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Upskilling and Empathy: HR’s Response to the 2025 Tech Layoff Crisis

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Upskilling and Empathy: HR’s Response to the 2025 Tech Layoff Crisis

The technology sector has recently grappled with significant workforce restructuring, leading to widespread layoffs and palpable uncertainty. 

While these shifts present immediate challenges for individuals and the broader economy, they also compel Human Resources (HR) and business leaders to confront a critical question: 

How can organizations respond to such crises with both strategic foresight and deep empathy? 

Beyond the immediate impact of job reductions, the long-term health of an organization hinges on its ability to support its remaining workforce, retain institutional knowledge, and prepare for future demands. 

This HR Spotlight article distills invaluable insights from leading business executives and seasoned HR professionals, exploring their innovative strategies for managing the aftermath of layoffs, emphasizing crucial concepts like upskilling, cross-training, and fostering a culture of genuine care to ensure both employee and organizational well-being in turbulent times.

Read on!

Leila Rao
Author and Business Strategist, Agile Coach

Leila Rao

Like many in the DEI and tech-adjacent space, our organization faced real turbulence; federal contracts were terminated, and with them, jobs.

Some team members had to leave through layoffs; others left on their own as the future felt too uncertain. It was a hard chapter. But what came next was powerful: the remaining team doubled down on articulating our value to existing clients and began looking beyond the federal space for more stable opportunities.

Rather than “upskilling” as a buzzword, we treated it as a lived practice – growing into roles we hadn’t expected, supporting each other in real time, and adapting how we deliver value. That collective resilience (not just the strategy) made the difference.

Sara Green-Hamann
Founder & CEO, Tallwood Human Resources Consulting

Sara Green-Hamann

I’m seeing two different things with my clients experiencing reduced sales.

I generally work with small to mid-sized businesses, so they often engage me later in the process after their finances have come to a head. For those who need to lay off employees, we are looking at severance packages that include outplacement services.

In addition to updating resumes and cover letters, we are also using services that will review an employee’s visible social media and if possible, remove anything that could be considered controversial.

For employers who have the financial ability, we are going back to basics with retention and engagement strategies. Often, these employers haven’t had a dedicated HR representative and don’t know the cost savings of a combined retention and engagement strategy. When we look at the potential ROI associated with that option, employers are often receptive to pursuing it as an option.

Finally, I work with several restaurants and therefore have connections within the industry. I’ve worked with business owners to help place employees with other businesses I work with who are in growth mode. This has been beneficial to the growing business who is receiving a seasoned employee that has already been vetted and referred by a colleague. It helps the struggling employer because they can keep a positive relationship with the employee.

Chris Putrimas
CEO, Teak Warehouse

Chris Putrimas

At Teak Warehouse, we focus on cross-training and internal mobility to minimize the risk of layoffs.

When sales decline seasonally or certain roles change, we actively seek ways to redeploy team members into other areas, like customer support, logistics, or content. We also engage employees early in discussions about changes so that no one feels blindsided. In a few cases where transitions were unavoidable, we provided extended notice, resume coaching, and connected people to our supplier or partner network for new opportunities.

It’s not just about doing the right thing—it also protects our brand and culture. Our team understands that we value them, and that loyalty is evident in how they treat our customers.

Alejandro Meyerhans
CEO, Get Me Links

Alex Meyerhans

Facing the wave of 61,000+ tech layoffs in 2025, we’re tackling the challenge head-on by reinventing how our teams operate. Currently, we are synergizing functions and integrating automation to create a more agile, future-proof workforce. Instead of traditional silos, our experts cross-train and leverage AI-driven tools to boost productivity even with a low headcount.

This approach not only reduces redundancy but also empowers our staff to adapt quickly as technology evolves. In addition, we focus on upskilling alongside automation, preparing our people for roles that blend human insight with AI efficiency through the education of tools.

This fusion fosters retention and creates career paths less vulnerable to layoffs, proving that embracing tech can safeguard, rather than threaten, jobs.

Justin Azarias
CEO & Founder, Property Homebuyers CA

Justin Azarias

We only hire more employees when absolutely necessary.

Each learns how to handle transactions, conduct home inspections, and communicate with sellers. In this manner, nobody is forced to perform a single task all the time. It keeps everyone productive and ready for a slowdown.

We ride the market together when it changes. We reduce hours or distribute duties among team members in place of layoffs. To keep people employed, I’ve even taken a lesser cut myself.

We don’t hold people back if they’re ready for anything new. We connect them with our network, assist with references, and offer guidance where appropriate. If I were in their position, that is what I would desire. Treating people well always pays dividends, both in real estate and in life.

Eli Pasternak
Founder & CEO, Liberty House Buying Group

Eli Pasternak

Personally, I believe that RIFs are distressing and challenging for all individuals to manage.

HR managers and leaders should not be expected to maintain a positive attitude or attempt to make the situation more tangible than it is. This may diminish the severity of the termination and disrespect individuals’ emotions. It is important to recognize that the reduction is a difficult experience for all employees, including those who are being terminated, those who are remaining, and the HR administrators who are responsible for implementing it.

