50 AI Examples from the World's biggest companies
Written by Luke Renner
Manceps has gathered 50 Artificial Intelligence and Machine Learning examples from the first 50 companies in the Fortune 500.
We sought to answer a few questions:
• How are the world's biggest companies deploying artificial intelligence today?
• What AI examples are out there?
• What lessons can stakeholders everywhere apply to their own organizations?
AI Super Power
Optical Character Recognition
Uses
Brick-and-mortar Stores
Corporate Campuses
Factory Floors
Emergency Rooms
Airports
Walmart
Walmart is deploying machine learning and image processing to all of its locations to make it easier for employees to keep their stores running smoothly. Using thousands of video cameras, weighted sensors on shelves, and other technologies, Walmart’s in-store tech can tell employees when certain products are running low or produce is starting to go bad.
In one example, image processing systems could identify bananas that had started to brown, eliminating the need for employees to manually inspect fruit. Similarly, traffic flow systems could anticipate downtimes, giving employees the chance to restock shelves or gather up shopping carts from the parking lot.
Key Takeaways
The lesson here is that visual data and image processing can improve the efficiency of nearly all the physical spaces in which organizations operate. By training an extra set of AI eyes onto all sorts of workspaces, artificial intelligence can automate inspections, catching things that humans may have missed and/or leaving them free for other tasks.
Ask Yourself: Does my business operate in any physical spaces that could benefit from an extra pair of eyes?
Free Guide
Discussion Questions for AI Readiness
AI Super Power
Automated Operations
Uses
Sourcing job candidates
Conducting research
Prospecting for business opportunities
Diagnosing Patients
Designing products
Exxon Mobil
ExxonMobil deployed an AI-powered algorithm to make it easier for its deepwater prospecting teams to drill at the bottom of the sea. Trained on a dataset that leveraged drilling specifications of previous jobs as well as survey information from the ocean floor, this Drilling Advisory System empowered their team to “automatically optimize drilling parameters”. The algorithm led to improved drilling and safety performance, and lower costs.
Key Takeaways
Like ExxonMobil and their ongoing effort to drill for oil, repetition within your organization is a good indicator that you might be able to deploy an AI system. In this case, ExxonMobil could transform its experience with off-shore prospecting into an algorithm that could automatically apply that experience to future jobs.
Ask Yourself: Is repetition a major part of my business?
AI Super Power
Optical Character Recognition
Uses
Fashion
Retail
Marketing
Customer Service
Media & Content Creation
Apple
Apple is infusing as much AI into their products and operations as possible. In 2019, the world's largest technology firm made several key artificial intelligence acquisitions, which served the following purposes:
1. Bring greater personalization to web search and Siri results;
2. Shore up their competitive disadvantage in the self-driving car space;
3. Make images “shoppable” by allowing users to search using photos, rather than keywords;
4. Improve iPhone photography with AI-powered photo enhancement.
Siri, of course, is another example of how Apple runs on AI. The voice-powered assistant is designed for continual, at-the-edge improvement, which means it uses customer communications to further train itself without having to transmit those private communications to Apple servers.
Key Takeaways
Just like the digital transformation of the ’80s and ’90s that led to a computer on every desk in America, Artificial Intelligence represents a massive opportunity for transformation within your business. AI, combined with machine learning, gives organizations an unprecedented ability to personalize the products, services, and search results of each of their customers. Personalization is one of the drivers of the digital economy. Today, consumers have come to expect that their experiences will be customized and individually curated.
Ask Yourself: Would my customers benefit from increased customization and personalization?
AI Super Power
Data-driven Decisions
Uses
Mortgage Lending
Insurance Underwriting
Credit Card Applications
College Admissions
Background Checks
Berkshire Hathaway
GUARD, a Berkshire Hathaway insurance subsidiary, has partnered with an AI data platform to optimize the underwriting process for its small and medium business segment. The goal of the partnership is to spare underwriters from having to spend so much time finding, gathering, and organizing client data. By structuring the data in a more efficient and actionable way, the organization hopes to dramatically reduce the time it takes from submission to quote. According to the announcement, “with only a business name and an address as inputs, insurers can obtain the information necessary to evaluate the ‘right’ risks and profitably grow and maintain their portfolio.”
Key Takeaways
Regardless of whether your organization has its own datasets or needs to acquire it from other sources, this example illustrates the fact that artificial intelligence puts customer data to work. Any time your organization uses data to make decisions about the services it offers its customers, artificial intelligence could be used to bring greater speed, accuracy, and fairness to the process.
Ask Yourself: Does my organization collect a lot of data to make decisions?
AI Super Power
AI-Centered Corporate Strategy
Uses
Supply Chain Management
Customer Recommendations
Logistics
Amazon
When it comes to making the best use of artificial intelligence, there’s an argument to be made that Amazon sits at the front of the pack. The company has employed AI across its entire operation with its leaders famously required to explain how they intend to deploy AI in their annual business plans.
In one of its first forays into AI, Amazon built a recommendation engine to make it easier for customers to surface more of the things they might like to buy. Today, their entire distribution infrastructure runs on AI. Combined with robotics and other innovations, Amazon’s warehouses are some of the most advanced artificial intelligence incubators on earth.
How else could they offer one-hour delivery to an expanding slate of markets?
Key Takeaways
Speaking at the inaugural re:Mars event, Amazon Go VP Dilip Kumar told the crowd that "If you start with a genuine customer problem, you can use the power of machine learning... to build a stellar customer experience." This philosophy drives Amazon’s AI strategy.
Similarly, artificial intelligence should inspire all organizations to take a look at their (and their customers’) pain points to see if greater analytics, efficiencies, and/or automations could solve some of their toughest business challenges.
Ask Yourself: Could greater analytics, efficiencies, or automations make my customer's lives easier?
AI Super Power
Natural Language Processing
Uses
Law
Medicine
Journalism
Liberal Arts
Customer Service
United Health Group
Like Amazon, the massive healthcare organization is deploying natural language processing across large swaths of its business. One of the biggest responsibilities of the organization is to authorize (or deny) payments for doctor-recommended medical procedures. This process of getting pre-approval can be costly, which is why Unitedhealth Group is using machine learning to streamline and automate as much of this process as possible.
Unitedhealth has also turned to natural language processing to sort the more than one million calls they get to their customer service line each day. Their goal is to deliver a better customer experience to all of their 115 million customers.
Key Takeaways
Natural language processing gives Machine Learning algorithms the powerful ability to organize, summarize, and understand language. Whether words are handwritten, scanned, scraped from a PDF, downloaded from a database, or spoken into a phone, these systems can glean not only the words being used but also their deep intrinsic meaning.
If your organization traffics in files, books, records, customer records, etc., natural language processing can streamline and automate almost everything you can think to do with that content.
Ask Yourself: Is reading a major part of my organization's daily activities?
AI Super Power
Smart Contracts
Uses
Digital Identity
Finance
Real Estate
Internet of Things
Healthcare
McKesson
Like Unitedhealth Group, McKesson is also in the healthcare industry; however, one of the ways the organization is deploying artificial intelligence is less about managing their patients and more about managing their business. For several years, McKesson has maintained a partnership with a global professional services firm focused on bringing about digital transformation. According to Emerj, this partnership was designed to bring smart contracts and Artificial Intelligence to several business processes, including:
• Customer payments, such as hospital stays and prescription drugs;
• Patient information gathering, storage, and processing;
• Automated contract management;
• Vendor payments and reimbursements.
Key Takeaways
Smart Contracts bring a layer of automation to how businesses operate and respond to market conditions. Adding artificial intelligence to these types of contracts opens the door to all sorts of novel business practices. We’re talking factories that automatically shift their output based on supply-chain fluctuations; prices that automatically shift based on demand; vendor payments that go out the instant certain conditions are met.
Smart contracts are particularly useful to your accountants, promising to automate and simplify much of the work they do over and over again.
Ask Yourself: How would your business change if you could automatically execute contracts?
AI Super Power
Automated Diagnostics
Uses
Transportation
Machinery
Logistics
Telecommunications
Medicine
CVS Health
2 years ago, CVS Health entered into an agreement with AI startup Buoy Health to deliver AI-powered customizations and healthcare recommendations to their more than 1,100 Minuteclinics across the US. Delivered via in-store kiosk, the CVS system automates the patient intake process by asking customers a series of questions about their current symptoms and previous health history. AI then uses these answers to direct patients to either an on-site healthcare professional, a medical consultation via webcam, or an approved over-the-counter solution.
