The AI Revolution Is Coming, Here’s How Industries Are Optimizing

This article was originally published on ValueWalk

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AI is clearly the most buzzworthy topic of the year but it’s not new. It’s been around for a very long time. Phones and social media don’t wow with innovation anymore but AI can and will. Unsee all the articles about robot uprisings and impending doom for a moment. There are real, positive applications already occurring across almost every industry. Here are how some are already using it now.

Is Your Business AI Ready?

The current drug discovery process is expensive, disconnected, time-consuming, and involves mountains of diverse data. Bringing a single drug to market costs around $2.5 billion dollars and takes 10 years. For every drug that gets approved, ten thousand compounds will fail.

AI allows scientists to test infinitely more possibilities for potentially life-saving therapies in a less costly, more time-efficient manner. However, AI cannot be done on a broad scale without first having access to clean data.

Data liquidity is the creation of flexible, scalable systems designed to handle and make accessible the rapidly increasing amount of data that will be needed to power AI. 2 million scientists, including those from Bristol-Myers Squibb and Merck to research universities like MIT and Oxford work with a platform, Dotmatics that is specifically designed to make research science data AI ready.

Worldwide the amount of data reportedly doubles every 2 years. Developing and testing each new drug creates terabytes or even petabytes of data at every stage, and broadly speaking genomics research is expected to generate between 2 and 40 exabytes of data within the next decade. Scientists are now faced with massive datasets that require sophisticated analysis techniques and computational tools to extract meaningful insights. This will require infrastructure to truly get predictive in their approach, and be able to apply data science to their science data.  

Healthcare itself is also using AI on the backend to be more efficient. One of the top contributors to wasted healthcare dollars is due to the claims process, reported at more than $250 billion per year according to Humana. 

Predictive denials evaluates each claim and its likelihood of being denied to help determine which item(s) on the claim are the leading cause for denial before submitting. This allows the right specialist to intervene before claims go to payers with a “clean” submission. The product also leverages large-scale historic payment data tuned to providers’ denial trends for continuous learning and adaptation.

Turning Data Into Real-Life Action

There are also rapid challenges companies face when it comes to data connectivity. There are data silos, networks are unreliable, they can’t get data in real-time, the data comes from varied sources, and the whole system is difficult to unify.

Mercedes Benz, Sirius XM, BMW and more use a service called HiveMQ to navigate data and accelerate IoT. It is the central nervous system for the Internet of Things, giving companies a way to overcome all of these obstacles with a secure platform so they can do more with their data. Features like zero message loss, consistent communication, zero-downtime upgrades and a cluster architecture for no single point of failure may be too technical but for a practical example BMW cut time to open the car door with a phone app from 30 seconds to 1 second.

Businesses want to focus on their core value – whether it be a connected car platform, a drone that carries medical supplies, or a connected dishwasher.  They don’t have the time or the expertise to worry about the data moving from point A to point B to make sure their product delivers an exceptional user experience, they want to spend their time building more features. HiveMQ takes that piece, the movement of IoT data, and solves it. So the car door opens in sub one second instead of 30, the drone gets the medical sample to the hospital 75% faster, and the dishwasher tells the owner when the detergent is empty.

Fast and fair access to capital is huge. Accelerating data in highly regulated industries is always more challenging. Some banks are now utilizing AI-enabled underwriting. This allows banks and credit unions to provide faster decisioning while safely offering credit access to more consumers and in some cases has already led to a 25% increase in approval rates for customers.

Travel is a trillion-dollar-industry without a ton of current disruption. Mondee just rolled out what Fox is calling the most powerful travel assistant ever. Abhi is mobile-first A.I. platform. leveraging Generative A.I., deep learning, and computer vision to provide personalization and immersive local travel experiences. Uniquely, Abhi creates custom travel guides based on users’ interests. It is also empowering travel experts globally, presenting monetization opportunities for influencers and freelancers that use the platform.

Finally, payment technology is always evolving. Open banking is a  bet on the future of payments. Banks have often held a monopoly on financial data. Open banking enables individuals and businesses to share data with other financial services providers, such as budgeting apps, investment platforms, and more. While the process isn’t as complex as other advancements in data science it’s transformative in what the end result can and will be. Personalized financial products, increased access to credit, better financial inclusion and simplified international transactions will all be possible.

That’s the main takeaway with AI and all the data that is being used right now. There are stunning examples that will grab headlines and lead to profound breakthroughs but really every business, every industry will be using AI to power its next generation of offerings. That’s why it’s the buzzword of the year and might be of the decade.