What Are Upskilling and Reskilling? (And Why They Matter)

Upskilling and reskilling have become the defining workforce challenges of the AI era. The World Economic Forum projects that 39% of core skills will change by 2030, and Gallup finds that 22% of employees fear their jobs could disappear due to technology. Global search interest in “upskilling and reskilling” reached record highs in 2025, underscoring both rising demand and growing anxiety.

For HR leaders, the barriers are clear: tight budgets, outdated tools, and limited time to design development programs. What’s different now is employee sentiment, from FOBO (fear of becoming obsolete) to expectations for continuous learning. Meeting these needs requires moving beyond static training and embracing AI-powered workforce transformation. 

Let’s dive into how business leaders can leverage upskilling and reskilling to deliver modern solutions for evolving workforces.

Summary:

• Upskilling and reskilling are now the #1 workforce challenge in the AI era. Upskilling strengthens current roles, while reskilling prepares employees for new ones.

• AI in learning & development accelerates both by running skills gap analysis, mapping career paths, and powering adaptive learning journeys

• For HR leaders, the result is better retention, stronger internal mobility, and clear ROI on training investments

• Case studies from Kuehne+Nagel and Merck KGaA, Darmstat, Germany show how AI workforce transformation creates agility at scale

  In This Article:

What Is Upskilling?

Upskilling is the process of helping employees expand their capabilities within their current roles so they can keep pace with shifting business and industry demands. For HR leaders, it’s a way to protect productivity, strengthen engagement, and reduce the risk of turnover by giving employees the tools to grow where they are.

In the AI era, upskilling has become faster and more precise. Learning & development uses skills intelligence to map strengths, analyse gaps, and recommend tailored next steps from microlearning and mentoring to certifications aligned with career goals. This replaces one-size-fits-all training with personalised learning journeys that adapt to each employee’s role and trajectory.

Common upskilling strategies include:

• Personalised learning paths tied to specific roles and goals

• Mentoring and coaching to transfer knowledge in real time

• Online courses and microlearning are available on-demand

• Cross-functional projects that provide hands-on exposure

LinkedIn estimates that 70% of today’s job skills will change by 2030, much of it driven by generative AI. For HR, upskilling is the bridge between an employee’s current strengths and the future skills needed to remain competitive.

What Is Reskilling? 

Reskilling is the process of preparing employees to move into new roles within the organization, equipping them with capabilities for jobs the company urgently needs. While upskilling strengthens expertise in a current role, reskilling enables transitions, ensuring both employees and organizations remain relevant as automation and AI reshape entire functions.

Using AI-powered skills gap analysis, HR teams can identify transferable strengths, map adjacent career paths, and recommend role-specific training. For example, a customer support agent with strong communication skills could transition into an AI trainer role — and later pivot into prompt engineering.

Practical reskilling tools include:

• Career transition programs with clear role roadmaps

• Role-specific training for in-demand positions

• Short-term projects that provide cross-team experience

• AI-enabled learning platforms that combine certifications, microlearning, and adaptive learning to accelerate development

Upskilling vs. Reskilling

Upskilling and reskilling are often paired together, but they address different workforce needs. The table below outlines where upskilling initiatives can help while also highlighting the role reskilling plays within a modern workforce development strategy. Both are critical for organizational agility in the AI era.

The Impact of AI on Upskilling and Reskilling

AI is not only automating tasks; it’s reshaping the nature of jobs. Within just a few years, roles with the same title may carry entirely different responsibilities and required skill sets. For HR leaders, this means skills gaps are opening faster than external hiring can close them.

Why urgency has accelerated:

• Roles are being rewritten: Automation is removing routine work while adding new, AI-driven responsibilities

• Skills expire faster: The half-life of technical skills is shrinking from years to months

• Static training is insufficient: Development can no longer be a once-a-year exercise; it must be continuous and adaptive

• Retention risks are rising: Employees who don’t see visible growth opportunities are more likely to leave

• Agility defines survival: organizations that reskill quickly can pivot with market change; those that don’t, face obsolescence.

