A counterintuitive pattern has emerged in AI deployment: the market is barbelling toward extremes—transformative AI at the top, “good enough” AI everywhere else, and an increasingly hollow middle. Understanding why “good enough” is winning reveals the true economics of AI adoption.

The 7% of companies achieving transformative AI results share a common trait: they’re not optimizing for perfection—they’re optimizing for core business integration. Meanwhile, the vast majority have discovered that “good enough” AI delivers 80% of the value at 20% of the cost and complexity.
Why Perfect Is the Enemy
The companies stuck in AI purgatory are those pursuing perfection before deployment. They’re waiting for models that never hallucinate, systems that require no human oversight, accuracy rates that match human experts in every edge case. While they wait, competitors deploy imperfect but useful AI and iterate their way to dominance.
This mirrors classic minimum viable product thinking applied to AI. The winning strategy isn’t to build the best AI—it’s to build AI that’s good enough to create value, then improve through real-world feedback.
The Strategic Implication
The barbelled AI economy creates a clear strategic choice. Either you’re competing at the transformative end—massive investment, deep integration, fundamental reinvention—or you’re competing at the “good enough” end—fast deployment, iterative improvement, pragmatic expectations.
The mistake is targeting the middle: investing significantly but not enough for transformation, expecting perfection from pragmatic tools. That middle ground is where AI projects go to die.
The asymmetric opportunity? Most competitors are stuck in that deadly middle. Choosing either extreme creates differentiation.
For AI strategy frameworks, explore The Business Engineer.









