Atlassian On Why AI Must Deliver Measurable Business Outcomes

Feb 18, 2026

Andrew Boyagi, Customer CTO at Atlassian who advises enterprises on delivery, collaboration and AI adoption. He discusses why AI must solve concrete business problems and how developers’ roles are evolving toward system design and product outcomes. Conversation covers keeping humans accountable, leadership and training for AI collaboration, and measuring AI’s real impact on flow, quality and ROI.

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INSIGHT

Focus AI On Specific Team Problems

Companies that see the biggest AI gains solve specific team-level problems rather than rolling AI out broadly.
Treat AI as a teammate and invest in upskilling to get real measurable outcomes.

ANECDOTE

Mercedes Cut Duplicate Tickets 85%

Mercedes-Benz built a duplication detection agent that cut duplicate bug tickets by ~85%.
That freed engineers to focus on higher-value work rather than wasting time on repeats.

INSIGHT

Engineers Are Productivity Multipliers

Engineers already optimize flow and productivity through practices like agile and DevOps.
AI will amplify that role, letting engineers spend more time solving business problems and improving organizational productivity.

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At Davos this year, some of the biggest names in tech sent a clear signal. AI is no longer a novelty. It is no longer a proof-of-concept exercise. As Demis Hassabis of Google DeepMind suggested, AI will shape more meaningful work. And Satya Nadella of Microsoft was even more direct. AI only matters if it improves real outcomes for people.

So what does that look like inside the enterprise?

In this episode of Tech Talks Daily, I'm joined by Andrew Boyagi, Customer CTO at Atlassian, to unpack how the conversation has shifted from experimentation to execution. Developers, in many ways, are the perfect lens for understanding this moment. Over the last two decades, their role has expanded far beyond writing code. They now own products, infrastructure, operations, and business outcomes. AI is simply the next chapter in that evolution.

Andrew argues that AI will not replace engineers. It will raise expectations. As intelligent tools absorb repetitive work, the real value moves up the stack. System design. Architectural thinking. Reviewing and refining AI-generated output and orchestrating solutions that solve genuine business problems. And through it all, humans remain firmly in the loop.

We also explore what this means for leadership, why mindset is starting to matter more than technical skill alone, how organizations can avoid layering AI on top of broken processes. And why the companies pulling ahead are treating AI as a strategic discipline, not a feature upgrade.

This is a conversation grounded in reality. It speaks to product leaders, CTOs, CIOs, and anyone asking a simple but powerful question. If we are investing in AI, what are we actually getting back? And before we close, we look ahead to Team '26 and the themes Andrew and his team are already working on.

If this year has been about proving value, what will the next chapter demand from enterprise leaders? As always, I'd love to hear your thoughts. Are you seeing proof of value in your organization yet, or are you still working through the pilot phase?