With AI-powered skills gap analysis and adaptive learning, HR can move from reactive training to proactive workforce transformation, turning disruption into a competitive advantage.

Related: Why the Time to Invest in Skills is Now ft. Deloitte

Benefits of Upskilling and Reskilling

AI is rewriting the rules of workforce development. Instead of guessing where the gaps are or hoping employees engage with generic training, HR can now see skills in real time, deliver personalized development, and measure impact on retention, productivity, and growth. Upskilling and reskilling become not just learning activities, but levers for business strategy.

Automates skills audits

Traditional skills audits such as surveys, performance reviews, and manual tracking lag behind reality. By the time HR leaders gather insights, the needs of the business have already shifted. 

AI automates the process of skill mapping by continuously scanning workforce data and surfacing gaps the moment they appear. This gives managers, leaders, and c-suite executives a live, accurate picture of organizational capabilities, allowing for faster alignment between employees and evolving job requirements. 

With real-time data views comes an enhanced ability to make informed, data-driven decisions that positively impact the workforce and each individual employee. Actionable insights also help reduce lag between identifying gaps and closing them. 

The businesses that have easy access to widespread insights are the ones that can pivot faster, implement solutions quicker, and stay ahead of the competition. 

Delivers adaptive learning journeys

Out-of-the-box learning paths often leave employees disengaged because they rarely connect to career aspirations or day-to-day challenges. AI enables adaptive learning pathways that consider role, goals, and performance data. Instead of one-size-fits-all training, employees receive development opportunities that feel practical and motivating. This makes learning stick and increases return on investment.

• Tailored recommendations based on each employee’s strengths and career goals

• Multiple formats (mentoring, microlearning, certifications) to fit different needs

• Higher engagement with learning content that feels personally relevant

• Career pathways designed to support both individual growth and business priorities

Offers clear growth paths to reduce FOBO

A lack of visible career progression is one of the biggest drivers of attrition. Employees who cannot see a path forward often look outside the organization. AI-powered career pathing creates transparency by showing how current skills connect to future roles. This directly addresses FOBO (fear of becoming obsolete), strengthens loyalty, and reduces external hiring costs by keeping talent engaged internally. Here’s how:

• Visible career pathways keep employees motivated and committed

• Internal mobility reduces reliance on external recruiting

• Succession planning pipelines are strengthened with ready internal candidates

• Culture of growth improves long-term retention and stability

Related: How Employee Feedback Drove Improved Career Mobility at BAE Systems

Optimises content by role and industry

Training budgets are often wasted on irrelevant content. AI addresses this common issue by matching employees to resources that are directly tied to their role, function, and industry. This precision ensures that every learning investment accelerates development rather than sitting unused in a learning platform. By implementing personalized learning and development plans, rooted in skills data, teams can:

• Reduce wasteful spending by eliminating irrelevant training content

• Surface role- and industry-specific recommendations delivered at scale

• Employees become productive in new or expanded roles faster

• Maximize budget by targeting investments with measurable impact

Tracks ROI with skill analytics

HR has long faced pressure to demonstrate ROI from learning and development. Course completions alone do not prove business value. AI closes this gap by linking skill attainment directly to workforce outcomes, showing how training affects productivity, retention, and internal mobility. For HR leaders, this turns learning into a measurable driver of strategy.

• Track skill attainment and completion rates in real time

• Measure productivity and performance gains from upskilling

• Demonstrate retention improvements tied to visible development opportunities

• Quantify cost savings through lower turnover and reduced external hiring

Related: 3 Strategic Workforce Planning Examples for HR Teams in 2025 

How to Launch AI-Powered Upskilling and Reskilling (Actionable Framework)

Every organization approaches workforce development differently, but the key is connecting employee growth to business priorities. With AI, what was once a slow, manual process becomes a continuous cycle of learning and adaptation. To stay future-ready, leaders can follow this five-step framework:

Step 1: Start with a skills audit powered by AI

Traditional audits, based on surveys or outdated reviews, often leave teams with an incomplete picture. AI strengthens this process by creating a live skills inventory that combines employee data, performance records, and market insights. With this foundation, hiring managers and executives can clearly see both current strengths and future risks.

