I used to work for an organization that served hundreds of different industries. What was fascinating about it was how insights and best practices would translate from one segment to another. For example, principles from adhesive technologies formulated to adhere devices to a neonatal patient would be shared with R&D teams formulating adhesives to hold on airplane wings.
It got me thinking more deeply about my current space in clinical AI. We all know AI is transforming healthcare, from predictive analytics to workflow automation. But while the industry is making strides, other sectors like finance, aviation and retail have been perfecting AI applications for years. Their experiences offer valuable lessons in how to scale AI, improve adoption and maximize impact.
So, what can healthcare learn from these industries? Let’s take a look.
Banks and financial institutions don’t just use AI to analyze past data. They use it to predict what’s coming. Fraud detection, risk assessments, stock market forecasting – AI helps them see around corners.
What healthcare can learn: Right now, AI in healthcare often focuses on analyzing what’s already happened including reading scans and reviewing patient records. But what if we leaned into AI’s predictive power more? Imagine AI models forecasting staffing shortages before they happen, predicting patient deterioration earlier or optimizing hospital resources in real-time. Finance has figured out how to trust AI for proactive decision-making, and healthcare has an opportunity to follow suit.
What’s particularly interesting about finance is that it operates in a highly regulated environment, governed by the FTC, SEC and other regulatory bodies which should, in theory, make AI adoption slow and complex. And yet, financial institutions have successfully implemented AI while maintaining trust, security and ethical standards.
Healthcare, which faces its own regulatory hurdles, can take a similar approach by ensuring AI is explainable, transparent and aligned with ethical guidelines. Trust in AI doesn’t happen overnight, but the finance industry has shown that with the right safeguards, AI can become a critical, reliable tool for proactive decision-making.
Retailers have mastered AI-powered customer engagement. Think about the chatbots that guide you through online shopping, the recommendation engines that know exactly what you want before you do or that company that keeps you coming back by knowing when you’re out of your favorite shampoo exactly when you need it.
What healthcare can learn: We have patient portals, AI chatbots and automated appointment reminders but are they as seamless and user-friendly as what retail has built? Patients expect the same ease of interaction in healthcare as they do when shopping online. AI can help make patient engagement more intuitive, personalized and proactive rather than just another clunky system people avoid.
Pilots rely on automation to assist with navigation, flight stability and safety protocols but they remain in control. The aviation industry is starting to integrate AI into areas like predictive maintenance (anticipating mechanical failures before they happen), optimized flight paths (adjusting for weather and efficiency) and air traffic management (reducing congestion and delays).
What healthcare can learn: The key takeaway from aviation is that AI isn’t there to replace pilots. It’s there to support them. The same should be true for healthcare. AI can reduce cognitive overload, automate routine tasks and provide decision support, but trust and oversight remain critical.
Some specialties, like radiology, have already embraced AI to help address backlogs, workforce shortages and increasing imaging volumes. AI is helping radiologists prioritize urgent cases, detect anomalies faster, and reduce administrative burden. But other areas of healthcare, from hospital operations to clinical decision-making, are still in the early stages of AI adoption. Aviation shows us that AI can be trusted to assist without taking over, and more healthcare fields should follow suit leveraging AI as a partner that enhances, rather than replaces, human expertise.
Ever noticed how Netflix or Spotify seem to “know” what you’ll like next? Their AI engines constantly learn from your behavior to deliver hyper-personalized recommendations.
What healthcare can learn: Personalized medicine is a hot topic, but we’re still scratching the surface. AI can help tailor treatment plans, predict medication adherence risks and even personalize patient education based on individual learning preferences. The same AI that figures out your next binge-worthy show could be used to guide patients toward better health choices without a one-size-fits-all approach.
Manufacturers rely on AI-powered computer vision to catch defects, optimize processes and ensure precision. A factory floor isn’t just a bunch of machines, it’s an orchestrated system where AI monitors, analyzes and corrects issues before they become costly.
What healthcare can learn: In healthcare, small errors can have massive consequences. AI-driven imaging is already helping radiologists find suspected anomalies, but there’s room to do more. Imagine AI reducing medication errors, catching potential surgical mistakes in real time or ensuring hospitals maintain the highest standards for infection control. The same AI that guarantees product quality on an assembly line could be used to enhance patient safety.
AI’s impact isn’t just about having the right technology. It’s about how industries apply it. Finance, retail, aviation and other sectors offer valuable lessons in scaling AI adoption, building trust and driving meaningful outcomes. By learning from their successes (and missteps), healthcare can accelerate AI’s potential to improve patient care, streamline operations, and enhance overall efficiency.
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