60% of Healthcare Firms Use AI for Chatbots

It’s about trying to relieve the pressure those professionals encounter on a daily basis.

That is the clearest healthcare takeaway from “Financial Services Pulls Ahead in the Enterprise AI Race,” a PYMNTS Intelligence report based on a March survey of 60 senior technology executives at enterprises in the United States with at least $1 billion in annual revenue.

The report compares AI adoption across financial services and insurance, healthcare, and media and advertising. All three sectors use AI across the 75 tasks studied, but healthcare has reached high adoption on just 10 tasks, compared with 27 in financial services and insurance and 16 in media and advertising.

That narrower footprint does not mean healthcare is standing still. It means the sector is applying AI first to the places where the strain is most visible. Healthcare firms are using AI to manage customer service demand, workforce planning, model development and logistics. Those are not abstract use cases. They are the kinds of operational pressure points that can affect patients, employees and costs at the same time.

The pattern suggests a practical form of AI adoption. Healthcare executives appear less focused on broad enterprise transformation than on targeted relief. In a sector known for fragmented systems, heavy regulation and labor constraints, that may be the more realistic starting point.

Key healthcare findings include:

• The share of healthcare firms that use AI for customer service chatbots and virtual agents is 60%. That is the sector’s highest-reported AI use case and reflects the pressure to manage high volumes of patient, member and customer interactions more efficiently.
• The share of healthcare firms that use AI for workforce planning and skills gap analysis is 55%. That finding points to AI’s role in helping healthcare organizations understand staffing needs and better allocate talent across complex operations.
• Over the next 12 months, 60% of healthcare firms are increasing AI budgets. Many are still in an exploratory phase, with 60% citing pilot funding without formal return-on-investment requirements as a driver.

The optimistic read is that healthcare is putting AI where it can help with immediate operational strain. Chatbots can handle routine inquiries. Workforce planning tools can help organizations see staffing gaps earlier. Logistics tools can improve routing and delivery in supply chains that support care delivery.

However, the report also shows why healthcare’s AI expansion may be slower than in other sectors. The industry has not yet scaled AI deeply across many business functions. Regulatory compliance monitoring sits at 30%, despite the sector’s compliance burden. Customer journey orchestration is only 5%. In-product recommendations and guidance are at 20%. Those figures suggest healthcare has not yet connected AI adoption to a broader, end-to-end experience strategy.

The main barriers are structural. Healthcare executives cite integration with existing systems and data quality and fragmentation as the sector’s two biggest AI obstacles, each at 30%. That is a practical problem.

Healthcare organizations often operate across clinical, operational and financial systems that were not built to work together easily. AI cannot reach its full value when the data it needs is siloed, inconsistent or hard to access.

That makes the next phase less about adding more AI tools and more about connecting the underlying infrastructure. Healthcare organizations appear willing to invest, and the use cases are real.

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.