A few years ago, while serving as ClickUp’s General Vice President of Solutions and Success, I found myself staring at a whiteboard, trying to map out how my teams actually got work done. What started as a simple organizational diagram quickly turned into a tangled web—lines connecting people, tools, and processes in every direction. It was a moment of clarity: our biggest challenge wasn’t a lack of effort or talent. It was the invisible sprawl that made even simple projects feel overwhelming.
If you’ve ever wondered why your team’s best intentions get lost in the shuffle, or why progress feels slow despite everyone working hard, you’re not alone. This is the reality of what I call “Work Sprawl”—a problem I’ve observed firsthand with hundreds of customers across industries. This isn’t just about having too many tools or chaotic workflows; it’s about what happens when strategic vision loses its connection to daily execution.
Work Sprawl represents the fragmentation of people, processes, and technology within organizations. It’s the shadow side of digital transformation—an unintended consequence of disconnected work systems that leaves teams feeling like no one has the complete picture. Every new tool or workflow adds another layer of complexity, creating what feels like organizational fog.
This fragmentation manifests in three critical areas:
People fragmentation occurs when teams become isolated islands, each with distinct priorities and communication patterns. Even within single organizations, departments can feel completely disconnected, with overlapping roles and responsibilities that create confusion rather than collaboration.
Process fragmentation happens as workflows multiply without proper governance. Sub-processes and dependencies accumulate, making it nearly impossible to trace how individual tasks connect to broader strategic objectives. Change management becomes an afterthought, further complicating an already complex landscape.
Technology fragmentation emerges when tools proliferate without meaningful integration. Each new application promises to solve specific problems, but collectively they create information silos that strip away crucial context. The more tools organizations add, the less visibility they have into their actual work patterns.
Artificial intelligence now weaves through all these layers, sometimes helping to clarify connections, sometimes adding to the noise. The challenge lies in distinguishing between AI that enhances understanding and AI that simply creates new forms of digital clutter.
The most dangerous effect of Work Sprawl isn’t wasted time—it’s the breakdown of the connection between strategic vision and daily execution. In theory, strategy should flow seamlessly from leadership to every individual contributor, while results should flow back up through clear reporting lines. In practice, Work Sprawl fractures this essential chain of communication and accountability.
I’ve witnessed teams work for weeks on projects, only to discover another department was tackling identical challenges. I’ve seen executives struggle to answer fundamental questions like “How does this initiative connect to our quarterly goals?” The larger the organization, the more pronounced these disconnections become.
This fragmentation isn’t merely inefficient—it’s profoundly demoralizing. People want to understand how their work contributes to meaningful outcomes. When they can’t see the impact of their efforts, engagement and motivation inevitably suffer.
Research reveals that tool proliferation significantly impacts team performance. High-performing teams consistently use nine or fewer tools, while low-performing teams are four times more likely to juggle fifteen or more applications. Approximately one-third of teams fall into the middle range of five to nine tools, suggesting that most organizations struggle to find the right balance.
This data illustrates a crucial principle: effective teams maintain lean tool stacks, prioritize integration over accumulation, and preserve context across their work environments. They focus on reducing noise and sprawl rather than adding new capabilities that might fragment their workflows further.
When teams constantly switch between disconnected applications, they lose critical context with each transition. What starts as a simple task—reviewing a document, updating a project status, or responding to a client question—becomes a complex navigation exercise across multiple platforms, each with its own interface, data structure, and communication patterns.
Artificial intelligence promises to automate routine tasks, optimize workflows, and transform how organizations operate. However, AI’s effectiveness depends entirely on the context and integration it receives within existing systems. When AI tools operate in isolation, disconnected from core business processes, they often create what I call “AI Sprawl”—simply replacing one form of fragmentation with another.
Current data suggests that over one-third of workers use AI tools with zero integration into their primary work areas. Many organizations remain stuck with only partial or minimal AI integration, leading to fragmented processes and missed opportunities for genuine productivity gains. True comprehensive integration—where AI seamlessly weaves into daily workflows—remains relatively rare across most industries.
Layering AI capabilities onto already fragmented systems typically produces more complexity, not less. Teams end up managing both their original tool sprawl and a new collection of AI applications that don’t communicate with each other or with existing business systems.
