AI is not a new system to deploy or a capability to roll out. It is a permanent shift reshaping how work gets done – how tasks are executed, how decisions are made, and how value is created. And because work itself is changing continuously, talent strategy can no longer be static. It must evolve alongside it.

Many organizations still approach AI through the lens of efficiency, automating tasks, reducing costs, and accelerating decision-making. But efficiency is only the starting point. AI is amplifying productivity and efficiency, and fueling growth. The deeper transformation begins when leaders recognize that AI fundamentally alters the relationship between people, jobs, and skills – and redesign their talent strategy around this new reality.

From workforce planning to skills-first strategy

Traditional workforce planning starts with jobs. AI demands that we start with skills. As AI absorbs repeatable work, human value increasingly lies in judgment, creativity, problem‑solving, and leadership – capabilities that outlast rapidly changing job titles.

A skills‑first approach gives leaders visibility into current capabilities and emerging gaps. But hiring alone isn’t enough. Skills must also inform performance, learning, compensation, and mobility to avoid fragmented decision‑making. As organizations place greater emphasis on human‑centric capabilities, from analytical thinking to resilience and curiosity, transparency around how AI informs these decisions becomes foundational to trust and scale.

Learning by doing: AI agents in real work

One of the clearest ways to understand AI’s impact is to apply it internally. As AI has evolved from basic automation to agent‑based systems, its role has expanded from answering questions to orchestrating workflows and executing complex processes. At IBM, this evolution is reflected in AskHR, our AI‑powered HR agent that supports employees and managers at enterprise scale.

In 2025, AskHR handled more than 16 million employee interactions – a 65% increase year-over-year – while significantly reducing transaction times and simplifying a previously disjointed technology landscape. These outcomes matter, but the more important insight is what they reveal about work. At scale, AI agents expose which activities can be automated, which require human judgment, and how that balance is shifting across the organization. This visibility should inform how work is redesigned.

Defining higher-value work and redesigning roles

As AI agents take on more routine and transactional tasks, employees can focus more on higher-value activities. But it also introduces a more fundamental question: What, exactly, is that higher-value work? In many organizations, this question remains unresolved. AI is being applied to existing roles, but the roles themselves are not being fundamentally redesigned. The result is a growing gap between how work is performed and how it is structured.

If AI is changing the nature of work, then talent strategy must define how that work should evolve, including which skills are required, how roles are shaped, and how performance is measured. Nowhere is this more consequential than in entry‑level roles.

As automation expands, pressure grows to reduce entry-level roles. While that can deliver short‑term savings, it creates long‑term risk. Entry‑level roles have historically been the places where employees build judgment, learn the business, and develop leadership capability. Without entry-level employees, companies’ future talent pool will dry up, causing pipeline challenges. Deep domain expertise – which many develop as entry-level employees – is critical in an AI-powered world.

The question isn’t whether these jobs will change – they already are – but whether they will be intentionally redesigned. As AI absorbs routine tasks, higher‑value work becomes clearer: analysts focus on insights and recommendations, developers spend more time on design and quality, and HR partners shift from transactional work to coaching leaders, identifying workforce trends, and driving change. In an augmented workforce, AI scales execution while humans focus on judgment, context, and leadership growth. This balance won’t emerge on its own. It must be designed.

The CHRO’s role in continuous transformation

Today’s CHROs are not just stewards of policy and process. They are architects of how work is designed, how skills are developed, and how enterprise value is created through people. That includes building AI fluency across the organization, embedding skills into every talent process, and ensuring AI is used responsibly and effectively.

It also requires a new operating system. Talent strategy can’t be revisited in fixed cycles. In an AI‑driven environment, it must evolve continuously, guided by real‑time insight and a clear point of view on how work is changing.

A strategy built to evolve

AI transformation has no finish line. The organizations that succeed will design talent strategies that adapt over time, move beyond static workforce models, and embed AI across the talent lifecycle. This will be done while continuously aligning skills, roles, and work as change accelerates.

Resilience is no longer just a workforce trait. In the AI era, it is a design principle for talent strategy itself.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.