Scale AI CEO Aims to Turn AI Hype into Profitable Reality for Companies

With 95% of businesses failing to see returns on AI, Scale AI’s Jason Droege is reshaping how organizations leverage artificial intelligence to drive revenue and operational efficiency.

The artificial intelligence industry faces a pressing challenge: despite widespread adoption, the vast majority of companies are not profiting from AI. A recent report from the Massachusetts Institute of Technology revealed that 95% of businesses attempting AI initiatives fail to generate revenue. Jason Droege, CEO of AI startup Scale AI, believes he has a solution.

“There has been this general promise of, hey, you’ll just plug in the AI model … and everything will work,” Droege said in an interview. “The reality is a little bit different.”

Scale AI, a leader in data labeling for AI models, has been crucial for training large language models with massive amounts of structured data, such as differentiating a photo of a cat from a fish. Meta recognized this value, purchasing a 49% stake in Scale AI in June for $14.3 billion, valuing the company at $29 billion. Following the deal, founder Alexandr Wang and several top executives moved to Meta, yet Scale’s data-labeling business continues to grow steadily.

Now, under Droege’s leadership, Scale AI is also focusing on helping companies build custom datasets and deploy AI tools to automate processes, aiming to make AI implementation profitable rather than a costly experiment. “I think companies thought it was a bit easier than it actually is,” Droege said. “But there is a ton of value when you get it right.”

The reality for many organizations is that AI is not a magic wand. Most failed AI projects apply the technology to the wrong problems. Droege emphasizes that AI excels in areas where humans are slow, inconsistent, or error-prone, such as analyzing documents, processing insurance claims, or summarizing patient medical histories.

Scale AI counts prominent clients including the Mayo Clinic, Cisco, Global Atlantic Financial Group, and the Qatari government. Recently, it secured a $99 million contract with the U.S. Defense Department to develop AI applications for the Army, further cementing its expertise in high-stakes environments.

The company’s approach integrates human expertise with AI to ensure meaningful outcomes. In healthcare, for instance, senior doctors provide feedback to fine-tune AI systems, creating tools that genuinely assist medical professionals rather than replacing them. Similarly, government agencies have used Scale AI solutions to streamline building permit approvals, learning from historical data to make processes faster and more accurate.

While analysts warn that it may take years before AI tools consistently generate profits across large organizations, the potential rewards are substantial. Gil Luria, head of technology research at DA Davidson, said, “Once we figure out how to do AI in the organizational context, those tools will be very valuable and generate a tremendous amount of revenue.”

Competition in the AI applications space is fierce, with Amazon, Microsoft, and thousands of other startups vying to develop enterprise solutions. However, Droege sees opportunity for companies that deeply understand AI’s capabilities and limitations. MIT’s research supports this, finding that firms who deploy AI successfully often rely on outside expertise rather than attempting to build solutions entirely in-house.

Droege is confident about the trajectory of Scale AI. “Going into all of this, the application side of our business was already in the hundreds of millions of dollars in revenue,” he said. “And on the data side of the business, we’ve grown every month since the Meta deal … It’s a large business for us and we’re very happy with it.”

As the AI market matures, Scale AI’s strategy exemplifies a pragmatic path to turning AI hype into tangible business results, bridging the gap between technological promise and profitable application.