However, when organizations implement what I term “Contextual AI”—artificial intelligence that understands the relationships between people, processes, and technology—they can achieve genuine clarity and connection. For example, properly integrated AI can surface duplicate work across departments, highlight dependencies between related projects, and identify bottlenecks before they impact delivery timelines.
The key is treating AI as a connective tool rather than another standalone application. It should enhance visibility and coordination, not create new silos that require additional management overhead.
Most leaders initially approach AI as a way to accelerate existing processes—imagining it as a turbocharger that makes current workflows faster and more efficient. We think in terms of familiar equations: if Process A plus Resource B normally equals Outcome C, then AI should help us reach Outcome C at lightning speed.
This perspective, while understandable, limits AI’s transformative potential. Instead of asking “How can AI help us achieve current goals more efficiently?” we should ask “What new goals, outcomes, and experiences does AI make possible?”
This shift in thinking opens up three fundamental areas for reimagination:
Organizational structure can evolve from rigid reporting hierarchies to dynamic “work charts” that reflect how value actually flows through the organization. Rather than focusing solely on who reports to whom, these new structures emphasize how work moves, where decisions get made, and how information travels to support better outcomes.
Work definition itself becomes more fluid when AI dissolves traditional boundaries. The concepts of projects, teams, and individual job roles can expand to enable new forms of collaboration and shared ownership. AI can help identify natural working relationships that transcend departmental lines, creating more effective problem-solving groups.
Customer experience can extend far beyond traditional service improvements. Instead of simply using AI to process support tickets faster, organizations can anticipate customer needs, personalize interactions at scale, and create entirely new value propositions that weren’t feasible with human-only processes.
The leaders and teams who thrive in this environment will view AI not as a shortcut to current objectives, but as an invitation to fundamentally rethink what’s possible within their industries and markets.
Traditional organizational charts show reporting relationships but reveal little about how work actually gets accomplished. To address this gap, I began creating “work charts”—visual maps that trace how projects, people, and tools interact in real-world scenarios.
Work charts reveal bottlenecks that don’t appear on traditional org charts, highlight redundancies across departments, and identify opportunities for enhanced collaboration. They help leaders understand where strategic vision breaks down during execution and where teams excel despite structural obstacles. Most importantly, they make invisible workflow patterns visible to decision-makers.
AI can enhance this mapping process by analyzing communication patterns, identifying hidden connections between projects, and suggesting workflow improvements based on successful patterns from other parts of the organization. However, the foundation remains a clear, unified view of how work actually flows through the company.
For example, a work chart might reveal that the marketing team’s campaign approval process creates a bottleneck that delays product launches, even though the marketing and product teams appear unconnected on the organizational chart. Or it might show that customer success and engineering teams have developed an informal but highly effective collaboration pattern that could be replicated across other departments.
Working with hundreds of organizations has revealed several consistent patterns, regardless of industry or company size:
Redundant work appears everywhere. The most common breakthrough moment occurs when teams discover that multiple departments are solving identical problems in parallel, often using different approaches and reaching different conclusions. AI can help identify these duplications, but only when organizational data is properly connected and accessible.
Lack of awareness creates costly inefficiencies. Teams frequently operate without understanding what’s happening in related departments. This isn’t just about efficiency—it’s about building trust and shared purpose across organizational boundaries. When teams can see how their work connects to other efforts, they make better decisions and collaborate more effectively.
Strategy requires bidirectional communication. While top-down alignment ensures everyone understands organizational priorities, bottom-up feedback provides crucial insights about execution realities. The most successful organizations create robust channels for both directions of communication, ensuring that strategic decisions incorporate ground-level insights about what actually works.
One customer captured this perfectly: “We didn’t realize how much we were missing until we saw everything in one place.” This highlights the power of convergence—not just using fewer tools, but gaining more comprehensive context about organizational work patterns.
It’s tempting to address Work Sprawl by simply reducing the number of tools teams use. However, consolidation alone can backfire if it strips away the context teams need to perform their best work. Removing tools without understanding their role in maintaining workflow context can actually decrease productivity and collaboration.
Convergence represents a different approach. It focuses on bringing people, processes, and technology together while preserving the context that makes collaboration meaningful and effective. Sometimes convergence means using fewer tools; sometimes it means creating better integration between existing tools. The goal remains consistent: achieving clarity about how work flows and how individual contributions connect to broader objectives.
