Artificial intelligence has become the buzzword of the decade in healthcare. Every conference agenda highlights it, every boardroom strategy slide promises it and every vendor pitches it. Yet for all the excitement, healthcare’s adoption of AI still struggles with the same friction points: fragmented data, regulatory hurdles and the trust deficit between technology and clinicians. The question is no longer whether AI will influence the healthcare industry — but can it do so at scale and with integrity?
Charles Wong, an Enterprise Product Manager at Headway, has spent his career tackling precisely these questions. In a recent Forbes article on how AI exposes weaknesses in health care claims systems, he argues that fixing data quality is the first step toward meaningful automation.“The real challenge is far from building the AI; it’s about ensuring the data and workflows support it,” Wong notes. That emphasis on fundamentals is what makes his perspective unique: Wong sees AI, contrary to a mere magic fix, as a tool whose value depends entirely on the systems, governance and human expertise around it.
Learning from Enterprise Pilots Outside Healthcare
Healthcare is far from the first industry to face operational complexity at scale. Mining, energy and logistics have long wrestled with siloed data, compliance risks and high-stakes decision-making. And while their contexts differ, the lessons are transferable.
A Forbes Councils Member, Wong knows this firsthand. He led a pilot project between Palantir and Rio Tinto, one of the world’s largest mining companies. The task was deceptively simple: integrate scattered shipping invoices, tariffs, warehousing costs and inventory records into a unified system that could inform better decisions. The reality was anything but simple. Data lived in incompatible formats, costs were hidden in fine print and decision-makers were skeptical that algorithms could surface insights they could trust.
The pilot succeeded because it paired robust AI models with human oversight. Automated systems identified underutilized shipping routes or mismatched cost structures, but executives validated and acted on those recommendations. The result was millions in annual savings, alongside a cultural shift in how Rio Tinto approached data-driven operations.
“What worked in mining was counter to merely the algorithm: instead, the trust executives built by seeing results they could act on. Healthcare needs the same balance,” Wong reflects.
Why Healthcare Needs Cross-Industry Thinking
Unlike consumer technology, healthcare cannot afford failure as a learning exercise. A flawed recommendation in logistics may cost money; in medicine, it may cost lives. That is why healthcare leaders should look beyond their own sector for lessons on how to scale AI responsibly.
Today’s challenges in healthcare, from denied claims and referral breakdowns to compliance ambiguity, mirror the operational bottlenecks of industries that learned through hard enterprise pilots. What they discovered is that algorithms alone are never enough. AI succeeds when paired with systems that embed accountability and processes that ensure human oversight.
Wong has consistently reinforced this principle in his work. In his Hackernoon article, titled “Finding Product-Market Fit in Healthcare: Lessons from Blending Automations with a Human Touch,” he writes about the necessity of looping in humans, leveraging touchpoints and automating. He describes how automation can eliminate repetitive, error-prone tasks; even then, his automation-with-a-human touch narrative cautions that without clinical staff interpreting results and managing exceptions, systems risk reinforcing the very inefficiencies they were meant to fix. That machines-for-scale, humans-for-nuance philosophy echoes the lessons of Rio Tinto and extends directly into medicine’s most pressing debates.
From the industry side, policymakers are now underscoring this balance. The latest federal guidance on AI in healthcare emphasizes explainability, clinician oversight and accountability for outcomes. Standards like HL7 FHIR are also accelerating interoperability, forcing organizations to connect data silos if they want AI to be effective. What this means is that healthcare’s future with AI will depend, as opposed to bold claims of disruption, on the same principles that guided enterprise adoption elsewhere: trust, infrastructure and governance.
Building Healthcare AI That Clinicians Can Trust
Scaling AI in healthcare is beyond the scope of building flashy features; it concerns constructing the invisible infrastructure that clinicians can rely on. His approach has been consistent: focus on the messy data plumbing, ensure systems are interoperable and design workflows where clinicians stay in the loop.
This thinking aligns with his academic contributions as well. In his co-authored scholarly paper, titled “Securing Production Engineering: Data Science and Cybersecurity in Product Development,” the author Wong explores how combining machine learning with cybersecurity frameworks creates more resilient production systems. He details how anomaly detection models like Isolation Forests can safeguard critical environments without introducing fragility, thus underscoring the importance of security and trust in data-driven systems. The relevance to healthcare is obvious: without resilient, secure infrastructure, AI risks compounding vulnerabilities rather than solving them.
The common thread through Wong’s work, from mining and healthcare operations to research and publications, as evident in his slew of other scholarly articles, is that trustworthy AI is less about algorithms and more about architecture. Clinicians need confidence that the recommendations they see are based on reliable data, governed by rigorous systems and safeguarded against misuse. That is the foundation on which automation can scale without eroding trust.
Medicine’s Call to Look Beyond Medicine
Healthcare is at an inflection point with AI. The promise is undeniable: reducing administrative waste, closing referral gaps and helping clinicians focus more on patients than paperwork. But the pathway forward will be found beyond mere grand visions. Instead, it will come from applying cross-industry lessons about how to make AI work at scale, responsibly and sustainably.
As Wong, who also authored a Product-Led Alliance article, titled “Finding product-market fit in healthcare: Building & scaling a human experience,” puts it, “AI can surface patterns, but it takes rigorous systems and human judgment to make them matter.” That belief has guided him from the mining industry’s boardrooms to the infrastructure of modern healthcare, and it offers a roadmap for how medicine can finally harness AI in ways that clinicians trust and patients deserve.