It is also a favorable moment to be candid about the organization’s activities. Helping those who are left behind to manage and progress can be achieved by responding to inquiries regarding the necessity of the reduction. While it may be effortless to personalize a dismissal, it is not a productive effort.

Managers should maintain an impartial perspective when selecting positions to eliminate. They must be cautious of the language they use when discussing the reduction. Personnel are not being eliminated; rather, positions are minimized, which impacts individuals. Additionally, they must keep in mind their decisions.

The organization will be haunted by the slightest indication of favoritism or bias on Glassdoor and other websites that collect employer ratings.

Sara Bandurian
Operations Director, Online Optimism

Sara Bandurian

At Online Optimism, we’re working to strategically pivot our approaches, as we continue to grow our team. We’re staying on the forefront of emerging technologies and platforms, and always looking to further adapt.

We train employees in new AI tools to help traditional marketers transition into AI-enhanced roles, an approach that allows existing team members to become more valuable and versatile rather than being replaced by automation.

We also look to rising platforms such as Reddit to continue diversifying our approaches, and expanding into new territories of growth as a hedge against industry changes and economic downturns. We see these challenges as opportunities to evolve–a philosophy at the core of the company.

Miko Pasanen
Owner, Mikollc

Miko Pasanen

As a general contractor working across residential, commercial, and government sectors, we’ve seen firsthand how workforce stability impacts every stage of a project—from planning to execution. With the wave of tech layoffs in 2025 and so many skilled individuals struggling to re-enter the job market, we’ve taken a proactive approach.

Instead of cutting roles during slowdowns, we’re investing in cross-training our teams. A project engineer might pick up scheduling or safety oversight skills, while admin staff are learning more about project management software.

We’ve also partnered with local trade schools and tech programs to offer transitional roles for displaced tech professionals—especially those with data or BIM experience, which aligns surprisingly well with modern construction tools.

Our goal is not just to keep our people working, but to help them grow with us as the industry evolves. Upskilling has become not just a retention tool, but a long-term resilience strategy.

Vishnu P
President, Hale Cosmeceuticals

Vishnu P

We haven’t had to lay off a single full-time employee in the past five years—not because we’re magical, but because we’ve designed our workforce model around capability fluidity. Instead of rigid roles, we cross-train. Our R&D staff attend quarterly marketing labs. Our warehouse team? Trained in customer service protocols. It’s not just about keeping people “busy”—it’s about future-proofing their relevance.

Most layoffs happen when people are locked into narrow functions. We break that cage.

What helped us avoid the talent cliff everyone’s falling off? Honestly, we ignored the typical “skills-based” upskilling playbook that floods LinkedIn with certificate jargon. We focused on domain intelligence + adaptability + internal mobility. We started running 6-week sprint shadow programs where a junior lab tech could shadow e-commerce, or marketing could sit in on supplier negotiations. That raw exposure was more powerful than any LMS module.

We track one key metric: functional redundancy without burnout. How many departments can absorb an adjacent function if needed? Last year, when a supplier crisis forced us to rework packaging logistics, two non-ops employees stepped in to coordinate timelines. Zero delay. Zero panic.

There’s a human story behind every layoff stat. People aren’t disposable, they’re just misallocated. Our job as leaders is to reallocate before it’s too late. Upskilling isn’t a buzzword. It’s a daily operational mindset. Most companies remember that only after the pink slips.

The HR Spotlight team thanks these industry leaders for offering their expertise and experience and sharing these insights.

Do you wish to contribute to the next HR Spotlight article? Or is there an insight or idea you’d like to share with readers across the globe?

Write to us at connect@HRSpotlight.com, and our team will help you share your insights.

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AI Recruitment Risks: Experts Uncover Biases and Share Fixes

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AI Recruitment Risks: Experts Uncover Biases and Share Fixes

Get ready for a deep dive into the future of hiring!

AI-driven recruitment tools are speeding up talent acquisition with incredible efficiency, but they’re also raising eyebrows over bias and fairness.

These systems can supercharge hiring, yet their potential to entrench inequities or miss diverse talent is a real concern.

To tackle this hot topic, the Techronicler team connected with HR gurus, AI experts, visionary thought leaders, and business trailblazers to answer a big question:

Despite concerns of potential bias, AI-driven hiring is gaining traction. In your opinion, what’s one serious adverse consequence of this practice in your industry, and how is your organization addressing it?

Their insights unpack real challenges—from amplifying biases to misreading candidate potential—while showcasing smart solutions like transparent algorithms, diverse data sets, and human oversight.

Join us as we uncover the risks of AI in hiring and the bold strategies organizations are using to champion fairness.

Discover how these leaders are striking a balance between cutting-edge tech and equity to pave the way for a more inclusive recruitment future!

Read on!

David Case
President, Advastar

David Case – Advastar

As a recruiting firm leader, I’ve seen firsthand how AI tools can improve the efficiency and accuracy of hiring. But I’ve also seen the risks they pose when used without proper oversight, especially in industries like construction and manufacturing, where our firm focuses most of its work.