Related: How Skills Technology Drives Talent Acquisition Excellence 

Step 2: Define program goals

With the skills landscape in view, talent management teams must set clear objectives. Should employees be upskilled to remain competitive in their current roles, or reskilled to transition into emerging ones? Without defined goals, training becomes scattered and disengaging. AI insights allow segmentation for employees by role, potential, and business needs, ensuring that development plans are tied directly to long-term strategy.

Step 3: Match employees to pathways with AI personalisation

Clear goals only matter if they translate into action. Employees need pathways that feel relevant, motivating, and achievable. AI recommends journeys tailored to each individual’s skills and aspirations, ranging from microlearning and mentorship to certifications and immersive hands-on apprenticeship. This makes learning personal and ensures that business objectives are connected to everyday actions that support employee growth.

Step 4: Track and optimise progress

Progress is more than course completions. Without insight into the skills gained and how those skills support business needs, leaders are left guessing about internal mobility pipelines or employee readiness for future roles. AI analytics provide this real-time visibility, allowing leaders to measure impact and adjust programs quickly to close remaining gaps.

Step 5: Close the loop with predictive AI

Even the best initiatives lose relevance if they don’t evolve. Predictive AI combines workforce data with market signals to forecast emerging skill demands, giving decision-makers a glimpse into the potential future. This future outlook helps ensure learning pathways are updated continuously, keeping employees prepared not only for today, but for the roles of tomorrow.

Learning Inclusivity in the AI Era

Upskilling and reskilling programs cannot succeed if they are not accessible and engaging for all employees. HR leaders know that traditional approaches often leave certain groups behind, whether because content is offered in a single format, assumes English fluency, or overlooks differences in generational learning styles. This not only limits adoption but also risks widening existing skills gaps.

AI helps close these gaps by making learning more inclusive and adaptable:

• Offer multiformat learning: AI-powered platforms recommend training in the format that best fits each learner, whether that’s video, gamified modules, microlearning, or long-form courses. Employees can engage in the way that feels natural and effective for them.

• Support multiple languages and accessibility features: AI translation and speech-to-text tools expand access across global teams. Closed captions, screen-reader compatibility, and multilingual content make learning available to employees regardless of location or ability.

• Adapt to generational preferences: AI can tailor delivery by demographic need — shorter, interactive modules for Gen Z learners, or structured, certification-focused content for mid-career professionals who prefer more formal development.

Weaving inclusivity into every stage of development helps ensure the continued expansion of the program while fostering widespread adoption. Put yourself in the shoes of your employees. Are you likely to take action on a vague or general development opportunity? Offering clarity and a tailored experience sparks engagement, leading to stronger outcomes for both the people and the business.

Examples of How Leading Companies Are Scaling Career Growth with AI

There’s no one-size-fits-all solution for closing skills gaps through upskilling or reskilling initiatives. Some organizations focus on unlocking internal mobility, while others prepare talent for future roles that don’t yet exist. Both strategies show how successful development strategies drive measurable business value.

Kuehne+Nagel: Upskilling to Unlock Internal Mobility

Kuehne+Nagel launched Career Explorer, an internal talent marketplace powered by the Phenom platform. Employees could complete profiles, discover recommended roles, and access personalised learning opportunities, while recruiters gained visibility into internal candidates.

In just six months, they achieved:

• 20% decrease in time-to-fill for internal requisitions

• 2× higher conversion rate for internal candidates

• 74% employee satisfaction with the tool

By automating skills audits with AI, keeping profiles continuously updated, and tailoring recommendations to individual career goals, organizations can scale these outcomes further. Recruiters don’t just see a snapshot of talent readiness; they gain real-time insight into employees’ evolving capabilities, while workers receive personalized learning journeys that expand their mobility options.

