The healthcare industry has become a major testing ground for artificial intelligence, but Kam Thindal believes the sector’s transformation will look slower and more operational than many headlines suggest.
Thindal, Managing Director of Core Capital Partners, says healthcare’s complexity makes rapid disruption unlikely. Unlike consumer software markets, healthcare organizations must balance efficiency with patient safety, privacy, regulation, and liability.
“The question is not whether healthcare needs disruption,” Thindal says. “It is whether AI is finally the tool that can deliver it, without breaking the safety, trust, and accountability that healthcare depends on.”
He argues that many investors focus too heavily on AI moonshots while overlooking the industry’s daily operational bottlenecks. Scheduling delays, billing disputes, coding errors, fragmented records, and prior authorization requests consume large amounts of staff time across the healthcare system.
For Thindal, these operational inefficiencies may become AI’s first meaningful foothold.
“They will show up as workflow improvements that quietly raise throughput, shorten cycle times, and reduce rework,” he says.
The healthcare sector now generates enormous amounts of digital information from electronic records, imaging systems, wearables, and lab platforms. Yet clinicians often struggle to process that information efficiently.
“AI’s value proposition is translation, summarization, and routing,” Thindal explains.
He believes products that integrate directly into existing systems will have a stronger chance of adoption than standalone AI applications requiring major workflow changes. Hospitals and clinics tend to resist tools that add extra steps or create uncertainty around accountability.
“AI will be judged on reliability, not novelty,” he says.
Regulatory considerations also shape his investment outlook. Thindal expects healthcare organizations to adopt AI gradually, beginning with lower-risk administrative applications before moving toward more advanced clinical support systems.
“Assistive, not authoritative,” he says of AI’s role in healthcare. “Decision support, not decision replacement.”
He also notes that healthcare incentives remain fragmented. Providers, insurers, employers, and patients may experience different financial outcomes from the same technology, slowing adoption even when the operational benefits are clear.
Despite those challenges, Thindal believes the opportunity remains significant for companies capable of improving healthcare efficiency without disrupting trust.
“The companies that win will be the ones that respect how healthcare actually works, not how it should work in theory,” he says.