One major concern is bias against candidates with non-linear career paths. These are common in both construction and manufacturing, which have also historically been male-dominated fields. AI hiring tools trained on historical data from such industries can end up favoring male candidates and overlooking others, and also tend to struggle with identifying transferable skills, meaning candidates with nontraditional backgrounds are often screened out unfairly.

Given the persistent talent shortages in the skilled trades and manufacturing sectors, employers simply can’t afford to lose strong candidates due to biased or incomplete algorithms. Overreliance on AI makes that more likely.

That’s why we pair AI tools with human oversight. For hard-to-fill roles, our recruiters manually review candidates who were initially screened out by AI. We also conduct regular audits of AI-driven decisions to spot and correct patterns of bias. I’d strongly encourage other employers using AI in hiring to do the same. Efficiency is important, but not at the cost of missing out on exceptional talent.

Justin Belmont
Founder & CEO, Prose

Justin Belmont – Prose

One major risk is automating bias at scale—if the AI’s trained on biased data, it’ll quietly filter out amazing candidates who don’t “look like” past hires.

In marketing, that can kill creativity and diversity fast.

We’re tackling it by keeping humans in the loop at key points and regularly auditing the tools for patterns that look off.

No set-it-and-forget-it.

If the AI’s making decisions, we’re making damn sure we know how and why.

George Fironov
Co-Founder & CEO, Talmatic

George Fironov – Talmatic

Despite the fact that AI has been with us for a long time, its use in different industries still raises many questions. And recruiting isn`t an exception.

A grave adverse effect of AI-powered hiring is the amplification of inherent biases in historical data, which can inadvertently exclude qualified candidates from underrepresented backgrounds.

To avoid this, Talmatic continuously audits our AI systems, employs training data sets that are diverse, and incorporates algorithmic recommendations into formal human review to guarantee fairness and accountability throughout the hiring process.

Vivek Mehta
Co-Founder & CEO, Weeve AI

Vivek Mehta – Weeve AI

A health system we advised saw applicant diversity drop sharply after deploying AI-powered hiring. The culprit? The model was trained on outdated job descriptions—rewarding familiar schools, linear resumes, and “no gaps.” It didn’t just miss out on great people—it reinforced the same old mold.

This wasn’t a tech glitch. It was a leadership miss.

AI doesn’t absolve us of judgment. It demands more.

Even the smartest systems drift without oversight. And in hiring, those drifts turn into quiet exclusions. That’s why high-impact leaders don’t just deploy AI—they guide it.

Here’s what they do:

Human-led, AI-augmented hiring: AI can flag patterns. People make the call. Always review for mission fit and lived context.

Bias audits beyond the checkbox: Track who advances—and who doesn’t. Patterns reveal what metrics alone can’t.

Transparency with teeth: Be clear with candidates about how AI is used. Offer opt-outs. Invite feedback. Build trust by design.

Design with lived voices: Involve ERGs, DEI leaders, frontline managers early. They see what the data misses.

There’s something more! What if the real breakthrough with AI in hiring isn’t speed at all—but finally seeing the people and potential we’ve always missed?

It’s not faster filtering. Not cheaper sourcing. Deeper understanding.

The best systems don’t just scan resumes—they talk to people.

Conversational AI engages applicants directly, surfacing what truly matters: how they think, connect, solve problems. You hear their values—the ones that already live in your organization, or the ones you wish did.

That’s the future—not automation for efficiency, but intelligence for alignment.

Great leaders use AI to spot brilliance others miss.

Not to filter people out—but to finally see them.

Eugene Mischenko
President, E-Commerce & Digital Marketing Association

Eugene Mischenko – E-Commerce & Digital Marketing Association

One of the most serious adverse consequences I see with AI-driven hiring is the risk of reinforcing legacy bias while creating the illusion of objectivity. In e-commerce and digital marketing, where growth depends on adaptable, creative teams, this is particularly dangerous. If a hiring algorithm is trained on historical data from a company that has favored a specific profile – consciously or not – it will perpetuate those patterns. This can quietly filter out unconventional talent, narrowing the team’s perspective and limiting innovation.

I have seen this first-hand in consulting engagements with multinational retailers and agencies. One client adopted an AI screening tool expecting it to broaden their talent pool. Instead, they noticed a subtle but consistent decline in candidate diversity – not only in demographics, but also in thought and experience. The system was favoring profiles that closely matched their legacy hires, even though the company’s strategy was shifting toward new markets and skills.

At the E-Commerce & Digital Marketing Association, we work with member companies to actively mitigate this risk. We treat AI as an efficiency tool, not a decision-maker. Every algorithm is audited by both HR and operational leaders before deployment. More importantly, we insist on regular outcome reviews, comparing AI-driven recommendations with business results and team performance. Where the data reveals patterns of exclusion, we adjust both the data inputs and the role definitions.

From a leadership perspective, it is critical to remember that hiring decisions shape the organization’s future capabilities. AI can streamline initial screening, but it cannot detect potential, adaptability, or cultural fit as a seasoned executive can. In my experience, the best results come when AI is paired with thoughtful human review, guided by a clear understanding of the shifting business context. This approach not only reduces bias, but ensures that teams stay dynamic and well equipped for rapid change.

