Atlassian’s Chief People and AI Enablement Officer, Avani Prabhakar, joined Workday Chief People Officer Ashley Goldsmith to share how HR leaders can turn AI adoption into meaningful organizational change.

At many companies, AI transformation follows a familiar script: technology teams set the direction, and HR enters later to help the organization adapt. Atlassian rewrote that order of operations, bringing people and technology to the same table from the start.

This means Avani Prabhakar, Chief People and AI Enablement Officer, has an unconventional but increasingly critical role: spearheading Atlassian’s AI transformation as a company-wide shift in how people work.

On Workday’s Future of Work podcast, Prabhakar joined Workday CPO Ashley Goldsmith for a candid conversation about AI’s enterprise ROI gap, why sweeping predictions about job loss miss the point, and how HR can redesign work for a future shaped by people and AI.

AI transformation is a people problem first

When Atlassian began its AI journey roughly three years ago, the work started as a cross-functional effort between the CISO, CTO, CIO, and Prabhakar’s team, first tackling security guidelines, then workflow questions. Eighteen months in, the group landed on a shared conclusion.

The technology, it turned out, was the easy part.

The people side, Prabhakar says, is where the real transformation has to happen, echoing a belief held by Atlassian’s founders that AI is fundamentally a people-led transformation.

This mindset transformed how both HR and technical teams operate:

  • HR’s Focus: Evolved from simple policy management to designing human-AI collaboration across the entire employee lifecycle.
  • Tech’s Focus: Shifted from just deploying tools to redesigning workflows and building the rich context needed to make AI genuinely useful.

AI is a people-led transformation—that is where the magic starts happening.”

Avani Prabhakar, Chief People and AI Enablement Officer at Atlassian

From experiments to real-world agents

Rather than enforcing top-down AI mandates, Atlassian fostered organic adoption through bottom-up “AI Builder Weeks” and internal super-users. This approach yielded powerful, custom AI agents:

  • Nora (Onboarding Agent): Built by the onboarding team to solve top pain points for new hires.
  • Coco (Comp Conversation Agent): Generates personalized context profiles for managers handling tough compensation reviews

“For example, if my managers are having a conversation with me, Avani, they would know exactly what [I] give more importance to, whether it’s cash, whether it’s equity, what was the conversation we had last time, and what are the key points they need to hit,” Prabhakar says. 

It was important that leaders modeled the behavior themselves for employees. Atlassian’s Head of People Insights built her own agent alongside her team rather than simply asking them to adopt AI, and Prabhakar built her own AI-assisted operating system to demonstrate what deep AI usage could look like. The results were measurable: one team’s confidence using agentic AI grew from 27% to 91% in a single quarter.

Goldsmith points to that peer-learning model as a throughline worth copying. “It’s always been a really key way people learn, and it sounds like it’s been a critical part of how you’re driving this in your organization,” she says.

A framework for change

Prabhakar doesn’t buy into claims that AI will let five people do the work of fifty. “Eighty percent of that is BS,” she notes. 

Atlassian’s own data backs up her skepticism. Research with Fortune 500 customers found that individual AI use is generating a 33% personal productivity gain, but only 3-4% of that is translating into enterprise-level returns, which remain stuck in the low single digits. 

Workday research presents a similar phenomenon: roughly 37% of the time employees save through AI use is silently lost to rework, highlighting a critical blind spot in organizational assessment of AI productivity gains. 

To close that gap responsibly, Atlassian built a framework that starts at the task level, then moves to skills, and only then to roles. Small, empowered teams of about five people were given greenfield problems and let self-organize; one shipped a major product-conference feature 7.3 times faster than a typical team—not because of raw output, but because role boundaries blurred in productive ways: designers shipped code, engineers prototyped in Figma, and everyone invested heavily in context creation and judgment.

Only after understanding how tasks and skills were shifting did Atlassian begin rewriting job descriptions, starting with design, where AI-forward designers were already shipping more code and warranted a new job family: design technologist.

“We are not saying: will we need this role, will we need less of it, more of it,” Prabhakar explains. “Our approach was, let’s see how this role is really changing.”

Redefining what adoption actually means

Atlassian’s first adoption metric—showing how many employees used any AI feature—hit 90% within three months and told the company almost nothing useful. So the team rebuilt its measurement approach around a maturity model: non-users, simple AI users, strategic AI users, and, at the top, strategic AI collaborators who treat AI as a thinking partner rather than a task-doer.

That distinction matters because it changes what AI can access and accomplish. A strategic AI user might ask AI to draft a market research report. A strategic AI collaborator builds a hypothesis with AI, testing variables and reasoning through open-ended problems, which requires a much deeper context layer connecting product, performance, and workflow data.

The payoff is real: strategic AI collaborators produce roughly 2x the output of simple users. More importantly, they actively unblock AI adoption for the teams around them by building the connections and context that make AI more useful for everyone.

Architecting organizational capacity

Looking ahead, Prabhakar believes HR is uniquely positioned to own a question no single function currently answers well: what is the right mix of human talent and agentic capability at every level of the business?

Right now, she says, that responsibility is scattered. IT often owns token spend, the CTO owns productivity, HR owns upskilling, and a separate function typically owns AI transformation ROI. Prabhakar argues HR should be the horizontal thread that connects all four, and Atlassian is already hiring a director of capacity planning for humans and agents to test that belief.

She also expects HR’s operating model, talent programs, and even performance management to look fundamentally different within the next six to twelve months—not years—as the function evolves from managing the employee lifecycle to building what she calls the organization’s context layer.

HR is uniquely positioned to own a fundamental question: what is the right mix of human talent and agentic capability at every level of the business?

What HR leaders can do right now

Prabhakar’s challenge to HR leaders is both practical and ambitious: be willing to redesign your own work first.

Choose a workflow with real business weight, one that sits at the organization’s nerve center rather than inside a single function. Build with the people closest to the work, look beyond surface-level productivity metrics, and create a small community of peers who can pressure-test ideas. No one has fully figured out AI transformation yet, which makes honest exchange part of the work.

“This is a once-in-a-career opportunity, so lead it,” Prabhakar says. “Be willing to redesign your work first.”

AI is already changing how work gets done. HR now has the opportunity to shape what follows: which tasks move to agents, where human judgment remains essential, and how teams build confidence through the transition. Atlassian’s experience offers a living model, shaped by experimentation, shared learning, and a willingness to let evidence guide the next move.