“Enterprises that treat AI as a teammate & not just a tool see greater productivity and innovation”: Jamil Valliani, Atlassian

In an exclusive interaction with Express Computer, Jamil Valliani, Head of Product – AI, Atlassian highlights how important it is for IT and business teams to work closely together, especially when building AI-ready infrastructure. Valliani also explains that when IT and business teams are aligned and working toward shared goals, AI becomes a powerful tool to unlock new opportunities and drive meaningful change.

AI has been touted as a game-changer, but what tangible results can organizations expect in terms of revenue growth, operational efficiency, or customer experience? How should businesses measure the ROI of AI investments to ensure that it’s delivering true value?

AI is already delivering measurable results for businesses, especially in terms of time savings. Our research shows that teams collaborating with AI save a full day or more each week. That’s transformative.

However, the real value of AI goes beyond efficiency; it’s about improving the quality of work. Take Rovo, Atlassian’s AI-powered solution, for example. It helps customers streamline workflows, build smarter processes, and serve as a sparring partner on high-value tasks. The result is work that’s not only faster but also of higher quality and better aligned with strategic business goals.

Measuring AI’s ROI needs a multidimensional approach. While time savings and efficiency gains are important, improvements in work quality and strategic impact are equally crucial—a perspective we believe will increasingly define how AI’s ROI is measured in the workplace.

In your view, what key capabilities should organizations look for in the AI ecosystem? How can they integrate these technologies to empower their teams to drive productivity and transformation or innovation?

Organisations should focus on AI capabilities that directly enhance how their teams work. Key features to look for include:
• Leverages company knowledge: AI should do more than simply analyse individual documents or projects. It should connect the dots across teams, projects, and relationships within your company. Look for AI solutions that leverage data graphs to capture and visualize all the interconnected data about your team’s workflows. Vendors with a deep understanding of teamwork, like Atlassian, are well-positioned to offer these insights.

• Integration with daily tools: The best AI solutions work seamlessly within the tools your teams already use. When AI understands the context of your workflows and acts as a true partner, it becomes more intuitive and impactful.

• Custom workflows: While AI features are widely available, true value comes from personalised data that’s tailored to your organisation’s needs. AI that adapts to your unique workflows makes a huge difference in productivity and ensures alignment with business goals.

• Enhanced collaboration: In today’s distributed world, AI can bridge gaps, making it easier for teams to communicate and collaborate seamlessly, no matter where they are.

• Real-time insights: AI should provide timely, real-time data that empowers teams to make informed decisions faster.

• Scalability: As your business grows, your AI solution should scale to meet evolving demands, delivering long-term value.

The key is to treat AI as a teammate, not just a tool. Organisations that embrace this mindset see greater productivity and innovation. Leaders play an important role here—by encouraging experimentation and supporting a learning environment, they help teams build the skills they need to collaborate with AI effectively. The more leadership supports AI collaboration, the more time teams save and the better the quality of their work.

What are some of the foundational elements that organisations need to consider when building an AI-ready infrastructure? How critical is it for IT teams to work closely with business units to ensure that the infrastructure is scalable and optimised for AI workloads?

At Atlassian, we’ve seen just how important it is for IT and business teams to work closely together—especially when building AI-ready infrastructure. Too often, IT operates in isolation, leading to misalignment and delays. When these teams collaborate, AI investments remain focused on solving real business challenges and delivering meaningful results.

A key foundation is strong security and data access controls. AI often processes vast datasets, and if permissions aren’t properly set, it can create serious roadblocks. By ensuring these controls are solid before rolling out AI, organisations can protect sensitive information while giving teams the access they need to get the most out of AI.

Equally important is fostering a culture of experimentation with clear goals and guardrails. While IT departments naturally want to minimise risks, overly tight restrictions can stifle creativity and slow progress. A better approach is for IT and business leaders to partner on identifying specific problems or workflows where AI can make a measurable difference. This collaboration gives teams the space to experiment with new solutions while staying within defined boundaries, so they can learn, iterate, and make progress quickly.

This kind of partnership not only builds scalable, optimized AI infrastructure but also helps teams explore innovative ways to use AI. When IT and business teams are aligned and working toward shared goals, AI becomes a powerful tool to unlock new opportunities and drive meaningful change.

Implementing AI can be a complex journey, especially when it comes to aligning it with business goals. What are the key challenges that organisations face in AI adoption, and how can they overcome these barriers to achieve a smooth and impactful transformation?

One of the biggest challenges is focusing too much on adoption rates—how often AI is used—rather than how it’s used. It’s like measuring a carpenter by how often they pick up a hammer. What really matters is how people collaborate with AI, how they approach it, and how they think about it.

Our research shows that mindset is more important than frequency when it comes to optimising AI. Teams that approach AI as a collaborator—clearly defining tasks, setting goals, and partnering with AI for brainstorming and problem-solving—see the best results.

At Atlassian, our goal is to make working with AI as natural as Googling or messaging a teammate. People are used to a system interaction where you press a button and get a response, but it’s important that we start to think about conversing with AI like you would a teammate instead. When AI is intuitive and easy to use, it doesn’t just improve efficiency—it transforms work quality and empowers teams to deliver more impactful results.

With AI automating repetitive tasks and transforming job roles, what strategies can organizations implement to ensure that employees are up-skilled and equipped to work alongside these new technologies? How can leaders foster a culture of innovation and learning to support this evolution?

Leadership sets the tone for AI adoption. If employees sense fear or hesitation from leaders, they’ll avoid using AI. But when leaders share their own AI learnings and encourage experimentation, they demystify the technology and inspire teams to explore its potential.

Starting small is the key. Tackling simple, well-defined problems first allows teams to see quick wins and build confidence. Many organisations make the mistake of jumping straight into complex challenges, which can stall progress and enthusiasm.

Hands-on learning is another essential strategy. Instead of passive training sessions, challenge employees to use AI to find creative solutions to specific problems. This practical approach makes AI feel accessible and immediately valuable.

By fostering a culture of experimentation and continuous learning, leaders empower employees to embrace AI with confidence. This not only drives innovation and productivity but also strengthens adaptability across the organisation.