AI in Healthcare: 3 Predictions for 2026

If 2025 taught the healthcare industry anything, it’s that it is no longer playing catch-up — it’s quite literally jumping over previous limitations. Many companies are putting aside a gradual approach to digital evolution. They are diving straight into AI-native operations, bypassing decades of legacy systems and solutions. 

This leapfrog moment is driven by intense external pressures, including policy changes to ACA subsidies that are reshaping how payers calculate risk, an ongoing staffing crisis and acute economic pressures —making AI-enabled efficiency increasingly seen as an important lever for addressing short- and long-term financial and operational pressures. These forces have created a climate where the technological status quo is simply unsustainable.

At the heart of this transformation is a fundamental shift in how the industry views its most strategic asset: data. The growing sophistication of AI solutions is shining a bright light on high-quality, multimodal data that many organizations have been sitting on for years. They now recognize it as essential training fuel for ML models, a foundation for trusted AI, and a potential revenue stream for delivering future efficiencies and innovations.

To seize this opportunity and move from merely piloting technology to delivering measurable return on investment (ROI), executives across the industry must focus on definitive, strategic shifts. Based on what we’re seeing across the industry, here are the top three predictions we believe will define success for the healthcare ecosystem in 2026.

1. The definitive shift to governed, agentic AI for measurable ROI

Across the industry, AI deployment is moving from experimental pilots to integrated, autonomous agents operating within core, high-value workflows — all working under  strict governance controls and the need to demonstrate value to the organization. Healthtech, in particular, has the mandate to build AI-first, workflow-native products that are infused into business processes — not just standalone features. This requires making governance, including drift monitoring and bias detection, a core capability of the technology platform.

2. Data interoperability becomes the foundation for value-driven ecosystems

The era of siloed data is collapsing. True interoperability, driven by standardized access and quality data, is necessary to supply sophisticated AI models with the scope of insights required to unlock value-based care (VBC) across the entire patient journey.

• For healthcare payers and providers: Interoperability is the catalyst for success in value-based care. It enables the creation of both a longitudinal and 360-degree view of the patient and their journey, which is essential for accurately calculating risk, making data-driven care decisions, measuring outcomes and effectively managing population health.

• For health techs: Implementing interoperability at scale enables them to deliver solutions for and more effectively collaborate with payers and providers. As a result, these organizations can better achieve the required projects of VBC initiatives, for example, patient journey mapping, creating a master patient index and so on. 

3. Data as a strategic asset to navigate market volatility

High-quality, multimodal data is no longer a byproduct of healthcare research but a critical, high-value financial asset. It’s now understood as essential training fuel for AI’s machine learning models (which underpin all AI solutions) as well as a source of competitive and strategic advantage against the current backdrop of intense market and economic pressures.

• For healthcare providers: Current cost pressures require them to lean into AI-driven models to drive operational and clinical efficiencies (doctors’ notes transcription, staffing forecasting, etc. ) that rely on high-quality, near real-time data to help ensure financial sustainability and reduce operational burden.

• For healthcare payers: The volatility from policy changes (for example, ACA subsidy shifts) mandates the use of data for rapid-response risk management and personalized member engagement to help manage churn and secure member loyalty.

• For health techs: The focus is on unlocking insights from deidentified health data and real-world data to generate new potential revenue streams. The strategy is to leverage secure data clean rooms and platform architectures where payers and providers can bring their analytics to the data, eliminating the need to transfer raw protected health information. Using this approach allows them to improve their VBC initiatives, for example.

Do you want to explore our healthcare predictions in more depth, watch our Healthcare and Life Sciences AI + Data Predictions 2026 webinar.