As companies rush to integrate artificial intelligence into their operations, Matt Wood has observed an unexpected pattern: the fastest adopters are often firms traditionally viewed as slow to change.
Wood, global vice-president of AI products at Amazon Web Services (AWS), says the rapid adopters include regulated sectors such as health care, life sciences, financial services, insurance and manufacturing. That trend surprised him, given the reputation of many of these industries for cautious, slow-moving change.
“If you’d have told me a year and a half ago that 160-year-old life insurance companies were going to be in the vanguard of artificial intelligence usage, I probably would have been a bit surprised, but that’s turning out to be the case,” Wood said, citing the example of Sun Life Financial Inc. during an interview after a visit to Toronto for the Collision tech conference.
This shift challenges assumptions about who embraces innovation first. Across industries, organizations are evaluating how AI—especially generative AI—can boost productivity, improve customer outcomes and accelerate digital transformation.
How different industries are using AI
Regulated organizations are finding practical, high-value use cases for AI. In the life insurance sector, for instance, companies are applying AI to review decades-old policies to identify liabilities and risks that may materialize when claims are paid in the future. By parsing large volumes of historical policy documents, AI helps insurers surface patterns and projections that would be slow or impossible to compile manually.
In health care, clinicians are using AI tools to transcribe doctor-patient conversations and generate concise appointment summaries. According to Wood, blind tests revealed that care providers preferred AI-generated summaries over human-written ones about seven out of 10 times—an indication of how quickly the technology can improve workflow efficiency and documentation quality.
Manufacturing and life sciences organizations are also leveraging AI to analyze research, clinical trials, maintenance logs and production data. The common thread is access to deep data sets: these industries often hold extensive internal records, experiment results and operational histories that can fuel powerful AI-driven insights when properly governed.
Why regulated sectors have moved quickly
Wood identifies several reasons regulated industries have surged ahead in AI adoption. The first is data availability: many of these organizations possess large, private collections of structured and unstructured data that AI models can analyze to discover connections, trends and anomalies.
Equally important is that regulated companies have already established robust data governance and privacy practices. They understand what data they hold, how it can be used, who can access it and what compliance constraints apply. That prior work reduces the friction in deploying AI responsibly, enabling faster, safer experimentation and rollout.
“They’ve already figured out … what data they have, what it can be used for, who it can be used by, what tools it can be used with, all those sorts of things,” Wood said. That clarity gives them a head start compared with organizations that haven’t yet grappled with these issues.
Wood also points out a perception challenge among some organizations: a belief that succeeding with generative AI requires sacrificing data privacy. “There is a kind of schism in some customers’ minds that in order to be successful with generative AI, you have to make some sort of negative trade-off when it comes to the privacy of the data that you’re using,” he said. That view, he added, stems partly from cases where data handling was careless or poorly governed.
Balancing privacy and potential
Wood emphasizes there are practical ways to balance data protection with AI’s potential. Many companies work only with anonymized or de-identified datasets, while others create secure, isolated environments for testing AI models so sensitive information does not leak or get used to train public models.
AWS, he noted, does not use data from paid corporate customers to train its underlying models and provides customers with control over where data resides, how it moves and which networks it uses. The company also maintains policies to prevent internal or third-party staff from reviewing client prompts, reinforcing customer control over sensitive inputs.
Beyond technical controls and contractual safeguards, the impetus for regulated firms to adopt AI also comes from a desire not to be left behind. Many of these organizations watched earlier waves of digital transformation occur in other sectors and now see generative AI as an opportunity to catch up or even leapfrog competitors.
“They’re looking at generative AI not just as a way to kind of catch up, but as a way to leapfrog, significantly kick-start that digital transformation,” Wood said. For many regulated industries, AI is becoming a strategic means to enhance decision-making, streamline operations and deliver better outcomes while maintaining the safeguards required by regulators and customers.
As AI tools mature, the combination of deep, private datasets, established governance practices and careful deployment strategies will likely keep regulated sectors among the leaders in practical, responsible AI adoption.