflect the values and needs of the entire workforce.

Collaboration across these groups is essential to balance innovation with accountability, minimize bias, and protect employee well-being. Here’s a blueprint for organizations that apply these strategies in the real-world context:

Accountability Areas, Recommendations, and Stakeholders

Conclusion: Accelerate with Awareness

AI’s trajectory in workplaces across all industries is extraordinary. Early adoption brings tangible gains: time saved, efficiency unlocked, and innovation enabled. But studies reveal that beneath polished interfaces, deep-seated biases can persist and exacerbate roadblocks for some people who face barriers in actualizing their ambitions.

Senior leaders, technology experts, AI governing bodies, and policymakers must act not only with speed but also with vigilance. The six-step accountability framework outlined here provides a path forward: embed checks on bias, protect employee well-being, and ensure inclusive adoption. By aligning speed with vigilance, organizations can harness AI’s potential while safeguarding the people at the heart of work.

The post AI at Work: The 6-Step Accountability Framework for Inclusive Workplaces appeared first on Seramount.

But efficiency comes with a catch. Only a quarter of candidates say they trust AI to evaluate them fairly. They might be right to worry: A recent investigation revealed that AI-powered salary negotiation tools often advise women and minority candidates to ask for lower pay than their White male peers. And just this summer, a judge allowed a class-action lawsuit alleging bias in Workday’s AI hiring tools to proceed.

The pattern is clear: Without careful oversight, AI risks may amplify the very inequities in recruiting that inclusion leaders are working to eliminate.

How AI Hurts and Helps Recruiting Fairness

AI has transformed how organizations find and evaluate talent. What once required hours of manual screening and résumé sorting can now be done in minutes. Algorithms can scan thousands of applications, flag candidates with relevant experience, and even predict which ones might be the best fit. In theory, this should help broaden the pool, surfacing applicants with unconventional career paths or transferable skills who might otherwise be overlooked.

But the risks are just as real. These same systems can unintentionally filter out qualified candidates: those with career breaks or nontraditional résumés or those who use assistive technologies. And because the process is automated, those exclusions can happen at scale and without human awareness.

Recent headlines show that these risks aren’t hypothetical, and bias shows up not just once but across different tools and contexts. The consistency of these missteps should be a warning sign: Fairness won’t happen by default.

This is where inclusion leaders come in. Your role isn’t to fine-tune search strings but to ensure the systems themselves have checks and balances. That means asking the right questions:

• How is the AI being tested for bias?
• Who is accountable for monitoring its outcomes?
• What human oversight exists to ensure efficiency isn’t coming at the expense of equity?

By shaping these guardrails, inclusion leaders can help organizations harness AI’s promise without letting it hardwire discrimination into the hiring process.

Guardrails for Inclusive AI in Hiring

While most inclusion leaders may not be writing Boolean strings or running LinkedIn searches themselves, they play a critical role in shaping how their organizations use AI in hiring and beyond.

Here are a few ways to lean in:

• Be transparent: Tell candidates when AI is used in your hiring process, and make sure a human always reviews final decisions.
• Audit regularly: Test your AI-driven tools for evidence of bias. Look for trends in data: Is one group being advanced at a higher rate than others? Are certain résumés consistently flagged down? Adjust accordingly.
• Engage critically: Treat AI like a teammate, not a decision-maker. Ask follow-up questions, challenge its recommendations, and compare its outputs against your own judgment. Even go so far as to ask: “What biases may exist within this response?”
• Continue the fundamentals: Even the smartest tech doesn’t replace inclusive hiring practices such as standardized interview processes, clear evaluation rubrics, and strong referral pipelines. Get more strategies here.

An Opportunity: Using AI to Advance Skills-Based Hiring

Back in 2020, skills-based hiring gained momentum when the White House issued an executive order encouraging federal employers to waive degree requirements and open doors to qualified workers without four-year diplomas. The idea was simple but powerful: Reduce unnecessary barriers, expand opportunity, and modernize recruitment.

Yet follow-through has lagged. Research shows that among companies announcing a move to skills-based hiring, nearly half made little meaningful change in practice, even after removing degree requirements from job postings.

Now is a great time to recommit to skills-based hiring. AI could be used to breathe new life into the shift, helping recruiters identify candidates based on the specific capabilities needed for a role rather than credentials alone. By asking AI to prioritize skills and experience, organizations can minimize the degree bias that still shapes too many hiring decisions.

