Across industries, companies are pouring money into AI tools, and employees are embracing them. According to a 2026 report from Atlassian , 85% of knowledge workers now use AI, yet only 29% say it has fundamentally changed how they work.

For business leaders, that gap raises a much bigger question: Where is all that investment going? And is it actually driving meaningful business outcomes?

Right now, many companies don’t know, says Asha Thurthi, Atlassian’s senior vice president of product for Strategy Collection, the company’s solution for AI-native strategic portfolio management . AI (and the compute behind it) have quickly become a major category of enterprise investment, yet many organizations still lack visibility into where those resources are being spent, how they support strategic priorities and whether they’re generating measurable returns.

That’s a governance problem, Thurthi says. Without a clear view of how AI investments work alongside human talent and financial resources to drive business results, leaders can’t effectively govern one of the larger new line items on their books.

“It’s no longer just about people, headcount and labor cost,” she says. “It’s now also about AI cost — human and AI capital coming together. Is the combined investment moving the priorities that matter?”

The Numbers Behind The Problem

Separate research from Atlassian suggests many organizations are still operating with funding models built for a different era , even as AI is accelerating the pace of business and forcing leaders to make decisions faster.

Among the 308 senior enterprise leaders Atlassian surveyed, 42% still rely on fixed annual budgeting, while another 36% use project-based funding tied to deliverables. Together, that means nearly eight in 10 organizations are operating with funding models designed for a world where strategy changed once a year — not one where priorities shift weekly or even daily. “Strategy definitely can no longer be something that leaders set and forget,” Thurthi says.

The disconnect became even clearer when leaders were asked how they fund strategic work. Just 8% reported using dynamic, outcome-based funding models that allow investment to move as priorities change, yet 92% acknowledged they need that capability. “That’s one of the widest gaps in our research: say versus do,” Thurthi says. “And that’s exactly the muscle AI governance requires.”

Three Systems, No Shared View

That disconnect stems from the way companies track three major cost centers. Human resources monitors the workforce. Finance tracks budgets. IT controls spending on AI tools and processing power.

Few enterprises have a single view across all three. So when leadership shifts priorities, reallocating people, budget and AI resources becomes slow and fragmented, making it difficult to understand whether investments are supporting the outcomes the business is trying to achieve.

As AI becomes embedded across the enterprise, governing people, budgets and technology through separate systems becomes “increasingly unsustainable,” says Avani Prabhakar, Atlassian’s chief people and AI enablement officer.

“Planning, work and investment outcomes often live in different places, with no shared, real-time context,” Prabhakar says. “AI needs living signals from a unified system, a shared context layer, to deliver meaningful insights. When AI is forced to reason over disconnected systems, it can’t answer the questions that matter most to leaders.”

Atlassian’s research reinforces that point: Domain connectivity, or how well an organization’s systems are integrated, was the strongest predictor of AI effectiveness, ahead of tool selection, use and investment level.

“The real trust gap isn’t just about AI model quality,” Prabhakar says. “It’s about whether the organization has a connected operating model that AI can leverage.”

Atlassian has experienced the benefits of that approach firsthand by bringing together its IT and People teams, along with strategic initiatives, engineering and customer support under one leader — Prabhakar.

“We can now manage people, budgets and technology through a single source of truth, unlocking the full potential of both our culture and our technology,” Prabhakar says. “We also operate on a principle of working open by default and connecting everything. It means that our institutional memory is rich, up-to-date and accessible to AI as it embeds across the enterprise.”

Organizations beginning to close this gap are treating AI and compute as portfolio-level resources that are governed alongside people and financial capital within a connected operating model.

“A dynamic, outcome-based operating model connects what matters most to enterprise leaders: priorities, goals, investments, outcomes, accountable teams, people, AI usage and work, all within a single, living system,” Prabhakar says. “What makes this model truly dynamic is the organization’s ability to pivot. When priorities move, the organization can react with speed and shift people, budgets and technology resources to what matters most.”

That connectivity gives leaders answers to questions that are difficult to answer without that unified view, such as:

  • What happens if we cut funding for this initiative — which goals, teams and systems are affected?
  • Where are we overcommitted relative to capacity, and which portfolio shifts would increase strategic return?
  • What skills are needed to enable our highest-priority initiatives, and can existing people be reallocated?

The shift, Thurthi emphasizes, is about visibility, not headcount. “This isn’t about people versus AI,” she says. “The question is how does AI augment the people in an organization so the organization can move from strategy to execution — the entire lifecycle — faster?”

Organizations that connect people, investments and AI within a single operating model will be better positioned to move faster and make informed decisions, while those managing each separately will continue to struggle to understand the return on their AI investments.

Atlassian is applying that same thinking internally by evolving its own governance practices, including creating a director of human and AI capacity planning role and publishing responsible technology guidelines to help teams use AI consistently and transparently.

Without that connected view, governing AI becomes largely guesswork. With one, leaders can begin managing AI investment with the same rigor they apply to people and financial capital.

“Successful organizations will treat investments in people and technology as a single, unified commitment, recognizing that AI skills development and technology adoption must go hand in hand,” Prabhakar says. “They’ll also focus on governance because trust is non-negotiable.”