Key Takeaways
While Artificial Intelligence has been driving innovation across both medical diagnostics and customer service, respectively, these AI-powered Minuteclinics represent one of the first attempts to integrate these capabilities and roll them out at scale.
Whether providing technical support or processing returns, the work of customer service is to diagnose a problem and deliver a solution. CVS’s foray into automating this process is a good reminder that other organizations should consider doing the same. Regardless of whether your team spends its time diagnosing a mechanical issue, a technical problem, or a delivery concern, artificial intelligence can surface solutions more quickly and accurately.
Ask Yourself: Does your organization leverage diagnostics to identify and solve problems?
AI Super Power
Image Recognition
Uses
Passports
Xrays
Engine Parts
Pumpkin Patches
Archival Footage
AT&T
AT&T is one of those organizations that has tried to bring artificial intelligence to almost every aspect of their business. Unlike the majority of the Fortune 100 organizations on this list, AT&T is more than willing to tout how artificial intelligence is driving their business. The organization owns dozens of media companies including HBO, AOL Time Warner, and ESPN, which means many of their AI plays are related to cataloging their video library and serving relevant ads.
On their dedicated Data Science and AI page, they outline several initiatives including:
• Video processing and image recognition to generate highly detailed descriptions of video content, “to power enhanced content experiences and more relevant advertising.”
• Natural language processing to improve the accuracy of genre and category labels across their video library.
• Predictive algorithms to better match advertisers with their ideal customers.
• Geospatial data analysis
• Machine learning for cybersecurity.
Key Takeaways
AT&T understands the almost limitless, seemingly magical capabilities that artificial intelligence can bring to their business. By thinking creatively about opportunities for automation, AT&T can deliver a better product for both its customers and its advertisers.
Their work with image recognition is particularly notable. Image Recognition is an extremely powerful tool that works on all sorts of visual data such as images, video, drone footage, or LIDAR surveys. Beyond the media examples above, training machines to perceive the visual world has all sorts of use cases that can be customized to the individual needs of your organization.
A good place to start is to make a list of all the things people in your organization regularly inspect. In most cases, an AI system can be trained to complete those inspections automatically.
Ask Yourself: What are things your organization regularly inspects?
Get the Ebook
50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now.
AI Super Power
Automated Data Processing
Uses
Finance
Insurance
Real Estate
Medicine
Amerisource Bergen
Amerisourcebergen, the world’s most profitable pharmaceutical company, is bringing artificial intelligence to its benefit verification process. In 2018, the pharmaceutical conglomerate announced that one of their subsidiaries had launched an AI-powered electronic benefit verification solution that would leverage a dataset of “health coverage and payer data collected from millions of manual verifications” to evaluate each incoming benefit verification. Today, their system can predict outcomes and process data in real-time with only a tiny fraction getting forwarded to a human clinician for further investigation.
Key Takeaways
AmerisourceBergen joins virtually every other major healthcare company in its quest to bring greater automation to the benefit verification process. The ongoing challenge of healthcare conglomerates is to balance expensive treatment options with their need to drive profitability. To achieve this balance, treatment plans must be efficacious and carefully considered.
More broadly, the thrust of their effort here is to transform a crush of data into action. Regardless of whether your information overload comes in the form of healthcare data or multinational financial information, artificial intelligence can bring order to chaos.
Ask Yourself: Is your organization drowning in data?
AI Super Power
Operational Optimization
Uses
Predictive Maintenance
Surveying
Process Optimization
Data Extraction and Interpretation
Chevron
In 2016, Chevron rolled out a machine learning system that could help it identify new well locations and stimulation candidates. Trained on a dataset that included the company's large collection of historical well performance data, the system has since contributed to a 30% increase in productivity.
At the time, the company was also rolling out a system that analyzed the performance information of thousands of pieces of equipment to bring predictive maintenance to their operation.
Today, Chevron is expanding its use of artificial intelligence beyond the needs of their oil-drilling operations. They recently announced a partnership with Microsoft in which they’ll use natural language processing to scrape information and insights from millions of drilling reports.
