Reskilling Vs Upskilling: The Difference, And When To Use Them

October 22, 2025

Kirsty Cooke

In a world where technology, AI, and new business models are reshaping the nature of work, the ability to adapt has become an organization’s most valuable competitive advantage. But adaptation takes many forms – and understanding the importance of reskilling and upskilling (and the difference between them) is key for organizations who want to stay competitive.

Reskilling and upskilling employees are vital strategies for closing skills gaps, supporting internal mobility, and future-proofing your workforce – but they serve different purposes.

Here’s why they matter so much today, how to tell them apart, when to use each, and how data and AI can make both smarter and more scalable.

Why Reskilling & Upskilling Matter Now

As AI and automation transform business models, the skills that drive success are changing faster than ever. By 2030, 70% of the skills used in most jobs will have evolved, with AI acting as a major catalyst (LinkedIn).

According to the World Economic Forum, 77% of employers plan to upskill their workforce in response to these changes, while nearly half expect to redeploy staff from roles most exposed to automation into new areas of the business.

The priority is clear: 70% of HR professionals say their organization is already investing in upskilling initiatives in 2025 to build critical capabilities internally – in areas such as AI fluency, soft skills, and sustainability (LinkedIn). Meanwhile, 47% of business leaders list upskilling existing employees as a top workforce strategy for the next 12–18 months (Microsoft).

What Is Upskilling?

Upskilling means helping employees deepen or expand their existing skills so they can perform their current role more effectively – or step into an adjacent role within their field.

For example, a marketing specialist learning to use generative AI tools, or a data analyst advancing their knowledge of predictive modeling, would be considered upskilling.

Benefits of Upskilling

Boosts productivity by improving performance within current teams
Improves retention – employees see clear paths for growth without leaving the company
Increases agility. by preparing the workforce for incremental technological or process changes
Supports succession planning, ensuring you have a ready talent pipeline for key roles

Upskilling is ideal when roles are evolving – not disappearing – and when an organization wants to help people stay ahead of emerging trends and technologies.

What Is Reskilling?

Reskilling is about training employees in entirely new skills so they can transition into different roles within the organization.

For instance, you might retrain a factory technician to become a robotics maintenance specialist, or move a customer service representative into a digital operations role.

Benefits Of Reskilling

Reduces layoffs and hiring costs by redeploying existing employees instead of replacing them
Fills critical skill gaps faster than external hiring
Protects institutional knowledge – reskilled employees already understand company culture and processes
Drives workforce transformation, by aligning people to new business models and growth areas

Reskilling is essential when automation, restructuring, or new technologies are changing what work needs to be done – not just how it’s done.

When To Choose Reskilling vs Upskilling

The choice depends on business strategy and workforce insights. Organizations should choose upskilling when their teams need sharper skills to keep pace with new tools, regulations, or customer expectations. Upskilling ensures employees can thrive in evolving roles without leaving the company.

By contrast, organizations should choose reskilling when business transformation – such as new products, automation, or market shifts – requires redeploying employees into different functions.

In practice, most enterprises need both strategies. An AI-powered workforce intelligence layer can reveal where to focus: which teams can be upskilled to boost productivity, and where reskilling is needed to prevent talent shortages or redundancies.

The Role of Task & Skills Intelligence

Nearly half of leaders (45%) say expanding team capacity with digital labor is a top priority in the next 12–18 months. (Microsoft)

Traditional training programs often start with job titles. But today, work is changing faster than job descriptions can keep up.

AI-driven skills intelligence and task intelligence provides the missing context. It helps organizations understand what work actually gets done, what skills are being used, and how those tasks are evolving.

With this insight, HR leaders can:

Identify which tasks could be automated, and what new skills will be needed
Map skills adjacencies: showing how current capabilities can transfer to new roles
Build personalized learning and redeployment pathways based on real workforce data, not guesswork

By combining workforce data, labor market insights, and AI-powered skills and task inference, companies can make smarter decisions about where to invest in reskilling or upskilling.

Data Foundations (What You Need)

To make reskilling and upskilling successful at scale, organizations need a connected foundation of data. That means integrating:

Employee data (roles, experiences, skills, learning history)
Job data (tasks, requirements, and success criteria)
External labor market data (emerging skills, market trends, salary benchmarks)

When these sources are unified – for example, within an AI-powered talent data platform – organizations gain visibility into their skills landscape. This enables more accurate workforce planning, personalized learning recommendations, and smarter internal mobility or redeployment decisions.

How to Implement (Step-by-Step)

Assess your current workforce data: understand what skills and roles exist today
Define future capabilities: identify which skills will drive business performance in the next 2–5 years
Run a gap analysis: compare current and future states to find where reskilling or upskilling is most needed
Prioritize initiatives: focus on roles critical to business continuity and growth
Create pathways: develop learning journeys linked to measurable outcomes (e.g. internal moves, certification, or project readiness)
Integrate technology: use AI tools that match people to opportunities and learning based on their existing skills and potential
Monitor and adapt: continuously refine based on performance data and shifting market demand.

Note: This doesn't have to be a difficult, manual exercise. AI can infer skills and task data from existing data sources: job descriptions, resumes, and learning history – helping you build, enrich and maintain a workforce intelligence layer that also plugs directly into Workday, SAP, and your talent marketplace.

Metrics & Measurement

To measure success, go beyond training completion rates. Key metrics include:

Internal mobility rate (how many employees move into new roles)
Skills growth velocity (how fast new skills are acquired and applied)
Time-to-productivity after redeployment or promotion
Reduction in external hiring or turnover costs
Engagement and retention scores among participants

The most effective organizations track outcomes over time – using skills data to quantify both individual and business impact.

The Future: AI-Driven, Skills-First, Task-Aware

The next era of workforce transformation will be AI-assisted and skills-first. Instead of one-size-fits-all training programs, organizations will deliver customized learning and mobility pathways based on dynamic data about tasks, skills, and performance.

AI will not only infer the skills people have, but predict the skills they could develop next, guiding HR leaders on where to invest in development, redeployment, or external hiring.

Reskilling and upskilling are two sides of the same coin: both are essential for building a future-ready workforce. The difference lies in intent: are you helping people evolve within their current role, or preparing them for a new one?

With the right data foundations and AI-powered workforce intelligence, organizations can make that distinction clearly, act on it decisively, and create a more adaptive, resilient workforce for whatever comes next.