Here are the trends I’m watching most closely across technology, operating models, and the day-to-day mechanics of work.
AI exists, or is currently being built, in most enterprise platforms. The bigger shift this year is in how companies will use it. That means less experimenting and more putting it into real workflows.
Leaders should start asking:
What decisions can AI help with?
What actions can it take safely?
What data can it use?
Who is accountable for the output?
Most companies won’t be limited by AI capabilities. They’ll be limited by governance: access controls, auditability, data rules, and clear review processes. The organizations that do these things well will get more value, faster, without introducing unnecessary risk.
AI is only as helpful as the information it can reliably access. If documentation and decisions are scattered or outdated, AI will only serve to scale confusion. Nothing is more unsettling than when AI is confidently wrong, and the end user doesn’t know it.
In 2026, the organizations that will get the most from AI will be the ones that do the basics well, like:
Clear structure and naming conventions
Owners and review cycles
Capturing decisions in a consistent place
Practical runbooks and standards teams actually use
Hygiene of both structured and unstructured organizational data
This is also where the Atlassian stack can be an advantage. Jira for structured work, Confluence for durable knowledge, and Jira Service Management for work intake can create strong end-to-end workflows, as long as systems are designed intentionally.
Most enterprises have accumulated too many tools over time, often for understandable reasons. But the cost shows up in licensing, security risk, duplicate and/or siloed data, and disjointed processes.
More organizations will push to simplify. The goal isn’t one tool, but fewer core platforms with clearer standards for what belongs where and how systems connect.
Atlassian Cloud adoption used to be treated as a migration project. Moving forward, more organizations will approach it as a business decision that affects how teams work.
That means getting more disciplined about:
Reducing customization where it adds complexity without real value
Standardizing workflows and issue types
Defining a clear app and extension strategy
Setting governance that supports speed and consistency
Atlassian Cloud will continue to attract investment because it’s where vendors are building the most capabilities, especially in AI, administration, and cross-product experiences.
Service management is a lot about work intake: requests, approvals, escalations, incidents, and handoffs. More organizations are applying ITSM best practices and tools beyond IT, true Enterprise Service Management, to teams like HR, marketing, security, facilities, finance, and procurement, because the alternative is still too often email and ad hoc processes.
Where teams invest in the fundamentals, things like service catalogs, ownership, escalation paths, and smart automation, enterprise service management really works and can deliver much better outcomes across an organization.
AI can help here in practical ways, including better triage, faster routing, clearer summaries, and easier access to relevant knowledge.
Projects and committees don’t scale well. This year, more organizations are shifting toward product-style execution with teams aligned to outcomes (not just deliverables), clear ownership, better measurement of value, and portfolio decisions that reflect capacity.
This also creates more transparency about what’s in, what’s out, and why. Visibility is uncomfortable for some organizations, but it’s often what drives better prioritization and decision-making.
The hybrid work debate will continue, but the best organizations will focus less on blanket rules and more on clear working agreements about where async work is expected, when meetings are necessary, and when in-person time is worth it (planning, alignment, relationship building).
Now, a strong culture matters more than collaboration features. Teams need transparency, accountability, and good support systems to empower innovation and execution.
If you want to be prepared…
Identify a few AI use cases tied to outcomes you can measure, like cycle time, resolution time, quality, or risk reduction. Do not start with AI everywhere all at once.
Treat platform choices as business operating model decisions. If you’re standardizing on Atlassian (or anything else), decide what “good” looks like and ensure ROI through effective organizational change management, enablement, and governance.
Fix the work context layer: knowledge hygiene, decision capture, and service intake discipline. AI amplifies the system you already have. That’s not good news if you’re behind on data cleanup, system audits, or process standardization.
2026 will reward organizational coherence: clean intake, clear priorities, durable knowledge, and accountable teams. Most of this isn’t glamorous. It’s the hard work day-to-day to align strategy, execution, and results.