Key Takeaways
Chevron’s adoption of cognitive intelligence echoes a pattern we’ve seen with many of our clients. First, companies will use AI to exclusively address their most mission-critical business challenges. Then, after seeing the transformation AI has brought to their business in one area, leaders will look to deploy AI systems in other contexts.
If you’re bringing AI to your company for the first time, we recommend working on a project that has an immediate and measurable ROI. This brings an enthusiasm to your project that will make the transition to AI easier for your entire company.
Ask Yourself: For our first foray into AI, what is a problem we can solve that would directly affect our bottom line?
AI Super Power
Autonomous Vehicles
Uses
Transportation
Robotics
Drones
Defense
Ford
Ford is at the forefront of the transportation transformation. It was one of the first companies on earth to deploy a neural net at scale and has since brought artificial intelligence to both their assembly lines and in the operation of the vehicles they sell. Ford Edge’s all-wheel-drive system, for example, uses artificial intelligence to automatically determine if all-wheel drive is needed — more quickly and accurately than a human driver. In the factory, AI can detect wrinkles on seat fabric.
In 2019, to better compete in the race for full vehicle autonomy, the organization made a $1b investment into Argo.ai. The company expects to roll out a Geo-fenced self-driving car fleet within 3 years.
Key Takeaways
Self-driving cars may well be artificial intelligence’s greatest challenge. While most cars operate in predictable ways and follow predictable patterns, the long tail of outliers and special cases can quickly confuse the technology, leading to severe consequences and, as we’ve already seen, death.
While transportation and tech companies may disagree on the systems required for full autonomy, every single organization agrees that AI will be part of the solution.
While researchers have yet to completely solve the self-driving car problem, anything that moves can be imbued with some level of autonomous mobility. Some examples may include trains, drones and submersibles, consumer electronics, and even luggage.
Ask Yourself: What does my organization make that could move on its own?
AI Super Power
Generative Design
Uses
Product Design
Marketing
Video Game Design
Architecture
Manufacturing
General Motors
In 2018, General Motors announced that it had partnered with Autodesk to capitalize on its brand new technology: generative design. With generative design, an engineer can feed basic design parameters into the program such as materials needed, strength requirements, weight constraints, and the part’s intended method of manufacture. From there, artificial intelligence can deliver hundreds of variations of the original design. According to The Drive, General Motors has seen a 40% reduction in weight and a 20% increase in strength.
Key Takeaways
Generative design is just one of the many ways that artificial intelligence can support (or completely handle) the creative process. Already, AI can compose music, generate photo-realistic faces, and write high converting ad copy.
For the product designer, however, AI is an extremely robust tool. After the AI spits out hundreds of sample designs, design teams can then go and tag their favorites and repeat the process. This allows even computer-generated designs to improve generation by generation.
Ask Yourself: How could AI support your organization's creative endeavors?
AI Super Power
Geographic Datasets
Uses
Retail
Real Estate
Land Management
Agriculture
Costco
For at least a decade, Costco has used the purchasing history of its 90 million customers as inputs to help them determine new store locations. Speaking at a thought-leadership forum last year, CIO Paul Moulton explained how over time, the organization’s pattern recognition algorithms could help them define “what a successful Costco looked like in terms of who lived where, how far they would travel, and so on.” This initiative has led to millions of new customers in recent years.
To shore up their competitive disadvantage against the retail juggernaut, Amazon, Costco added Kenneth Denman to their board in 2017. Mr. Denman founded the company, Emotient, which used ML to measure facial expressions and predict emotions. The company was acquired by Apple in 2016.
Key Takeaways
Costco is a good example of how proprietary and publicly available datasets can be combined to drive greater sales and profitability. Amazon has taught us to expect that customer data will naturally be mined and processed. However, Costco brings additional layers to these processes such as geographical information about different neighborhoods and the people who live there.
If the questions you’re trying to answer require more data than your company currently has, you can likely round out your set by either launching a data collection effort or licensing an existing set.
Ask Yourself: What geographic data does your company use to make decisions?
AI Super Power
Deeply trained AI models
Uses
Natural Language Processing
Optical Character Recognition
Speech Recognition
Facial recognition
Image Processing
Alphabet (Google)
Last year, Google released Tensorflow, its open-sourced platform for machine learning, giving everyone access to one of the most advanced machine learning platforms ever created. More than 50 Google products have adopted the platform to put deep learning to work.
