Fawad Butt, CEO and Co-Founder of Penguin Ai – Interview Series

Fawad Butt, CEO and Co-Founder of Penguin Ai, is a seasoned healthcare data and AI executive who founded the company after leadership roles at Kaiser Permanente, UnitedHealthcare, and Optum, where he oversaw large-scale data governance, analytics, AI, and enterprise platform initiatives. His experience leading data strategy inside some of the largest payer and provider organizations shaped Penguin Ai’s focus on solving healthcare’s administrative inefficiencies with purpose-built AI, rather than forcing generic platforms into complex healthcare workflows. Under his leadership, Penguin Ai is positioned around a practical thesis: healthcare needs AI systems designed specifically for payer and provider operations, with the ability to reduce manual work, improve accuracy, and help teams spend more time on higher-value clinical and operational decisions.

Penguin Ai is a healthcare-native AI company focused on automating administrative and clinical operations for payers, providers, revenue cycle management companies, and healthcare technology partners. Its platform combines healthcare-specific language models, digital workers, AI agents, governance, compliance capabilities, and workflow automation for use cases such as prior authorization, claims processing, medical coding, appeals management, risk adjustment, payment integrity, medical chart summarization, clinical documentation summarization, and eligibility verification. The company secured $29.7 million in venture funding, including a $25 million Series A led by Greycroft, to expand hiring, product development, and deployments with payers and providers, and it has also launched Gwen, a build-your-own AI platform designed to help healthcare teams create governed digital workers for administrative workflows.

After leading data and analytics organizations at Kaiser Permanente, UnitedHealthcare, and Optum, what specific experiences convinced you that healthcare needed a purpose-built AI platform, and how did those lessons shape the founding vision for Penguin Ai?

I’ll give you the exact moment it clicked. I was sitting in a review at one of the big payers, staring at a queue of prior auths that had each bounced three times, and not one of them had a real clinical question attached. They bounced because a field did not match or the documentation showed up in the wrong format. Some of the best operators in the country were spending their day feeding a machine that produced nothing for the patient waiting on the other end. I saw that same waste at Kaiser, at UnitedHealthcare, at Optum, just wearing different costumes. That is when I stopped buying the industry line that friction is the price of scale. The vision for Penguin Ai came straight out of that frustration: build a platform that speaks payer and provider, put the healthcare context inside the system so an Ai Worker applies your own criteria and shows its reasoning, keep a person accountable for the clinical judgment, and take the grunt work off everyone else. That is the whole game.

Penguin Ai is focused on tackling what you describe as healthcare’s trillion-dollar administrative burden. Which workflows did you prioritize first, and why did prior authorization, claims adjudication, medical coding, and appeals emerge as the biggest opportunities for AI-driven transformation?

We went where the pain screams loudest. Prior authorization, claims adjudication, medical coding, and appeals rose to the top for one reason: they are enormous piles of repetitive work where most cases follow a pattern, and the exceptions are rare enough that a human should only ever see the exceptions. Prior auth led the list because that is where you can literally watch a patient suffer while paperwork sits in a queue, and nobody defends that with a straight face. When our Ai Workers carry the routine volume and route the genuine outliers to a person, the results are not subtle. Reviews run up to 97 percent faster, denials drop by more than half, and roughly 92 percent of cases resolve without a manual touch. I picked these four because they let me prove the thesis fast: strip the administrative burden and you make healthcare better for the patient, the clinician, and the person doing the work. Everyone wins in the same move, which is exactly why we started there.

Having evaluated countless enterprise technology vendors as a healthcare executive, what are the most common reasons AI startups fail to gain traction inside large health systems and payers?

I sat on the buying side long enough to watch startups torch their shot the same three ways. One, they treat healthcare like any other enterprise account and get blindsided by the integration mess, the legacy systems, and the regulatory weight nobody warned them about. Two, they oversell autonomy and then cannot answer the first hard question, which is always some version of “show me how it got there.” No audit trail, no reasoning a compliance team will accept, and the pilot dies right there in the room. Three, they fix one side of the payer-provider relationship and never grasp that the friction lives at the seam between them. And honestly, every buyer I know is exhausted. They have a drawer full of AI-powered tools that added a dashboard and moved zero work off anyone’s plate. The bar is high and the patience is thin, and anyone who cannot show a real path from pilot to production gets shown the door.

Many healthcare organizations remain cautious about deploying AI in mission-critical operations. What does successful AI adoption actually look like inside a large payer or provider organization, and what mistakes do leaders make when trying to scale it?

Real adoption starts small and earns its way up. Pick one workflow that genuinely hurts, keep your people in the loop so they can watch the work get done and trust it, and let the proof buy you the expansion. When a team sees an Ai Worker clear the routine cases correctly and hand them the ones that actually need judgment, they stop resisting and start asking for more. The mistakes are painfully predictable. Leaders try to boil the ocean and deploy everywhere at once. They buy technology and never redesign the workflow around it, so the burden just relocates to a different queue. Or they yank the humans out too fast, torch the trust, and then wonder why nobody touches the thing. Scaling AI in a health system is a change-management job wearing a technology costume. The teams that win treat it that way, and they commit to a hard metric on day one so everyone knows what success actually looks like.

Penguin Ai emphasizes healthcare-native AI models and workflows rather than generic enterprise AI solutions. What unique challenges in healthcare require a specialized approach, and where do general-purpose AI platforms fall short?

