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Forbes
3 days ago
- Health
- Forbes
Code Meets Cabinet: How AI Is Whispering In The Halls Of Government
Arpan Saxena is the COO/CIO at (based out of Harvard University), a leading healthcare AI solutions company. getty In recent years, efficiency has become more than a budgetary aspiration—it's a political imperative. Government agencies around the world are under pressure to modernize legacy processes, deliver faster services and meet rising expectations from citizens who've grown accustomed to the responsiveness of the private sector. In this climate, AI—particularly generative AI (GenAI)—has begun to play an increasingly influential, if quiet, role in government agencies. While debates around AI tend to swing between utopian dreams and dystopian fears, a quieter transformation is taking place in the government back office. In pilot programs and procurement meetings, GenAI is being evaluated not as a sci-fi curiosity but as a pragmatic tool for paperwork-heavy bureaucracies. GenAI's utility in government is not found in headline-grabbing robots or automated surveillance systems. It's showing up in the trenches of civil service—where policy briefs are written, compliance forms are reviewed and workflows are clogged with procedural friction. Governments are experimenting with GenAI to: • Draft regulatory summaries. • Analyze and synthesize public comments. • Review contracts and legal language. • Generate templates for benefits processing. • Translate bureaucratic language into plain English. In the U.S., for example, the Department of Veterans Affairs has piloted the use of GenAI to help streamline correspondence and claims processing, areas long plagued by backlogs and inconsistencies. In the U.K., HM Revenue and Customs is exploring AI-assisted tax advisory tools to better support tax advisors. These are not moonshots. They are efficiency plays—designed to support, not replace, the civil servant. Based on the current use cases, Boston Consulting Group predicts that the "government market for GenAI applications is projected to grow at more than 50% per year." Three converging pressures led to the adoption of AI in government agencies: 1. Operational Complexity: Government systems are layered with regulation, exceptions and historical patchwork. Human processing alone is no longer sustainable. 2. Public Expectation: Citizens expect digital government experiences to match the convenience of private platforms. Waiting weeks for a decision or form now feels outdated, if not unjust. 3. Cost Containment: With budget constraints tightening, governments are seeking ways to 'do more with less.' AI tools promise marginal gains at scale—translating into massive impact. Despite the potential, GenAI in government is not without its risks—and governments know it. Accuracy, bias, explainability and security are front and center in pilot discussions. The same GenAI model that summarizes a dense policy brief could also, if left unchecked, 'hallucinate' legal interpretations or misrepresent regulations. These tools cannot operate in a vacuum of oversight. Moreover, transparency is non-negotiable. Citizens have a right to know how decisions are made, particularly when those decisions impact healthcare, benefits or legal status. Any deployment of GenAI must come with clear auditability, ethical review and human-in-the-loop safeguards. This is why many governments are proceeding carefully: piloting, sandboxing and pairing AI output with expert validation. In this regard, slow may be smart. As AI quietly enters the policy sphere, the most forward-thinking agencies are not asking if they should use AI—but how. Some questions worth considering before getting started: • Where are our most friction-heavy processes? • Which use cases can benefit from GenAI without compromising trust? • How do we build AI governance that aligns with public values? • Are we investing in the right partnerships to maintain human and AI collaboration? These questions will shape the difference between AI as a trend and AI as a transformative tool. Generative AI's role in government isn't about flashy disruption. It's about quiet transformation. About supporting overburdened systems, improving public trust and returning time to the people who keep the engine of the state running. Whether this becomes a long-term success story depends not on the tools but on how thoughtfully they're deployed. As code begins to whisper into the ears of cabinet members and civil servants alike, the responsibility isn't to blindly follow—but to listen, validate and act wisely. This article was co-written with CEO and cofounder Amber Nigam, a Forbes Business Council member. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
20-05-2025
- Health
- Forbes
From Hype To Infrastructure: The Great Compression Of Health AI