Internally, Google has hundreds of employees who are working on machine intelligence. Their ultimate goal is to transform their panoply of AI-related services into a cohesive digital assistant that can proactively manage and automate your entire life.
Key Takeaways
By releasing Tensorflow to the Open Source community, Google is sending a clear message that artificial intelligence is for everyone. The platform makes available all sorts of pre-trained models and machine learning algorithms. Together, they represent millions of hours of computer training, meaning everyone has access to the most powerful AI tools in existence.
Today, machine learning is driven, in part, by major technology companies who train models using their massive data sets. These out-of-the-box tools are very powerful, but we can make them even more powerful by layering additional functionality, customized to your individual needs.
By applying pre-trained machine learning models to new datasets or information, we are able to efficiently apply complex rules and learning to a new problem, without having to reinvent the wheel.
Ask Yourself: How could your organization expand upon existing machine learning models by layering additional datasets from your niche?
AI Super Power
Data-driven Decisions
Uses
Retail
Health Care
Finance
Logistics
Engineering
Cardinal Health
Cardinal Health recently released a platform designed to support oncology professionals by making available a robust set of AI-powered capabilities including:
1. Tools to help deliver coordinated, comprehensive, high-quality cancer care for patients in all treatment settings;
2. Actionable and effective insights that help patients become active participants in their treatment plan;
3. Resources to help develop palliative care plans that respect the values and desires of the patient and his or her family.
Additionally, the platform uses machine learning to identify patients at risk of 30-day mortality who would have been missed by conventional predictive analytic approaches. According to their
self-published case study, the deployment of this platform led to an 80% increase in patients referred to palliative care and an increase in those getting flagged for depression.
Key Takeaways
One of the things AI is really good at is synthesizing a massive amount of information and using that information to drive decisions. In effect, this is also the work of medical professionals. They order up a bunch of tests (data) to diagnose the patient’s condition and then decide on a treatment plan. Perhaps this alignment explains why the healthcare sector has been so keen to bring predictive analytics and machine learning to their efforts.
Health Care, however, isn’t the only industry where data is driving decisions. Really, any organization that vacuums up data can use artificial intelligence to make recommendations. Cardinal Health is using it to identify patients for palliative care but your organization may use a similar model to make investments, choose a new store location, optimize a flight path, etc.
Ask Yourself: How would easy access to data foster better decision-making?
AI Super Power
Voice Interactivity
Uses
Retail
Customer Service
Smart Home/Office
Internet of Things
Walgreens
Last summer, Walgreens announced that it was rolling out Theatro, a voice-based assistant to all of its nearly 10,000 stores. The smart speaker will bring Alexa-like natural language processing to a retail context, making it easier for employees to get answers to common questions, serve customers with less distraction, and work with their coworkers more expediently.
Theatro is small potatoes compared to the blockbuster multi-year partnership they recently announced with Microsoft, the goal of which is to develop “new health care delivery models, technology and retail innovations to advance and improve the future of healthcare.” This is likely a defensive move against both Jeff Bezos’s foray into Healthcare and CVS’s deployment of AI in their MinuteClinics.
Key Takeaways
One of the challenges with any emerging technology is a failure of imagination. By imagining Alexa in a different context, the company behind Theatro developed a solution that will serve tens of thousands of Walgreens employees.
Ask Yourself: Would your customers enjoy accessing your services using their voice?
AI Super Power
Customer Personalization
Uses
Retail
Health Care
Finance
Marketing
Entertainment
JP Morgan Chase
In 2018, JP Morgan Chase tapped Apoorv Saxena as its global head of AI and Machine learning services. Saxena previously led product management for cloud-based artificial intelligence solutions at Google.
Since then, Chase has made headlines for its novel use of AI throughout its business. In the summer of 2019, the company signed a five-year contract with AI ad copywriters, Persado, after a pilot project revealed a 5x increase in click-throughs on AI-generated ads. By December, the company was touting its AI capabilities to process expense reports and expedite employee reimbursements.
Perhaps the most famous way that artificial intelligence has come to finance is through hedge funds and other investment products whose trades are guided—and in some cases wholly dictated—by artificial intelligence. Currently, the jury is still out on AI’s ability to make predictions. AI models are, after all, susceptible to market manipulation. Nonetheless, a 2018 study found that 53% of hedge funds were using AI in its decision-making, and by 2019, over
$17bn in assets were under AI management.
