The visual of an agile organization has transformed since the Agile Manifesto’s early days of stickies on whiteboards and collocated teams. Today, the most sophisticated agile teams adopt continuous planning, contribute to developing self-organizing standards, and deliver innovations that transform their organizations.

AI is now driving a remarkable reshaping of business, and not just in software development. The most Agile teams include marketers, data scientists, and HR leaders looking to deliver innovations and outcomes from AI agents and other genAI capabilities.

How AI can amplify agile collaboration

Today’s question should be, how should agile organizations evolve leadership responsibilities and delivery processes now that AI is being used almost everywhere?

AI code generators, vibe coding, spec-driven development, vibe design, and citizen analytics may change how agile teams complete their user stories. But they also raise new questions about how they should collaborate with AI agents, plan CX/EX innovations, and measure AI’s impact.

So, I was excited to attend Atlassian Team ’26 to learn how the leading company in agile, DevOps, and tools for knowledge workers is helping organizations develop AI natives.

A big woot to the Atlassian team, as I couldn’t help trying out some of the capabilities that CEO Mike Cannon-Brookes and team live demoed during the opening keynote. Full disclosure, StarCIO is an Atlassian customer, and we use Jira, among other tools, to run the business. We also guide other organizations in implementing StarCIO Agile across Atlassian tools as well as other platforms.

While many CIOs know Jira as the foundation of agile collaboration, some may not be aware of the full extent of Atlassian’s products and capabilities. Here’s what CIOs need to know about the AI they showcased at Team ’26.

1. Automate developing your organization’s knowledge graph

How can people, process, documentation, code, and other assets form your organization’s knowledge graph – an important contextual repository for AI agents – automagically?

Atlassian’s underlying architecture was on full display at Team ’26, as all activity within its tools, along with information from 50+ MCP integrations, automatically creates the organization’s knowledge graph –  what they call the Atlassian Teamwork Graph. I was able to visit StarCIO’s Teamwork Graph, all ready to go, without any upfront configuration.

I then enabled Rovo, Atlassian’s agent, and began testing its capabilities. It audited my workflow configuration and provided recommendations based on Jira’s newest capabilities. I then prompted Rovo to add a user story and connect it to its epic and release.

During the keynote, Atlassian’s team showcased more advanced use cases:

• Preparing for meetings by developing a briefing from relevant information.
• Finding one developer’s “To Dos” across a 1.5-billion-line codebase.
• Identifying applications that don’t adhere to the latest style/branding guidelines.

Atlassian reported that 75% of the Fortune 500 use Rovo.

Why this matters: Drive a smarter AI and richer team-with-AI collaborations:

Developing and evolving a knowledge base and an organizational taxonomy is very challenging and expensive when using traditional knowledge management tools that don’t fully integrate with the platforms knowledge workers use. Atlassian live-demonstrated their platform’s ability to accelerate with context (growing information in Teamwork Graph), harness that information (with Rovo and their workflow products), and surface it (with MCP and a CLI). 

2. Deliver AI innovations with product management

I last attended Atlassian’s conference in 2023 and wrote about how they were transforming collaboration. At that show, they announced Jira Product Discovery (JPD) and, at Team ’26, they shared that the product now has over 25,000 customers.

Participants of my workshop on World Class IT Organizations in the AI Era already know my opinion – that product managers and product owners have the toughest job. It takes years to develop all the competencies: Capturing ideas, researching buyers, learning from end-users, prioritizing stakeholder needs, articulating a strategic roadmap, delivering capabilities, driving adoption, and leveraging customer feedback.

And enterprises need more product managers and owners to help prioritize winning AI POCs, deploy a higher percentage to production, and iterate to deliver value. This is why top CIOs are evolving their organizations to product-based IT methodologies.

Only the tooling for product managers has been fragmented, especially for midsize and smaller enterprises. JPD helps product managers define the what so that agile teams use Jira to break down and deliver the how – a separation that’s challenging to achieve culturally when stakeholders often prescribe both, and when they want everything delivered yesterday.

Why this matters: Faster AI code generation requires smarter upfront prioritization.

Most organizations prioritize too many initiatives, too many POCs, and too many promises to stakeholders and customers. While product management is responsible for connecting business strategists to a deliverable roadmap, aspects of their work in many organizations are disconnected from agile delivery.

Translation: they spend a lot of time developing presentations and using spreadsheets, rather than focusing on upstream customer connections, product planning, and partnering with agile teams on the development process.

At Team ’26, Atlassian showcased a mature product for product management with an important new feedback app. This app completes the product development feedback cycle by pulling in signals from tools like Pendo and extracting qualitative feedback from virtual calls and other collaborations. Organizations that use these capabilities can evolve toward a data-driven, customer-driven, and strategy-driven roadmap.

3. Measure and benchmark the agile organization

Key questions for CIOs:

• Do you capture and measure the effectiveness of your agile process, including delivery efficiency, quality, and cost?
• When the team buys into DORA metrics and other devops KPIs, are they tracking them manually?
• When the CEO asks how the organization benchmarks DevOps against other companies of similar size, do you have to call in expensive consultants to perform an audit?

And now that AI capabilities are popping up across the SDLC, DevOps, and ITSM, are you able to measure their impact on team productivity, quality, and costs?

DX, a capability Atlassian acquired late in 2025, aims to address the gaps in streamlined, standardized, and benchmarkable intelligence. At Team ’26, they announced three new AI-related metrics:

• AI Effectiveness Score – Aggregates adoption, velocity, and quality around how teams use AI tools to improve their daily effectiveness.
• Agent experience – A score similar to developer experience, measuring workflow clarity as presented to AI agents, and reported by them when completing tasks.
• AI cost management – FinOps on token usage with drill-down to who’s spending how much on what.

Why this matters: CIOs must protect their teams when sentiment is to cut technology staff.

While AI can code, it takes collaboration with people and agile teams to pick the right priorities and deliver resilient solutions. For large DevOps organizations, standardizing on benchmarkable, automated metrics is the foundation of a data-driven story and a vehicle for continuous improvement.

Why Review Atlassian

Yes, I am biased toward Atlassian tools, but for very good reasons. I work with many organizations that use other agile tools, and many do a fine job of capturing backlogs, managing Kanbans, organizing sprints, and connecting requirements to code. But few drive collaboration from idea to operations, are used happily by non-techies, and automate the rolling up of intelligence to the PMO, product, and organizational levels. Now add a knowledge graph, AI for knowledge workers, and automated metrics to the mix, showcasing that Atlassian is a leading platform for agile organizations.