In this interview from Atlassian Team '26 in Anaheim, Asha Thurthi, head of product strategy collection and head of strategy and ops at Atlassian, joins theCUBE's Alison Kosik and Christophe Bertrand to discuss how Strategy Collection is closing the gap between enterprise priorities and execution. Thurthi draws on a survey of 300-plus senior enterprise leaders revealing that while 80% say their priorities are clear, only 11% have work linked to those priorities in a single system. She frames this not as a tooling problem but a connectivity one, introducing the concept of "mean time to pivot" — which Strategy Collection, powered by Atlassian's Teamwork Graph with 150 billion-plus objects and relations, is designed to compress from months to a single day.

The conversation also explores how Strategy Collection addresses a deeper enterprise risk Thurthi calls the "multi-billion dollar productivity tax" — the cost of deploying AI on top of fragmented, disconnected systems rather than fixing underlying structural gaps. She highlights the platform's human and AI capital management capability, which places human labor costs alongside AI usage and spend in a single view, elevating the question from individual productivity to organizational outcomes. Customer proof points anchor the discussion: Datasite's chief strategy officer describes Strategy Collection as providing a "factory to flow" view for leaders, while CNA Insurance credits it with connecting executive-level strategy down to the teams doing the work. Thurthi positions 2026 as the inflection year when AI must prove itself — not by replacing people, but by augmenting them and delivering measurable ROI for organizations making the journey from AI curious to AI native.

In this interview from Atlassian Team 2026, Alicia Lenart, vice president of HR business partners at Atlassian, joins theCUBE Research's Christophe Bertrand and Alison Kosik to discuss why the real barrier to AI adoption is culture — not technology. Lenart argues that the technology has advanced, but cultural transformation is what most organizations are getting wrong. She explains how Atlassian approaches AI as a people transformation first — with HR, not engineering, at the center of that shift. A centerpiece of that strategy is NORA — the New Atlassian Onboarding Rover Agent — built by three non-technical HR staff in two weeks. Deployed to every new hire, NORA has answered over 19,000 questions, saved the operations team more than 2,000 hours and demonstrably accelerated AI adoption among incoming employees.

The conversation also explores Atlassian's "ShipIt" quarterly innovation event, where non-technical participation surged — HR involvement jumped from less than 1% to roughly a third of the team, and finance reached 20%. Lenart draws a sharp distinction between efficiency and "rewired work," pointing to the design technologist role as a concrete example: by embedding AI into the software development lifecycle, the team achieved a 35% month-on-month increase in prototype output, reaching 12,000 prototypes and unlocking the capacity to pursue ideas previously deemed out of reach. She also addresses where AI should not go, drawing firm lines around hiring decisions and performance reviews, and outlines how Atlassian's responsible AI committee — spanning HR, legal and compliance — keeps governance current as the landscape evolves. From reframing every individual contributor as an orchestrator of agent fleets to setting AI norms the moment a new employee walks in the door, Lenart provides a practical blueprint for embedding AI into the fabric of workforce strategy.

In this interview from Atlassian Team '26, Dr. Molly Sands, head of Atlassian's Teamwork Lab at Atlassian, joins theCUBE's Alison Kosik and Christophe Bertrand to discuss the hidden cost of misaligned AI adoption and what it takes to build truly coordinated, AI-forward teams. Sands introduces the "AI fragmentation tax" — a coordination gap that widens as AI accelerates execution while alignment falls further behind. Drawing on research spanning 12,000 knowledge workers across six countries and more than 170 Fortune 1000 executives, she reveals that the performance gap between high- and low-functioning teams amounts to roughly six hours of wasted coordination per week, translating to an estimated $161 billion in annual opportunity cost across Fortune 500 companies. Despite surging AI adoption, only 6% of executives can point to clear ROI from their investments.

The conversation also explores why team-level AI implementation remains the exception rather than the norm — with just 24% of executives directing AI at the teams that perform 80% of the work. Sands breaks down why leaders are 84% more likely to invest in new tools than in developing the people and skills needed to use them, and why closing that gap is where most transformations will stall. She advocates for redesigning full workflows at the team level, building shared skills libraries and cultivating the psychological safety that allows leaders to model learning alongside their teams. Rather than chasing usage metrics, Sands urges organizations to anchor AI investments to specific, measurable problems and define success by outcomes — not adoption rates.

In this interview from Atlassian Team 2026, Andrew Boyagi, customer chief technology officer of Atlassian, joins theCUBE Research's Christophe Bertrand and Alison Kosik to discuss how developer experience is becoming the blueprint for enterprise-wide productivity in the AI era. Boyagi outlines four flows of enterprise productivity — purpose, workflow, knowledge and intelligence — explaining why purpose flow is the critical foundation that ensures teams are aligned on strategy before AI accelerates their execution. Drawing on his transformation work with Atlassian Williams Formula One, he details how the Teamwork Graph continuously aggregates organizational context, enabling teams to surface instant answers on car parts, race incidents and project ownership through Rovo — and why that same model applies to any enterprise navigating the shift to AI-powered work.

The conversation also explores how Atlassian helped Williams break down legacy system silos by integrating manufacturing parts data through its assets solution, making that information searchable and AI-ready across the entire team. Boyagi pushes back on the emerging trend of "tokenmaxxing," arguing that measuring AI productivity through token consumption misses the point entirely — enterprises should track value delivered faster, not raw output. He reframes the cultural shift around shared intelligence, noting that documenting knowledge doesn't dilute expertise; it eliminates low-value interruptions and raises the quality of every human interaction. From legacy integration to the F1-proven principle that fast, well-informed decisions separate winners from the field, Boyagi offers a concrete framework for enterprises ready to operationalize AI across the full flow of work.

In this interview from Atlassian Team '26 in Anaheim, Tanguy Crusson, product lead of Jira Product Discovery at Atlassian, joins theCUBE's Alison Kosik and Christophe Bertrand to discuss how AI is reshaping the discipline of product management and why choosing what to build has never mattered more. Crusson, who oversees a prioritization and roadmapping tool used by more than 25,000 customers, announces the new Atlassian product collection at the event. He warns that AI's speed advantage has made the "build trap" — shipping feature after feature that fails to move the needle — easier than ever to fall into, and explains why the real shift is from relying on secondhand customer summaries to querying signals directly at the source.

The conversation also explores how Atlassian is tackling one of product management's oldest problems: fragmented, filtered data. Crusson details the new feedback app, which aggregates customer signals across sales, support and customer success conversations into a searchable data mart — and pairs it with product analytics so teams can see how those signals map to actual feature usage. He describes how Atlassian's teamwork graph connects feedback to insights, ideas, roadmaps and delivery work in Jira, enabling teams to close the loop with customers the moment a feature ships. Crusson also introduces a three-bucket prioritization model — new capabilities, existing product quality and reliability — as a practical framework for teams navigating competing demands. From a 300,000-customer distribution network to a developer platform supporting agentic workflows, he outlines how Atlassian's scale gives product teams the foundation to move from gut-driven assumptions to continuously validated, data-informed decisions.