Samantha Gregory
Self-Care Strategist & Culture Consultant, Workplace Alchemy

Samantha Gregory – Workplace Alchemy

One major consequence of AI-driven hiring is the exclusion of qualified, diverse candidates due to flawed training data. I’ve seen this firsthand as a SCORE business consultant supporting small business owners expanding their teams. These entrepreneurs often rely on AI tools to save time but unknowingly inherit biased algorithms trained on outdated, homogenous hiring patterns.

In my own work, I’ve built S.A.M.I., a digital well-being coach I trained on my original intellectual property, not general machine learning data. This personalized approach ensures culturally competent, context-aware support. Companies can adopt a similar model by customizing their AI tools, enhancing inputs, and incorporating values-aligned data to eliminate bias.

Diverse hiring isn’t just a checkbox; it’s a strategy. When AI is paired with inclusive design and human insight, it can surface well-rounded candidates who bring hard-won experience, education, and fresh perspectives that strengthen workplace culture.

Ulad Stsepuro
HR Director, ScienceSoft

Ulad Stepuro – ScienceSoft

I see two serious consequences here.

The first is discrimination. Since machine learning models are trained on historical hiring data, they may inherit past biases related to gender, ethnicity, or age, for example.

The second is an increase in conflicts within teams.

In my experience, human recruiters are still better at evaluating a candidate’s soft skills and their ability to integrate into a specific team. It’s not all just about technical skills — a poor team fit can quietly erode morale and productivity for months. It often takes a while to identify the source of the issue and even longer to reorganize the team or part ways with someone who is the wrong fit.

At ScienceSoft, we use a complex, multi-step hiring process managed by people, not AI.

Our recruiter initially selects candidates whose profiles best match the role, then forwards their resumes to technical specialists. This ensures that qualified candidates are not overlooked due to non-technical judgment.

Only those approved by the technical team proceed to the next step. Then, the selected candidates are invited for a behavioral and culture-fit interview with our HR team.

After that, the candidate undergoes a technical assessment. Depending on the role, that could be a technical test or a practical task relevant to the position. Those who pass the assessment are then interviewed by our technical team for a more in-depth evaluation.

A final interview with the department head ensures alignment with team goals and expectations. Successful applicants undergo thorough background checks, which include verification of their identity, employment history, education, and professional references.

Another important point is that the recruiter receives a bonus if the candidate they recommend is hired and proves to be a strong fit for the role. This way, the recruiter is highly motivated to remain objective and focus on finding the most qualified candidates.

James Francis
Founder, Artificial Integrity

James E. Francis – Artificial Integrity

When AI drives hiring, the hiring process is far more efficient, but it can also entrench bias in recruiting. If an AI model is trained on historical data that captures biased hiring decisions (for example, bias on the basis of gender, race, or age), it could replicate these biases in future decisions.

For example, an AI system may unintentionally reward candidates who are similar to past hires if it filters out equally competent brains. By weakening fairness, this also hampers organizational diversity, which, according to several studies, is essential for innovation and success.

At Artificial Integrity, we try to minimize this problem by ensuring that our AI tools are regularly audited for fairness and bias-free algorithms. By ensuring such biases are not a part of our training data and implementing checks for equity, we are creating systems that promote inclusion.

Eric Walczykowski
CEO, Bespoke Partners

Eric Walczykowski – Bespoke Partners

The old software principle, “garbage in, garbage out,” still applies in AI. Train your model using data only from your previous talent searches and hiring and you’ll repeat the same patterns.

Everyone using AI Chatbots for candidate discovery is likely affected by bias and recycling former candidates instead of finding new ones.

We take a completely different approach. AI’s real power is processing huge amounts of data, recognizing patterns, and forming logical connections.

Instead, our AI-driven talent market mapping platform, the Executive Index, maps every executive in the US software industry. It’s nearly 700,000 executive profiles, assembled from 53 million executive background data lines from 575,000 sources.

Our clients can see the entire talent market, filter it in real-time, and see who could solve their search.

There is no possibility of bias or narrow, repetitive thinking because you see the whole market, not a narrow slice based on past work.

The HR Spotlight team thanks these industry leaders for offering their expertise and experience and sharing these insights.

Do you wish to contribute to the next HR Spotlight article? Or is there an insight or idea you’d like to share with readers across the globe?

Write to us at connect@HRSpotlight.com, and our team will help you share your insights.

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Resistance to Readiness: How Leaders Can Upskill Teams in AI and Analytics

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Resistance to Readiness: How Leaders Can Upskill Teams in AI and Analytics

The rise of AI and analytics is transforming industries, pushing organizations to urgently upskill their teams to stay competitive in a data-centric world.

Yet, preparing employees for this shift comes with significant challenges.

From addressing resistance to new technologies to managing limited budgets and diverse skill levels, HR and business leaders face a multifaceted journey to drive effective upskilling.

The HR Spotlight team posed a critical question to leading HR and business experts:
What practical challenges should leaders anticipate when helping their workforce advance in AI and analytics skills?

