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.


Build your competitive advantage with intelligent technologies. If you are interested in leveraging AI & ML technologies for your business, contact Crescense today!

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!

The Impact of Software and Digital Transformation on the Life Sciences Industry

At a faster rate than ever before in history, the life sciences industry is experiencing unprecedented technological change, primarily due to the blurring of the line between “life sciences” and “technology.” As a result, every new technological breakthrough, in fields ranging from AI and machine learning to DNA sequencing and genomics, has contributed to the ongoing digital transformation of the life sciences industry.

Of course, ever since the launch of the modern software industry in the mid-1970s , technology has had a significant impact on the way companies in the life sciences industry operate. Enterprise Resource Planning (ERP) software, for example, made it possible to coordinate sophisticated supply chains spanning multiple countries (and even continents). Sales force automation software made it possible for pharmaceutical companies to transform the selling ability of their sales teams. And nearly every aspect of the life sciences industry was made better, faster and more efficient by the introduction of new software that enabled different units of the enterprise to “talk” with one another. That, in turn, helped to speed up the R&D cycle, improve the overall quality of healthcare, and spur new breakthroughs in the way we treat infectious and chronic disease.

You can think of that phase as the first round of digital transformation in the life sciences industry. It largely fulfilled vision of famous Silicon Valley venture capitalist Marc Andreessen, who once famously opined that “software is eating the world.” What he had in mind was a brave new world where every process, every operation and every aspect of industries would be transformed by software. But even visionaries like Marc Andreessen could not anticipate what happened next.

The next wave of digital transformation involved the introduction of the cloud, Big Data, mobile devices and the Internet of Things. Each of these huge technological breakthroughs had a tremendous impact on the future growth trajectory of the life sciences industry. The cloud, for example, made it possible for doctors on one side of the world to analyze data from patients located in another part of the world in near real-time. Big Data meant that doctors and medical professionals had more patient data than ever before to come up with cures and treatments, while R&D professionals had many more insights into how to develop the next Miracle Drug.

That brings us to the situation today, where big digital trends like the cloud, Big Data, and mobile have already transformed life sciences companies. But now comes an even more exciting era, brought on by technological innovations in fields such as artificial intelligence and machine learning. Already, there are AI-powered supercomputers capable of diagnosing patients and disease symptoms almost as well as today’s top medical professionals. It is only a matter of time before a “trip to the doctor’s office” means hooking up with an AI-powered online avatar that you communicate with via your smartphone. Moreover, the Internet of Things is leading to the development of innovations like “digital pills” that can track and monitor your body from inside once you consume them.

For the past half-century, the impact of software and digital transformation on the life sciences industry has truly been profound. The blurring of the line between the life sciences and tech industries is only going to intensify in the future.  SAP’s intelligent software solutions are helping life sciences organizations with transformation that will allow them to thrive in the digital age.  Contact us to learn more!

The Advantages of In-Memory Databases – Delivering Data in Real Time

In-memory databases are differentiated from other database management systems by the way they store data. Instead of using a disk storage mechanism, an in-memory database relies on main memory for computer data storage. For non-technologists, this might seem like a purely semantic distinction, but relying on memory for data storage actually offers several powerful advantages.

First and most importantly, in-memory databases are faster than traditional database management systems. A key reason for this is because memory access is much faster than having to access disk storage. All data is stored and managed exclusively in main memory. The speed upgrade should make intuitive sense: you are essentially eliminating an entire step in accessing data. Another key reason for this boost in speed is the fact that internal optimization algorithms for in-memory databases are less complex and require fewer CPU instructions to get the right data at the right time.

Secondly, in-memory databases are more efficient than traditional database management systems because there is less “seek time” when querying data. This, too, should make intuitive sense: when looking up data, it’s a lot easier to find what you need when the data is stored right there in main memory. This is known as minimal request time, and is absolutely essential for any industry where real-time performance is crucial.

That being said, in-memory databases are not a perfect solution. Until recently, for example, there were questions about what happens to data stored in RAM in the event of a power loss or similar event. But that drawback has largely been addressed by a number of workarounds, such as the creation of non-volatile RAM technology that can continue to run, even in the event of a power failure.

Recently, so much attention has been focused on in-memory databases because they are a much better fit for any real-world application that requires microsecond response times, as well as any application that must deal with extremely large spikes in traffic at any time. In the mid-2000’s, for example, in-memory databases became popular as a way of offering real-time analytics to large corporate customers. As the price of RAM continues to fall, and as new, more powerful multi-core processors hit the market, it’s easy to see why this trend will continue.

Thus, as can be seen, in-memory databases are superior in terms of speed, efficiency and reliability. For any application where response times are measured in microseconds, they offer an obvious value proposition. Just a decade ago, it might have been possible to use disk storage mechanisms, but the creation of a truly real-time digital landscape has also created the obvious need for in-memory databases.

Contact us to learn more about how SAP HANA in-memory technology can help your business thrive in the Digital Economy.

3 Ways That Cloud Software Has Impacted the Retail Industry

Cloud software is having a significant impact on the retail sector, especially when it comes to boosting revenue, cutting costs, and providing the type of real-time analysis needed to make better business decisions. From small retail operations with just a single store, to national and even international retail superstars, cloud software has become an important way to build competitive advantage and improve overall business performance. Here’s a brief overview of exactly how that is happening…


Reduced IT costs


By using a network of remote servers hosed on the Internet, cloud enables a “pay-as-you-go” IT system that is affordable and convenient for small retailers, as well as very robust and scalable for larger retailers. If you’re running a small clothing retailer, for example, you don’t have to worry about installing your own equipment or maintaining your own servers – everything runs smoothly on the cloud.


Instead of spending money each month on excess IT costs, retailers can then re-invest those dollars into running the actual business. And for larger retailers, it’s possible to trade up to enhanced functionality on the fly, all without the need for calling in IT vendors or installing new equipment.


Real-time visibility into the business


If you’re not running your retail business in the cloud, you simply won’t have access to the types of real-time insights that cloud software enables. That’s especially true in the digital device era, when retail store managers with smartphones and tablets can literally have real-time insights into how their business is running regardless of where they are in the world.


That’s especially important when it comes to supply chain logistics, where real-time insights can be the key to avoiding inventory stock-outs, lost deliveries and sub-optimal inventory levels. With cloud software solutions designed for the retail sector, store managers will always know exactly how much inventory they have on hand, as well as the status of deliveries and orders.


This type of real-time visibility does more than just empower store managers – it also empowers the retail staff on the floor. When these sales associates have tablets or other digital devices, they can tell customers exactly which items are in stock, when a delivery is set to arrive, or how to find a specific item at any retail location in the store’s network. It all adds up to a superior customer service experience.


Enhanced productivity through cross-department collaboration


At many companies, information about a business is “siloed” into different departments, making it difficult for, say, the marketing team to interact with the finance team, or the supply chain team to interact with the sales team. Ultimately, that results in a lot of lost productivity.


But imagine what can happen when retail store managers can see exactly how a specific marketing campaign is performing, or a budgeting team can see exactly what’s happening at the warehouse. It means much better insights into the overall operation of the business, and the end of the silo mentality. It also makes it possible to generate reports on an as-needed basis, rather than waiting for monthly or quarterly updates.




It’s no wonder, then, that cloud has become a multi-billion dollar market opportunity within the retail sector. It’s not just that retail stores have better information and better data – they also have the right tools to cut costs, boost sales and improve overall profitability. For any retail business looking to boost its performance, cloud software offers a very compelling opportunity.