Right-Skilling: The Workforce Strategy for the AI Era

There’s a pattern I’m seeing across industries that’s both expensive and destructive: 

Company identifies a strategic shift → Company lays off people in “old” or outdated roles → Company scrambles to hire people for “new” roles → Company spends months onboarding → Company repeats this cycle a couple of years later. 

It’s the corporate equivalent of running in circles. Burning cash, damaging morale and trust, and with it the institutional knowledge that makes great companies last. 

AI is accelerating this pattern. The pace of change is faster, the roles are shifting sooner, and it’s clear just how unprepared most organizations are. 

McKinsey reports that 92% of companies plan to increase their AI investments over the next three years, yet only 1% of those companies describe their current AI deployment as mature. Amid this race, it’s also clear that most candidates don’t have the skills required for the new roles that are emerging. The World Economic Forum claims that most workers will require training before 2030. 

Organizations now face a stark choice: develop a workforce that can evolve as quickly as your strategy, or watch your best talent slip away... or fall behind. 

Everyone is talking about “reskilling”—preparing employees for new roles, new tools, new demands. And yes, it's important, but it’s only part of the story. 

The problem with most reskilling efforts is that they’re usually generic and mistimed. Organizations roll out one-size-fits-all training programs, hoping performance will eventually catch up. They train people for some future job based on assumptions rather than actual performance signals, treating learning as something that happens away from work, not as part of it. 

The same goes for “upskilling” or building new capabilities within the same role or career path. For example, a customer support specialist getting trained to become a quality analyst. 

What’s missing is the intelligence to know which skills matter most and when each employee should learn them. 

This is where right-skilling comes in. 

Right-skilling connects today’s performance with tomorrow’s readiness, creating continuous, intelligent mobilization of your existing workforce across roles as your business needs evolve, without the expensive hire/fire cycle. 

Right-skilling is about making smarter, more intentional decisions based on real performance data. 

Here's what that looks like: 

If an employee is struggling with a critical KPI today, and a specific behavior or skill would move the needle, that gets prioritized. 

If another employee is performing well now but needs to prepare for what’s next, that gets prioritized differently. 

Same workforce, different needs, different timing. 

Instead of treating role transitions as discrete events, right-skilling creates gradual, supported transitions. An employee starts at 100% in their current role. They begin learning and practicing skills for a new role—maybe 5% of their time at first. As they gain confidence and capability, they start taking on tasks from the new role. Eventually, they shift fully, but they’ve been building muscle memory throughout the entire journey. 

It’s also about horizontal mobility, i.e. helping your best people move across roles as your organization’s needs shift, rather than limiting growth to vertical ladder-climbing or forcing them out entirely. 

Here are the benefits: 

McKinsey’s research confirms this approach: companies that excel in people development are more profitable and resilient, and they also have attrition rates about five percentage points lower than competitors who focus primarily on financial performance. 

Across industries, the new skills that matter the most are increasingly human. Beyond learning how to use new tools and integrate them into day-to-day work, workers are expected to develop skills like: 

Frontline roles are evolving from script-followers to trusted problem-solvers. And yesterday’s skills are not enough. 

In his book The Next Renaissance: AI and the Expansion of Human Potential, Zack Krass defines “behavioral adaptability”: the ability to alter one’s practices and habits in response to changing conditions. “Unlike technical skills, which expire, behavioral adaptability endures because it reflects a disposition toward flexibility, experimentation, and unlearning,” he writes. 

Companies need to be thinking about how to identify and cultivate these qualities in their workforce. 

In the Centrical Performance Intelligence Platform, we use the General Performance Score (GPS), a normalized performance score that aggregates all relevant KPIs into a single, comparable signal for every employee, team, and region. 

Because GPS is normalized, it allows organizations to compare performance fairly across roles, teams, and time, even when individual activities aren’t directly tied to a single KPI. 

This makes it possible to see how behaviors like coaching, learning, and daily engagement correlate with (and predict) real performance outcomes, as well as to measure the effectiveness of coaching itself. 

As a result, teams get practical performance signals they can actually act on: which skills to develop, for whom, and when. 

But performance intelligence alone isn’t enough: you also need a way to develop those skills effectively. 

Two ways we do that are manager-led coaching and microlearning.

At Centrical, we recently launched AI Role-Play simulations and coaching, a new capability offering realistic, on-demand practice environments where employees can build confidence and master behaviors and new skills-- and continue refining their skills as they grow in their roles.

One thing that’s particularly great about it is that it’s fully integrated into our platform, creating a closed-loop system that connects performance gaps directly to targeted practice, coaching workflows, and ongoing reinforcement: 

Performance intelligence → targeted practice → skill development → improved performance. 

This closed-loop system is right-skilling in action, followed by re-onboarding within the flow of work.

We should stop treating training as an add-on and start designing work itself as a primary engine for development and growth. 

We can all agree that people will need to adapt to new roles and expectations in the AI era. But are we building the infrastructure to help them do it fast enough? 

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