It’s not because they are more innovative.
There is a common myth in tech that smaller, nimbler companies always win the adoption race. But with Generative AI, we are seeing the opposite. While startups are still “tinkering,” enterprises are productionizing. According to recent data shared by Nathaniel Whittemore (a.k.a. NLW, host of the AI Daily Brief & CEO, Super.ai) at the at the AI Engineer World’s Fair, full production deployment of AI agents in billion-dollar enterprises jumped from 11% to 42% in just the first three quarters of 2024 [03:15]. Why? It comes down to a brutal reality of economics, automation, and what I call the “2% vs. 20% ROI Gap.”
AI is Automation (Just Less Consistent)
Many AI enthusiasts argue that automation isn’t AI. That’s true in the sense that not all fruits are apples, but all apples are fruits. AI is automation. The primary difference? Traditional automation is deterministic (consistent); AI is probabilistic (less consistent, but more capable). Smaller companies are already masters of traditional automation because they have to be. They use it to survive with fewer people. But for a massive corporation, the “low-hanging fruit” of basic automation hasn’t even been picked yet. This creates a massive opportunity for Information Gain—the ability to apply AI to “messy” processes that were previously too expensive to automate.
The Math: The 2% vs. 20% Rule
The biggest “moat” for big business isn’t their data or their brand—it’s their Scale ROI. Because a large company doesn’t need significantly more resources than a small company to build a single AI agent or workflow, the math of deployment looks very different:
- For the Small Business: To pay for the initial R&D and resource overhead, a new AI tool might need to deliver a 20% improvement in efficiency just to break even.
- For the Enterprise: Because they are applying that tool across thousands of employees or millions of transactions, a mere 2% improvement creates an ROI that justifies the entire department.
Furthermore, as NLW points out, these large organizations are moving toward Systemic Adoption [17:00]. They aren’t just doing “spot experiments”; they are thinking cross-disciplinarily. They can afford to go slower, spend more on high-quality resources, and leverage volume discounts that drive their production costs down even further.
The “Risk Reduction” Transformation
Interestingly, while most companies start with “Time Savings” (the default ROI metric), the real “transformational” wins are happening elsewhere. NLW’s study found that Risk Reduction—while the least common primary goal—was the most likely to result in “transformational” impact [14:59]. Large companies have massive back-office, compliance, and risk burdens. AI can handle the sheer volume of these tasks in ways a human team never could [15:17]. This is a “moat” that small businesses simply don’t have to worry about yet.
The Cycle: From Moat to Commodity
This scale is the moat that gives big business a temporary advantage. But here is the irony: The more they use that advantage, the faster the moat shrinks. As enterprises productionize these efficiencies, they effectively commoditize them. What cost a Fortune 500 company $1 million to develop today will be a $20/month SaaS plugin for a small business tomorrow. We are in a cycle of:
- Hype: Everyone talks.
- Value: Big companies productionize at scale.
- Cheap: The tech becomes a commodity.
- Reverse Leverage: Small, disruptive players use those same cheap tools to move faster and out-innovate the giants.
The giants are winning the production race today, but they are also building the very tools that the next generation of “disruptors” will use to tear them down.
© Scott S. Nelson