Not only would this open the door to more candidates, but it also can boost performance: Employees hired for skills over pedigree are 1.9 times more likely to perform effectively.

For inclusion leaders, this is a chance to reframe AI not just as a risk to be managed but as a tool to actively advance inclusion when paired with intentional, skills-first hiring strategies.

The Bottom Line

Like any tool, AI reflects how it’s used. In recruiting—a process already vulnerable to inequity—it can accelerate progress or entrench bias. Without thoughtful oversight, organizations risk sidelining the very people they hope to attract.

That’s where inclusion leaders come in. You are uniquely positioned to make sure fairness doesn’t get lost in the pursuit of speed and to champion ways AI can actually expand opportunity, such as recommitting to practice skills-based hiring.

The post Will AI Close—or Widen—the Equity Gap? appeared first on Seramount.

Here’s why that matters—and what you can do about it.

Why AI-Generated Résumés Pose a Risk

1. Generic, Surface-Level Content

AI-written résumés often produce generic phrases and buzzwords (“proven track record,” anyone?). They may pass ATS keyword checks but reveal little about actual job performance, leaving recruiters struggling to differentiate candidates.

2. Misleading Qualifications

Generative AI tools are known to embellish or even fabricate skills and achievements. One study found nearly 49% of hiring managers automatically dismiss résumés suspected of AI authorship due to authenticity concerns.

3. Increased Recruiter Burden

AI-generated résumés often exaggerate experience or use vague, identical phrasing, which forces recruiters to manually cross-check details, add interview rounds, or administer skills tests to confirm qualifications. This flood of superficially perfect applications increases recruiter workloads as hiring teams must spend more time verifying claims and conducting deeper candidate assessments, potentially delaying hires and increasing costs.

How to Combat AI-Generated Résumés

Talent acquisition teams can shift away from résumé-dependent processes toward practical, skills-based hiring. Here’s how:

1. Prioritize Skills Assessments over Résumés

Rather than relying solely on keyword-driven ATS screening, integrate practical assessments into your hiring process. Real-world job simulations, such as those offered by Forage, provide candidates an opportunity to demonstrate tangible skills through task-based simulations.

Companies partnering with Forage report compelling outcomes:

• One leading law firm found Forage simulation participants comprised 44% of their applicants yet represented 90% of eventual hires.
• An investment bank discovered that Forage participants, making up just 9% of applicants, received 70% of offers with a 100% acceptance rate.

By screening based on actual performance, companies can reliably separate qualified candidates from AI-assisted pretenders.

2. Enhance Your ATS with Human and AI Screening

Ironically, AI can also help detect AI-generated résumés. Advanced ATS systems using contextual analysis and cross-referencing can flag inconsistencies or generic language. Pairing these insights with human judgment means trained recruiters quickly spot unnatural phrasing or implausible career achievements, enabling more informed candidate assessments.

3. Foster Authenticity and Transparency

Clearly signal your organization’s preference for authenticity. Consider asking candidates up front if AI aided their application materials—62% of hiring managers support this transparency. Frame this positively, emphasizing that the goal is understanding the candidate’s genuine qualifications.

Encourage interviewers to discuss specific résumé details during conversations, probing deeper into experiences. Genuine candidates typically respond confidently and specifically, while those relying heavily on AI tend to falter.

4. Build Curated Talent Pipelines

Reduce your reliance on the open résumé flood by creating pipelines from candidates who have already proven interest and aptitude. Forage job simulations offer this advantage by engaging thousands of motivated, skill-tested candidates who have proven relevant competencies. Leveraging such curated pipelines simplifies screening and elevates candidate quality.

5. Train Recruiters to Stay Agile

Educate your hiring team about emerging recruitment trends, including AI-generated applications. Regularly update screening criteria to prioritize skills, authenticity, and practical assessment outcomes over keyword-matching alone. An informed, agile recruitment team better identifies high-quality hires despite evolving candidate strategies.

Turn AI Challenges into Hiring Advantages

AI-generated résumés don’t have to overwhelm your hiring strategy. By shifting the focus from traditional résumé screening to practical skill validation, leveraging authentic tools such as Forage job simulations, and combining AI tools with trained human judgment, talent acquisition leaders can confidently separate outstanding talent from AI-assisted noise.

Your ATS might currently be cluttered with AI-assisted applicants, but with the right approach, you’ll spot genuine talent faster, reduce recruiter workloads, and ultimately make smarter hiring decisions.

