60% of Healthcare Firms Use Chatbots Before Further Automation

Healthcare organizations are deploying enterprise artificial intelligence primarily as a pressure valve to alleviate operational strain rather than as a tool for immediate, system-wide transformation. According to the PYMNTS Intelligence report, The Enterprise AI Benchmark Report, healthcare firms are prioritizing targeted AI use cases—such as customer service chatbots and workforce planning—to address immediate friction points while trailing financial services in total task adoption.

Where is healthcare applying AI today?

Healthcare firms are focusing their AI investments on three specific areas to manage patient demand and operational complexity, according to data from a March survey of 60 senior technology executives. Currently, 60% of healthcare organizations utilize AI for customer service chatbots and virtual agents to streamline routine interactions. Additionally, 55% of firms apply AI to workforce planning and skills gap analysis to optimize staffing, while 53% leverage the technology for logistics routing and delivery of medications and supplies.

Pro Tip: Focus on automating high-volume, low-complexity tasks first. The data suggests that healthcare organizations gain the most immediate relief by using AI to handle routine patient inquiries before attempting complex clinical integrations.

How does healthcare AI adoption compare to other sectors?

Healthcare’s approach to AI is more disciplined and narrow compared to the financial services and insurance sectors. PYMNTS Intelligence reports that healthcare firms have reached high adoption in 10 specific tasks. In contrast, financial services firms have achieved high adoption in 27 tasks. This disparity indicates that healthcare is prioritizing foundational stability, using AI to solve specific bottlenecks before attempting to scale the technology across broader clinical or administrative workflows.

What challenges remain for widespread AI integration?

Despite current momentum, significant gaps in infrastructure hinder deeper AI adoption. According to PYMNTS Intelligence, customer journey orchestration is used by only 5% of healthcare firms, suggesting a lack of connectivity across the patient experience. Furthermore, regulatory compliance monitoring remains at 30%, and data labeling—a critical requirement for reliable AI—stands at 25%. Industry leaders must improve these data inputs and system integrations to move beyond isolated use cases toward a more cohesive operating model.

Are healthcare firms increasing their AI budgets?

Investment in AI is expected to rise over the next 12 months, with 60% of healthcare firms planning to increase their spending, according to PYMNTS Intelligence. Organizations are currently funding these pilots through two primary drivers: 60% of firms utilize funding that carries no formal return on investment (ROI) requirement, while 50% of firms are explicitly targeting productivity and efficiency gains. This spending pattern suggests that executives are in a learning phase, testing AI’s efficacy before committing to long-term, large-scale budgets.

Did you know?

While chatbots are the most adopted AI tool in healthcare, only a small fraction of firms have successfully connected these tools to the full patient journey, leaving significant room for future growth in orchestration technologies.

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Frequently Asked Questions

Why is healthcare prioritizing chatbots over other AI tools?

According to PYMNTS Intelligence, chatbots serve as a high-impact “pressure valve” that absorbs routine requests, allowing human staff to focus on more complex patient needs.

What is the biggest barrier to scaling healthcare AI?

The data indicates that insufficient data labeling (25% adoption) and a lack of integrated customer journey orchestration (5% adoption) are the primary hurdles preventing a broader, more unified AI strategy.

Is healthcare AI meant to replace medical staff?

The current application of AI in healthcare is designed to support staff by accelerating workflows, identifying patterns, and simplifying administrative tasks rather than replacing clinical judgment.

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