As AI and analytics transform industries, upskilling workforces presents practical challenges, with 46% of leaders citing skill gaps as a barrier, per McKinsey’s 2025 report.
This HR Spotlight article compiles insights from business leaders and HR professionals on preparing for these hurdles.
Experts highlight resistance to change, fear of job displacement, and integrating AI into workflows as key issues.
They stress tailored training, fostering psychological safety, and aligning tools with business goals to bridge gaps.
By addressing data access, ethical concerns, and cultural shifts, leaders can empower employees, ensuring sustainable AI adoption and enhanced productivity in a rapidly evolving tech landscape.
Read on!
Arshad Khalid
Technology Advisor, No Strings Public Relations
A key challenge for leaders supporting their workforce in AI and analytics upskilling is ensuring access to quality, relevant data and the right tools. Without clean, well-organised data, learning and experimentation become frustrating and ineffective.
Leaders need to invest in data infrastructure and create environments where employees can safely test and iterate. Another practical hurdle is overcoming resistance to change. AI can feel intimidating, especially for those unfamiliar with the technology.
Leaders should focus on building confidence through clear examples of AI’s benefits, practical use cases, and ongoing mentoring. It’s also essential to foster collaboration between technical and non-technical teams to break down silos and encourage knowledge sharing.
Data Access Blocks AI Upskilling
Eugene Stepnov
Chief Marketing Officer, 1Browser
When helping their teams grow their skills in advanced tools and data analysis, managers should focus on crafting a clear strategy that connects these improvements directly to business objectives.
Offering access to suitable learning programs is essential, but ensuring the material is aligned with employees’ unique tasks and duties makes the experience more productive and engaging.
It’s crucial to build a culture of curiosity and innovation, where team members feel supported in exploring new tools and methods without the fear of making mistakes.
Leaders should also emphasize practical uses of advanced tools and data insights to show how these skills can benefit both the company and individual career development.
Regularly appreciating and rewarding achievements inspires teams to keep progressing. Encouraging collaboration is equally important—breaking down barriers between departments and promoting shared learning can boost the effectiveness of skill-building initiatives.
Being an accessible and encouraging leader throughout this journey creates the foundation for a successful transition.
Misaligned Training Stifles AI Progress
Riken Shah
Founder & CEO, OSP
Upskilling a healthcare workforce in AI and analytics isn’t just about training—it’s about reshaping mindset, culture, and workflows. One challenge I’ve seen is the disconnect between technical capability and clinical context.
Many healthcare professionals don’t see how AI directly improves outcomes until they’re shown practical, patient-centered applications. At Ochsner Health, for example, embedding data science into care delivery worked best when frontline staff were involved early and training was tied to real clinical problems.
Another issue is psychological safety—people need room to experiment without fear of failure. At Mayo Clinic, success came when AI literacy was embedded into roles across departments, creating a shared language and sense of ownership.
Based on my experience, effective strategies include role-specific learning paths, storytelling to demystify algorithms, and fostering peer champions. Long-term success depends on treating AI not as a project, but as a mindset shift that evolves with your organization.
Mindset Shifts Challenge AI Training
Des Anderson
Co-Founder & CTO, LearnUpon
AI is set to make learning more adaptive, contextual, and proactive. In terms of upskilling, AI has the power to transform customer education from AI-driven personalization and tailoring learning paths and ensuring customers get the right information at the right moment to predictive analytics and providing support to individuals before users even ask for help.
As AI has made significant progress, it will grow and rapidly change the customer experience in the L&D industry. It’s essential for leaders to prioritize human oversight where possible.
The technology can create skill gaps within companies, making it challenging to fully leverage its capabilities and achieve its intended business results. Like any new tool, users need to know how to use AI to get the most out of it.
It’s critical when developing fully customized learning experiences for individuals and making sure the information produced by AI is accurate and appropriate.
By investing in a robust corporate learning strategy, businesses can effectively train employees on key skills and competencies to succeed in their workplace. Otherwise, they are wasting their time and resources.
Skill Gaps Limit AI Customization
Rebecca Trotsky
Chief People Officer, HR Acuity
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.
Fear, Bias Slow AI Adoption
Expect almost every aspect of your workforce and teams to soon be using AI to enhance their conversations, and even their decision-making as the younger generations are starting to use AI as real companions and assistants.
And that’s why you need to hold all communications to a higher standard, and put in place additional teams to review all outbound communications.
While AI is a great tool it can make mistakes just like human beings, requiring us to be extra vigilant and approve all outbound information.
AI Errors Demand Review Teams
James Francis
Founder, Artificial Integrity
The Hardest Part of AI Upskilling? It’s not the tech. You can teach someone to use AI tools in a week—but reshaping how they think with data? That takes cultural rewiring.
Mindset shift is one of the biggest challenges that most leaders often overlook in AI and analytics. Training teams to use AI dashboards or prompts is not what it is all about. It’s more about helping them move from intuition-based decisions to data-backed judgment. That’s a leap, not a step.
There will be resistance from high performers who have built their careers on instinct. Build in time for reflection, experimentation, & safe failure.
Also, beware of the “tool trap.” Rolling out shiny AI tools without clarifying their need leads to surface-level adoption and wasted investment. Upskilling isn’t a tech project—it’s a change management challenge in disguise.
Cultural Rewiring Delays AI Upskilling
It’s important for HR leadership to stay engaged with the C-suite, board, and shareholder/stakeholder viewpoints. The truth of the situation is this: employees may be training tools that ultimately displace roles, including their own.
The C-suite and board are already weighing this tradeoff between upskilling and strategic workforce reduction. HR must be prepared to navigate sensitive implications around reskilling, job design, and ROI justification.
Budget decisions hinge on whether the AI investment drives measurable operational advantage without eroding morale or stakeholder trust.
The real challenge is aligning talent strategy with a future that prizes adaptability over job security. This places HR leadership in ownership of the task of executing ethically and transparently while keeping C-suite, board, and shareholder values in mind.
Job Security Fears Hinder AI Training
William Soliman
Founder & CEO, Accreditation Council for Medical Affairs
Historically, big pharmaceutical industry manufacturers have been slow to adopt newer technologies. AI has been no different. As AI and analytics reshape the pharmaceutical and healthcare sectors, leaders face a critical responsibility: ensuring their workforce is ready.
Beyond just training, the real challenge lies in overcoming resistance to change, bridging digital skill gaps, and integrating new tools into daily workflows. Many professionals, especially in heavily regulated industries, like pharma, may fear automation or lack confidence in applying AI practically. That’s where targeted upskilling becomes vital.
At the Accreditation Council for Medical Affairs (ACMA), we work with 300+ pharma and biotech companies and address this challenge through our accreditation, certification and training offerings for the life sciences.
For example, the Board CertifiedMedical Affairs Specialist (BCMAS) board certification, which now integrates AI literacy as a core competency, has become the standard board certification for medical science liaison and medical affairs professionals worldwide.
Along with our other training and certification programs, we help life sciences professionals not only adapt to evolving technologies but also apply them responsibly within medical affairs and field reimbursement functions.
Today’s leaders must invest in structured, credible learning frameworks because future success depends not just on having AI tools, but on empowering people to use them effectively and compliantly.
Resistance Hinders Pharma AI Adoption
The HR Spotlight team thanks these industry leaders for offering their expertise and experience and sharing these insights.
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