Reskilling for Automation: Hard Truths, Smarter Paths, and the Future-Ready Workforce

Why Reskilling and Upskilling for Automation Must Be Reinvented Now

If you feel like “reskilling” is the buzzword of the decade, you're not alone. From executive boardrooms to factory floors, organizations are urgently asking: how do we prepare our global workforce for an AI-powered world where automation and technological disruption are rewriting the rules?

In parallel, businesses are scrambling to find efficiency gains, and AI is already delivering significant benefits. According to Bain & Company, for example, organizations can save an average of 15% to 20% of HR labor time through AI automation and augmentation. That’s not just time saved; it's time that can be reallocated toward employee development, leadership training, and high-impact strategic initiatives. But the question remains: are we equipping people to actually step into these more valuable roles?

AI and automation are transforming how work gets done at an unprecedented pace. In some cases, they're creating new jobs and career paths, while in others, workers may switch to a different occupational category. In others, they're displacing workers in occupational categories such as clerical, logistics, and even finance, as noted by SHRM’s 2025 research, which estimates that 19.2 million U.S. jobs face a high or very high risk of automation displacement.

So, where does that leave us?

In a liminal moment, to say the least. A time where skills development, continuous learning, and workforce transformation aren’t just good practice, they’re a matter of survival. But to get this right, we need to rethink reskilling from the ground up.

Want to see how workforce intelligence can make reskilling work? Book a demo of Aura’s platform to future-proof your team today.

Why Traditional Reskilling Fails in the Age of Automation and Artificial Intelligence

The historical playbook for reskilling and upskilling remains rooted in public retraining programs that emerged during the Great Depression and gained scale through policies such as the Manpower Development and Training Act (MDTA), Job Training Partnership Act (JTPA), and Workforce Innovation and Opportunity Act (WIOA). But decades of evaluations reveal a difficult reality: most U.S. federal retraining initiatives, for example, offer mixed or modest outcomes at best. And the private sector is little bettter.

A Brookings study published this year explains why. Retraining programs frequently struggle with:

• Poor targeting: Workers often retrain from one at-risk role to another automation-vulnerable job.

• Access and equity: Those who could benefit most from learning opportunities, like older workers or those with financial constraints, are least able to participate.

• Data limitations: We lack reliable methods to measure which training programs actually lead to career advancement and lasting business outcomes.

• Mismatch risk: Employers and policymakers alike face a lack of clarity about what new skills will actually be in demand in the near future.

The result? We risk spending millions on reskilling options that don't meaningfully close the skills gap, especially in the context of fast-evolving artificial intelligence.

Rethinking Reskilling: Smarter Paths for Future-Ready Skills and Automation Readiness

This doesn’t mean reskilling is dead. It means we need to get smarter and more strategic about how we do it. Here are five key ways to rethink reskilling for automation, grounded in evidence, enabled by AI, and designed for the real world.

1. Align Reskilling with AI, Business Goals, and Future Roles

According to the World Economic Forum, the jobs of the future will require continuous improvement and future-ready skills. Generative and agentic AI can help organizations achieve this, providing key benefits. Unlike static platforms, AI-enabled platforms potentially can tailor training to an individual’s strengths, career goals, and organizational needs.

AI-powered learning tools can:

• Align training with business objectives and key performance indicators

• Recommend self-improvement paths based on role and company trajectory

• Adapt content in real time to optimize knowledge retention

• Track progress and notify HR when someone’s ready for promotion or pivot

This shift isn’t hypothetical. Leading companies are already building knowledge-first cultures where machine learning powers everything from employee development plans to leadership training.

2. Train to Complement Automation, Not Compete With It

SHRM data reminds us that most jobs won’t be replaced entirely, but job roles will shift. That’s where employee upskilling becomes crucial.

Think of it this way:

• Automation handles the routine tasks

• Humans handle the relationships, judgment, and creativity

Therefore, reskilling should encompass not only new techniques and technologies, but also interpersonal skills, problem-solving, and adaptability: skills that machines can’t replicate well.

Roles that blend technical fluency with a human touch, such as AI prompt engineers, chatbot trainers, or digital trust advisors, are increasingly in demand. Companies that prioritize lifelong learning will remain competitive by ensuring their people evolve as fast as their platforms.

3. Make Reskilling Inclusive and Modular for Maximum Reach

The Brookings report highlights a significant obstacle: the people most at risk of automation displacement often face the most significant barriers to participation in reskilling programs. That includes:

• Older workers hesitant to leave current jobs

• Entry-level employees with low savings or competing priorities

• Workers with limited digital literacy or basic education gaps

So, we must stop treating reskilling as a one-size-fits-all, classroom-based experience. The future of work demands hybrid models that include:

• Mobile-first, on-demand courses

• Bite-sized microlearning

• Job-embedded coaching

• Apprenticeship-style experience

And AI can also help here. By assessing current existing skills, cognitive styles, and availability, generative and even agentic AI can potentially deliver personalized training regimens that adapt to learners’ lives, not the other way around.

4. Reskilling for the New Flexible, Fragmented Workforce

The workforce is fragmenting. Freelancers, contractors, gig workers, and remote employees now comprise a growing segment of the labor market. The World Economic Forum estimates 90 million jobs will be remote by 2030.

Traditional employee training models are no longer effective in this new reality. That’s why reskilling for automation must expand beyond internal HR systems to become part of broader workforce transformation strategies. These strategies should likely include:

• Digital credentialing and skills verification

• Cross-sector partnerships between employers and EdTech providers

• AI-driven assessments of job fit and occupational categories

This not only ensures employee engagement and that people are learning the right things,  but it also enables organizations to redeploy talent more effectively across geographies, functions, and projects.

5. Build Agile, Iterative Reskilling Systems That Adapt

We’re living in a moment of technological change so rapid that even the experts struggle to keep up. Brookings rightly cautions that reskilling programs are operating in a data-poor, high-uncertainty environment.

Instead of waiting for perfect information or a guaranteed ROI, the best path is to build agile systems that allow employees to experiment, fail, and try again.

As SHRM puts it, the goal for HR and L&D leaders isn’t just to anticipate automation displacement, but to actively shape the transformation of roles and skills. That means:

• Embedding learning opportunities into daily workflows

• Treating reskilling as an iterative, ongoing process

• Using AI to surface trends early and respond fast

• Redefining success not as perfect outcomes, but as progress and adaptation

The Future of Reskilling: From Policy Talk to Real Workforce Impact

Let’s be clear: reskilling won’t "save" our current workforce and traditional setups from automation. It’s not a silver bullet, and we shouldn’t oversell it as a catch-all cure for AI-driven disruption.

But when done right, with the help of essential tools like Aura’s data-driven workforce intelligence platform, reskilling and upskilling can:

• Help employers understand their own workforce and their competitors'

• Open doors to new types of work

• Make organizations more resilient and adaptive

• Empower workers to pursue meaningful, secure careers, even as new technologies change the playing field

As AI advances, the need to remain relevant will only intensify. The ability to stay ahead will increasingly depend on how well companies build a future-proof, future-ready workforce, not just through training, but with intention, innovation, and empathy.

Aura helps companies lead in the age of automation by turning workforce insights into more innovative business strategies. Schedule a demo today and build a workforce that’s ready for what’s next.