Financial services firms have scaled artificial intelligence across three times more tasks than healthcare providers, according to a new report from business news outlet Pymnts. The gap reveals an industry under operational strain that is deploying AI reactively - chasing immediate relief rather than building long-term infrastructure.
The analysis draws on a survey of 60 verified senior technology executives at U.S. enterprises with at least $1 billion in annual revenue. Pymnts tracked AI adoption across 75 specific tasks in eight business functions, comparing healthcare providers with financial services and media/advertising companies. Insurance firms were grouped with financial services.
"The financial services sector has deeply embedded AI into revenue recognition, credit scoring and sales forecasting," the report's authors write. "Healthcare, by contrast, has concentrated its AI investments in a handful of workforce and operational areas, leaving most tasks unautomated."
Where healthcare is concentrating its AI investments
Customer service chatbots lead at 60% adoption, a domain explored in AI for Customer Support resources. Workforce planning and skills gap analysis follow at 55%, with logistics routing and delivery optimization at 53%. The Pymnts analysts said these choices reflect an industry under acute staffing pressure. "Healthcare is using AI where the pressure is most acute, and right now, that means anywhere it can take duties off the plates of overburdened staff."
The numbers show a sector reaching for tools that absorb demand without adding headcount. Yet the concentration in a few operational areas also signals that AI has not spread into higher-value clinical or strategic functions.
The gaps that tell the real story
Customer journey orchestration - coordinating the full sequence of patient interactions from first contact through ongoing care - sits at just 5%, the lowest figure in the entire survey. Regulatory compliance monitoring, a high-stakes function for any healthcare organization, reaches only 30%. The analysts described this pattern as "managing symptoms rather than building infrastructure."
These gaps suggest that healthcare providers are deploying AI in response to immediate operational pain, not where it would deliver the greatest long-term value. The report notes that healthcare organizations have abundant clinical, operational and financial data, but fragmented systems prevent its consistent use.
Constraints holding healthcare back
Two constraints tie as the top barriers, each cited by 30% of healthcare respondents: system integration challenges and data quality issues. "These organizations sit on enormous volumes of data, but that information lives in disconnected systems that don't easily communicate with one another," the analysts write. "Until a shared language is in place, even high-quality data remains difficult to access and use at scale."
Sixty percent of healthcare respondents fund AI pilots without formal ROI requirements. The Pymnts team frames this as pragmatism rather than recklessness. In a sector facing workforce shortages and fragmented infrastructure, organizations often cannot wait for a rigorous business case. Still, the report adds, "These are the motivations of an industry still in experimentation mode, deploying AI without the governance infrastructure needed to measure what's working."
The broader challenges of AI adoption in healthcare are addressed in AI for Healthcare training, which covers the skills needed to move from isolated pilots to scaled, governed systems.
Why this matters for healthcare professionals
For healthcare leaders, the Pymnts findings underscore a hard truth: AI cannot scale on top of broken data infrastructure. The financial services sector moved faster not because it had better algorithms, but because it had already done the unglamorous work of integrating systems and cleaning data. Healthcare professionals who want AI to deliver more than point solutions need to prioritize that foundational work - building shared data languages, connecting siloed platforms, and establishing governance that measures what actually works. Without it, AI will remain a triage tool for overworked staff, not a strategic asset.
Your membership also unlocks:
