Many companies have been experimenting with artificial intelligence over the past two years. Writing text, summarizing meetings, drafting emails, generating code: much of it now works surprisingly well. And yet, after the initial successes, the same question often remains:Where does the real, sustainable value actually come from?
According to the recently publishedAI Collaboration Index by Atlassian, 96% of leadership still see no significant ROI from the use of artificial intelligence.
The next AI push won't come from better prompts, but from better context.
Philipp Göllner, XALT Founder & CEO
The answer to how to create measurable value through AI was made very tangible at theAtlassian Community Event in Leipzigin February. In his talk, he showedPhilipp Göllner, XALT founder and AI enthusiast, why the next major development step in AI doesn't come from even better individual answers. The real lever lies elsewhere: incontext, integration, and actionability.
Because companies don't need AI that just sounds good. They need systems that understandwho wants to know what, what a team is working on, which pieces of information belong together, and what action should follow. That is exactly where the next phase of enterprise AI begins:Agentic AI.
The first wave of AI was useful but too generic
The first phase of AI adoption was defined by experimentation. Companies tested chatbots, copilots, and assistants. The results were often impressive but also limited. Many answers remained too generic, too poorly contextualized, and too far removed from the real working context.
The problem is not the quality of the models. The problem is that a model without the right context will, at best, produce very convincing responses without actually providing precise help.
That is precisely why the focus is shifting now. No longer just:What can the model do?But rather:What does the model know about my working reality?
The real progress is context
One central point became very clear in the presentation: AI only becomes truly valuable in day-to-day business when it doesn't work in isolation, but in context.
Who worked on which ticket? Which documents are associated with it? Which decision was made when? What information is in Jira, Confluence, SharePoint, Slack, or other systems? And most importantly: What content is a user even allowed to see?
These questions are critical. Because they turn a generic AI response into reliable support in everyday work.
This is precisely where the current strategic focus of many platforms in the enterprise market lies. It's about bringing together knowledge, work, communication, and permissions so that AI doesn't just generate content, butbecomesoperationally effective.Within the Atlassian ecosystem, Rovo AI leverages the Teamwork Graph (Atlassian's data and relationship layer) to understand people, tasks, tools, and assets in context, enabling precise answers and efficient collaboration.
What is Agentic AI? The real paradigm shift
Perhaps the most exciting aspect of the current development is the shift from classic generative AI toAgentic AI. This is more than a buzzword. It refers to an AI that doesn't just respond to questions, but actively supports within defined boundaries: it gathers context, prepares decisions, drafts summaries, triggers actions, and handles operational intermediate steps.
This fundamentally changes the role of AI. It is no longer just a tool for individual responses but becomes part of the workflow. Rather than merely generating content, it carries out preparatory or execution tasks, relieving people in their daily operations.
Philipp Göllner, XALT Founder & CEO
Typical real-world examples illustrate what this looks like in practice:
- automated summaries of team performance,
- drafts for weekly updates or town halls,
- identifying connections between processes and documents,
- preparing communications based on current work progress
- or triggering downstream actions through integrated workflows.
Especially in enterprise andservice management contextsthis is highly relevant. Because in these contexts, it is rarely just about knowledge questions. It's about recurring tasks, status checks, handovers, approvals, documentation, and cross-team coordination. It is precisely in these areas that AI agents deliver their greatest value, because they don't just provide information but actively support the next meaningful step in the process.
Why this is becoming a critical topic right now
The fact that this development is gaining momentum now is no coincidence. Three things are converging at the moment.
- Modern models can process significantly more context than just a short time ago. That means: not just individual questions, but entire work contexts, documentation, and histories can be taken into account to a greater extent.
- The barrier to building first prototypes is lowering. What used to be a dedicated development project can often be implemented much faster today as a first use case. This is massively changing the pace of innovation.
- Companies are increasingly willing to view AI not merely as a productivity gadget, but as a structural lever for processes, collaboration, and service organizations.
