Unfinished Work Emerges as Major Productivity Challenge for Indian Workforce: Atlassian

Atlassian released new research on “Zombie Projects,” unfinished initiatives that linger in organisations without delivering outcomes while consuming time, energy, and resources.
Unfinished Work Emerges as Major Productivity Challenge for Indian Workforce: Atlassian
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Atlassian released new research on “Zombie Projects,” unfinished initiatives that linger in organisations without delivering outcomes while consuming time, energy, and resources.

As teams across India return to work after the year-end holidays, many face unfinished legacy work rather than new priorities. Instead of beginning the year with clarity and renewed momentum, employees find themselves navigating a backlog of stalled initiatives that delay decisions and divert focus from what matters most.

The study finds that more than half (53%) of Indian knowledge workers are currently stuck with zombie projects, with many reporting that these stalled initiatives are harming productivity, increasing stress, and contributing to missed deadlines.

Zombie projects are taking a measurable toll on Indian teams

  • 45% of Indian workers feel stressed or overwhelmed by zombie projects.

  • 40% say these projects reduce their productivity.

  • At a team level, 40% worry zombie projects contribute to missed deadlines, while 39% say they consume valuable resources.

Why zombie projects persist

Despite their negative impact, many zombie projects continue because teams struggle to shut them down:

  • 43% fear negative perception if they suggest retiring a project.

  • 42% say there is no clear authority over who gets to make the decision to close an ongoing project.

  • 41% feel that too much time or money has already been invested to justify stopping.

  • 47% say if a project is perceived to deliver high business value, they would rather revive it than retire it, even if it has been stalled.

Together, this highlights not just a process problem, but a cultural one: many teams lack clear permission to let go of work that no longer delivers value.

Indian workers see AI as a way to bring clarity, prioritisation and closure. In fact, many Indian knowledge workers are optimistic about AI’s role in helping to resolve this challenge:

  • 79% believe an AI teammate could guide decisions on whether to revive or retire a project.

Top ways AI could help include:

  • Summarising what’s been missed (62%)

  • Creating realistic time estimates based on calendars (55%)

  • Extracting action items from emails and drafting quick replies (51%)

These findings point to a growing role for AI not just in accelerating tasks, but in helping teams triage work, distinguish high-impact initiatives from low-value clutter, and start the year with clearer priorities and fewer legacy burdens.

“The start of the year shouldn't be haunted by the ghosts of past projects,’ says Sven Peters, AI Evangelist at Atlassian. ‘The January reset offers a unique opportunity to start fresh. It's essential to give teams the clarity and cultural permission to retire what doesn't serve them, so they can channel their energy into the work that really matters.”

“AI is not a distant promise, but a practical solution directly integrated into workflows. It enables teams to report, sort and finally set aside pending initiatives. Intelligent tools integrate where teams work, helping them identify what is worth reviving and what is ready to be abandoned, transforming Blue Monday from a difficult moment into a springboard for renewed focus and productivity,” Sven concludes.

From productivity to coordinated impact

While much AI discussion focuses on individual efficiency, Atlassian’s research highlights the need to shift towards AI-powered teamwork. Without better collaboration, prioritisation, and shared visibility, organisations will struggle to realise AI's full productivity potential.

To realise the full value of AI, Atlassian recommends organisations:

  • Build connected knowledge systems that AI can access and reason over.

  • Create clear decision frameworks around prioritisation and project closure.

  • Encourage leaders to model AI use and foster a culture of safe experimentation and permission to stop low-value work.

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