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Healthcare gets an AI pulse check

ARTICLE | July 9, 2025

Healthcare gets an AI pulse check

Despite data management challenges, healthcare and life sciences companies are using AI to accelerate innovation, according to new research from ServiceNow and NVIDIA

By Evan Ramzipoor, Workflow contributor

Last year, AstraZeneca announced its intention to analyze up to 2 million human genomes by 2026. This project would have been impossible if not for recent advances in AI technology. As more healthcare and life sciences companies leverage AI, “there is enormous opportunity to expedite discovery for treatments and cures and to improve healthcare access,” says Deana Kraft, global head of healthcare and life sciences at ServiceNow.

To get a full picture of how organizations are putting AI to work, ServiceNow surveyed almost 4,500 executives worldwide, including 502 from healthcare and 278 from life sciences companies. Their answers power our second annual Enterprise AI Maturity Index, which measures each respondent’s progress on a 100-point scale. The surprising result: The average score across all organizations dropped nine points from last year.

Healthcare and Life Sciences are investing more in AI

ServiceNow and NVIDIA teamed up to analyze AI maturity across a range of industries and found that in healthcare and life sciences scores declined even more, falling an average of 11 points for both industries.

(Click here to read the full Healthcare AI Maturity Index report.)

The news wasn’t all bad, however. Most of the organizations we surveyed are already seeing benefits from AI adoption. Healthcare companies report a 5.8% increase in gross margins from their use of AI last year, while life sciences organizations are seeing even greater impact (7.4%).

The main challenge is that AI technology is evolving faster than these industries can deploy it. “Although AI should boost performance, most healthcare companies face tremendous economic headwinds,” Kraft says. Given the need to ensure that new technologies don’t put patient health at risk, it makes sense that organizations are “thoroughly evaluating, testing, and retesting potential innovations.”

Despite these challenges, 16% of healthcare and 15% of life science companies are Pacesetters. These top-ranking firms are ahead of the pack on all five pillars of AI maturity: AI strategy and leadership, workflow integration, talent and skills, data governance, and realizing value in AI investment. These Pacesetters had higher scores than other firms in each of these areas, with an average score of 44 for both industries, compared to 32.3 for others.

Pacesetters saw greater bottom-line growth from AI than did their competitors, and they achieved substantial improvements in productivity, competitive positioning, risk management, and speed of innovation.

Here are five best practices that distinguish Pacesetter firms in the healthcare and life sciences industries.

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AI Maturity in Healthcare and Life Sciences

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Healthcare and life sciences companies thrive on innovation. Pacesetters foster innovation by creating a culture of experimentation, encouraging employees to play around with AI applications to solve problems on the job (74% vs. 45% of others).

Pacesetters then translate experimentation into growth by creating innovation centers. More than half of Pacesetters have built AI innovation centers to develop and lead AI transformation, as opposed to 41% of others.

Organizations are increasingly using modern IT platforms to onboard AI solutions across the enterprise. Nearly three-quarters of Pacesetters employ enterprisewide platforms with built-in AI capabilities.

These platforms provide the agility, speed, and customizability that companies need to apply AI at scale, says Rory Kelleher, senior director of global business development for healthcare and life sciences at NVIDIA

“Without a platform, companies seeking to drive AI transformation would be bogged down with internal technical debt, extended development times, and a hodgepodge of systems that don’t communicate,” he says.

 

Pacesetters in the healthcare and life sciences sectors are enthusiastic about human/machine collaboration. More than 70% of Pacesetters in these industries have run AI training programs, hosted learning events, and identified AI champions to drive innovation. Upskilling initiatives primarily seek to develop data scientists (63%), experience developers (59%), and machine learning engineers (56%).

To foster a talent pipeline, Pacesetters work closely with academic institutions. For example, Roche recently launched an innovation center at Harvard's Enterprise Research Campus to leverage the AI talent pool in the Boston area and foster collaboration with Harvard researchers in fields such as AI and data science.

As more healthcare and life sciences companies leverage AI, there is enormous opportunity to expedite discovery for treatments and cures and to improve healthcare access.

Healthcare and life sciences companies handle vast quantities of data. These data are often sensitive and difficult to parse. However, effective data management is crucial for any organization looking to get ahead.

“AI can extract unstructured data from electronic health records and other sources,” says Manish Shah, a chief transformation officer at ServiceNow. “It’s a true opportunity to convert to digital analytics and to help caregivers make better and faster decisions.”

Compared to competitors, Pacesetters have made more progress with data governance schemes that mitigate AI privacy risks—55% have done so, versus only 38% of others.

“AI systems interacting with patients need to be certified and auditable and comply with HIPAA security and privacy standards,” says Jon Cohen, head of life sciences industry for the Americas at ServiceNow. “Pacesetters make sure that the data is staying within systems that have been risk assessed, tested, and hardened.”

Pacesetters are all in on agentic AI, a form of AI that operates with minimal human oversight. Just over a quarter are already using the technology, while under half are considering adopting it in the next year.

To implement agentic AI, companies need to create a process that lets AI learn. It starts with supporting human agents in their work and develops from there, says Cohen. “Then, as AI assimilates that body of knowledge and skills, AI agents start to handle low-risk, high-confidence actions under the supervision of a department manager,” he says. “Over time, AI can begin to handle more complicated cases, creating more and more efficiencies.”

As human supervisors become more comfortable with AI, they can then empower it to act even more autonomously, gleaning even more benefits.

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Author

Evan Ramzipoor is a writer based in California.

Articles by Evan Ramzipoor