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The issue of reimbursement by payers is an important factor that should be discussed. Is it possible that if radiologists use AI to read scans, they’ll receive less reimbursement? Or to approach this from the other angle, if payers are reimbursing for the use of AI, will they pay radiologists less as a result? My discussions with insurance executives have shown that they don’t think this is likely. If the use of these technologies will improve patient outcomes and lead to fewer errors, there are benefits to them that will motivate executives to pay for them in addition to radiologists’ reading fees.

Oil companies start every war around the world, insurance companies, the medical field, pharmaceutical companies are partners in crime. Financial institution collect all that the little man earned in the past 10 years and the AI will end humanity once and for all

novel The Diamond Age: Or, A Young Lady’s Illustrated Primer, author Neal Stephenson gives readers a glimpse of what AI experts call a “lifelong learning companion”: an agent that tracks learning over the course of one’s lifetime, both insuring a mastery-level education and making exquisitely personalized recommendations about what exactly a student should learn next.

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University scholars Carl Benedikt Frey and Michael Osborne ranked about seven hundred occupations on their likelihood of being disrupted by the early 2030s.[14] At a 99 percent likelihood of being able to be automated were such job categories as telemarketers, insurance underwriters, and tax preparers.[15] More than half of all occupations had a greater than 50 percent likelihood of being automatable.

Another AI-oriented company that has established several ecosystems—also in partnership with Palantir, in which it has made a substantial investment—is SOMPO Holdings, a large insurance and elder care company based in Japan. SOMPO

The deep learning powering Ganesh Insurance’s apps has been trained to determine the likelihood that each insured may develop serious health problems, and then set premiums accordingly.

Of course, in a future where “distributed autonomous organizations (DAOs)” will control fleets of autonomous taxis, there will soon come a point that a DAO’s AI will be talking to Daimler’s Sarah about that financing, leasing, and insuring. It’s an AI-to-AI negotiation, no human required.

The Insurance Institute for Highway Safety analyzed five thousand car accidents and found that if autonomous vehicles do not drive more slowly and cautiously than people, they will only prevent one-third of all crashes.

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My six-year-old son Thomas won’t need a driver’s licence to own a car and it’s highly likely he won’t even own a car; he’ll simply rent car “time” instead. Throughout his entire life, he will never be without a smart device which will soon tell him when to go to the doctor for advice (and his insurer will require him to wear it), he’ll live in a smart house where robots clean and fridges or a household AI order groceries (delivered by a robot), he’ll never use a plastic card or chequebook to pay for anything (and likely no cash either) and he’ll interact with hundreds of computers every day that won’t have a mouse or keyboard. Thomas is part of the so-called Generation Z which is growing up in a world so dramatically different from the world that their grandparents were born into that if you had predicted these changes 100 years ago, it would have simply been called science fiction.

So which country will lead in the broader category of business AI? Today, the United States enjoys a commanding lead (90–10) in this wave, but I believe in five years China will close that gap somewhat (70–30), and the Chinese government has a better shot at putting the power of business AI to good use. The United States has a clear advantage in the most immediate and profitable implementations of the technology: optimizations within banking, insurance, or any industry with lots of structured data that can be mined for better decision-making. Its companies have the raw material and corporate willpower to apply business AI to the problem of maximizing their bottom line.

Here are a few examples of different levels of risks, ordered from high to low: If you don’t do this, competitors with AI can make you obsolete. If AI poses a major existential threat to your business, incorporating AI must have the highest priority. In the 2023 Gartner study, 7% cited business continuity as their reason for embracing AI. This is more common for businesses involving document processing and information aggregation, such as financial analysis, insurance, and data processing. This is also common for creative work such as advertising, web design, and image production. You can refer to the 2023 OpenAI study, “GPTs are GPTs” (Eloundou et al., 2023), to see how industries rank in their exposure to AI. If you don’t do this, you’ll miss opportunities to boost profits and productivity. Most companies embrace AI for the opportunities it brings. AI can help in most, if not all, business operations. AI can make user acquisition cheaper by crafting more effective copywrites, product descriptions, and promotional visual content. AI can increase user retention by improving customer support and customizing user experience. AI can also help with sales lead generation, internal communication, market research, and competitor tracking. You’re unsure where AI will fit into your business yet, but you don’t want to be left behind. While a company shouldn’t chase every hype train, many have failed by waiting too long to take the leap (cue Kodak, Blockbuster, and BlackBerry). Investing resources into understanding how a new, transformational technology can impact your business isn’t a bad idea if you can afford it. At bigger companies, this can be part of the R&D department.

