AI is rapidly adopted and enhances personal productivity, yet it does not translate into transformative team outcomes
AI is clearly accelerating at work. According to Atlassian’s new research, 55% of Australian knowledge workers now use generative AI daily. Globally, workers report a 33% productivity boost, saving roughly 1.3 hours each day. In Australia, that’s about 78 minutes.
But here’s the issue: all this saved time and individual efficiency isn’t adding up to real team or organizational breakthroughs. Only 3% of executives surveyed said AI has brought actual transformation. That’s low. Most organizations are still dealing with fragmented platforms, scattered workflows, and poorly aligned team objectives. In reality, it’s still chaos, just happening faster.
So, while individual gains are evident, we’re not seeing systems evolve. Acceleration without structural alignment doesn’t create transformation. If you want lasting value from AI, you need strategies that connect people, data, and outcomes directly, not just grind through more tasks at higher speed.
C-suite leaders often over-index on task-based productivity metrics. But productivity at a personal level doesn’t scale unless the surrounding system – workflows, tools, and team coordination – is set up to benefit from it. AI adoption should focus on cross-functional improvement, where multiple teams benefit from gains.
Focusing solely on individual productivity undermines innovation and long-term returns on AI investments
AI is proving itself in task execution. People are getting things done quicker. But there’s a catch. When companies focus mainly on automating individual output, they miss the point. They don’t fix how teams collaborate or how solutions scale.
The Atlassian report is direct about this: organizations that prioritize individual productivity over AI-driven coordination are 16% less likely to see innovation rise. The consequence is financial. If this pattern holds, the Fortune 500 stands to lose about USD $98 billion every year in unrealized returns on AI. That matters.
This means isolated productivity isn’t just inefficient, it’s expensive. You can’t create next-gen output with last-gen structures. If AI doesn’t connect strategy across the company, from product teams to operations to leadership, it remains a tool, not a transformational driver.
C-level executives should measure AI success not just in time saved, but in impact created across departments. That includes how often AI outputs feed into decision-making, innovation cycles, and cross-departmental projects. A dollar saved is useful, but a dollar multiplied by collaboration is more valuable. Leadership must push for AI architecture that scales laterally, team-wide, not just top-down or individually.
AI is reshaping how employees access information, altering traditional collaboration habits
The way people get answers at work is changing fast. Atlassian’s data shows that 51% of workers now prefer to ask AI over asking a colleague. That number is higher than the 44% who go to coworkers. It’s a quiet but important shift in behavior. Employees are becoming more self-reliant, and AI is filling the role of go-to assistant for real-time information.
Speed is good. Quick answers reduce delays. But when fewer people speak to each other, knowledge sharing declines. Teams become less coordinated. Informal collaboration – the stuff that drives creativity and shared context – takes a hit. Replacing one-to-one exchanges with algorithmic answers increases efficiency, but it can reduce team alignment over time.
This shift is a signal to leaders. If managers and executives don’t build new systems for knowledge flow and team interaction, information becomes compartmentalized. Productivity goes up, but team cohesion weakens.
C-suite leaders need to track AI usage patterns beyond adoption rates. How employees access and apply information directly shapes collaboration. Relying too much on AI for isolated problem-solving can lead to drift in team alignment. Leaders should redefine internal communication, incentivizing team-driven problem-solving even when AI is used in parallel.
Inadequate data integration limits AI’s effectiveness and undermines user trust
Employees are using AI more, but they’re also running into frustrations. Atlassian’s numbers show that 80% of Australian workers feel blocked by AI tools that aren’t connected to the right data. That’s the real bottleneck. Globally, 79% say they’d use AI more if tools had access to better information. These are not minor gaps, they’re core limitations.
The quality of input directly defines the quality of output. If AI systems are stuck pulling from incomplete, outdated, or poorly linked data, then even advanced algorithms can’t deliver meaningful results. Tools appear inconsistent. Trust erodes. Usage plateaus.
Integration isn’t just a technical upgrade; it’s a strategy move. Companies that put clean, structured, and unified data behind their AI tools get better performance and higher employee confidence. Without tight data integration, AI turns into a surface-level solution, one that looks promising but rarely scales.
For executives, solving this is a matter of systems architecture and strategic oversight. Data accessibility should be viewed as infrastructure, not just IT. Ensuring AI tools operate with full context is essential if you want results that are accurate, actionable, and repeatable across teams. Don’t treat AI adoption as complete until data readiness is in place.
Leadership support and modeling are critical for deepening AI adoption
AI adoption is accelerating, but what drives meaningful usage across teams isn’t just the tech. It’s leadership. According to Atlassian’s report, 74% of knowledge workers now say their leaders encourage safe experimentation with AI, up from 60% the year before. That’s a significant cultural shift. People are more likely to test and integrate technology when they feel supported by senior management. Even more important, 65% of employees have directly observed their managers using AI in tasks and problem-solving.
