AI has become a must-have topic in every strategic discussion. From CEOs to department heads, everyone is talking about the potential to boost productivity, cut costs, and support better decision-making with AI. Yet, when it comes to real-world implementation, many organizations quickly realize there's a significant gap between expectations and results.
Businesses invest in AI, experiment with various tools, but overall operational efficiency barely changes. Productivity doesn't improve noticeably, collaboration remains slow, and information is still fragmented. According to a 2024 McKinsey survey, while 72% of global tech companies have deployed at least one form of AI, only 11% have seen a significant impact on business outcomes. This raises a critical question: Where exactly is AI being embedded in the way your business works?
To answer this, we need to revisit a foundational concept: the System of Work.
The core issue with most AI initiatives isn't the technology itself, but the lack of a clear system of work for AI to plug into. AI can process language, analyze data, and make suggestions at lightning speed, but it can't understand how your organization operates if you haven't defined your own ways of working.
This is precisely what Atlassian refers to as the System of Work.
According to Atlassian, a System of Work isn't just a set of tools. It's a mindset and a framework for how work is organized and flows across the business. It describes how strategy is translated into plans, how work is executed across teams, how decisions are made, and how knowledge is retained so it isn't lost after each project.
In other words, a System of Work answers the questions AI can't guess: What are the priorities? What work is actually happening? Which decisions have been made, and what context led to those decisions? When these elements don't exist in a clear system, AI can only work with fragmented information like a new hire with no org chart, no documentation, and no one to explain the context.
That's why AI can't deliver sustainable value if it sits outside the System of Work. It might help with isolated tasks, but it won't improve how your organization collaborates, makes decisions, or learns over time. On the other hand, when a System of Work is in place, where work is tracked consistently, knowledge is systematically captured, and workflows are transparent, AI finally has the soil it needs to thrive.
Here, the System of Work isn't the end goal, but the necessary foundation. It provides the context, data, and structure for AI to understand what your organization is doing, why it's doing it, and where support is needed. Only then does the story of AI-powered Systems of Work truly make sense. Without a clear System of Work, you can still deploy tools, but they'll remain disconnected, mirroring the lack of alignment in your ways of working.
In reality, many organizations approach AI from the wrong direction. They start with technology, pick a few AI tools, and try to bolt them onto existing workflows. Marketing experiments with ChatGPT for content. Engineering uses GitHub Copilot to code faster. Sales drafts emails with AI. The result? AI exists as a scattered layer, serving individuals or small teams, but never becoming part of the overall operating system.
AI can help you write faster, analyze quicker, and answer questions better. But it doesn't understand your organization's workflow. It doesn't know what's truly a priority, which decisions have been made, or what knowledge is buried in old documents. A product manager might ask AI to prioritize features, but AI won't know that the team tried a similar idea three months ago and failed for well-documented technical reasons.
When AI isn't part of the System of Work, it doesn't create operational advantage. It just makes individual tasks faster, while the overall system remains slow and disconnected. Gartner estimates that knowledge workers spend an average of 2.3 hours per day just searching for information and switching between tools. Disconnected AI doesn't solve this problem. It can even add to the tool sprawl.
An AI-supported System of Work doesn't replace your ways of working. It builds on them. This is when AI is directly integrated into your existing systems, rather than sitting outside as a standalone tool.
In such a system, AI understands the context of work because it accesses data from where work actually happens. It knows which tasks are blocked, who's waiting for feedback, which decisions were made in the last cycle, and which documents are relevant to the current issue. It supports teams not just with answers, but with actionable suggestions, risk alerts, and timely knowledge connections. When an engineer encounters a bug, AI doesn't just suggest a fix. It surfaces similar incidents, past reports, and lessons learned.
The key difference is that AI doesn't require users to change how they work or learn new interfaces. Instead, AI is designed to fit seamlessly into existing workflows, showing up at the right time, in the right place, with the right information.
When you position the System of Work correctly, as an organizational philosophy for how work gets done, the role of collaboration platforms needs to be redefined. They're not the end goal, but the way Atlassian brings the System of Work to life in real operations.
