A Day in the Life of Work Sprawl
It’s 9:00 a.m. on a Monday. You open your laptop and are immediately greeted by a barrage of notifications—Slack, Outlook, Jira, Salesforce, Google Docs, and a dozen other apps.
You spend the first 30 minutes just piecing together what happened last week. By noon, you’ve toggled between 15 different tools, chased down three people for status updates, and manually copied data from one system to another. By the end of the day, you realize you’ve spent more time juggling fragmented workflows and battling context switching than actually doing meaningful work.
This is Work Sprawl in business operations. And it’s not just an inconvenience; it’s a trillion-dollar drag on productivity, digital transformation, and employee morale. I’ve lived it, I’ve fought it, and now, at , I’m helping organizations conquer it for good.
Recent research backs this up: A Forrester Total Economic Impact™ study found that organizations using saw a 384% return on investment over three years, saving teams thousands of hours previously lost to fragmented workflows and manual processes.
But these numbers aren’t just statistics—they reflect a reality I experienced firsthand.
Solving Work Sprawl: ’s Head of Solutions Architecture on AI and the Future of Work
The Early Days: Work Sprawl Was the Norm
Before joining , I led a business systems team at a large healthcare and investment company. We owned every SaaS tool under the sun—accounting, HR, investment management, engineering, marketing, you name it. Each department had its favorite platform, and none talked to each other.
The result? Silos everywhere.
I’d watch support or marketing teams walk over to engineering, breaking developers’ flow just to ask for a status update. We tried to “fix” this by rolling out Jira to the marketing team. They used it for training, then promptly went back to their whiteboard. Why? Because the tool didn’t fit their workflow or language. It was a classic case of IT mandating a solution that didn’t solve the real on-ground problem.
This experience taught me two things:
- Fragmentation isn’t just about tools—it’s about context and culture
- Forcing teams to adapt to a tool (instead of the other way around) is a recipe for resistance and wasted effort
The “Aha” Moment: Discovering the Power of Convergence
When I first saw , it was like a lightbulb went off.
Here was a platform flexible enough to meet the needs of every team, but unified enough to break down silos. For the first time, I could imagine a world where a task lived in both marketing and engineering, where context wasn’t lost in translation, and where teams could work in their own language, without losing sight of the bigger picture.
But the real magic wasn’t just in the features. It was in the philosophy: build a workspace that adapts to people, not the other way around. That’s the only way to drive real, lasting change.
Why Sprawl Persists: The Human Side of Fragmentation
Let’s be honest: teams don’t want to give up control over their tools. It’s natural. Tool selection is as much about identity and autonomy as it is about functionality—especially in enterprise tech stacks where software adoption impacts digital workflows. That’s why so many “digital transformation” projects fail—they ignore the human element.
The cycle perpetuates itself: IT tries to standardize, business units resist, and new point solutions get added to the stack. The result? More silos, more friction, and more manual workarounds.
The challenge is only growing. In a 2025 study on the side effects of digitalization, enterprises face “role overload,” where the burden of managing fragmented workflows and tool sprawl impairs execution and employee well-being. As digital tools multiply, so do the demands on employees’ attention and energy—making unified, context-rich workspaces more critical than ever.
The only way out is to meet teams where they are, help them see what’s possible, and then guide them—step by step—toward a more unified way of working.
The New Frontier: Eliminating AI Sprawl and the Critical Role of Context
Just as we started to get a handle on app sprawl, along came AI. Suddenly, every team wanted its own AI tool. But here’s the catch: AI is only as good as its access to the context.
If your AI doesn’t know the latest project updates, customer conversations, or strategic priorities, it’s just another siloed tool—one that worsens AI Sprawl and increases confusion and manual effort.
The real breakthrough comes when AI is deeply integrated into your workspace, automatically loading and updating context in real time. That’s when you move from “AI as a shiny object” to “AI as a true partner in work.”
What It Takes: The Technical and Organizational Prerequisites for AI Transformation
But achieving true AI Convergence isn’t just about plugging in a new tool. It requires:
- Unified data architecture: All your work, communication, and knowledge must live in a single, accessible platform
- Live context feeds: AI must have access to real-time updates—no more stale data or manual uploads
- Process mapping: You need to understand and document your workflows so AI can augment, not disrupt, them
- Change-ready culture: Teams must be open to experimenting, learning, and iterating as AI becomes part of daily work
At , we invested heavily in building a platform where context is always live and actionable. Our AI agents don’t just answer questions; they proactively surface insights, automate routine tasks, and adapt as your business evolves.
Here’s a visual summary of these prerequisites for successful AI transformation:
Case Study: From Sprawl to Convergence—A Before and After
To truly understand the cost of Work Sprawl and Convergence’s power, you have to see it in action. Here’s how one global marketing team went from fractured workflows and scattered tools to a unified, AI-powered workspace that streamlined execution and gave time back to creators.
Before:
A global marketing team managed campaigns across six tools. Project updates were lost in email threads, creative assets lived in Dropbox, and reporting was a manual nightmare. AI tools were piloted, but each required manual context uploads and quickly fell out of use.
After:
The team migrated to , consolidating projects, docs, and communication. AI agents now monitor campaign progress, generate weekly status reports, and even draft creative briefs based on live project data. Status meetings are shorter, reporting is automated, and the team spends more time creating and less time chasing information.
Driving Change: The Power of Incremental Wins
One of the biggest mistakes I see is trying to “boil the ocean” with AI transformation. Instead, I advocate for incremental wins: finding specific workflows where AI can deliver immediate, tangible value.
