leveraging AI

How Tech Giants are Leveraging AI

Artificial intelligence has integrated into the very fabric of lives in such nuanced ways that we barely notice it. The recommendations on Netflix, scheduling appointments through Alexa, advertisements, services all have their roots in AI. Digitization has enabled AI to leverage unified data and deliver value for more business sectors than conceived before.

AI will alter work as we currently know it; and therefore, it must be on the radar of business leaders to stay competitive in the digital economy. However, we have only begun to explore the potential of AI and what it can do for our world. This blog post aims to assess how capable AI already is, by looking at how tech giants are leveraging this technology.

E-commerce giants like Amazon use recommendation algorithms to personalize the choices for each customer, based on interest, purchases and browsing history. Netflix uses this to generate top 10 recommendations for each user in a household based on their preferences. Seventy five percent of what users watch on Netflix come from product recommendations, according to McKinsey.
Let’s take a deep dive into how these different companies are leveraging AI.

Google

Google was built on its search engine. It has come so far to replace “look it up” with “Google it.” From being a dictionary to converting currency, to knowing how to draft a letter, figure out the time difference or what to say/do, it is quite literally a one-stop shop for all odds and ends. A few years back, when AI was still a novelty, Google was using it in its search engine to refine results.

In 2014, Google bought DeepMind for $400 million. This allowed a more seamless integration and helped in finding the fastest route between stations, improving healthcare and learning about how AI evolves and learns through a Differentiable Neural Computer, the board game “Go.” Google Assistant depends on natural language processing and can quite literally take on the role of an assistant in several situations from a reservation for dinner to setting reminders and more.

In an effort to study machine learning and make it more widely available, Google launched TensorFlow, an open source library for numerical computation that makes machine learning faster and easier. People + AI Research initiative by Google [PAIR] brings together researchers, academics and those interested to advance the state-of-the-art in the field, applying AI to products and new domains, developing tools to ensure that everyone can access AI.

Amazon

Amazon, through its many e-commerce sections, has put the spotlight on the retail value of AI. Recommendations build on past purchases, browsing history to curate what the customer needs, improving cart value. The algorithms behind those systems have been tweaked again and again over the years. These days, thanks to machine learning, the recommendations have gotten more dynamic and sophisticated, and accounts for 35% revenue generated.
At the scale that Amazon processes orders, efficiency needs to be at its highest. Magnified over several hundred million orders a year, even a second or two saved per order makes a huge difference at the back-end. Amazon Robotics fulfills millions of orders at a dizzying pace, potentially saving the company, by some estimates, up to $2.5 billion.

Amazon Echo, featuring Alexa has been one of the most popular end products of machine learning from Amazon. Many companies now have Alexa skills that add value to the customer’s life such as Liberty Mutual and Capital One. Liberty Mutual provides auto insurance information to customers and Capital One enables them to make a payment through their Amazon device.

Imagine a world where you never wait in line, or even open your wallet for a shopping experience. Amazon Go delivers just that. Usage of Amazon Web Service’s machine learning tools has grown 250% over the last year, and since last November, more than 100 new features or services have been added to its machine learning portfolio. One of them is DeepLens, designed so that developers can build and fully train a machine learning model within minutes of unboxing, it is already being used in ways Amazon never imagined. Using AI and machine learning has enabled Amazon to enter the trillions in terms of market value.

Spotify

Spotify uses AI to power its renowned music recommendations through collaborative filtering that analyse behaviour, Natural Language Processing that interpret text, and audio models that review the sounds of the songs. Other features including Release Radar, Spotify for Artists are powered through big data as well.

The Discover Weekly feature on Spotify reached 40 million people in its first year. Every user gets a personalized playlist each week from Spotify consisting of music they have not heard before on the service, but something the listener is expected to enjoy.

A dynamic map by Spotify, made possible through machine learning and interpreting the data generated, maintains a playlist that updates for each city, showing the world what makes each city unique and what its residents love about it. Spotify was always data driven and The Echo Nest brought a different approach. They combined what people said to what music actually is.

