The Unscalable Truth That Could Disrupt McKinsey

According to Inc., Invisible Technologies founder Francis Pedraza has identified what he believes is a massive blind spot in the enterprise technology market. Since founding his company in 2015, Pedraza has developed a business model called “AI services” that combines proprietary software platforms with human consultants who work inside client organizations to solve complex problems. Despite investor resistance to the “services” component—which they viewed as limiting scalability—Pedraza became convinced he’d discovered a lucrative opportunity to disrupt both traditional enterprise software companies like Salesforce and consulting giants like Accenture. His approach involves breaking customer processes into modular “LEGO-like” steps, automating what’s possible while deploying a global network of human agents for steps requiring human judgment.

The Scalability Obsession That’s Creating Market Gaps

Pedraza’s experience reveals a fundamental tension in venture capital thinking that’s creating significant market opportunities. Investors’ obsession with pure software solutions that are “infinitely scalable from day one” ignores the reality that most enterprise problems are messy, context-dependent, and require human expertise. This creates what economists call a business model innovation gap—where market needs exist but funding flows only toward solutions that fit predetermined patterns. The irony is that many of the most successful tech companies, including early Amazon and Apple, combined software with significant human-powered services during their growth phases, only achieving pure software margins after establishing market dominance.

Why Traditional Consultancies Are Vulnerable

The consulting industry, particularly firms like McKinsey & Company, operates on business models that haven’t fundamentally evolved in decades. They rely on high-priced human expertise delivered through standardized methodologies, creating exactly the kind of inefficiency that digital disruption typically targets. What makes Pedraza’s approach particularly threatening to this establishment is the combination of proprietary technology with flexible human deployment. Traditional enterprise software companies face similar vulnerability—their one-size-fits-all solutions often require expensive customization and integration services, creating the very inefficiencies that modular, human-augmented approaches can eliminate.

The Hidden Complexities of Human-Augmented Automation

While the vision of breaking processes into “LEGO-like” steps sounds elegant, the execution presents significant challenges that investors rightly question. Managing a global network of subject matter experts requires sophisticated coordination systems, quality control mechanisms, and cultural alignment that pure software companies don’t face. The margin compression from human labor—even at global rates—creates financial pressures that pure proprietary software businesses avoid. More fundamentally, the “last mile” problem of enterprise automation—those final, messy steps that resist codification—often represents the most valuable and complex parts of business processes, making them both expensive to solve and difficult to scale.

Where This Model Could Reshape Industries

The hybrid approach Pedraza describes has particular relevance in sectors where processes combine standardized and unique elements. Financial services compliance, healthcare administration, and legal operations all involve repetitive tasks that could be automated alongside expert judgment that requires human intervention. The real competitive advantage may come from building data flywheels—where each human-assisted process generates training data that improves automation over time, gradually reducing the human component while maintaining quality. This creates a path toward the scalability investors crave, but through an evolutionary rather than revolutionary approach.

The Pattern Recognition Behind the Vision

Understanding Pedraza’s perspective requires looking at his track record. As documented in his background, he’s navigated multiple ventures across technology and services, giving him unique insight into where pure technology solutions fall short. His previous experience with Everest—which he acknowledges failed partly due to business model issues—informs his current conviction about combining services with technology. This pattern of learning from failure and identifying gaps between investor preferences and market realities often characterizes founders who successfully identify overlooked opportunities.

The Coming Services Revolution

We’re likely entering a period where the distinction between software and services becomes increasingly blurred. The most successful enterprise companies of the next decade may be those that master the art of combining proprietary technology with precisely deployed human expertise, rather than pursuing purity in either direction. As artificial intelligence handles more standardized tasks, the value of human judgment in complex, context-rich situations may actually increase, creating new opportunities for businesses that can efficiently scale both technological and human capabilities. The companies that crack this code could indeed become the Netflix to traditional consulting’s Blockbuster—but they’ll need to navigate investor skepticism about their “unscalable” components along the way.