Their responses highlight key barriers—such as cultivating a culture of continuous learning, securing resources for robust training, and designing inclusive programs that meet varied employee needs—while providing practical solutions to navigate them.

As AI expertise becomes essential for organizational success, these leaders stress strategic foresight, transparent communication, and tailored approaches to empower teams.

Discover their insights below to learn how to overcome these obstacles and build a workforce ready for the demands of 2025.

Read on!

Chris Hunter
Director of Customer Relations, ServiceTitan

Personalized Development Paths Ease AI Transition Fears

Leaders have to take into consideration different levels of competence across teams, which means personalized development paths are required.

In addition, obstacles exist when people do not want to change or fear that machines will take jobs away from them. Thus, access to top-level training materials is essential, as is the ability to develop a culture of consistent improvement.

Finally, the integration of new AI software should not disrupt old workflows, which means stressed leaders have to take part in detailed planning and communication to ease employees into the transition.

Chris Brewer
Managing Director, Best Retreats

Lead by Example: Curiosity Drives AI Adoption

Expect resistance and uneven learning curves. Not everyone will be tech-savvy or excited.

Budget for ongoing training, not just one-off sessions.

Be clear about the “why” behind the upskilling so it feels relevant, not forced.

Create space for experimentation without fear of failure.

Most importantly, lead by example because curiosity is contagious.

Joel Miller
President, Miller Pest & Termite

Tailor AI Training to Roles for Faster Adoption

Leaders should prepare for resistance to change and varying skill levels across their workforce when introducing AI and analytics training.

Many employees may feel intimidated or unsure about how these tools fit into their daily work. It’s essential to address these concerns through clear communication and tailored training.

Another challenge is integrating new AI tools without disrupting current workflows.

Leaders need to plan for time and resources to support learning while maintaining productivity. Practical training must focus on real-world applications relevant to employees’ roles to build confidence and drive adoption.

Amir Husen
Content Writer, SEO Specialist & Associate, ICS Legal

Map Individual Gaps Before Building AI Skills

When upskilling teams on AI and analytics, leaders must prepare for several hurdles.

Uneven skill baselines: A one-size-fits-all bootcamp won’t work—map individual strengths and gaps, then offer tiered learning paths.

Tool proliferation: Bombarding learners with every new library or platform breeds confusion. Start with one core stack (e.g., Python + pandas + a BI tool), then expand.

Data quality & access: Without clean, well-governed datasets and clear ownership, analytics projects stall. Audit your pipelines before training begins.

Time constraints: Carve out protected “learning sprints” or micro-learning slots—don’t expect people to upskill on top of full workloads.

Change fatigue: Promote quick wins, celebrate early successes, and keep leadership visibly invested to maintain momentum.

Anticipating these challenges turns training initiatives from checkbox exercises into lasting capability builders.

Louis Costello, MD
Founding Physician, Dynatech Lifestyle Mind Body Care

Address Biology First for Effective AI Training

The dominant narrative frames AI literacy as a content issue, solved with more courses and longer modules.

That misses the actual bottleneck: cognitive fatigue and information rejection.

Most employees can handle 60 minutes of high-intensity abstract learning before the prefrontal cortex disengages and starts defaulting to rote behavior. Stretch that to two hours with no breaks and retention drops below 40%. Instead of expanding access to AI resources, more companies should be reducing training blocks to 45-minute intervals, followed by physical reset tasks that spike dopamine and improve memory encoding.

Without structured rhythm, upskilling becomes an intellectual treadmill.

Any AI training rollout that skips lifestyle recalibration will collapse under mental dropout.

Sleep compression reduces data absorption by 25% in 48 hours. Multitasking through Slack or email during training destroys analytical engagement. Movement, fuel timing, and environment temperature under 72degF all impact neuroplasticity.

These variables do not show up on a curriculum checklist, but they determine whether the content lands or bounces.

Every executive designing AI training must address biology first. Otherwise, the content is brilliant but the brain is unavailable.

Yoad Bet Yosef
Owner, Nature Sparkle

Gradual Learning Process Builds AI Confidence

When I started helping my team get better at AI and analytics, one of the biggest challenges was getting people comfortable with the new tools.

Many of our employees, especially those in customer service and marketing, were used to more traditional methods of working. Transitioning them to data-driven decisions required patience and clear, simple explanations of how AI could make their jobs easier.

One thing that worked was offering bite-sized training sessions that focused on real-world applications, like how AI could help with predicting customer preferences.

After implementing this approach, we saw a 22% increase in marketing team productivity, as they became more confident in using analytics to create personalized campaigns.

The key takeaway is: Make the learning process gradual, show the direct benefits to daily tasks, and celebrate small wins along the way. This way, your team can embrace the changes rather than resist them.

Marcus Denning
Principal & Senior Lawyer, MK Law

Align AI Tools with Daily Legal Practice

Often, lawyers struggle with using statistics because they perceive them as separate from the legal process. I helped a company explain the basics of data analytics by using daily examples and easy-to-understand legal terms for probabilities and trends. Presenting legal ideas as simple data allows people to learn them more quickly and retain them for a longer period.

