Healthcare is one of the largest, most complex industries in the real economy, and it still runs on workflows that feel stubbornly analog. Phone calls to confirm coverage. Faxed forms. Clinicians spending evenings finishing documentation. Patients navigating a maze of portals, referrals, and opaque billing codes.
Kam Thindal, Managing Director of Core Capital Partners, has been watching the healthcare AI space closely as an investor. His view? The disruption is coming, but not the way most people expect.
We sat down with Kam Thindal to discuss where AI will actually penetrate healthcare first, why the biggest wins won’t be clinical, and what investors are getting wrong about the sector.
The Central Question: Can AI Actually Disrupt Healthcare?
Is healthcare finally ripe for AI disruption?
“The question is not whether healthcare needs disruption,” Thindal says bluntly. “It is whether AI is finally the tool that can deliver it, without breaking the safety, trust, and accountability that healthcare depends on.”
In his view, most sectors would have rewritten this level of friction years ago. But healthcare is not software in a clean-room sense.
“Healthcare is a regulated service business, delivered by humans, constrained by capacity, and shaped by incentives that often reward volume over outcomes,” he explains. “AI can be powerful here, but only if it moves from impressive demos to reliable systems that people actually use.”
For Kam Thindal, that gap between demos and deployment is where many investors are getting the story wrong.
Why Now? Three Converging Forces
What makes this moment different for healthcare AI?
Thindal points to a pattern he has seen across sectors: healthcare disruption usually arrives when multiple pressures stack at once. Right now, three forces are converging.
First, labor is tight where it matters most.
“Clinician burnout is real, and administrative staffing is not a bottomless pool,” says Thindal. “If you view healthcare as a production system, the bottleneck is increasingly human time. AI is not replacing care, but it can compress the non-care work that consumes the day.”
Second, data is more available, even if it is still fragmented. Electronic health records, imaging archives, claims datasets, wearables, and lab systems have created a digital substrate that did not exist at scale a decade ago.
“The paradox is that healthcare has more data than ever, yet clinicians often have less usable information in the moments that count,” Kam Thindal observes. “AI’s value proposition is translation, summarization, and routing.”
Third, the technology has crossed a threshold.
“The leap from narrow automation to language and multimodal systems expands what can be automated,” he says. “You can now tackle unstructured text, forms, conversations, and clinical notes. That is where a meaningful share of waste and delay lives.”
For Kam Thindal, this convergence creates a different investment landscape than previous healthcare tech waves.
Where the Friction Lives
Where do you see AI penetrating healthcare first?
“If you want to predict where AI penetrates first, follow the pain, not the headlines,” Thindal says.
Diagnostics and drug discovery get the spotlight because they sound transformational. But healthcare’s daily cost structure is dominated by operations: scheduling, triage, coding, prior authorization, denials management, care coordination, discharge planning, and call centers.
“These are not glamorous problems, but they determine whether the system flows or jams,” he says.
As an investor, Kam Thindal sees AI as especially well suited to this layer because the tasks are repeatable, high volume, and constrained by documentation burden.
“A model that drafts a prior authorization appeal, summarizes a chart, pre-populates a note, or flags missing coding elements can create real leverage,” he explains. “Not because it is ‘smarter,’ but because it reduces the cost of moving information from one place to another.”
This is why he believes the earliest AI wins may look smaller than the hype cycle suggests.
“They will show up as workflow improvements that quietly raise throughput, shorten cycle times, and reduce rework.”
For Kam Thindal, that is where the immediate returns are hiding.
From Pilots to Workflows
What separates AI pilots from AI products that actually scale in healthcare?
“Healthcare does not adopt technology the way consumer markets do,” Thindal says. “A hospital cannot ‘move fast and break things’ when the downside is patient harm, privacy breaches, or clinical liability. AI will be judged on reliability, not novelty.”
That means the battlefront is integration.
“Tools that live outside the workflow die in the workflow,” he warns. “If a clinician needs to open a separate app and copy outputs back into the EHR, adoption will stall. If an administrative team has to redesign its process around a model that is not traceable, it becomes a risk, not a solution.”
Kam Thindal believes the AI products that scale will feel less like standalone intelligence and more like embedded infrastructure. They will sit inside the EHR, inside radiology worklists, inside contact center systems, and inside claims pipelines. They will have guardrails, audit trails, and clear ownership when something goes wrong.
“In other words, the winners will not just have models,” he says. “They will have distribution and workflow capture, paired with product discipline that respects clinical reality.”
