Healthcare IT Today’s annual predictions roundup on AI and automation for 2026 brings together perspectives across the ecosystem—from clinical workflow and bedside automation to back-office transformation and life sciences operations. A consistent theme runs through the predictions: the next phase of AI value depends less on novelty and more on governed deployment, trusted data flows, and measurable outcomes.
What’s in the piece
- Governance becomes the gate: Predictions emphasize that infrastructure readiness and data governance will determine which AI initiatives scale in 2026.
- AI becomes a “teammate” in workflows: Expect copilots and automation that reduce documentation burden, support triage, and strengthen throughput and safety KPIs.
- Integrated systems win: Value is tied to AI that integrates natively into EHR and clinical/revenue workflows—reducing denials, improving margins, and easing burnout.
- Edge and bedside automation grow: A shift toward more reliable, near-point-of-care automation is expected as health systems demand outcomes and operational proof.
- Agentic automation expands: Predictions include AI agents that execute multi-step tasks (e.g., drafting notes, preparing referrals, preparing prior auth materials) under human oversight.
- Automation extends beyond hospitals: Forecasts also cover manufacturing, workers’ comp, specialty pharmacy, and administrative workflows—where automation can increase speed and resilience.
Why it matters
Healthcare leaders are increasingly measuring AI by trust, integration, and outcomes. As automation touches sensitive patient and operational data, teams must ensure security and compliance are embedded in the same pipelines that power AI—so innovation can scale without introducing new risks.
Protegrity POV
Protegrity’s Iwona Rajca frames 2026 as a pivotal year where the systems that combine automation with responsible data protection will advance—shifting the focus from experimenting with models to governing the data that fuels them. Protecting sensitive information through methods such as encryption and anonymization helps data move more safely between cloud environments, medical devices, and AI systems, while building compliance into pipelines. Interoperability standards such as TEFCA are positioned as a key enabler of consistent, controlled data flows.
How Protegrity helps
- Data-centric protection: Apply field-level tokenization and encryption to keep sensitive data protected while remaining usable across systems and workflows.
- Privacy-preserving approaches: Support safer analytics and AI usage with techniques such as anonymization and synthetic data strategies where appropriate.
- Policy-driven control: Align protection with governance requirements so compliance can be operationalized across pipelines and environments.
Key Takeaways
- Modernize with governance: In 2026, scale comes from clean data, clear rules, and workflow-native automation.
- Build trust into the data layer: Protect sensitive information (encryption/anonymization) and enforce controlled exchange (e.g., TEFCA-aligned flows) so AI can move safely across environments.