I tried an AI tool for litigation and found it impressive at the start, but it was not fully aware of the details used in Victoria’s courts. They quickly rejected it because it did not fit with what they dealt with every day. It demonstrated to me that any software that does not align with the law or lawyers’ thought processes will not find use.

Furthermore, I have suggested that companies reconsider their expectations as people adapt to new ways of doing things. In some cases, junior lawyers did not use AI tools since their time was only recorded by billable hours. We changed the benchmarks for a brief period to encourage students to try different activities, and very soon students became excited about working together.

Mary Rizutti
HR Advisory & Compensation Resources group, EisnerAmper

Skill Analysis, Identifying Application, and More

While AI is changing so many aspects of business, with change comes challenges. There is clearly and expectedly a learning curve in this space. Companies are facing the challenge of a workforce that has had limited to no exposure and/or training in AI.

To work effectively with AI, a combination of technical and soft skills is needed. Technical skills such as knowledge of programming languages like Python, Java, R and C++ are commonly used in AI development. Individuals with backgrounds in computer science, data science, artificial intelligence, robotics, mathematics and statistics and software engineering may possess skills upon which they may rely to begin to understand large language and algorithm model development, as well as prompt engineering (the ability to optimize prompts for AI tools), as an example. may be acquired through self-study.

It’s important for companies to assess the current workforce to help them understand which employees might be suited to support an AI integration process.

One initiative many companies are undertaking is to perform a skills analysis on its workforce to identify those in-house who possess the capability to engage in identifying areas where AI may be appropriate.

Companies should also be prepared to deal with the challenge of identifying the application for AI within their companies. Some questions they should consider include: How far down the road should we go with AI? Are there controls in place to test and trust AI’s output? Do we have policies in place to monitor and provide guardrails for individual usage?

These challenges call upon leaders to not only possess, but to also instill and encourage keen problem-solving skills among their teams, to create ethical awareness around AI biases, privacy concerns and the responsible use of AI.

Fostering an environment of continuous learning, adaptability, curiosity, communication and collaboration needs to be a deliberate focus for leaders to enable their companies to travel the AI journey that is ahead.

Rebecca Trotsky
Chief People Officer, HR Acuity

Allow Employees to Shape AI Use

As HR leaders, one of our biggest priorities is helping our people leaders reskill and upskill their team members.

Many are excited by AI’s potential; yet, some challenges and concerns remain.

Fear of job displacement, lack of understanding, concerns about privacy and bias. Knowing these sensitivities, organizations that are adopting AI have to remember that trust and transparency are just as critical as training.

That means involving your employees from the start, allowing them to help shape how AI is used.

Making sure that they understand how AI is an enhancement not a replacement. And setting clear policies on how tools are used and what data is collected.

Danielle Pickens
Chief Program Officer, Urban Schools Human Capital Academy

Support Not a Substitute for Human Judgment

One key challenge for education leaders is preparing their workforce to effectively adopt AI and analytics. This goes beyond technical training as it requires a mindset shift toward data-informed decision making.

Educators are the heart of schools, yet many lack exposure to AI tools and face time constraints, making targeted professional development critical. Leaders must ensure equitable access to technology to prevent deepening disparities, while addressing ethical concerns like data privacy and bias.

AI should be seen as a support, not a substitute, for human judgment. It all starts with a strategic, empowered Human Resource team ready to lay the foundation for continuous learning.

By prioritizing upskilling and fostering an open culture, schools can begin to leverage AI to improve efficiency, accessibility, and ultimately, student outcomes.

The HR Spotlight team thanks these industry leaders for offering their expertise and experience and sharing these insights.

Do you wish to contribute to the next HR Spotlight article? Or is there an insight or idea you’d like to share with readers across the globe?

Write to us at connect@HRSpotlight.com, and our team will help you share your insights.

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Navigating the AI Skills Gap: Practical Challenges and Solutions for Leaders

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Navigating the AI Skills Gap: Practical Challenges and Solutions for Leaders

As AI and analytics reshape industries, organizations face the urgent task of equipping their workforce with the skills to thrive in this data-driven era.

However, upskilling employees in AI and analytics is not without its hurdles.

From overcoming resistance to change to addressing skill gaps and resource constraints, HR and business leaders must navigate a complex landscape to ensure successful adoption.

The HR Spotlight team asked top HR and business leaders:
What practical challenges should leaders prepare for when helping their workforce level up on AI and analytics skills?

Their insights highlight critical obstacles—such as fostering a learning culture, securing budget for training, and tailoring programs to diverse employee needs—while offering actionable strategies to overcome them.

In a world where AI proficiency is becoming a competitive necessity, these leaders emphasize the importance of strategic planning, clear communication, and inclusive approaches to empower employees.

Explore their expert advice on preparing for these challenges and building a future-ready workforce in 2025.

Read on!

Grace Savage
Brand & AI Specialist, Tradie Agency

Address Fear First: AI as Teammate, Not Threat

The fear of replacement is real, and it’s the #1 challenge I see when helping teams adopt AI.

