Scale AI targets enterprise AI deployment gap with healthcare use cases
Enterprises shift from AI pilots to real-world deployment as regulated sectors prioritise reliability, integration, and measurable outcomes
The next phase of artificial intelligence (AI) adoption is shifting from model capability to execution, as enterprises struggle to turn powerful systems into usable, reliable applications.
This gap is becoming the industry’s defining challenge, particularly in complex environments where accuracy, trust, and workflow integration are critical. As companies move beyond experimentation, the emphasis is shifting toward embedding AI into core business processes rather than running isolated pilot projects.
“There’s actually a pretty big gap between the model capability and the user of the model. What people now call agents is essentially about building real use cases for end customers to truly unleash that capability,” said Emily Xue, Head of Enterprise AI at Scale AI.
“To close that gap, you need to understand what the model can do and how to steer it, but also deeply understand the customer. Connecting these two sides is not easy.”
The shift toward execution is forcing enterprises to rethink workflows, data integration, and how success is measured. Rather than focusing solely on model performance, organizations are prioritizing reliability, explainability, and operational fit.
This pressure is most visible in healthcare, where AI systems must operate within strict safety and operational constraints.
“We treat reliability and the quality of our deliveries very highly. We focus on high-stakes, high-impact domains where trust really matters,” Xue said. “We work with one of the largest medical centers in the United States, Mayo Clinic, and help reduce administrative workload and the cognitive load on physicians.”
“Doctors often need to review hundreds of pages of medical records before treating a patient. Our agent helps extract the relevant information and present it in a clear way, reducing a lot of review time.”
Beyond clinical workflows, deployments are also streamlining patient intake. Context-aware systems reduce repetitive questioning and improve patient experience. These early use cases are helping build trust in environments where errors carry significant consequences.
From data to deployment
Xue told TechJournal.uk in an interview during the AI and Business Innovation Summit, organized by Economist Impact, in London on March 25. The discussion focused on how enterprises can operationalize AI at scale and move beyond fragmented tool adoption.
Scale AI, based in San Francisco, was established in 2016 as a data annotation provider supporting early machine learning systems, particularly in computer vision and autonomous driving.
“Scale AI started with data labeling for very data-heavy machine learning models like vision systems. That was the foundation of our business,” she said.
The company later expanded into supporting large language models (LLMs) through reinforcement learning with human feedback (RLHF), a training method in which human evaluators guide model outputs to improve performance, reasoning, and safety alignment.
“What truly makes ChatGPT work is RLHF, where human feedback is used in post-training to improve model quality and safety alignment,” Xue said. “Scale AI was actually the first data partner to work with OpenAI, providing annotated human data to help large language models become usable.”
Scale AI has since evolved into a broader AI infrastructure provider. Its services now span data annotation, model evaluation, and enterprise software for building and deploying AI applications. Its Safety, Evaluation and Alignment Lab focuses on model alignment and benchmarking, while its platforms support both the private and public sectors and customers.
Beyond early language tasks, the company now supplies increasingly complex training data, including reasoning datasets used to improve model performance on multi-step problems. It also supports customers with deployment engineering, helping integrate models into enterprise systems, connect data pipelines, and meet security and compliance requirements.
Xue said Scale AI works with global customers, including large UK-based enterprises and organizations in the Middle East, reflecting demand for localized, domain-specific AI implementations.
The company supports the full lifecycle of AI adoption, including data preparation, model evaluation, system integration, and production deployment. Its role increasingly centers on helping enterprises operationalize AI systems within existing infrastructure and regulatory constraints.
It operates across multiple business lines, including enterprise AI deployment, government applications, and foundational model support. It also runs large-scale data operations through subsidiaries specializing in training data for computer vision and large language models.
The company’s founder, Alexandr Wang, is now the Chief AI Officer at Meta Platforms. He identified early the importance of high-quality training data and human feedback in building scalable AI systems.
This positioning has enabled Scale AI to act as a bridge between model developers and enterprise users. As organizations look to translate general-purpose models into domain-specific applications, demand for this intermediary role is increasing. The company’s expansion into Europe and the Middle East reflects that trend.
Governance challenges
As foundation models become more widely adopted, questions around governance, ethics, and ownership are becoming more prominent.
“Foundation models have, to some extent, become a public asset. It’s almost like electricity in how people rely on it,” Xue said.
“They need to bring public value and hold a high ethical standard. But what defines ‘good’ is still evolving, and there are large gray areas.”
“There are still areas we don’t fully understand, like user privacy and data ownership. These are questions that will need regulatory and industry alignment over time.”
The debate is being shaped by growing government involvement in AI development and deployment. This raises questions around access, control, and cross-border data flows. Enterprises operating globally must navigate emerging regulations while maintaining consistent standards for safety and compliance.
Execution reality
Despite concerns about automation, enterprise AI adoption is increasing demand for skilled workers rather than reducing it.
“AI is improving the velocity and quality of delivery for people already in the company. We actually have more demand than we can meet,” Xue said. “It’s not about reducing people. It’s about needing both people and AI to make teams more effective.”
“Code generation is only one part of the problem. You still need to understand use cases, design products, test systems, and monitor performance in production.”
Enterprise deployments require multidisciplinary teams that combine technical expertise with domain knowledge, particularly in regulated industries. This is driving demand for roles that sit at the intersection of engineering, operations, and business strategy.
Looking ahead, the company’s priority is to translate AI capabilities into measurable business outcomes.
“Our goal this year is to make AI actually produce business impact,” Xue said. “To do that, we need to build foundational technologies and work closely with high-stakes customers.”
She said success will depend on balancing domain expertise with execution discipline as enterprises move from experimentation to scaled deployment. Companies that integrate AI into core workflows, rather than treating it as a standalone tool, are likely to see the most significant gains.