When leaders model AI adoption, it sets a behavioral standard. This changes how teams behave. Employees take cues from how managers work. If leadership uses AI only for short tasks, that’s how the rest of the team will see it. If leaders experiment, collaborate, and integrate AI into strategy and operations, broader adoption follows naturally.
Molly Sands, Head of Teamwork Lab at Atlassian, summed it up well. “AI is everywhere, tackling to-dos, summarising docs, and crunching data, but teams are still drowning in tasks… Silos persist, work remains scattered, decisions are fractured, and goals are disconnected.” Her point is clear: without alignment from the top, AI adoption risks amplifying disorganization instead of solving it.
C-level executives need to go beyond endorsement. They must be visible users of AI and clearly communicate their objectives for the technology. This creates a tone throughout the company that AI is not just allowed, it’s expected and strategically valuable. When leadership use is thoughtful and visible, it moves AI from tactical tool to operational standard.
Developing new AI-related skills is essential to maximize the technology’s potential
AI isn’t plug-and-play. Even with the best tools, output depends on user input. That’s where skills matter. Atlassian’s research shows 48% of Australian employees identify prompt engineering, specifically writing accurate and actionable AI prompts, as the most important AI-specific skill to learn. Another 45% say creativity is the key general skill to build in the next year. These aren’t traditional learning gaps; they’re strategic capability shifts.
Prompt engineering drives better outcomes. The clearer the prompt, the more useful the response. Creativity, on the other hand, gives workers the flexibility to combine AI outputs into unique, effective solutions. This combination, precise instruction and creative application, gives teams an edge.
For companies already investing in tools, skills training is the multiplier. It enables scale. If people use AI narrowly or inefficiently, the technology underdelivers. When employees know how to work with AI, not just around it, you unlock real value.
C-suite leaders need to treat AI skills not as optional add-ons, but as core competencies. Prompt engineering, critical thinking, and creative application should be built into training, onboarding, and team development. Companies that fund tool access but not capability-building will fall behind competitors who train for precision and innovation.
Strategic AI collaborators drive team-wide, rather than solely individual, outcomes
Adoption alone isn’t enough. How employees use AI determines its impact. Atlassian’s research identifies a growing segment of workers called “Strategic AI Collaborators.” In Australia, 43% of respondents fall into this group. These individuals don’t just use AI to complete tasks faster, they integrate it into broader team workflows. They’re driving project coordination, improving strategic outcomes, and extending AI’s value across their teams.
This group also tends to follow the behavior of management. When leaders use AI not just to shortcut work but to solve meaningful problems, employees are more likely to apply it in cross-functional ways. Strategic AI users coordinate their efforts, generate insights that benefit entire teams, and improve decision-making by sharing AI outputs.
This matters. Because most organizations are still stuck in the cycle of individual gains and scattered results. Strategic AI collaborators are proving that with the right intent and usage, AI can become more than just a personal productivity tool. It becomes a mechanism for team performance and higher-level alignment.
Executives should recognize and scale the behaviors of these early strategic users. Provide visibility into successful cross-functional AI use cases. Optimize tools to support teamwork rather than isolated usage. When strategy and execution converge around AI, the organization levels up, not just the individuals.
The time saved through AI is being strategically reinvested
AI is saving real time, and employees are making smart decisions with it. In Australia, 49% of workers said they use their saved time for work-life balance, 43% for strategic thinking and planning, and 41% for improving processes. These aren’t superficial activities. They reflect a shift from reaction to intention, employees are reinvesting time in areas that deepen performance and long-term outcomes.
This suggests a clear pattern: employees want to push beyond just doing more work. They want to work more intelligently, and focus on areas that benefit both their personal well-being and the business. If the organization supports and structures that reinvestment, the time saved with AI becomes a lasting competitive advantage.
The reported behavior shows potential for compound improvement: better processes, better ideas, and sustainable energy across teams. But without a system in place to capture and scale these improvements, you risk missing the upside.
C-suite leaders should structure internal programs to harness this reinvested time. Allow strategic thinking to feed into planning cycles. Recognize and implement employee-driven process improvements. AI-driven time savings are only valuable if leadership knows where they’re going, and ensures the organization moves with clarity and discipline toward those gains.
Final thoughts
The numbers are real. AI is saving time, making work faster, and freeing up space for bigger thinking. But productivity alone isn’t the prize, it’s just the starting point. If the gains stop at the individual level, you’re leaving most of the value on the table.
Execution matters. Leading with AI is about more than rolling out tools, it’s aligning systems, empowering teams, and modeling strategic use from the top. Decision-makers who treat AI as an engine for collaboration, not just automation, will see competitive advantage scale across their organizations.
The next wave of impact won’t come from adding more AI. It will come from using it better, with smarter data, better skills, and connected workflows. That’s how you turn velocity into progress.