Read the full story: How Kuehne+Nagel Empowers Employees To Own and Grow Their Careers

Merck KGaA, Darmstadt, Germany: Reskilling for Future Roles

Merck KGaA, Darmstadt, Germany, shifted from headcount-based planning to a skills-based model that integrates capacity, skills, and costs. Using AI-driven scenario planning and a living skills ontology, the company created a dynamic view of roles and transferable skills to keep workforce strategies aligned with evolving business needs. 

Impact:

• Ability to reskill employees into emerging roles ahead of demand

• Ongoing alignment of workforce planning with market shifts

• Greater agility and foresight in talent strategy

By extending these practices with AI, organizations can run continuous scenario simulations, map adjacent career paths in real time, and adapt reskilling journeys dynamically. This ensures employees see clearer routes into future-ready roles, while HR leaders keep strategies aligned with market change.

Read the full story: Mastering Workforce Dynamics: Merck KGaA Germany’s Cutting-Edge Approach 

FAQs

When should organizations choose AI-powtered upskilling vs AI-powered reskilling?

Organizations should choose AI-powered upskilling when roles are evolving and reskilling when employees must shift into entirely new positions. AI supports this decision by:

• Analysing workforce data to identify skills gaps and strengths

• Mapping adjacent roles and transferable capabilities

• Recommending targeted pathways for career growth or transitions

• Guiding leaders on whether to develop internally or hire externally

This makes workforce planning more precise, helping HR balance current role demands with future business needs.

How does AI measure ROI of training programs?

AI measures ROI of training programs by linking skills gained to clear workforce and business outcomes. It does this by:

• Tracking employee skill attainment in real time

• Connecting development progress to productivity and retention metrics

• Highlighting internal mobility improvements from visible career paths

• Calculating cost savings from reduced turnover and external hiring

With these insights, HR can prove that upskilling and reskilling drive measurable business value rather than just a learning activity.

What are some best practices for integrating upskilling into overall business strategy?

Some best practices for integrating upskilling into overall business strategy include aligning learning with company goals and using workforce data to prioritise critical skills. HR leaders typically focus on:

• Linking training to business priorities like digital transformation or market expansion

• Running regular skills audits to identify gaps

• Making learning continuous with microlearning, coaching, and cross-functional projects

• Connecting development to career growth and internal mobility

• Measuring outcomes such as retention, productivity, and time-to-fill

By embedding upskilling into strategy, organisations build agility and stay competitive.

What is the role of AI in recommending learning opportunities for employees?

AI helps recommend learning opportunities by analysing employee skills data and tailoring training to each person’s role, goals, and career path. Its main contributions are:

• Spotting skills gaps through continuous analysis

• Delivering personalised learning journeys with mentoring, certifications, or microlearning

• Matching employees to relevant content at the right time

• Forecasting future skill demands so training stays aligned with business needs

This AI-driven personalisation makes learning adaptive, engaging, and directly tied to workforce readiness.

Future-Proof Your Workforce with Phenom

Upskilling and reskilling have always been vital to employee growth and retention. What’s changed in the AI era is the speed of disruption, and the need for HR leaders to act with more precision than ever. AI doesn’t replace workforce strategy, but it makes it faster, smarter, and scalable.

With Phenom, organizations can bring this vision to life. Our platform combines skills gap analysis that highlights strengths and risks, personalized career pathing that engages employees with clear growth options, and automated learning recommendations that connect people to the right training at the right time. Together with predictive workforce planning, these capabilities help HR leaders prepare not just for today’s needs, but for the skills their business will require tomorrow.

By turning learning into a continuous advantage, Phenom empowers organizations to close gaps, open opportunities, and build a workforce ready for the future of work.

Request a Skills Snapshot for insights into your organization’s roles, skills, and progressions — plus, get practical guidance on where to start your upskilling and reskilling journey.