In the rush for AI gold, healthcare is getting left in the dust by finance

If the economy were an inter-sector track meet in which AI adoption propels the runners, healthcare would be getting repeatedly lapped by financial services. 

That’s because financial services firms have scaled AI across three times more tasks than healthcare providers. 

The business news outlet Pymnts analyzes the race in a new report. 

“The financial services sector has deeply embedded AI into revenue recognition, credit scoring and sales forecasting,” the authors write. “Healthcare, by contrast, has concentrated its AI investments in a handful of workforce and operational areas, leaving most tasks unautomated.”

Pymnts arrived at its play-by-play coverage and color commentary after surveying 60 verified senior technology executives at U.S. enterprises with at least $1 billion in annual revenue.

The analysts divided contestants into three equally sized groups. Along with healthcare providers and financial services, they also looked at media and advertising. (And they lumped insurance companies with financial firms.) 

The survey tracked adoption across 75 specific AI-supported tasks spanning eight business functions, the authors state, noting the approach allowed for mapping of “not just whether companies [and organizations] are using AI but also where and how deeply.”

Among the analysts’ observations of AI adoption in healthcare are these five. 
 

1. All three sectors report some AI use across every function surveyed. The divergence lies in whether AI is a tool that a few teams experiment with or something most of the organization depends on. 

Financial services firms cross that threshold often, while healthcare and media are still in the early stages, the authors found. 

“What makes this finding particularly striking is that it isn’t about technological access or organizational enthusiasm,” they write. “It’s about the internal stuff: messy data in financial services, siloed systems in healthcare and, in media, a lack of basics such as clear governance and leadership buy-in.” The authors add: 

‘Fix those, and AI can potentially scale. Leave them unaddressed, and it can stagnate as a support tool.’

2. Of all the places healthcare organizations have concentrated their AI investments, the leading use case is customer service chatbots, at 60% adoption. 

That number reveals more about the workforce than about technology, Pymnts remarks. 

“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 analysts point out. They report that healthcare workforce planning/skills gap and model development/training each follow at 55%, followed by logistics routing and delivery optimization at 53%.

‘These use cases reflect an industry under operational strain, reaching for tools that can absorb demand without adding headcount.’

3. In healthcare, the gaps are just as telling. Customer journey orchestration (coordinating the full sequence of interactions a patient moves through, from first contact to ongoing care) sits at just 5%—the lowest figure in the entire survey. 

Regulatory compliance monitoring, which tracks whether an organization is meeting its legal obligations—arguably one of the highest-stakes functions in any healthcare organization—reaches only 30%, Pymnts reports. 

The numbers show that healthcare providers are deploying AI “reactively, as a response to an immediate operational challenge rather than as long-term strategic design,” the analysts state. “Healthcare [providers] have abundant clinical, operational and financial data, but fragmented systems prevent its consistent use.” 

The result is that, in healthcare, AI is “managing symptoms rather than building infrastructure.” 

‘The tools are going where the immediate operational pain is most acute, not where they would deliver the greatest long-term value.’

4. Sixty percent of healthcare respondents cite pilot funding without formal return on investment (ROI) requirements as a justification. This means they’re committing a budget to AI without requiring proof it will pay off. 

That isn’t recklessness so much as pragmatism, the Pymnts analysts write. In a sector under immediate operational strain, they note, “facing workforce shortages and fragmented infrastructure can’t always wait for a rigorous business case before acting.”

‘These are the motivations of an industry still in experimentation mode, deploying AI without the governance infrastructure needed to measure what’s working.’ 

5. Healthcare faces two equally binding constraints, each cited by 30% of 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,” Pymnts observes before adding: 

‘Until a shared language is in place, even high-quality data remains difficult to access and use at scale.’

The authors conclude their comments on healthcare AI adoption by underscoring the two-track nature of the sector’s challenge.

“System integration and data quality must improve in parallel,” they write. 

‘Unlike financial services, healthcare doesn’t yet have mature AI systems waiting around for better inputs. The infrastructure itself needs building.’

The report is available in full for free.