When LinkedIn's "Work Change" report came out in January 2025, they said out loud what we're all thinking.

"Professionals can no longer ignore AI or assume it does not apply to their job. AI will be relevant to every job in the future and woven into most of our tasks." (Scary bolding mine.)

And I'm apt to believe them. LinkedIn scans thousands of profiles that have added new skills. They also survey 2,500 businesses and 2,000 leaders.

The work landscape is quickly shifting thanks to the AI era, which requires a new skill set for all workers. But this is especially true for anyone working in HR.

​McKinsey reported that 46% of survey respondents expect to decrease their HR headcount by at least 3-10% due to gen AI use.

With that in mind, I spent 3 days reviewing several skill reports on top of LinkedIn's, including the World Economic Forum's Future of Jobs Report 2025 and Microsoft's Frontier Firm Report, to answer the question: What skills do HR folks need to stay relevant in the AI era?

In this first part, we'll outline 5 AI skills they'll need (and that all of us can learn):

1/ AI Literacy

What it is: More than prompt engineering and working with specific Gen AI tools, AI literacy involves understanding the basics of how different AI tools work in practice, an awareness of their capabilities, and the ability to effectively use them.

Why it's important: AI literacy lays the groundwork for other skills in this list, such as AI quality assurance and threat modeling. But, nearly two thirds of leaders recently said there's an AI literacy skill gap.

This gap is particularly concerning as AI literacy has become a critical decision-making factor across the entire talent lifecycle. As the Microsoft Frontier Firm data shows, organizations are already using AI literacy to determine who to hire, promote, and invest in developing.

HR professionals without this foundational skill will struggle to both implement AI tools in their own work and make informed decisions about AI readiness across the workforce they support.

Source: Microsoft Frontier Firm. AI literacy will factor into all of these workforce decisions.

2/ AI Quality Assurance

What it is: AI QA skill involves testing and verifying AI outputs. Specifically, knowing how to check for quality outputs by comparing model responses, understanding how to apply different models by use case, using automated fact-checking tools, and gathering feedback.

Why it's important: My work at Codility, a technical skill assessment platform, showed me that software engineers using AI tools often relied on underlying knowledge to verify and review AI outputs.

When using AI to better understand novel documentation, for example, they would use other methods such as unit testing to verify response accuracy.

​A recent KPMG survey found that half of employees have said they've made a mistake in their work due to AI. For HR professionals, this translates directly to critical workflows. When using AI to draft job descriptions, for instance, an HR manager needs to verify if the AI-generated text accurately reflects the role requirements, avoids bias in language, and meets compliance standards.

AI evaluation is a lattice of output verification techniques, domain expertise, and external checks that are critical for effective and efficient AI use.

3/ Threat Modeling

What it is: Anticipating what could go wrong, intentionally or unintentionally, when using a system, tool, or process such as generative AI, and taking steps to reduce risk.

For example, understanding AI capability limitations or hallucinations when citing sources, creating summaries, or working with data. This also includes knowing when and how legal, reputational, and security risks can occur when working with or building AI systems.

Why it's important: While "responsible AI" is often discussed broadly, threat modeling gives HR professionals specific tactics to prevent AI mishaps. For example, an HR manager using AI to screen resumes needs to anticipate potential biases against protected demographic groups and verify data privacy compliance when collecting candidate information.

Without some threat modeling skill, users will be disappointed in AI output quality and capability or expose themselves and their companies to unnecessary risk. It's also a crucial skill for adopting powerful new technology and earning trust as systems evolve.

Source: McKinsey's State of AI Report, March 2025

4/ Product Management

What it is: While not specifically about AI, product management (PM) is becoming a critical meta-skill for the AI era. It's a combination of several skills and mindsets that involves discovering, delivering, and evolving products that solve customer problems in ways that add value.

Why it's important: As AI tools increasingly become the building blocks of our work, the ability to think like a product manager - identifying where AI can create value, designing effective implementations, and iteratively improving them - becomes essential for everyone, not just those with "product" in their title.

The "HR as product" movement is a good example of how product skills are finding themselves in non-product roles. HR as product is best described by workers asking themselves: "Is the customer going to buy it?" when designing services.

5/ Systems Thinking

What it is: Systems thinking is the skill of understanding the complex, dynamic interconnections between processes, people, or points. This helps one understand and predict how things work, change, and work together. To learn more, read Adam Treitler's excellent piece on systems thinking history and components.

Why it's important: AI assistants and agents are better some tasks because they can hold thousands of mechanistic rules about how things work, but they can't create mental models about why things exist and how they interact.

This means that while AI can perform tasks, it will be our job to coordinate these agents.

We already see this in software engineering. Developers are now not only focusing on the codebases in their personal sphere of influence, AI assistants are doing this quickly and at increasing quality levels. Instead, developers are now thinking systemically.

In non-technical roles, Josh Bersin's Systemic HR is an example of how HR pros need to think about how all HR practices connect to one another and the business's objectives. This thinking is not only required for future work, it's also a defensible human competency.

Wrapping Up

Of course, your role and background will dictate which skills you need to develop first or the most.

An HR recruiter might prioritize AI Quality Assurance to validate AI-screened candidates, while an HR strategist might lean into Systems Thinking to redesign talent processes.

The key isn't mastering everything at once: it's identifying where AI intersects most critically with your responsibilities and building competency there first.

Technical AI skills are just the beginning. In part 2, we'll explore the five 'core,' 'durable,' or 'soft' skills you'll need for success in the AI era. Join us!