For decades, healthcare IT has grappled with the tension between innovation and regulation, between transformation and risk. Today, that tension has reached a boiling point—and nowhere is this more evident than in the conversation around artificial intelligence.

32% of hospital CIOs identified AI and machine learning as their top health IT priorities in 2023, a substantial increase from 6% in 2022, according to a survey by Stoltenberg Consulting.

A survey by Healthcare IT Leaders revealed that 66% of hospitals and health systems plan to increase AI spending over the next one to three years, with 64% initiating AI pilot projects within the next 12 to 24 months. ​

This momentum is driven by the potential of AI to address critical challenges in healthcare, particularly the administrative burdens that contribute to clinician burnout. The American Medical Association’s 2023 survey found that two-thirds of physicians are already using AI tools, primarily for administrative tasks, indicating a growing acceptance of AI in routine healthcare operations.

This momentum isn’t simply hype—it’s driven by urgent need.

The Administrative Burden Is Unsustainable

Healthcare is one of the few industries where professionals still spend as much time with paperwork as they do with people. From clinical documentation to billing reconciliation to regulatory compliance, the administrative burden is relentless. For every physician or nurse burned out by late-night EHR sessions, there’s an IT team buried under manual workflows, disconnected systems, and data entry tasks that should have been automated years ago.

AI, when implemented thoughtfully, offers a release valve. It can read, extract, summarize, and route document-based data in ways that reduce human error and accelerate decision-making. And in healthcare, where timing can be the difference between effective treatment and delayed care, that speed matters.

The Reality: AI That Works Starts with the Mundane

There’s a misconception that AI’s greatest value in healthcare lies in predictive modeling or algorithmic diagnostics. While those headlines draw attention, the true ROI often begins with lower-profile wins—document classification, intelligent routing, smart redaction, and data extraction.

These are not glamorous use cases, but they are high-impact. Automating them reduces hours of manual labor, ensures greater data accuracy, and—perhaps most critically—lays the foundation for broader AI transformation.

Take document management: PDFs still underpin vast swaths of healthcare operations, from claims and contracts to discharge forms and HIPAA-compliant communication. Integrating AI-powered automation into these workflows—such as summarizing referral notes, redacting protected health information, or extracting key values from forms—can radically simplify the burden on staff while ensuring compliance.

Why Caution Is Healthy—and Necessary

Despite the clear momentum, CIOs are treading carefully. The integration of AI into clinical and administrative workflows brings up tough questions: How do we ensure data quality? What are the ethical implications of algorithmic bias? How can we secure protected health information in AI environments?

These are valid concerns—and they’re why healthcare IT leaders aren’t just looking for AI. They’re looking for AI with guardrails: tools that are transparent, accountable, and interoperable. Tools that don’t train on private health data. Tools that don’t disrupt existing workflows but enhance them.

Laying the Infrastructure for Long-Term Impact

For AI to scale in healthcare, it must plug into the infrastructure already in place—EHRs, CRMs, cloud storage, and yes, the document workflows that tie it all together. CIOs are realizing that AI doesn’t have to start with a moonshot. It can start with a form. A contract. A signature. A redaction.

And that’s where companies like Foxit are stepping in—not just to provide AI, but to integrate intelligent automation into the everyday. Whether it’s using AI to instantly summarize lengthy documentation, route files to the right department, or ensure PHI is redacted before sharing, these are the real-world, compliance-ready applications that make AI viable at scale.

The Bottom Line

AI in healthcare isn’t coming—it’s already here. But adoption will belong to those who treat it not as a silver bullet, but as a strategic extension of existing operations. CIOs know the opportunity is real. Now it’s about putting it into practice—securely, intelligently, and sustainably.

Because in the race toward digital transformation, the organizations that win won’t be the ones who talk about AI the most. They’ll be the ones who figured out how to use it to make the small things faster, the complex things simpler, and the critical things automatic.