The post Your ATS Is Filling Up with AI-Generated Résumés—Here’s How to Fight Back appeared first on Seramount.

The job market is shifting quickly. Baby boomers are retiring at a rate of 10,000 per day, and younger generations are entering the workforce with different expectations. Workforce planning—the ability to anticipate talent shortages, skills gaps, and hiring demands—is a top concern for Talent Acquisition (TA) leaders today.

Why Workforce Planning Matters for TA Leaders

For years, workforce planning was viewed as a separate HR function. But in today’s labor market, top TA leaders are taking a more proactive role in shaping the future workforce. As companies reevaluate strategy in the wake of reorganizations, digital transformation, and shifting market demands, workforce planning has become a cross-functional imperative—aligning people strategy with business execution and ensuring TA, HR, and business leaders are working toward shared goals.

Consider these workforce trends:

• The labor force is shrinking. As older workers retire, fewer younger workers are available to replace them, creating increased competition for talent.
• Skills gaps are widening. By 2027, nearly half of core job skills will be disrupted by technological advancements such as artificial intelligence (AI), meaning companies must rethink how they identify and develop talent.
• Generational shifts are accelerating. Younger generations prioritize flexibility, purpose-driven work, and development opportunities, while companies face the challenge of replacing retiring expertise with future-ready skills.
• Roles are being redefined. According to the World Economic Forum, AI and tech advancements are expected to transform 86% of businesses by 2030, creating 11 million new jobs while displacing 9 million. Roles in areas such as AI, big data, and fintech are growing quickly, while administrative and clerical jobs are in steep decline.

Workforce planning helps businesses align teams around the future by tracking what’s changing, what’s staying the same, and what’s being phased out—not just reacting to the present.

How Forage Helps TA Leaders Plan for the Future

Forage makes it easier for TA teams to align hiring efforts with workforce planning by helping companies build evergreen talent pipelines that are focused on contextual skills and designed to meet talent through digital channels. With Forage, organizations can:

• Build Evergreen Pipelines

TA teams often scramble to fill roles once a vacancy opens. Forage enables companies to engage prospective employees early, through virtual job simulations that showcase real work experiences. This means recruiters will already have a warm pipeline of candidates who understand the company and its expectations before they even apply.

This is especially valuable as companies build talent strategies around emerging job categories and prepare for generational turnover. By cultivating talent early, organizations can ensure continuity and reduce time to hire for future-critical roles.

• Provide Immersive Digital Experiences

Today, brands  are expected to be engaging and personalized, but there’s often a disconnect between employer branding and recruiting. Today’s candidates are looking for more than polished messaging—they want to know what it’s really like to work at a company. Virtual job simulations offer a scalable way to bridge that gap, providing an authentic way for employers to showcase their opportunities while giving candidates hands-on experience.

Forward-thinking companies are also using virtual tours to give candidates a deeper look into workplace culture and team dynamics. These experiences can help companies stand out in a crowded talent market.

• Stay Ahead of Hiring Trends

TA leaders who rely only on traditional recruiting methods will struggle to keep up with the pace of workforce change. Forage gives companies valuable data on which candidates are most engaged, what skills are trending, and how hiring strategies should evolve to meet future workforce needs.

Workforce planning isn’t a separate function from talent acquisition. This data can also support broader stakeholder alignment—helping HR, business leaders, and TA work together to understand emerging talent demands and proactively adjust workforce plans. The best TA leaders aren’t just thinking about today’s hiring needs—they’re planning for the next decade. This is the key to staying competitive in a dynamic job market.

With Forage, companies can take a proactive approach to hiring, building stronger talent pipelines and ensuring they have the right people in place for the future. Interested in learning more? Let’s talk.

The post How Talent Acquisition is Redefining Workforce Planning appeared first on Seramount.

Why? Accessibility. AI is no longer reserved for engineers and data scientists. Tools like ChatGPT are available to anyone with a computer or smartphone. No coding, no complex interfaces—just natural language inputs that allow users to engage seamlessly.

Another key factor is AI’s evolving capability. Today’s AI isn’t just about automation—it generates new, human-like content, from written articles to complex data analysis. The result? AI is becoming a general-purpose technology that will transform nearly every industry.

That transformation is already underway. AI investments are set to skyrocket, with 75% of companies planning to invest in AI within the next five years. Analysts predict AI-driven automation could impact up to 30% of work hours and 50% of tasks across industries. Goldman Sachs even estimates a 7% increase in global GDP over the next decade due to AI advancements.