That changes the diligence process entirely.
“I’m not just evaluating the AI. I’m evaluating the distribution strategy and the workflow integration plan.”
The Incentive Problem
You’ve written about misaligned incentives in healthcare. How does that affect AI adoption?
“Healthcare is not one market,” Thindal emphasizes. “It is a set of participants with conflicting incentives: providers, payers, employers, regulators, and patients. AI’s benefits often accrue to one party while the cost lands on another.”
He gives an example: a hospital may invest in AI to reduce documentation time, but the financial upside may show up as fewer denied claims or faster discharge. A payer may adopt AI to reduce costs, but providers may experience it as more friction. Patients want seamless care, but the system is fragmented across networks, billing entities, and data silos.
“This is why AI disruption will be uneven,” Kam Thindal says. “Use cases that align incentives will scale faster. Use cases that shift power or revenue will face resistance, even if the technology works.”
For him, the biggest near-term unlock is not a new diagnosis engine.
“It is reducing the administrative tax that exists because the system is adversarial by design.”
As an investor, he looks for companies solving problems where multiple stakeholders benefit simultaneously. Those are the use cases with the clearest path to adoption.
Regulators, Liability, and Trust
How do you think about regulatory risk and liability in healthcare AI investments?
“Healthcare is one of the few domains where AI’s margin for error is structurally small,” Thindal says. “A hallucination in a shopping assistant is annoying. A hallucination in a medication list is dangerous.”
But that does not mean AI cannot be used clinically.
“It means the role must be precise. Assistive, not authoritative. Decision support, not decision replacement. The human remains accountable, and the system must make it easy to verify outputs, trace sources, and understand confidence.”
Kam Thindal sees this creating a natural hierarchy of adoption:
“Low-risk administrative tasks first. Then documentation support. Then summarization and triage. Then clinical decision support in constrained settings where inputs and outputs are more bounded. Fully autonomous clinical decisions are a different conversation and likely a longer runway.”
His view is straightforward:
“Healthcare will not be disrupted in one sweep. It will be rewired module by module, and trust will be earned through consistency over time.”
This staged adoption pattern shapes how he evaluates investment timelines and risk-adjusted returns.
Kam Thindal’s Investment Lens: Operational Wins Over Clinical Moonshots
Where are you focusing your attention as an investor in healthcare AI?
“In the near term, AI’s most underappreciated impact may be operational: reducing the administrative drag that steals time from care,” Thindal says.
Over time, as workflows harden and trust builds, he expects clinical applications will expand, especially where AI can augment scarce specialists and surface relevant information faster. But that is a longer-term play.
“Right now, I’m more interested in the companies solving the boring problems,” he admits. “Prior authorization automation. Claims denial reduction. Clinical documentation workflows. These aren’t sexy, but they have clear ROI, quantifiable time savings, and alignment across stakeholders.”
He contrasts this with the clinical moonshots that attract media attention but face longer validation cycles, higher regulatory hurdles, and complex liability questions.
“I’m not saying those won’t work,” Kam Thindal says. “I’m saying the path to revenue and adoption is longer and less certain. As an investor, I need to balance conviction with realism about timelines.”
Closing Thoughts: Gradually, Then Suddenly
How should investors think about the timeline for healthcare AI disruption?
“Healthcare is ripe for change, but not the kind that fits neatly into a single ‘AI revolution’ headline,” Thindal concludes. “The opportunity is real precisely because the constraints are real. Regulation, safety, privacy, and incentives slow adoption, but they also create moats for solutions that prove themselves.”
For Kam Thindal, the key insight is understanding the pace of change in healthcare.
“The disruption is coming,” he says. “It will just arrive the way healthcare always changes: gradually, unevenly, and then suddenly, when the new workflow becomes the default.”
He sees this as an advantage for patient investors who understand the sector’s unique constraints.
“The companies that win will be the ones that respect how healthcare actually works, not how it should work in theory.”
As Kam Thindal puts it: “This isn’t a sprint. It’s a multi-year build. But the returns will go to the people who understand that operational leverage compounds, trust is earned slowly, and integration beats innovation every time.”
About Kam Thindal
Kam Thindal is Managing Director of Core Capital Partners, where he focuses on healthcare technology investments, operational efficiency plays, and sectors undergoing regulatory-driven transformation.
Connect with Kam Thindal: LinkedIn
Learn more about Core Capital Partners: ccpartnersinc.com
