The AI Revolution Won't Wait: Why Gradual Adoption Is Doomed to Fail
The AI Revolution Won’t Wait: Why Gradual Adoption Is Doomed to Fail
The Parallel to E-Commerce Revolutions
The adoption of AI in business reminds me of what we witnessed with e-commerce. Just as Zalando revolutionized retail by building a completely new business model rather than simply digitizing existing stores, I believe AI adoption will follow a similar path. The most successful AI implementations won’t come from grafting AI onto existing business structures but from creating entirely new companies built with AI at their core.
The Adoption Challenge: Insights from Founders
Over the past year, I’ve spoken with founders across diverse industries—from legal tech to healthcare, manufacturing to financial services. Despite their different verticals, they all share a common struggle: adoption. Existing companies face significant resistance when implementing AI tools, not just from technological limitations, but from organizational and human factors. The executives I’ve spoken with repeatedly emphasize that selling the technology is often easier than getting people to use it effectively.
Why Traditional Adoption Approaches Fall Short
The conventional approach to AI adoption will likely fail in most industries because the timeline is simply too slow. While tech companies might embrace and integrate new AI tools within months, traditional industries often operate on adoption cycles spanning years. By the time many organizations complete their AI transformation initiatives, the technology will have already evolved several generations forward, leaving them perpetually behind the curve.
The Problem with “Tools over Tools” Approach
Many vendors are creating AI interfaces that sit on top of existing software—what I call the “tools over tools” approach. However, this strategy fundamentally misunderstands the challenge. True AI transformation isn’t about adding another layer of technology; it requires reimagining entire processes.
This involves complex change management across organizations, which is typically:
- Prohibitively expensive
- Painfully slow
- Fraught with resistance from stakeholders invested in the status quo
When process redesign is necessary, gradual AI adoption becomes increasingly impractical.
The Future: Purpose-Built AI-Native Companies
I believe we’ll see the greatest success from completely new companies built from the ground up with AI at their center. Consider a law firm designed not to add AI to existing processes but one where every workflow is engineered around AI capabilities from inception.
These AI-native companies will:
- Design client intake systems optimized for AI processing
- Create document workflows built for AI collaboration
- Develop pricing models that reflect AI-enhanced productivity (next story)
- Build team structures that maximize human-AI synergy
A concrete example would be a new legal practice where lawyers never start with blank documents, but instead provide strategic oversight to AI systems handling document creation, research, and initial analysis. This isn’t about replacing lawyers but creating an entirely different type of legal practice.
Conclusion: A Bifurcated Future
Rather than seeing gradual, universal AI adoption across existing businesses, I anticipate a bifurcated future: legacy companies slowly implementing limited AI tools while struggling with adoption, alongside rapidly growing AI-native companies built from scratch.
These new enterprises won’t just use AI—they’ll be fundamentally designed around it, with processes, pricing, and organizational structures that maximize AI’s capabilities while focusing human talent on the areas where it truly adds value.
The winners won’t be those who best integrate AI into existing workflows, but those who boldly create entirely new business models with AI at their foundation.
What do you think about this perspective? Does your experience suggest a different adoption pattern for AI technologies?
Just write me on LinkedIn!