From Experimentation to Execution: What Vendors Really Think About Generative AI in Healthcare 

Generative AI has moved rapidly from hype to habit in healthcare IT. Over the past two years, vendors have announced pilots, launched new features, and rebranded roadmaps around large language models and foundation AI. But beneath the surface enthusiasm, an important question remains: how confident are vendors really, and what is now holding adoption back?

To answer this, Signify Research conducts its Vendor Sentiment Index (VSI), a quarterly survey capturing the views of healthcare IT vendors on generative AI adoption, confidence, barriers, and value creation. The Q4 2025 edition offers one of the clearest signals yet that the market is entering a new phase.

This is no longer a story about whether generative AI will matter. It is a story about execution, integration, and commercial reality.

Confidence is rising, but it’s becoming more grounded

Vendor confidence in generative AI strengthened further in Q4 2025 across short-, medium-, and long-term outlooks. Near-term confidence (three and twelve months) rebounded sharply from mid-year caution, while five-year confidence remains near-unanimous.

However, this confidence looks different from earlier waves. Optimism is no longer abstract or speculative; it is increasingly tied to market products, customer implementations, and real-world operational use cases.

Long-term belief in generative AI as a foundational capability is effectively locked in. The divergence now lies in how quickly value can be delivered and captured.

Availability and implementation are accelerating, unevenly

A growing share of vendors now report that generative AI features are already commercially available, with most others targeting availability within the next 24 months. At the same time, customer implementation timelines are shortening, particularly among Digital Health and EMR vendors, where deployments are increasingly live or underway.

Medical and Clinical IT vendors show longer timelines, but not weaker intent. Their slower pace reflects higher integration complexity, validation requirements, and regulatory exposure, rather than lack of demand.

In short: adoption is moving forward, but not uniformly.

The biggest barriers have changed

One of the most important findings from the Q4 VSI is what no longer dominates the conversation.

Regulatory uncertainty and basic model efficacy, once the primary blockers, have eased in relative importance. Instead, the leading barriers are now:

1. Integration with existing systems
2. Data privacy and security
3. Operational fit and execution complexity

This shift is telling. Vendors are no longer asking, “Is this allowed?” or “Does this work at all?” They are asking, “Can this be embedded into real workflows, governed at scale, and supported economically?”

Value perception has become pragmatic

Vendor views on where generative AI delivers value have also matured.

In Q4 2025, operational efficiency remains the clear top priority; automation, workflow simplification, and productivity gains dominate. Provider experience now ranks second, reflecting mounting pressure to address clinician workload and burnout.

Notably, patient outcomes have dropped to fourth place. This does not imply reduced importance, but rather a recognition that outcomes are downstream benefits, dependent on successful operational and workflow integration.

Revenue generation continues to rank last. Generative AI is increasingly viewed as a margin protection and efficiency lever, not a near-term growth engine.

Monetisation is starting, but remains uneven

Most vendors still derive only a small share of total product value from generative AI. However, a growing minority now report that AI contributes materially, over 50%, to their product value.

This divergence highlights a widening gap between:

• vendors still proving ROI and integrating AI incrementally, and
• those successfully embedding AI into core workflows and pricing models.

The market is not moving at one speed—and that gap is becoming strategically important.

Strategy remains diversified, not convergent

There is no single “right” approach to generative AI development. Digital Health and EMR vendors tend to prioritise off-the-shelf models, prompt engineering, and speed to market. Medical and Clinical IT vendors more often pursue fine-tuning or proprietary models to meet accuracy, validation, and compliance requirements.

Functionally, adoption is converging around workflow automation, documentation, orchestration, and system-adjacent support, rather than autonomous clinical decision-making.

What this means

The Q4 2025 VSI marks a clear inflection point. Generative AI in healthcare IT has moved past its exploratory phase and into one defined by execution discipline.

Success is now shaped less by model sophistication and more by:

• integration depth,
• workflow fit,
• governance and oversight, and
• clear economic value.

Vendors that can translate confidence into deployment, and deployment into measurable outcomes, will define the next phase of the market.

Signify Research’s Medical Imaging team formulates expert market intelligence for some of the leading Ultrasound, CT, MRI, and X-ray vendors. 

Signify Research’s AI in Healthcare team delivers in-depth market intelligence and insights across a breadth of healthcare technology sectors.