Acquiring Niland has allowed great use of big data and succeeded at bringing the most innovative products to the music streaming market with products for both fans and artists.

Addressing Key Challenges before the Collections Industry Today

Netflix

Netflix’s most-interesting use of data to inform creative decisions and original content development has resulted in an 80% success rate for Netflix original shows compared to a 30-40% success rate for traditional TV shows. The biggest proof is in the runaway success of House of Cards. Netflix skipped past the traditional process of making a pilot of House of Cards and committed to two seasons with 26 episodes. This was done by machine learning and analysis of 6 years worth of data and patterns.

Netflix not only has the largest worldwide subscriber base of any business but managed to keep growing it by +25% last year. Its market capitalization competes head to head with Disney, the most-valued entertainment company in the world.

 

JPMorgan Chase

JPMorgan Chase has invested in Contract Intelligence (COiN). This uses machine learning to review and interpret commercial loan agreements saving 360,000 hours. COiN is able to scan over documents in seconds and is less error prone than human beings.

The bank currently has a $9.6 billion USD budget for technology and is determined to automate some more mundane tasks and create new tools for bankers, making the process more streamlined, leaving more time for more strategic tasks.

The bank has developed its own cloud computing system Gaia and relies on cloud computing services from Amazon, Microsoft, and IBM. Firms can access balances, research and trading tools, allowing clients to easily access routine information.

Why these companies are after AI

Imagine Amazon without recommendations or Google without its relevant search terms. These companies work and are loved because they leverage AI. These companies have ushered in the digital age using their practices. They recognized the value of data and strove to use it and engage in a world of digitally native workflow. This culture of connectivity has created digital ecosystems and reinforced the data dominators that companies like Amazon, Netflix and Google are.

Is AI only accessible to tech giants?

While application of AI by these tech giants certainly enlightens us with the immense potential of AI, it’s important to understand that AI isn’t improbable for smaller companies. Startups from all over the world are coming up with intelligent products that can transform businesses of any size. Today, more companies including SMBs are becoming an early adopter of AI if they believe it can bring in an edge over competitors, while streamlining the process and adding value to the end user.

Democratisation plays a tremendous role in this. Less than two years ago, only 10% of internet users worldwide considered themselves experts on AI; they knew and understood the technology. Widespread knowledge of AI and machine learning is enabling more perspectives, more innovation and makes the field more accessible and less intimidating.

Which other business sectors can leverage AI: marketing and finance

If a company can access valuable data in a given space and provide a better product and user experience than everyone else because of it, they will be able to continue to get access to more of that data than anyone else. Today, AI has permeated into several other industries, and is now transforming even marketing, finance, loan management and debt collection, streamlining processes to deliver efficiency and improved customer experience. AI has become a key differentiator in the competitive landscape.
As more than 80% of marketers believe that targeted marketing is a priority for them this year, they are looking at employing latest technologies such as AI to enable them in doing so.

AI can propel next-generation marketing methods. In brand management, AI solutions can help you listen and comprehend what consumers are talking about your products and services, and further recommend actions for optimize business value.

For digital advertising, AI can tap into all the publicly available data such as social profiles and keyword searches to display more relevant ads to target personas; and hence, improve conversions for businesses with little to no manual effort.

Similarly, data about your prospects can be fed to AI solutions for an improved contact and treatment strategy. AI marketing achieves this through elaborate segmentations of different profiles and advanced algorithms. Data feeds consumer profiles which can then be leveraged by businesses using AI/ML to communicate the right message at the right time to their target customer.

It’s imperative that we achieve personalization in our messaging because today’s customer is likely to engage only with content relevant to him or her. Generic one-for-all messaging isn’t going to work anymore.

This is true for other industries like debt collection as well. Random calling is proving increasingly ineffective which is why creditors are exploring ways to personalize their contact and treatment strategy to boost collections and improve customer experience.

Dasceq, a FinTech startup, leverages intuitive intelligence, artificial intelligence, machine learning, and big data to power an improved contact strategy for collections. Feel free to reach out to us if you have any questions or if you would like to know more.