As Bill Gates put it: “AI’s impact will be as big as the introduction of the PC. Word processing applications didn’t do away with office work, but they changed it forever.”

AI Won’t Take Your Job—But Someone Using AI Might

Some jobs will be eliminated, but more will evolve. Journalism is already seeing AI-generated content at scale. Basic coding, technical writing, and financial analysis are also shifting. But AI isn’t just replacing jobs—it’s transforming them.

Job postings reflect this shift. Consider these real listings from Indeed:

• Graphic Designer: “Familiarity with AI and ChatGPT technology.”
• Protein Sciences Research Scientist: “Experience utilizing artificial intelligence for protein design and related applications.”
• Content Writer & Researcher: “Proficiency in AI prompting and navigation.”

AI skills are no longer confined to tech roles. Job postings requiring AI proficiency have increased by 1,800% over the past two years. This follows the historical pattern of disruptive technology—just as the Internet revolutionized business in the 1990s, AI is now reshaping work.

Economist Richard Baldwin captured this reality: “AI won’t take your job. Somebody using AI will take your job.”

The AI Skills Gap: Employers vs. Higher Education

Here’s the problem: Employers want AI-ready talent but struggle to articulate what they need that talent to do. And higher education is struggling to keep up.

• Job postings remain vague. In the past year, 71% of postings mentioning AI included no details on what AI tools or skills were required. Companies struggle to define specific AI applications in their roles, leaving students and universities unclear on what to teach.
• AI skills are barely mentioned in entry-level hiring. Only 7% of entry-level job postings request AI-related skills, and most are for technical roles, leaving nontechnical fields behind.
• Higher education operates on a different timeline. Universities analyze job market data, but curricula and new degree programs evolve too slowly to keep pace with AI’s rapid development. Without clear employer signals, universities risk teaching outdated or irrelevant AI skills.

This disconnect is a missed opportunity. The organizations that successfully integrate AI into hiring and the higher education institutions that weave practical use cases into their curriculum will shape the future of work.

AI as a Case Study: The Need for Employer-Education Partnerships

We’ve seen successful collaboration before. Consider the accounting field, where the National Accounting Board works directly with higher education faculties to shape curricula based on industry needs. This ensures students graduate with relevant skills, reducing the learning curve for new hires.

The same approach is needed for AI. Employers and universities must work together to identify:

• Which tasks AI will automate vs. augment. For example, cybersecurity analysts used to manually evaluate phishing attempts. With AI handling detection, analysts now focus on broader security strategy.
• Where AI complements existing work. The World Economic Forum predicts that for data scientists, 35% of tasks will be automated, 55% augmented, and 10% unaffected.

Understanding these breakdowns across fields, disciplines, and jobs will help universities adjust their programs accordingly.

Bridging the Gap with Job Simulations

This is where Forage comes in. Job simulations offer a scalable, real-world solution to closing the AI skills gap.

Forage connects early- to mid-career talent with leading companies through virtual job simulations that reflect real employer needs. Instead of theoretical coursework, candidates engage in practical tasks—like learning how a company uses AI for data analysis, marketing, or coding.

For companies, this provides a way to signal AI skill requirements more clearly than traditional job postings. For the candidate, it offers tangible AI experience before entering the workforce. Talent walks away knowing what to expect before applying for the role with skills employers actually need.

Preparing for the AI-Driven Workforce

AI’s impact on the workforce is undeniable. But the transition won’t be smooth unless employers and universities collaborate to define AI competencies. The future of work won’t be dictated solely by AI’s capabilities—but by how well we prepare the next generation to use it.

The post AI Is Reshaping Work—But Are Employers Ready? appeared first on Seramount.

I’m reminded of the iconic scene from Walt Disney’s Fantasia where Mickey Mouse places a spell on his broom to do his manual work of carrying water to the well. The plan backfires when Mickey falls asleep and the brooms automate the work too well: The unsupervised brooms take too many buckets of water to the well, and ultimately the room is flooded. It’s not until Mickey’s master (Yen Sid) dramatically intervenes that the situation is brought under control.

Right now, two-thirds of candidates are proverbially placing a spell on the broom and using AI to do the manual legwork of preparing job applications. This has led to a flood of candidate applications to open roles, putting immense pressure on talent acquisition teams. Many of Forage’s Fortune 500 employer partners are reporting 2x the candidate volume during the 2024/2025 recruitment cycle. TA teams don’t have the tools or strategies to tackle the dramatically higher candidate volume and effectively find great candidates within this sea of AI-generated documents.