Amber Nigam is CEO and cofounder of a Harvard-based company streamlining prior authorization for health plans with generative AI. getty The generative AI boom in healthcare feels inevitable. Every week, a new vendor promises to reimagine clinical workflows, redefine care management or reshape revenue cycle operations. Billions are pouring into startups, tech giants are repositioning and healthcare incumbents are scrambling to stake their claim. But beneath the noise, the real AI arms race is quietly underway—and winning it requires more than flash, jargon or even technical prowess. It demands discipline, clinical credibility and a long view of how the industry's foundations are shifting. As the cofounder of a health AI startup and someone who has spent a decade in healthcare and data science—from the halls of Harvard to the trenches of health plan operations—I've seen the real trenches and the real hype. And I can tell you: The AI arms race in healthcare is very real. But it's not what you think. Let's be clear: Healthcare has been burned before. Blockchain, virtual reality, wearables: Each had moments where hype outpaced reality. I think generative AI is different, but not because it's immune to exaggeration. The difference is that GenAI genuinely has the potential to replace rote administrative tasks, streamline care delivery and change how data moves through the healthcare system. Early proofs are real, and we are seeing firsthand streamlined prior authorizations, faster risk adjustment coding and more humanlike patient engagement. But these examples are narrow. A model that drafts a prior authorization letter isn't the same as a system that understands patient context, payer policy nuance and clinical appropriateness at scale. Copywriting is easy. Clinically meaningful transformation is not. Still, I see some vendors are serving up the same old drink. If a pitch centers around an AI that can "talk like a doctor" without having deep clinical oversight, be skeptical. If it relies solely on fine-tuning open models without access to proprietary, real-world healthcare data, be even more skeptical. And if it suggests that technology can replace clinical judgment wholesale—not just augment it—you're not dealing with a serious player. You're dealing with an "intention impostor": a founder who may believe in their mission but lacks the operational, regulatory or clinical sophistication to actually deliver it. Healthcare isn't a sandbox. It's a high-stakes, highly regulated environment where the cost of failure is patient harm, and the cost of overpromising is industry fatigue. So, how do you separate noise from signal? Start with team composition. True healthcare AI players blend technical excellence with clinical credibility. Look for former clinicians, policy experts and health plan operators at the leadership table—not just advisors used for window dressing. Second, scrutinize the problem they're solving. Is it rooted in collaboration with existing workflows, or does it presume clinicians and operators will change overnight? Healthcare doesn't bend easily to technology. The solutions that work will complement human decision-making, not try to outshine it. Third, assess their attitude toward regulation and liability. Serious players view HIPAA, CMS guidelines and AI-specific governance as the price of entry, not a hurdle to "hack." Finally, and most importantly, watch who trusts them. Meaningful pilots, real integrations with major payers or providers as well as tight partnerships matter. The best early indicators aren't flashy logos; they're renewal rates, expansion contracts and references from customers who are hard to impress. In an arms race, speed matters—but substance wins. The startups and incumbents pulling ahead are moving fast and building for durability. They're investing in proprietary data partnerships today to future-proof their models. They're building modular, interoperable platforms instead of narrow-point solutions. They're also deeply integrating into existing vendor ecosystems. The players who realize early that they must be an API, not an island, will pull ahead. Those who assume they'll own the whole stack will face slow adoption, frustrated users and dwindling investor patience. Healthcare is the ultimate trust-based system. Every new technology must prove it deserves a seat at the table—not just once, but every day. Perhaps the least discussed but most profound outcome of this AI arms race will be the collapse of the current vendor landscape. Historically, health plans and providers bought solutions for prior authorization, care management, risk adjustment and member engagement from different vendors—each with siloed data, rules engines and interfaces. I view AI as eventually breaking that model. Large language models (LLMs) and foundational healthcare AI platforms aren't task-specific; they're context-specific. The same longitudinal patient record that powers faster prior authorization can also power smarter care management workflows, risk stratification and value-based care initiatives. What does this mean? It means many of today's seemingly distinct categories will likely converge into a few dominant layers: • Data orchestration • Clinical knowledge modeling • Application-specific workflows I believe vendors that can plug into these layers will be the ones to thrive. Vendors that can only operate in isolation—no matter how good their specific application—will risk becoming redundant. Expect a wave of acquisitions, mergers and even outright closures over the next 24 to 36 months. Expect a few dominant AI platforms to emerge—ones that health plans, providers and life sciences companies use across functions. In the end, healthcare won't have 50 different AI partners. It will have a handful of trusted foundations, each deeply embedded into clinical, operational and financial lifeblood. The AI arms race in healthcare is real. But it won't be won by those shouting the loudest. It will be won by those who build quietly, partner wisely and think long term. In a world where data, models and workflows converge, the winners won't just automate tasks—they'll transform how healthcare is delivered, trusted and experienced. Sorting glitter from gold has never been more urgent—or more valuable. This article was co-written with CEO and cofounder Arpan Saxena, a Forbes Business Council member. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