What is actually changing in practice
What stood out most in the presentation was the abundance of practical examples—not as a show effect, but as a signal of a larger pattern.
AI now helps automatically prepare team updates, derive communication from the work context, build small integrations faster, automate routine processes, and make information usable across systems.
The strategic significance behind this far outweighs any individual example:AI doesn't just reduce effort; it shortens the path from idea to implementable solution.
This is particularly relevant because many companies don't fail for lack of ideas but because the path to implementation is too long. When AI helps make problems more visible, testable, and communicable faster, technology suddenly becomes a genuine organizational lever.
Vibe Coding is not the goal—but a signal
A term that keeps coming up in the context of this development isVibe Coding. It refers to the very rapid, often dialogue-based creation of prototypes with AI support.
You may like the term or not. What matters more is what lies behind it: The barrier to entry in software development is dropping. People who previously had ideas but no direct way to implement them can now make first solutions visible much faster.
This does not replace solid software development. It does not replace architecture. And it does not replace governance. But it significantly changes the early phase of innovation.
Abstract requirements suddenly become tangible prototypes. Long discussions become testable hypotheses. And that is precisely a greater advance for many companies than any single model improvement.
Without governance, new freedom quickly turns into new chaos
As great the opportunities are, the other side of the coin is equally clear. When more people are able to build solutions, automations, and small applications themselves, theThe Need for Governance.
This covers data, access rights, compliance, security audits, data processing agreements, integration control, and operational reliability. In enterprise environments, this is not a side issue but a prerequisite for AI initiatives to yield robust solutions.
The right response to this new speed is therefore not to prevent experiments. The right response isto create clean spaces for experimentation – and to define clear transitions into production environments.
Sandbox first. Governance second. Production deployment only with guardrails. That's how AI dynamics become genuine transformation rather than shadow IT.
Why ITSM and Service Management Benefit in Particular
ForIT Service ManagementandEnterprise Service Managementthis development is particularly interesting. This is where recurring tasks, documented processes, and high coordination needs converge.
Incidents, Requests, Changes, knowledge articles, approvals, handovers, or status communication: all of these areas are ideally suited for context-based AI support.
The great advantage is that Service Management already operates in a highly structured manner. As a result, AI can do more than just generate text—it can better categorize processes, prepare communication, and accelerate operational workflows.
That is precisely why this topic is so relevant for IT organizations today. Not because AI suddenly takes over everything. But because it removes friction where it matters.
Companies should now think bigger, not smaller.
Perhaps the strongest message from the talk was ultimately not technological but organizational:Brings bigger problems with it.
This is an important point. Many companies currently use AI primarily where it makes existing work a little faster. That makes sense, but it's often too short-sighted.
The bigger leverage lies in the questions that were previously too costly, too complex, or too inconvenient.
- Which internal processes are unnecessarily complicated?
- Where is knowledge lost in handovers?
- Which communication costs hours every month without creating real value?
- Which services could become significantly more user-friendly if context and automation work together seamlessly?
Those who view AI merely as an efficiency tool underestimate its potential. Those who connect it to real business problems use it strategically.
Conclusion: The next wave of AI is context-based, agentic, and workflow-native
Philipp Göllner's talk at the Atlassian Community Event in Leipzig made exactly this shift tangible. The future of AI in the enterprise doesn't lie in the next tool that writes better text. It lies in systems thatunderstand context, prepare work, and relieve teams along real processes.
For companies, this means a clear shift in perspective. Away from the fascination with individual model capabilities. Toward the question of how work context, knowledge sources, permissions, and processes can be connected to produce productive value.
The next phase of AI will therefore not simply be generative. It willcontext-based, agentic, and deeply embedded in workflowsto be. That's exactly where the difference between an exciting experiment and real business value is made.
You want to find out how AI agents, context data, and modern service processes can be meaningfully deployed in your organization? Let's take a look at your use cases together and discover where AI can already create real value today.
