Large corporations are often hamstrung by a silent killer of innovation: top-down bureaucracy. It’s one reason the next breakthrough AI tool is unlikely to emerge from a traditional corporate hierarchy. We’ve seen this before, many of today’s most influential productivity tools now seen as standard were born from a product-led growth model. In that model, products are designed to be so intuitive, accessible, and valuable that they attract users organically through free trials, freemium tiers, or simple onboarding, without relying on conventional sales or marketing.
Think of tools like Notion, Slack, and Figma. These products gained traction with individual users and small teams before making their way into enterprises.
Once a product achieves product-market fit, the race to secure enterprise customers begins, as they represent the most significant revenue opportunities.
I've witnessed this firsthand. At a startup I worked for, we were one of the first enterprise clients to adopt InVision (RIP). It was a no-brainer—it connected our globally distributed team, replacing our old Photoshop-to-Dropbox workflow with a much faster, more iterative process with engineers.
Was it perfect? No. But at the time, it significantly improved our collaboration and development speed. Since we were a smaller company, navigating procurement was relatively simple — accounting was on the other side of the room, and I just needed the CEO's approval to move forward.
The Complexity of Large-Scale Tool Adoption
Fast forward a few years to LinkedIn, where I was now part of a 300+ global design team spread across multiple business lines. The complexity of adopting new tools was exponentially greater. We were among Figma's first large enterprise customers, and it was clear they understood the demands of working with a large-scale operation and the necessary considerations, such as enterprise-scale licensing, security compliance, and change management. I remember that evaluating tools set itself apart from tools like Sketch and more specialized tools like Framer and Principle.
It was a classic buyer committee scenario. We ran a structured exploratory phase, rigorously testing Sketch, Figma, and InVision. Initially, there was some internal resistance, but Figma quickly stood out, especially regarding enterprise readiness.
The AI Tool Adoption Challenge
This experience illustrated how critical enterprise readiness can be. Now, as AI tools emerge, the same issues arise. I tell this story because the same challenges exist today as teams seek AI tools to enhance their workflows. Many companies won't approve these tools for enterprise use due to security, compliance, and procurement hurdles, and when you couple that with data compliance, there is a lot to consider. So, where will the next AI breakthrough come from? Most likely, side projects and grassroots adoption within teams experimenting outside of sanctioned workflows.
This highlights a key opportunity: companies that streamline the approval process for emerging tools will have a competitive advantage. Startups building AI-powered solutions should also consider how they can integrate with larger enterprises from day one. The cycle of enterprise tool adoption needs to move faster. Otherwise, the most innovative solutions will remain stuck in red tape while competitors move ahead.
If This Sounds Familiar:
If you're seeing friction in AI tool adoption at your company, here are some recommendations to consider:
For Individual Contributors & Team Leads:
Start small – Use AI tools in non-critical workflows to test their value before making a case for adoption.
Build momentum – Share wins with colleagues and create internal demand before pushing for formal approval.
Document impact – Track time saved, efficiency improvements, and collaboration benefits to strengthen your argument.
Find workarounds – If security policies block specific AI tools, look for compliant alternatives or offline versions to test.
For Managers & Decision-Makers:
Shorten approval cycles. AI moves fast, and companies that take months to approve tools will struggle to keep up.
Pilot AI tools in controlled environments – Allow teams to run structured trials before committing to full adoption.
Engage security & compliance early – Address concerns proactively to avoid unnecessary roadblocks.
Encourage experimentation – Create safe spaces for employees to test AI productivity tools within company guidelines.
The next wave of AI-powered productivity tools is already here. The question is whether your company will embrace them or risk being left behind. Even if your organization doesn’t adopt them, it’s still worthwhile to explore these tools on your own.
Looking to sharpen your design leadership and management skills? Join me for a personalized 1:1 coaching session, and we can work together on strategies that will help you truly thrive in your role.