TALKING

In 2024 , one of the top 100 global insurers will go out of business as a consequence of deploying generative AI too quickly .

inputs simultaneously , enabling more context-aware applications for effective decision making .

An example of this will be the generation of 3D objects , environments and spatial data . This will have applications in augmented reality , virtual reality , and the simulation of complex physical systems such as digital twins .

Technologies like AI and Internet of Things analytics drive important sectors of the economy , including manufacturing , energy and government . Workers on the factory floor and in the executive suite use these technologies to transform huge volumes of data into better , faster decisions .

In 2024 , the adoption of AI and IoT analytics will accelerate through broader use of digital-twin technologies , which analyse real-time sensor and operational data and create duplicates of complex systems like factories , smart cities and energy grids . With digital twins , organisations can optimise operations , improve product quality , enhance safety , increase reliability and reduce emissions .

Insurance claim modelling

After decades of anticipation , climate change has transformed from speculative menace to genuine threat . Global insured losses from natural disasters surpassed $ 130 billion in 2022 , and insurers worldwide are feeling the squeeze . US insurers , for example , are under scrutiny for raising premiums and withdrawing from hard-hit states like California and Florida , leaving tens of millions of consumers in the lurch .

To advance health and improve patient and member experiences , organisations will further develop generative AI-powered tools in 2024 for personalised medicine , such as the creation of patient-specific avatars for use in clinical trials and the generation of individualised treatment plans .

Additionally , we will see the emergence of generative AI-based systems for clinical decision support , delivering real-time guidance to payers , providers and pharmaceutical organisations .

And the crash . . .

To survive this crisis , insurers will increasingly adopt AI to tap the potential of their immense data stores to shore up liquidity and be competitive . Beyond the gains they realise in dynamic premium pricing and risk assessment , AI will help them automate and enhance claims processing , fraud detection , customer service and more .

However , in 2024 , one of the top 100 global insurers will go out of business as a consequence of deploying generative AI too quickly . Right now , insurers are rolling out autonomous systems at breakneck speed with no tailoring to their business models .

They are hoping that using AI to crunch through claims quickly will offset the last few years of poor business results . However , after 2023 ’ s layoffs , remaining staff will be spread too thin to enact the necessary oversight to deploy AI ethically and at scale . The myth of AI as a cure-all will trigger tens of thousands of faulty business decisions that will lead to a corporate collapse , which may irreparably damage consumer and regulator trust .

Public health is achieving technologic modernisation at an unprecedented rate . Whether overdoses or flu surveillance , using data to anticipate public health interventions is essential . Forecasting and modelling are rapidly becoming the cornerstone of public health work , but government needs help .

Enter academia . We will see an increase in academic researchers conducting AI-driven modelling and forecasting on behalf of government . It is clear after the pandemic that protection of our population will require exceptional technology and collaboration . p

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