The truth is, no tool works unless your people are on board. Right now, the most significant practical challenge across small and medium-sized enterprises isn’t the tool; it’s the trust. AI is moving faster than most employees can mentally process, and without the correct narrative from leadership, it quickly becomes a threat.

Here’s the framework we recommend leaders follow to close the fear gap and make AI adoption stick:

1. Hold the first conversation early and make it about value: Don’t wait for the tools to arrive before addressing the elephant in the room. From day one, tell your team, “We’re not replacing you; we’re upskilling you.” Let them know the great staff will always be valued. AI is here to remove repetitive tasks, not humans.

2. Reframe AI as a teammate, not a threat: We call AI a digital assistant, not a system. The language matters. When staff feel like AI is working with them – answering FAQs, handling follow-ups, drafting notes – they stop resisting it. Show them where it saves time, not where it replaces them.

3. Identify and invest in your early adopters: In every company, there’s someone who’s quietly curious. Support them. Train them first and then let them teach others. This builds internal momentum far better than top-down mandates or external consultants alone.

4. Make upskilling part of the culture: Create a culture where learning AI is a badge of honour, like becoming ‘fluent in digital’. You don’t need full technical literacy; you need familiarity and confidence. Normalize this by hosting 30-minute demos, walk-throughs, or mini-workshops

5. Check in often because fear doesn’t vanish, it evolves: Staff need reassurance during rollout, not just before. Create weekly check-ins, anonymous Q&A sessions, or pulse surveys to understand where the resistance lies. Trust builds with communication, not silence.

AI isn’t a threat to good people. It’s a multiplier for them.

My most practical advice is to build a narrative around value, not fear. Help people build an identity as someone who works well with AI. That’s what’s going to matter most in the next five years.

Vipul Mehta
Co-Founder & CTO, WeblineGlobal

Break Mindset Barriers for Successful AI Adoption

Expect resistance, even from smart teams.

One practical challenge is mindset—people often think AI and analytics are only for data scientists. Breaking that barrier means framing it as a tool, not a threat. Keep early use cases small, relevant, and quick to show value.

Another challenge is uneven learning curves. Some folks will sprint, others will drag. Avoid one-size-fits-all training. Pair fast adopters with slower ones, and use real business data so it feels connected to their daily work.

Also, leadership needs to walk the talk. If managers aren’t using the insights themselves, the team won’t either. The shift isn’t just tools—it’s how decisions are made, and that requires a culture shift more than a tech one.

Niclas Schlopsna
Managing Consultant and CEO, spectup

Meet Teams Where They Are, Not Where Expected

One of the first things I’d flag is the false sense of urgency that often creeps in—leaders feeling like they need to upskill their teams overnight.

That creates chaos.

I’ve seen companies invest in flashy AI courses without checking if anyone even has the baseline data literacy to understand what’s being taught. You’ve got to meet your team where they are, not where you wish they were.

At spectup, when we guide clients through AI readiness, we start by mapping out existing capabilities and aligning those with the business use cases that actually matter, not just the trendiest ones.

Another big challenge is the “fear factor.” People worry that AI will make them irrelevant, which leads to resistance or shallow engagement. I remember a session with a startup we were advising—everyone nodded through the AI onboarding, but no one actually used the tools after.

It wasn’t until we framed the tech as a support, not a replacement, and tied it to specific outcomes—like saving hours on reporting or refining investor insights—that people bought in.

Also, don’t underestimate how long it takes to operationalize what’s learned. You’re not just teaching tools—you’re reshaping workflows, KPIs, even mindsets. Make room for experimentation, and allow failure without penalty.

One of our clients only saw traction after they created internal “AI champions” to guide peers and offer real-world examples from their own work. That human layer made all the difference.

Vikrant Bhalodia
Head of Marketing & People Ops, WeblineIndia

Solve Today’s Problems to Overcome AI Adoption Fear

One of the biggest challenges we ran into was fear, not just fear of being replaced by AI, but fear of looking behind. No one admits it, but it shows up when people avoid trying new tools or stay quiet in sessions.

We shifted our approach. Instead of framing AI and analytics as “the future,” we made it about solving today’s problems. We ran short internal challenges, things like using AI to draft reports or prep for client calls. Once people saw how it saved time and effort, engagement went up.

We also realized that a one-time training wasn’t enough. So, we added five-minute mini-learnings to regular team meetings. We’d highlight something a teammate tried that week. It kept the momentum going without making it feel like extra work.

If I had to sum it up: address the emotional barrier first. Then connect the learning to something real. That’s when adoption starts to stick.

Derek Pankaew
CEO & Founder, Listening.com

AI Creates Identity Crisis, Not Just Skill Gaps

As a founder with a team that’s integrating more AI tools by the week, one challenge I’d flag for other leaders isn’t technical—it’s psychological.

The biggest hurdle?

The silent shame that creeps in when smart, capable employees feel like they’re suddenly behind. AI doesn’t just introduce new tools—it messes with people’s sense of competence.

You’re asking a mid-level analyst, who used to feel sharp and on top of their game, to admit they don’t understand a tool that a fresh grad just automated a dashboard with.