Effective convergence requires understanding what each tool or process contributes to the overall workflow before making changes. It means asking questions like: “What context would we lose if we eliminated this tool?” and “How can we preserve the valuable aspects of our current system while reducing unnecessary complexity?”
The solution to Work Sprawl lies in what can be called a “Converged AI Workspace”—an integrated environment where tasks, documents, goals, and communication exist together in a context-rich setting, enhanced by artificial intelligence. This represents a fundamental shift from managing multiple disconnected tools to working within a unified ecosystem.
A converged workspace enables teams to see the complete picture of work across people, processes, and technology. It leverages AI to surface insights, eliminate redundancies, and maintain alignment without requiring constant manual coordination. Most importantly, it preserves the context that makes collaboration meaningful while reducing the noise and fragmentation that characterizes most digital work environments.
This approach goes beyond simple tool consolidation. It creates a living ecosystem where strategy, execution, and innovation happen together, supported by AI that understands the relationships between different work elements and can provide relevant insights when needed.
For example, when working on a project proposal, team members can access related documents, see who else is working on similar initiatives, understand how the proposal connects to broader company goals, and get AI assistance with research or writing—all without leaving their primary work environment or losing context about their current task.
Solving Work Sprawl requires addressing cultural challenges alongside technological ones. When teams understand how their work fits into the bigger picture, they’re more likely to collaborate effectively, share knowledge openly, and contribute innovative ideas.
Transparency becomes the foundation for trust and organizational agility. The best insights often emerge from people closest to the actual work and customer interactions, rather than from senior leadership alone. Creating channels for feedback, celebrating cross-functional successes, and clearly connecting individual contributions to company outcomes are all essential elements of a healthy work culture.
Organizations that successfully address Work Sprawl typically develop strong feedback loops that allow information and insights to flow both up and down the organizational hierarchy. They create systems that make it easy for teams to share what they’re working on and to understand how their efforts connect to broader strategic goals.
Solving Work Sprawl is an ongoing process rather than a one-time fix. Organizations can begin with these foundational steps:
1. Map your current state comprehensively. Use whatever visualization tools work best—whiteboards, mind maps, or digital mapping software—to create a complete picture of how work actually flows through your organization. Include not just formal processes, but also informal communication patterns and workarounds that teams have developed.
2. Identify redundancies and gaps systematically. Look for places where teams are duplicating effort or where information gets lost between handoffs. Pay particular attention to areas where multiple departments interact, as these often reveal the most significant opportunities for improvement.
3. Prioritize convergence over simple consolidation. Focus on bringing work together in ways that preserve valuable context rather than just reducing the number of tools. Ask what each current tool or process contributes before deciding to eliminate it.
4. Integrate AI thoughtfully and strategically. Use artificial intelligence to enhance visibility and reduce manual coordination work, but keep humans involved in decision-making processes. Ensure that AI tools connect with existing workflows rather than creating new silos.
5. Foster transparency and systematic feedback. Make it easy for teams to share their work and to understand how it connects to broader organizational goals. Create regular opportunities for cross-functional collaboration and knowledge sharing.
6. Challenge fundamental assumptions regularly. Don’t just ask how AI can help you work faster—ask what entirely new outcomes become possible when AI is part of your toolkit. Consider whether current processes and structures still make sense in an AI-enhanced environment.
Work Sprawl represents a modern challenge, but the solution draws on timeless principles: clarity, connection, and purpose. Artificial intelligence can certainly help, but only when it brings people together rather than creating additional separation.
The goal isn’t just to make work more efficient—it’s to make it more meaningful. When teams can see how their individual contributions connect to larger purposes, when they have the context they need to make good decisions, and when they can collaborate effectively across organizational boundaries, work becomes more engaging and more impactful.
As organizations look toward the future, the most important question isn’t how AI can help them do the same things faster. It’s how AI can help them imagine new possibilities, develop new ways of working, and create new definitions of success that weren’t feasible before.
The future of work isn’t just about speed—it’s about vision. And with properly implemented converged workspaces, that vision becomes achievable for organizations of all sizes and across all industries.
Join hosts Anthony, Shane, and Francesca for essential insights on AI's impact on jobs, careers, and business. Stay ahead of the curve – listen now!
Join hosts Anthony, Shane, and Francesca for essential insights on AI's impact on jobs, careers, and business. Stay ahead of the curve – listen now!
No hype. No doom. Just actionable resources and strategies to accelerate your success in the age of AI.