Atlassian builds different collections for different organizational contexts: from Teamwork Collection for product and tech teams, to Service Management Collection for IT operations and internal services, to solutions for large-scale enterprise management. Each collection serves a slice of the organization, but all are built on the same mindset: work must be visible, knowledge must be retained, and teams must be able to collaborate seamlessly.
In this picture, Teamwork Collection is often the natural starting point, as it's where the core workflow of the business happens every day. Work is planned, executed, adjusted, and reviewed here. Jira provides structured visibility into work status, helping organizations understand what they're doing and why. Confluence preserves context, decisions, and knowledge, ensuring organizational memory isn't lost. Loom supports asynchronous communication, reducing meeting overload and ensuring information flows in distributed teams.
When these platforms are connected into a unified system, they don't just help individual teams work better, they create an operational backbone for the entire organization. This is the spine of the System of Work in practice. And it's here that AI starts to make sense.
If AI isn't present in the platforms where real work happens, it will always lack context. An external AI tool can answer generic questions, but it can't understand why a project is delayed, where bottlenecks repeat, or which decisions are shaping current priorities. Conversely, when AI is integrated directly into collaboration platforms, it can see the relationships between work, people, and information, the essential conditions for the System of Work to evolve, not just operate.
In this landscape, Rovo isn't just another AI tool in the Atlassian ecosystem. It's an AI layer built on top of the System of Work, designed to unlock and connect the value already present in your collections.
Rovo doesn't change how teams use Jira, Confluence, or Loom. Instead, it leverages the data and context already in your system to help people work smarter. When work, documents, and communication are in the right place within the System of Work, Rovo can access them as a whole, not as scattered pieces. A question is no longer answered with just information, but with context: related decisions, similar past issues, and lessons learned.
The difference is that Rovo doesn't force users to think like AI. It appears right in the flow of work, helping teams see what the system is telling them. When planning a new cycle, instead of digging through old documents and tickets, managers can quickly grasp key decisions, outstanding dependencies, and potential risks. Here, AI doesn't replace people. It amplifies decision-making based on the system.
This distinction clarifies the gap between "having AI" and "having an AI-powered System of Work." AI only delivers real value when it's built on a clear, disciplined, and connected way of working.
Let's be clear: AI can't fix a broken system of work. If your workflows aren't standardized, knowledge isn't documented, and data is fragmented, AI will only amplify those problems faster. AI trained on bad data gives bad results. AI integrated into chaotic processes just creates automated chaos.
That's why many AI pilots look impressive in demos but fail at scale. In controlled environments with clean data and clear processes, AI works well. But in the messy reality of undisciplined organizations, results are often disappointing.
On the other hand, when a business has a clear system of work, where work is tracked, documentation is maintained, and knowledge is shared, AI becomes a powerful catalyst for higher performance, reduced friction, and greater adaptability. Forrester Research found that organizations with mature digital work practices see 3.5x higher ROI from AI compared to those deploying AI on weak foundations.
An AI-supported System of Work isn't about chasing trends. It's the natural next step for organizations that have invested seriously in how they work, and are now ready to amplify that advantage with intelligent automation.
From our experience working with dozens of tech companies in Vietnam, BiPlus has seen that those who truly create value with AI always start by examining their system of work. They don't ask, "Which AI should we use?" but rather, "Where is our current system holding back performance?" They invest in standardizing processes before deploying AI. They ensure a culture of documentation exists before expecting AI to leverage organizational knowledge.
AI only delivers results when it's placed in the right context. And an AI-powered System of Work is how businesses move from AI experimentation to sustainable operational advantage. Not by changing everything, but by making current ways of working smarter, faster, and more effective, with AI as an inseparable part of the system.
At BiPlus, an AI-powered System of Work isn't about adding more AI to existing tools. It's a journey to transform how your organization operates. BiPlus acts as your partner to design, implement, and evolve your System of Work based on the Atlassian Teamwork Collection, from standardizing workflows, connecting knowledge, to integrating AI like Rovo into your real operational flow. The goal isn't just to have AI, but to boost performance, optimize operations, and enable continuous innovation sustainably.
If you'd like to explore how an AI-powered System of Work can unlock new value for your organization, connect with BiPlus for a tailored consultation.
