Machine Learning

How AI and Machine Learning Are Revolutionizing the Consumer Products Industry

Artificial intelligence (AI) and machine learning are having a disruptive impact on the consumer products industry, changing not only what types of products appear on retail shelves, but also how those products are brought to market and eventually delivered to consumers. As a result, leading consumer products companies are beginning to integrate elements of AI and machine learning into nearly every aspect of the product lifecycle.


New AI-powered products


A visible way to see the impact of AI on the consumer products industry is via the introduction of products that directly make use of language recognition and computer vision – both of which are only possible with the introduction of machine learning algorithms. Common products in this category include AI-powered virtual assistants, such as Amazon Echo and Google Home. Already, products that you use on an everyday may already integrate elements of machine learning without you even realizing it, including Facebook (photo recognition) and Netflix (personalized movie recommendations based on past viewing behavior). This type of recognition and recommendation functionality is quickly extending to more and more consumer products.


A new era of personalization and customization


Consumer products categories seeing a great uptake of AI-powered personalization include the food and beverage sector and the apparel and fashion sector. For example, consumer products brands are using sophisticated AI algorithms to analyze various taste, aroma and flavor profiles of products, and then coming up with new beverage or food products that have combinations of similar tastes, aromas or flavors. Eventually, the goal is to usher in an era of ultra-personalization, in which consumers no longer need to purchase mass-market goods that are completely identical to all other goods.


Manufacturing, logistics and delivery


At the manufacturing level, AI and machine learning are also impacting the consumer products industry. For example, AI is being used at manufacturing plants for quality control and supply chain optimization. Today’s modern logistics chains are so complex that humans are now using AI for what is often referred to as “augmented decision-making.” Thanks to AI algorithms, managers now have much greater visibility into all work-in-progress, and can then analyze the best processes to automate. This leads to faster time-to-market, as well as lower costs for consumers. Going forward, look for even more efficiencies in shipping and logistics. For example, Amazon has a patent for “anticipatory shipping,” which is a way of getting an order prepared for delivery before a product has even been ordered. By using AI algorithms to study user behaviors, Amazon can make very informed guesses about which items in your shopping cart will actually end up as part of your final order.


Marketing and promotion


Finally, AI is playing an important behind-the-scenes role in how consumer products are marketed and advertised to consumers. According to Gartner, by 2020, AI will help to manage 85 percent of all customer interactions in retail. The easiest way to see this is with e-commerce, where every step of your customer journey is being studied and analyzed, so that you can be shown ads, messages, and promotions that have the highest likelihood of getting you to purchase something.



As can be seen, AI and ML can play a variety of roles within the entire customer experience. They are impacting manufacturing and logistics, marketing and promotion, and even customer service (through the rise of AI-powered bots). Going forward, these technologies will continue to have a dramatic impact on the entire consumer products industry.


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The Impacts of Artificial Intelligence and Machine Learning on the Life Sciences Industry

Machine learning and artificial intelligence (AI) are having a dramatic impact on the life sciences industry. Many major life sciences companies have either partnered with an AI startup or acquired one. Many life sciences professionals have experimented with AI technologies in the workplace at one time or another. So why all the excitement?

Perhaps the best answer has to do with AI-powered drug discovery. The time to develop a new drug can run anywhere from 10-15 years, and the costs of gaining final approval from regulators can be staggering. By one estimate, pharmaceutical and biotech companies in the U.S. spend $75 billion annually on R&D to develop new drugs. The primary appeal of AI is a way to dramatically shorten the development cycle and also reduce the cost of getting approval.

And here is where AI and machine learning technologies really excel, because they are very good at making sense of incomprehensible amounts of data. Not only can they detect trends in a sea of data, but also they can predict the outcomes of trials with stunning accuracy.

Another key area for machine learning and AI technology in the life sciences industry involves greater drug personalization. It’s not just that AI-powered machines can find potential drug solutions faster and cheaper – they can also find solutions that are completely personalized and customized to a specific user. Machine learning really enhances the overall R&D process by being able to predict drug performance. Think about all the warnings on medications or treatments: depending on unique genetic code and physical condition, medication will likely impact you differently than it does someone else. And that’s where AI can make a huge difference – by personalizing medicine based on your unique identity at the molecular level, it can improve the overall treatment process.

And, of course, AI and machine learning can play a very important role in patient diagnostics. UK researchers at Oxford, for example, have used AI systems to diagnose heart disease and lung cancer faster than human practitioners can. One of the keys here is the ability of machine learning algorithms to study images and learn what a “healthy” person looks like and what a “sick” person looks like. Over time, the algorithm actually learns how to detect early warning signals of potential sickness just by studying images of a heart or a pair of lungs.

Just about any data-rich process within the life sciences industry can be made better through the use of machine learning and AI technologies. AI systems can collect, analyze and integrate text and data from just about any source. And they are also becoming much better at analyzing unstructured data that even humans might have a hard time analyzing. Thanks to AI and machine learning, the life sciences industry should see an accelerated pace of innovation in 2019 and beyond. That’s good news for people everywhere if it can bring down the cost of healthcare and lead to more effective treatments and possibly even cures.

To learn more about how you can incorporate intelligent technologies such as AI and ML into your business, contact Crescense today!