That’s not a technical gap. That’s an identity crisis. And nobody wants to talk about it.

If you want people to level up on AI and analytics, you can’t just throw them into a Notion doc of prompts and tutorials.

You have to actively defuse the ego threat. Normalize being clueless.

Create “sandbox hours” where teams can experiment without deliverables or pressure to be efficient. Celebrate learning curves, not just output. Otherwise, you’ll see people resist the tools they think are replacing them—because deep down, they’re mourning a version of themselves that used to feel valuable.

That’s the real work of leadership here. Not training people on GPT or Python—but helping them rewrite what “being good at your job” means in this new era.

Justin Belmont
Founder & CEO, Prose

Create Safe Spaces to Bridge AI Confidence Gap

The biggest curveball? The confidence gap.

Most employees aren’t resisting AI—they’re afraid of looking dumb.

The practical challenge is creating low-stakes learning environments where people can tinker, fail, and ask “obvious” questions without fear.

Gamified training, peer-led sessions, even AI mentors can help.

Upskilling isn’t just technical—it’s emotional. If you don’t manage that, your tools will outrun your team.

Edward Tian
CEO, GPTZero

Plan Training Around Those Who Need Most Help

Understand that not all of your workers are going to be able to adopt new AI and tech-related skills as quickly or easily.

This is especially true for cross-generational workforces.

It’s going to probably be a lot more common for Baby Boomer and Gen X workers to struggle more with learning these skills that it will be for Millennials and Gen Zers. So, you want to prepare for that.

Plan your training around those who you know will need the most help and require the most time.

Michelle Garrison
Event Tech and AI Strategist, We & Goliath

Assign Platform Ambassadors to Solve Tool Fragmentation

Tool fragmentation during content deployment feels exactly like trying to coordinate a hybrid event across six different platforms while your speakers are scattered across three time zones.

I think the real issue isn’t that teams need more integrated software—it’s that they’re trying to force editorial workflows into project management boxes that weren’t designed for creative iteration.

For our part, we discovered that video production actually flows more smoothly when we accept tool diversity instead of fighting it. We use Frame.io for visual feedback, Slack for quick decisions, and Notion for documentation, but we assign specific team members as “platform ambassadors” who translate information between systems.

The pain point isn’t multiple tools—it’s the cognitive overhead of context-switching without designated translators. Most editorial teams could solve 70% of their coordination problems by having one person whose job is simply moving information between platforms rather than trying to find the mythical “one tool that does everything.”

Josiah Roche
Fractional CMO, JRR Marketing

Rethink Workflows Before Adding AI Tools

One of the biggest challenges is getting people to unlearn outdated thinking. There’s a lot of excitement around learning prompt engineering or building dashboards, but not enough willingness to question whether current workflows still make sense.

So AI isn’t just a new layer of tools. It requires rethinking how decisions are made, how data flows through the business, and how fast teams can move. Without that shift, most AI efforts end up reinforcing broken systems instead of improving them.

Another challenge is emotional. When people hear “AI,” many worry it’s going to replace them. That fear can slow adoption more than any technical hurdle.

So the mindset shift is moving from doing the task to directing the system. It’s about becoming someone who uses machines to scale judgment, not just output. Some people adapt quickly. Others need time, examples, and a clear reason to change. Because of that, culture and incentives matter more than any training program.

Tool overload is also common. It’s tempting to roll out every trending platform like Power BI, ChatGPT, or Looker and expect productivity to follow. But more tools usually create more confusion. So what works better is starting with one narrow use case that clearly saves time or reduces cost. When people see impact, they start asking for more. That’s how adoption grows—when the value is obvious.

Accuracy gets over-prioritized. AI and analytics are probabilistic by nature. So if the bar is perfection, no one will take risks.

Teams need permission to test, learn, and adjust quickly. The advantage isn’t in getting everything right the first time. It’s in how fast feedback loops close and how quickly insights turn into action. That’s what makes AI useful at scale.

Robbin Schuchmann
Co-founder, EOR Overview

Connect Global AI Training to Business Outcomes

When helping a workforce level up on AI and analytics skills, I would say the biggest challenge is managing the diversity in learning curves and cultural expectations across global teams.

In international hiring, you encounter people with very different backgrounds and access to technology, so training programs must be designed to accommodate varying levels of familiarity with AI tools and data literacy. This requires a flexible, inclusive approach that respects local contexts while maintaining a consistent skill baseline.

I also emphasize the importance of aligning AI and analytics skill development with clear business outcomes. Upskilling efforts often fail when they’re too theoretical or disconnected from daily work.

For global teams, this means crafting training that directly supports the roles employees perform, making the learning immediately relevant and actionable. This practical connection helps maintain engagement and accelerates adoption of new technologies.

The HR Spotlight team thanks these industry leaders for offering their expertise and experience and sharing these insights.

Do you wish to contribute to the next HR Spotlight article? Or is there an insight or idea you’d like to share with readers across the globe?

Write to us at connect@HRSpotlight.com, and our team will help you share your insights.

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