Healthcare is not a generic enterprise use case, and pretending otherwise gets you burned. It has its own language, its own rules, its own consequences, and none of that is optional. A general model will hand you a confident, plausible answer that is quietly wrong, because it does not carry your medical necessity criteria and it cannot show its work in a way an auditor or a clinician will sign off on. Non-starter. We built Penguin Ai in layers for exactly this reason: a data layer that pulls the information together, a Context Layer that holds the healthcare-specific reasoning and governance, and the Ai Workforce that actually does the work, including our pre-built Ai Worker solutions and Gwen, the builder that lets your own team stand up new Ai Workers. General-purpose platforms fall short because they treat healthcare expertise like something you can prompt your way into. You cannot. You have to build it into the system, and that is the whole point of what we made.

Prior authorization remains one of the most frustrating processes for providers and patients alike. How do you see AI reshaping prior authorization over the next five years, and what safeguards are necessary to ensure automation improves outcomes without creating new barriers?

Five years out, the straightforward approvals come back in minutes, applied consistently against your own guidelines, with the clinician looped in on anything that calls for medical judgment. That is the good version. Here is the part people skip: automation can just as easily industrialize the barriers if you build it lazy. A faster way to deny care is malpractice with better throughput. So the safeguards are the whole ballgame. Every recommendation an Ai Worker makes has to carry its reasoning and a full audit trail, what we call a glassbox, so anyone can see exactly how it got there. Speed can never become cover for a quiet wall of automated denials. And a qualified human stays accountable for the clinical judgment, always. Get that right and prior auth stops being the thing patients and doctors dread. Get it wrong and you have built a faster version of the problem. I am only interested in the first one.

The industry is increasingly talking about AI agents and autonomous workflows. What are the biggest technical and operational hurdles to deploying AI that can reliably handle complex healthcare administrative tasks with minimal human intervention?

The hard part is not the demo, it is the edges. Exception handling, messy and inconsistent data, and legacy systems that were never built to talk to anything. That is where the real engineering lives, and it is deeply unglamorous. The operational hurdle is trust, which no health system hands you for free. If your people cannot audit it or explain it, they will not scale it, full stop. Now let me push back on the premise a little. “Minimal human intervention” is the wrong goal, and it worries me when I hear it. The point is to free your people up for the complex cases and the judgment calls, where their expertise actually earns its keep. Our Ai Workers carry the repetitive volume and know when to pull a person in. Nailing that hand-off, reliably and transparently, at real healthcare scale, is the hard problem worth solving. Everything else is a science fair project.

Healthcare is one of the most heavily regulated industries in the world. How do you balance innovation speed with compliance, privacy requirements, auditability, and the need for human accountability in AI-driven systems?

You earn the right to move fast by building things that hold up. In healthcare, governance is the foundation, and we poured it first. When you are handling patient data, enterprise data governance is a moral requirement, and I mean that literally. So every recommendation runs to a glassbox standard, reasoning plus a full audit trail, which is what lets a compliance team actually trust the speed. Human accountability is baked into the design, because a qualified person owns anything touching clinical judgment while the Ai Worker handles the assembly and the routine load. That is how you get fast and trustworthy in the same system, and in this industry you do not get to pick one and skip the other. Most people frame compliance and auditability as the brakes. I see them as the thing that lets you put your foot down at all, because without them you never reach production in the first place.

You made the transition from overseeing large-scale enterprise data and analytics organizations to building a startup from the ground up. What aspects of the operator-to-founder journey have been the most rewarding, and what challenges have been the most surprising?

The most rewarding part is that you finally get to build it right the first time. Inside a large organization, change moves at the speed of the institution. Every fix has to route through legacy systems, competing priorities, and layers of approval, so a change you know is correct can take eighteen months to land, if it lands at all. That pace will wear you down when you can see exactly what needs to happen. In founder space, that ceiling disappears. We get to build from scratch, design the platform around the problem instead of around thirty years of accumulated workarounds, and ship the right answer while it still matters. When I hear a nurse got her evenings back, or a patient got an answer in hours where it used to take days, that lands in a way no roadmap review ever did.

The most surprising part, and it genuinely humbled me, is how much of this job is storytelling. I came up through data and systems, and I assumed the technology would carry the argument. It does not. You are asking people to believe a different way of working is even possible, and you earn that belief one proof point at a time. The pace is brutal, you wear forty hats before lunch, and it is still the most meaningful work I have ever done.

Looking ahead, if AI successfully removes much of the administrative burden from healthcare, how do you think payers, providers, and patients will experience the healthcare system differently over the next decade?

A decade out, if we do this right, the system finally feels like care again. Patients get real answers when they are scared and vulnerable, and they stop having to earn a minor in insurance bureaucracy just to get what their doctor already ordered. Clinicians spend their hours on the art of medicine and on the people in front of them, instead of grinding through the eighty-seventh routine case before lunch. The operations and administrative staff, some of the most underappreciated people in this entire industry, finally do work that uses their real expertise. And payers and providers coordinate instead of lobbing paperwork back and forth across a seam nobody owns. That is the healthcare I am building toward. The scorecard I care about is boring and specific: time given back to patient care, and delay pulled out of the patient experience. Everything else we do is in service of those two numbers.

Thank you for the great interview, readers who wish to learn more should visit Penguin Ai.