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.
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