Another dire example involves an algorithm called “nH Predict”, used by UnitedHealth Group (the largest health care insurer in the U.S.) to determine the length of stays it would approve for patients in nursing homes and care facilities. In a class-action lawsuit61 filed in November 2023, the estates of the two named plaintiffs—deceased at the time of filing—alleged that UnitedHealth kicked them out of care too early, based on nH Predict’s output, even as the company knew the system had an error rate of 90 percent. The court filing says that UnitedHealth used this system anyway, counting on the fact that only a tiny group of policyholders appeal such denials, and that the insurer “[banked] on the [elderly] patients’ impaired conditions, lack of knowledge, and lack of resources to appeal the erroneous AI-powered decisions.” The families of the two plaintiffs spent tens of thousands of dollars paying for care that went uncovered by the insurer. Reporting from Stat News largely confirms62 the allegations in the lawsuit, namely that after acute health incidents, UnitedHealth aimed at getting elderly patients out of nursing homes and hospitals as fast as possible, even against the advice of their doctors. Moreover, when patients challenged denials, physician medical reviewers were advised by case managers not to add more than 1 percent of the prior advised nursing home stay. And case managers themselves were fired if they strayed from those targets.

For example, vehicles monitor their drivers’ behavior and share the data with the algorithms of the insurance companies, which raise the premiums they charge “bad drivers” and lower the premiums for “good drivers.” The American scholar Shoshana Zuboff has termed this ever-expanding commercial monitoring system “surveillance capitalism.

Automated tools for allocating care have major problems as well. In a well-publicized 2019 study60, public health scholar Ziad Obermeyer and his collaborators evaluated a prediction system used by hospitals, physician groups (including health maintenance organizations or HMOs), and health insurance groups to identify patients who may have complex health needs and provide more resources for care management. The algorithm they assessed had been applied to about 200 million people in the United States, nearly two-thirds of the population. Obermeyer and his team found that the algorithm dramatically underestimated the care needed for Black individuals, compared to white individuals. The team found that this was largely due to the lack of access to health care for Black people: the algorithm used previous expenditures to predict future expenditures on health care, rather than actual health care needs. Black people in the dataset were, on balance, sicker than white people, but were less likely to seek treatment (likely due to cost, availability, and potential discrimination).

The Insurance Paradox: We insure everything except for the one thing that pays for it all—our career.

Personal agency is the meta-skill that will define professional success all the way through 2050.

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Instead, the team could have prompted AI more effectively, saying, “I’m an agent with EMC Insurance. Here are five solutions I’ve come up with. Please identify alternative solutions you think would be more creative.

In one extreme case, U.S. health insurance company UnitedHealth forced employees to agree with Al decisions even when the decisions were incorrect, under the threat of being fired if they disagreed with the Al too many times. It was later found that over 90 percent of the decisions made by Al were incorrect. Even without such organizational failure, overreliance on automated decisions (also known as "automation bias") is pervasive. It affects people across industries, from airplane pilots to doctors. In a simulation, when airline pilots received an incorrect engine failure warning from an automated system, 75 percent of them followed the advice and shut down the wrong engine. In contrast, only 25 percent of pilots using a paper checklist made the same mistake. If pilots can do this when their own lives are at stake, so can bureaucrats. No matter the cause, the end result is the same: consequential decisions about people's lives are made using Al, and there is little or no recourse for flawed decisions.