But now is the time for TA teams to play the role of Master Yen Sid: They must craft thoughtful and deliberate strategies to retake control of their recruitment processes in this new AI-enabled world.

So how do you figure out which candidates are high-intent, especially when you’re awash with 10x the candidate volume you’ve been used to?

Using Positive Friction to Reclaim Control of Your Recruitment Process

Our view at Forage is that TA teams must introduce positive friction into the candidate experience process. Positive friction is where an employer introduces additional steps into the recruitment process to separate high-intent candidates from low-intent candidate, which is the first crucial hiring signal you should look for in the recruitment process. An application process that incorporates positive friction should (by definition) be more time-consuming, but it’s crucial that candidates extract value from engaging with that additional friction. This might include increased skills, a better understanding of the employer (and their culture), confidence to pursue the role, and so on. 

Deliberately introducing friction seems antithetical to every other macro trend in the recruitment world for the past 25 years. The talent acquisition industry (like most industries) has been focused on creating a frictionless candidate experience, which has culminated with a slew of one-click applications. Everyone now realizes that a frictionless process doesn’t serve the employer—or the candidate for that matter—which is why positive friction can be such a powerful tool to respond to higher candidate volume.

Forage job simulations are an effective way to introduce positive friction into the candidate experience to surface high-intent candidates. Job simulations are free, open-access, virtual experiences where candidates can road-test different roles at a specific employer by completing a series of interactive tasks that mimic the type of work you would do in a specific role. Many of the world’s leading employers, including Walmart, JPMorgan, Citi, and Pfizer, have leveraged job simulations to introduce positive friction into their hiring process. By engaging with a job simulation, candidates not only demonstrate intent but also gain tangible skills and insights into the role and company.

This approach is particularly effective in an AI-driven hiring landscape because it helps filter out low-intent candidates who are simply mass-applying with AI-generated applications. Unlike traditional application materials, which AI can generate in seconds, job simulations require real effort and engagement. A candidate who willingly invests time in a job simulation is far more likely to be genuinely interested in the role than one who simply clicks “apply” on a job board and submits their bundle of AI-generated application documents.

The Data Speaks for Itself

There is a great maxim that “data always has a better idea,” and hiring data that has been collected from +100 hiring partners now shows that positive friction unequivocally produces better hiring outcomes. Forage data demonstrates that candidates who complete a job simulation are:

• 4x more likely to convert to a hire than those who do not.
• Significantly more prepared for interviews, reducing recruiter time spent on qualification calls. Our partner TA teams feel more confident with the quality of candidates they are putting in front of their colleagues to interview.
• More likely to stay with the employer long-term, as these candidates are making more informed and deliberate career decisions in terms of where they want to work.

Rather than sifting through mountains of AI-generated resumes, recruiters can focus their efforts on candidates who have already demonstrated a clear interest in and aptitude for the role, saving time and resources.

The Future of Talent Acquisition in an AI World

AI isn’t going anywhere—it’s only getting smarter. Talent acquisition teams relying on outdated hiring signals such as resumes, cover letters, and pre-screening AI tools will soon find themselves overwhelmed, drowning in a sea of applicants but struggling to make strong hires.

But AI isn’t a replacement for human judgment. When used strategically, AI enhances hiring by refining processes and improving decision-making. The most effective recruiters won’t be those who process the most applications but those who engage with the best-fit candidates efficiently.

This means:
●  Prioritizing high-intent candidates over low-effort applicants.
●  Introducing positive friction to distinguish truly engaged candidates.
●  Leveraging job simulations to add value for both employers and applicants.
●  Using AI not to automate hiring decisions but to streamline screening and engagement.

A Call to Action for TA Leaders

The AI hiring flood is not slowing down. Just like Mickey in Fantasia, TA teams who fail to take control will find themselves drowning in applications, unable to separate the best candidates from the noise.

The solution isn’t to resist AI—it’s to build thoughtful, human-centered hiring processes that work alongside it. By prioritizing candidate intent, introducing positive friction, and embracing job simulations, TA teams can reclaim control and build stronger, more effective hiring pipelines.

The future of talent acquisition belongs to those who adapt. Will your team be one of them?

The post The Most Powerful Hiring Signal You’re Not Using in an AI-Driven World appeared first on Seramount.