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Snowflake Research Reveals 85% of Healthcare Leaders View Interoperability as Foundational to Scaling AI

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Key Terms

interoperability technical
Interoperability is the ability of different systems, devices, or software to work together smoothly and share information easily. It matters to investors because it enables more efficient operations, better data sharing, and faster decision-making across various platforms or technologies. When systems are interoperable, they can connect and communicate as if they were part of a single, unified system, reducing complexity and increasing overall effectiveness.
generative technical
Generative describes technology or systems that create new content, designs, data or outputs by learning patterns from existing examples — for instance generating text, images, code, or synthetic data. It matters to investors because generative tools can enable new products, reduce costs, speed innovation and disrupt business models, while also bringing risks such as quality errors, misuse and regulatory scrutiny; think of it as a machine that mixes known ingredients to produce novel recipes.
agentic ai technical
Agentic AI refers to computer systems that can make their own decisions and take actions without needing someone to tell them what to do each time. It's like giving a robot a degree of independence to solve problems or achieve goals on its own, which matters because it could change how we work and interact with technology in everyday life.
value-based care medical
A health-care delivery approach that rewards providers for keeping patients healthy and improving outcomes instead of charging for each test or visit. For investors, it matters because it shifts where profits and losses come from—favoring providers and technologies that lower long-term costs, prevent complications, and demonstrate measurable results; think of it like paying a contractor only when the house stays sound, which changes who wins and loses financially.
revenue cycle operations financial
Revenue cycle operations are the end-to-end processes a healthcare provider uses to turn services into paid revenue — from registering a patient and coding services, to submitting insurance claims, collecting payments and handling denials. For investors, efficient revenue cycle operations mean steadier cash flow, higher effective reimbursement and lower write-offs, similar to a well-tuned collection system that ensures money owed actually reaches the company’s bank account.
data governance technical
Data governance is the set of rules and practices that ensure information is accurate, consistent, and used responsibly within an organization. It is like a well-organized library system that keeps track of all the books, making sure they are correct, easy to find, and used properly. For investors, strong data governance helps ensure that the information they rely on is trustworthy and decisions are based on reliable data.
restricted stock units financial
Restricted stock units are a type of company reward where employees are promised shares of stock, but they only fully own these shares after meeting certain conditions, like staying with the company for a set time. They matter because they can become valuable assets and are often used to motivate employees to help the company succeed.
rule 10b5-1 trading plan regulatory
A Rule 10b5-1 trading plan is a pre-arranged schedule that allows company insiders to buy or sell stock at specific times, even if they have inside information. It helps prevent accusations of unfair trading by making these transactions look planned and transparent, rather than sneaky or illegal.
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Findings underscore a turning point in AI adoption in healthcare, where achieving measurable efficiency and cost savings with AI depends on breaking down data silos across fragmented systems

  • 85% of healthcare and public health agency leaders report that improving data sharing and interoperability is a higher priority today than it was two years ago as organizations scale AI and pursue broader operational and value-based care goals
  • 77% of organizations have already invested or plan to invest in generative or agentic AI technologies, prioritizing high-impact use cases such as administrative workflow automation, clinical documentation, and revenue cycle operations
  • More than half of respondents expect AI to deliver time savings of 10-50%, while 42% anticipate moderate cost savings as initiatives mature

MENLO PARK, Calif.--(BUSINESS WIRE)-- Snowflake (NYSE: SNOW), the AI Data Cloud company, in collaboration with Hakkoda, an IBM company, today released new research revealing that as healthcare organizations and public health agencies scale AI beyond pilot programs, improving interoperability is becoming foundational to broader AI deployment. With 77% of organizations investing in generative or agentic AI, 85% of leaders report that improving interoperability — the ability to securely share and use data across clinical, administrative, and financial systems — has become a higher priority over the past two years as they work to scale AI.

​​“Across healthcare, AI is moving into operational environments and leaders are holding it to a higher standard,” said Jesse Cugliotta, Global Head of Healthcare and Life Sciences, Snowflake. “Organizations want measurable efficiency gains, workforce relief, and better patient outcomes. That only happens when clinical, financial, and operational data can move securely and seamlessly across systems. Interoperability is no longer a compliance checkbox — it’s the engine that makes scalable AI possible.”

AI Investment Expands Across Core Healthcare Workflows

AI investment is accelerating across healthcare organizations, with 77% of respondents reporting they have already invested or plan to invest in generative or agentic AI technologies. Organizations are prioritizing high-impact use cases such as administrative workflow automation (60%), clinical documentation and scribing (50%), and revenue cycle operations, including billing and prior authorization (47%).

These priorities reflect the mounting pressure on healthcare organizations to reduce administrative burdens and support clinicians, enabling more time for direct patient care. By automating these workflows, organizations aim to increase revenue cycle efficiency amid persistent workforce constraints and reimbursement complexity. As a result, AI investment is shifting toward practical applications shaped by internal priorities.

Interoperability Becomes Critical as AI Scales

As AI initiatives expand, 85% of healthcare leaders report that improving data sharing and interoperability has become a higher priority compared to two years ago. Operational efficiency and decision making (74%), improving the patient experience (71%), and helping drive value-based care (64%) were cited as the primary drivers behind increased focus on interoperability.

This shift reflects a broader change in how healthcare organizations view interoperability — not only as a compliance requirement but as a strategic enabler of scalable AI initiatives. Compared to Snowflake’s 2023 survey, where improving patient care and coordination ranked as the top driver, operational efficiency and decision-making now lead as primary motivators for interoperability. While internal data sharing is already widespread at 82%, leaders acknowledge that connecting systems across departments and partners will be critical to expanding AI across the organization.

Leaders Expect Measurable Returns

Healthcare organizations increasingly expect AI investments to deliver quantifiable results. Fifty-two percent of respondents anticipate time savings between 10% and 50%, while 42% expect moderate cost savings as AI initiatives mature.

These findings reflect a shift toward evaluating AI through a more operational lens, with leaders placing greater emphasis on measurable productivity and efficiency gains. Respondents also cite mature data governance as an important contributor to AI effectiveness, underscoring the role of trusted, connected data in supporting scalable AI.

“AI is rapidly becoming embedded in mission-critical healthcare workflows,” said Chris Puuri, Global Head of Healthcare and Life Sciences at Hakkoda, an IBM Company. “The organizations that will see returns are the ones tackling data fragmentation head-on and building interoperable foundations. That’s what turns AI investment into measurable efficiency, financial strength, and better patient outcomes.”

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Methodology

The 2026 Future of AI + Interoperability in Healthcare Report is based on responses from 183 US senior healthcare leaders across providers, payers, health systems, non-profit health organizations and public health agencies. The online survey was conducted in collaboration with Hakkoda, an IBM Company, between October 8, 2025 and January 12, 2026. Figures in this release have been rounded for ease of presentation.

About Snowflake

Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 13,300 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW).

Media Contacts
Courtney Sbrocca
Customer & Industries PR Specialist, Snowflake
press@snowflake.com

Source: Snowflake Inc.

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