I’ll never forget a Secret Supper event in Mexico City. I demoed an agent that automatically scored sales calls using a custom rubric. In a couple of hours, I’d built a proof of concept that replaced hours of manual review. The reaction? Jaws dropped. Suddenly, AI wasn’t abstract—it was applied. People could see how it would change their day-to-day work.
These “aha” moments are the key to overcoming resistance. Show, don’t tell.
Make it real, relevant, and easy to adopt.
Change Management: Frameworks for Sustainable Transformation
AI transformation isn’t just a technical project—it’s a cultural one. Success requires both bottom-up engagement and top-down sponsorship.
Our change management playbook:
- Stakeholder Mapping: Identify champions, skeptics, and influencers across teams
- Communication Plan: Share the “why,” the “how,” and the “what’s in it for me” at every stage
- Pilot Programs: Start small, measure impact, and celebrate wins
- Metrics for Adoption: Track usage, satisfaction, and business outcomes—not just logins
- Continuous Feedback: Create channels for teams to share what’s working and what’s not
Above all, it’s about helping people see themselves in the future you’re building.
The Secret Sauce: Unified Context
The real power of —and the future of work—is in Converged context. When all your work, knowledge, and communication live in one place, AI can finally deliver on its promise. Not just theory—AI convergence is already proving to be the antidote to scattered workflows, helping teams reduce cognitive load, accelerate execution, and unlock creativity.
This isn’t just about efficiency. It’s about reducing cognitive load, breaking down silos, and enabling teams to move faster and smarter. It’s about creating a workspace where innovation can flourish—because everyone is working from the same playbook.
That’s the missing piece today. In a world drowning in disconnected tools and AI sprawl, brings it all together, the world’s first Converged AI Workspace. This is AI that doesn’t just assist—it converges.
That’s the promise of Brain.
Brain: Contextual AI in Action
Unlike bolt-on copilots, Brain is native to your workspace. It understands your projects because it lives inside them—summarizing meeting notes, generating updates, automating tasks, and surfacing blockers with full context intact. And when the moment demands, you can switch between leading AI models—ChatGPT, Claude, Gemini, and more—choosing speed, nuance, or depth without ever leaving your workflow.
And with Brain MAX, you unlock even more:
Talk to Text, which captures ideas as you say them and turns them into tasks, notes, or docs instantly—no typing required.
Context awareness, model flexibility, Talk to Text, and a unified workspace—that’s how you turn AI from a bolt-on tool into a lever for organizational AI Transformation.
Visualizing the Journey: From Sprawl to Convergence
Imagine a simple diagram:
- Left: A tangled web of disconnected apps, each with its own data and workflows
- Middle: A funnel representing the migration of work, knowledge, and communication into a unified platform
- Right: A single, integrated workspace where AI agents operate on live context, automating, advising, and accelerating work
Common Pitfalls (and How to Avoid Them)
Rolling out new technology isn’t just about switching on a tool; it’s about how you introduce, integrate, and measure it. Leaders often stumble in three predictable ways:
| Pitfall | Better approach |
|---|---|
| Mandating a new tool without team buy-in | Involve users early, co-create workflows, and pilot before scaling |
| Treating AI as a bolt-on instead of a core capability | Integrate AI into daily workflows and ensure it has access to live context |
| Measuring success by logins, not outcomes | Track business impact — time saved, errors reduced, innovation unlocked |
Looking Ahead: My Vision for the Future of Work
I believe we’re on the cusp of a new era. In the next 2–3 years, AI agents will become integrated members of our teams—always on, context-aware, and continuously learning.
But here’s the catch: the organizations that win will be the ones that master Convergence.
They’ll unify their tools, workflows, and knowledge, creating a fertile ground for AI to thrive.
The biggest opportunities are unlocking new levels of creativity, speed, and collaboration. The biggest risk is falling into the trap of “AI Sprawl,” adding more tools without solving the underlying fragmentation.
📮 Insight: 33% of people still believe multitasking equals efficiency.
In reality, multitasking only increases the cost of context switching. When your brain bounces between tabs, chats, and checklists, deep focus takes the biggest hit.
helps you single-task with intention by pulling everything you need in one place!
Working on a task, but need to check the internet? Just use your voice and ask Brain MAX to do a web search from the same window. Want to chat with Claude and polish the draft you’re working on? You can do that too, without leaving your Workspaces!
Everything you need—think chat, docs, tasks, dashboards, multiple LLMs, web search, and more—lives in one Converged AI Workspace, ready to go!
Advice for Leaders: Step-by-Step Recommendations
Turning strategy into real change requires everyday actions that build trust and momentum. Here are practical steps leaders can take to guide their teams through adoption:
- Start with empathy. Understand the real pain points of your teams. Don’t force change—co-create it
- Map your workflows. Identify where context is lost and where AI could add value
- Pilot, measure, iterate. Launch small experiments, track outcomes, and refine before scaling
- Invest in context. Make sure your AI has access to live, integrated data. Otherwise, you’re just adding noise
- Make it a team effort. Combine bottom-up experimentation with top-down leadership and support.
- Celebrate wins. Share stories of success to build momentum and trust
- Never stop learning. The landscape is evolving fast. Stay curious, stay humble, and keep iterating
If I could go back and do one thing differently, it would be to invest even earlier in building a culture of experimentation and learning. The future belongs to those who can adapt—and help their teams do the same.
Conclusion: The Path Forward
Work Sprawl is a trillion-dollar problem, but it’s also the biggest opportunity of our generation. By unifying our tools, workflows, and context—and by embracing AI as a true partner—we can build organizations that are not just more productive but also more human.
The journey isn’t easy, but it’s worth it. And if my experience has taught me anything, it’s this: the future of work will be built by those who dare to converge.
Devin Stoker is Head of Solutions Architecture at . Connect with him on LinkedIn to continue the conversation.
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