How to Use Human Skills & Skillspotting to Create an AI-Ready Workforce

For generations, people built workplace expertise through repetition: doing simpler tasks, making mistakes, and slowly taking on harder problems as they proved they were ready. That path is becoming less linear, and as AI takes on routine execution, employers expect people to bring skills like stronger judgment, AI delegation, and leadership to the forefront of their roles.

The question isn’t whether expertise still matters. It definitely does. The real questions are how people will develop expertise faster, and how employers will recognize those skills once they have them. That second question is harder to answer than it sounds because most companies simply don’t have good visibility into what their people can actually do.

That’s the gap a practice I call skillspotting is meant to close. Skillspotting is the practice of identifying what people can do today, including capabilities they’ve developed beyond the roles they were hired for. It’s about looking past static titles and credentials to understand real, demonstrated skills.

But knowing what to look for starts with understanding how AI is changing which skills matter, and how people build them.

AI is Changing What it Means to Be Skilled

Deloitte has described how automation can disrupt the entry-level work that employees traditionally learn by doing. When AI handles simpler tasks, the on-the-job repetition that once built expertise disappears. Employees lose that natural path to practice and employers lose a natural way to observe who’s developing real capability.

That loss makes skillspotting even more important, because the old signals people relied on to judge capability are getting less reliable too. A polished answer used to be a decent proxy for skill. Now AI can produce one instantly, so a good output no longer tells you much about the person who produced it.

What matters instead is what happens around the output: whether someone can direct the work, recognize when an answer is wrong, and explain the reasoning behind it. That’s really a test of judgment, not knowledge, and it shows up most clearly in how someone uses AI itself. When you break it down, using AI well is a form of delegation. You need to know what to ask for, what to challenge, and where the output can’t be trusted, and that requires enough grounding in the work to catch errors before they become expensive.

So the people with the advantage aren’t the ones producing the most polished work. They’re the ones who can name which human skills they most need to strengthen, and who use AI to take routine execution off their plate so they have room to strengthen them. If you can’t define the goal or evaluate what comes back, AI is doing the thinking for you.

That matters especially as the traditional skills ladder changes. So which skills are worth building? A few will hold up no matter how much the ladder changes.

4 Skills That Will Always Be in Demand

The World Economic Forum estimates that 39 percent of workers’ core skills will change by 2030. Organizations that keep evaluating and deploying talent against static job titles aren’t optimizing for an evolving workforce, which is why people need an agile skill set.

Tools will always change, but several human capabilities will remain important no matter what comes next.

1. Critical thinking and discernment come first. AI generates plausible answers in seconds. Employees still need to interrogate assumptions, spot flawed reasoning, and verify what’s actually true.

2. Adaptability and unlearning are equally important. The people who succeed through disruption are willing to adopt new ways of working and let go of habits that no longer serve them.

3. Humility matters because it creates opportunities for growth, feedback, and adaptability. People who can acknowledge gaps in their knowledge are more likely to keep learning.

4. Finally, collaboration and interpersonal connection matter. Working through conflict, coordinating priorities, and establishing trust are essential skills. AI can write a message, but it can’t replace the intentionality behind real communication or the human work of connecting with people.

When hiring talent at CodeSignal, we often use a framework from Patrick Lencioni: humble, hungry, and smart. “Smart” does not just mean intellectually capable. It means being good with other people. If you are hungry, humble, and smart, you’ll keep adapting and keep learning.

Build Human (and AI) Skills Through Practice

Watching content and marking the task complete has never built real capability. You don’t learn to drive a car or get good at playing basketball by watching videos about driving or shooting free throws. You get better through practice and repetition. The same is true for learning to use AI effectively, employ critical thinking skills, or work through conflicts.

Here are three easy ways for employees to practice these skills:

1. Use AI as an always-available tutor. Work through problems step by step, like you would with a coach or mentor.

2. Role-play conversations and simulations with agents. Create a list of challenges you face day to day, and practice solving them with an agent.

3. Get personalized training that includes learning and practice. One of the benefits of AI is that it enables most training tools to create lessons that are personalized to the level you’re at and the exact struggles you’re facing.

The goal can’t be generic training for everyone. Real learning combines personalized content, personalized tutoring, and hands-on practice based on where an individual is today and what they need to learn next. But building these skills is only half the equation. Organizations also need a way to see them once they exist, which is where skillspotting comes in.

Skillspotting the Talent You Already Have

A lot of employees are already building these skills on their own, without anyone telling them to: taking Claude Code courses to expand their capabilities beyond their traditional role, chatting with AI for hours to learn more about how to work with it, and even stealing prompts from social media to try out themselves. That’s the individual side. The harder problem is organizational: as a company, how do you know what skills your team already has versus what they need more training on?

Most organizations have limited visibility into the skills already present in their workforce. Much of their daily skills development never shows up in a job title, resume, or HR database. That’s where skillspotting comes in. In your own organization, you could have a support rep who has become the team’s go-to person for AI enablement, or a marketer who has taught herself enough SQL to pull her own campaign data.

For employers, skillspotting effectively creates an internal talent marketplace. Instead of assuming a new external hire is the only answer, leaders can identify employees who may already have the exact capabilities needed for a high-impact project. For employees, skillspotting creates agency. People shouldn’t be trapped by the narrow definition of a role they held years ago. They should have a clear, verifiable view of the skills they’ve built and the opportunities those skills can unlock.

That’s how organizations help their people stay relevant, and uncover more of the talent they already have.