AI’s speed paradox: why faster individuals don’t mean faster organizations

In the rapidly evolving landscape of work, the advent of artificial intelligence (AI) has created a paradox for organizations: while individual productivity may be soaring, overall organizational efficiency and coordination remain stagnant. This insight stems from a recent analysis presented at Atlassian’s Teamwork in an AI era event, featuring insights from Forrester Senior Analyst Will McKeon-White. The findings highlight a critical disconnect between the speed at which individuals can operate and the ability of teams to work cohesively.

The acceleration of work processes is evident. Tasks that once required significant time and effort, such as drafting presentations and responding to customer inquiries, can now be completed in a fraction of the time. Atlassian's survey of 12,000 knowledge workers and over 170 Fortune 1000 executives for their upcoming State of Teams report reveals that 89% of executives believe AI has expedited execution. However, this speed comes at a cost, as half of these leaders report no improvement in cross-team coordination.

The Challenge of Coordination

The primary concern for leaders is not merely to enhance individual speed, but to create value for the organization as a whole. Despite this understanding, 58% of executives admit they struggle to measure AI's return on investment (ROI) at the organizational level. This lack of clarity often leads to a focus on easily quantifiable metrics, such as individual productivity, rather than the more complex dynamics of team collaboration.

Research indicates that approximately 80% of enterprise work is collaborative, yet many organizations are primarily concentrating on individual AI advancements. This narrow focus risks overlooking the vast potential of AI to enhance teamwork and collective output. A staggering 70% of knowledge workers feel their workflows are not optimized for AI, and 87% of teams report a lack of time for coordination due to being entrenched in execution.

As the output of individuals increases—such as a designer producing multiple proposal versions or a support agent handling more tickets—teams find it increasingly difficult to manage this surplus without improved coordination. The introduction of AI agents adds another layer of complexity, necessitating coordination not just among people but also between human and AI agents. Without a shared context, the potential for chaos escalates, as disparate agents operate in isolation rather than in concert.

The Importance of Shared Context

Executives foresee structural changes in organizations, with 62% believing AI will reduce silos and 77% anticipating a shift toward more horizontal team structures. While breaking down silos can facilitate communication, it also raises the need for extensive coordination across various roles. A common theme in discussions with teams regarding the lack of AI transformation is the absence of a shared context. Many organizations lack a unified understanding of project goals, definitions, and decision-making processes, leading to fragmented knowledge.

The repercussions of this fragmentation are significant, as knowledge workers often spend hours in unrecorded meetings where vital decisions are made. This information typically does not find its way into formal documentation, remaining trapped in personal memories or scattered files. Consequently, AI systems may function like a confident new hire—quick and articulate but frequently misaligned with the organization's actual needs.

Fortunately, some teams are successfully navigating these challenges. Research indicates that teams utilizing a structured approach to collaboration experience a 68% reduction in what is termed the "fragmentation tax," which refers to the difficulty of working together. These teams are also nine times more likely to report that AI enhances their collaboration and three times more likely to trust AI outputs.

Strategies for Effective AI Integration

Top-performing teams do not rely on secret techniques; instead, they consistently implement three key strategies. First, they clarify the purpose of AI adoption, focusing on specific challenges rather than simply instructing individuals to use AI tools. This clarity fosters a deeper engagement with AI, allowing it to optimize for concrete objectives rather than generic productivity metrics.

Second, these teams treat organizational knowledge as a critical infrastructure component. By establishing a connected data layer, they ensure that decisions, key concepts, and metrics are documented and accessible. Atlassian’s Teamwork Graph exemplifies this approach, integrating various tools to provide a cohesive view of work, knowledge, and personnel.

Lastly, successful teams view AI as a long-term practice rather than a one-off implementation. They empower internal champions to explore use cases, share insights, and translate AI's potential into actionable workflows. This bottom-up approach, combined with clear objectives and shared knowledge, embeds AI into the fabric of teamwork.

As organizations look to the future, the challenge remains: how to harness AI's capabilities while fostering meaningful collaboration. Leaders are encouraged to shift their focus from merely automating tasks to creating opportunities for more coordinated and impactful work. By prioritizing alignment, shared knowledge, and continuous learning as integral parts of their AI strategy, organizations can transform the speed of individual output into collective success.