AI in HR: How Work, Skills, and Leadership Evolve in a New Tech Era

What AI readiness really means

AI readiness looks different across organizations. For Qualfon, it means aligning technology with strategies for growth and compliance. At Moody’s, readiness arrives when AI stops being viewed as something separate and becomes core to your strategy.

What began as cautious observation 12 months ago has accelerated dramatically. Organizations shifted from “let’s see who tries this first” to “we need to figure this out now.”

The three essential AI skills

Organizations moving fastest on AI adoption focus on three skill areas:

• GenAI capability awareness: Understanding what tools can help you do and which use cases apply to your role. These tools accelerate work speed and volume if you know how to use them.
• GenAI proficiency in use: Structuring prompts to be most efficient and productive. This looks different for finance versus sales versus HR, with functional, role-specific applications.
• Knowledge of GenAI limitations: Understanding current constraints. This isn’t “push a button and have it do your job.” There are specific subtasks you can delegate, important context you need to create, and things we should still do ourselves.

How client needs drive skill transformation

When Innodata started supporting clients building generative AI models, expectations moved to a different level. The skill sets required became more specialized and judgment-heavy: psychologists, linguists, writers who evaluate nuance and tone, legal professionals. These profiles are niche and the whole world is competing for them.

For Qualfon, AI enables individualized approaches to each client and learner. They can look at individual needs, gaps, and progressions, then tailor an approach very quickly and accurately.

Building effective AI workshops

The most effective AI training uses hands-on work with real problems, not exercises. At Moody’s, workshops bring people together to work on actual challenges. The goal is going from zero to a working prototype in one session.

The ability to build something you can actually use gives people satisfaction. That element of fear and the unknown dissipates when you explore the edges, figure out where models fail, and learn what instructions they need.

Jobs transforming into new roles

Organizations are switching from job-based architecture to skills-based talent architecture. The skills taxonomy drives everything. You combine skills in different ways, allowing agility as jobs change. Instead of only upward mobility, you create lateral moves and new role combinations.

AI pushes people to expand breadth across multiple areas while maintaining depth. The T-shaped professional is becoming more emphasized. A marketer now needs stronger understanding of sales, product, and engineering because AI tools enable covering more ground independently.

Innodata’s skills marketplace pilot

Innodata is eight months into piloting an internal skills marketplace where employees rotate across projects. Skills profiles sit inside their learning platform. Every employee builds a dynamic talent profile tied to skills they learn and projects they complete.

The foundation is clarity: a skills taxonomy that reflects actual work. Employees can explore roles mapped to them, see skill gaps, and start learning journeys. Managers can search for talent based on skills, readiness, and project history.

They treat it like a living system, refining skills as business direction shifts and technologies accelerate.

What universities should teach

Students need to understand what AI can be used for, how to use it, and what the limitations are. But there’s a critical fourth element for academia: how do we want you to NOT use it? Right now there’s a lot of “don’t use it,” but a balanced instruction of “don’t use it for this but use it for that” would be more helpful.

The other approach is to raise standards and expectations. You basically elevate the standard in a way that you couldn’t get to the expectations without leveraging AI and doing a good job at it. In most cases, it’s obvious if you’ve used AI and didn’t really deliver better quality and output. As an educator, if you can’t smell that from a mile away, you’ve got a problem.

What differentiates high-performing HR organizations

• People-product mindset: Viewing employees through a lens that emphasizes capability building.
• Proof and data: Showing that AI initiatives lead to better talent decisions, time savings, and compliance.
• Strategic innovation planning: Keeping eyes on what works best for your organization. It’s not one-size-fits-all.
• Deep AI proficiency: HR teams need to get really good at AI themselves to understand and support organizational needs.