Key Takeaways
For large organizations, artificial intelligence promises to increase customer personalization, decrease bureaucratic inefficiencies, and give teams a greater ability to generate demand at scale. Artificial Intelligence gives organizations the opportunity to bring personalization to their customers, at scale.
Ask Yourself: How would greater customer personalization transform your business?
AI Super Power
5G
Uses
Autonomous Vehicles
Virtual Reality and Telepresence
Remote Medicine and Surgery
Verizon
Verizon is leading the 5G revolution. The next-gen mobile data transfer technology is one hundred times faster than the 4G we currently find on our smartphones. 100xing the speed of the remote internet will profoundly alter how we interact with people at a distance and bring powerful technologies like virtual reality and autonomous vehicles to everyday consumers.
Already, Verizon is working to capitalize upon the promise of 5G by building customer loyalty from fleet managers today. Verizon’s Connect is an AI-powered fleet management solution that can automatically process in-car telemetry and dashcam footage for a variety of solutions, such as:
1. AI-based video filtering and search;
2. Advanced analytics;
3. Customized coaching for drivers based on in-vehicle sensors;
4. Risk mitigation from fraudulent insurance claims.
Key Takeaways
A faster mobile internet means more data: data from consumers’ phones, data from self-driving cars, and data from what Gartner predicts could be 20.4 billion internet-enabled objects and sensors by the end of this year.
Currently, companies already find it extremely difficult to monetize the vast amounts of data they collect. With the rise of 5G, we can expect corporations to deploy artificial intelligence with more urgency. Without AI, even more data will languish, un-utilized and under-monetized.
To get a jump-start on this onslaught, companies should work with data scientists today to create systems that efficiently structure and organize data as it flows into the company.
Ask Yourself: How could a lightning-fast mobile internet change the way you do business?
AI Super Power
Forecasting
Uses
Supply Chain Management
Retail
Sales
Kroger
Kroger is taking machine learning very seriously.
In 2015, they purchased a UK analytics firm, brought it to the US, and renamed 84.51°, a tribute to the longitudinal analysis the company employs. The acquisition eventually led to an ongoing initiative called “Embedded Machine Learning”, which is charged with framing, building, deploying machine learning solutions across their business regularly.
Supply chain management is a key use case for the retailer. Way back in 2018, their AI-powered sales forecasting application could predict day-by-day sales numbers for each of the items in their 2500 stores, for each of the subsequent 14 days.
Imagine what the organization can do today.
Key Takeaways
Kroger is one of several non-tech companies on this list that have allocated the resources necessary to bring an entire AI and ML team in house. This team is responsible for designing, building, and deploying all kinds of solutions, across all areas of the company’s business.
By going all-in on AI, Kroger hopes to reduce costs by reducing inefficiencies and maximizing every opportunity.
Ask Yourself: How would your business change if you could double the accuracy of its forecasts?
AI Super Power
Sufficient Investment in AI
Uses
Generative Design
Health Care
Utilities
General Electric
Our research found that GE is one of those massive companies that are making a big bet on artificial intelligence. In 2019, they made several announcements, which demonstrate the impact AI is already having on their business.
For example, GE cut the design process for jets and wind turbines in half, got FDA approval for an AI-powered x-ray machine, and released three utility analytics tools to make it easier for power companies to use more of their data and better “orchestrate their networks and the workers who operate them.”
The first of these tools is called Storm Readiness, which integrates outage history, crew response and GIS data, and high-resolution weather forecasts to accurately predict storm impact and prepare response crews and equipment ahead of impending weather.
Next, is a tool called Network Connectivity, which uses ML to automatically detect errors and polish the dataset that utilities use to maintain their network.
The final tool, Effective Inertia, uses AI and ML to bring greater stability to power grids that are impacted by fluctuating renewable resources such as sunlight on solar panels and wind on turbines.
Key Takeaways
One of the considerations that come with deploying AI applications is the accuracy and usability of datasets from which to train the models. GE’s deployment of Effective Inertia demonstrates that it’s not always necessary to have crystal clean source material. In fact, AI systems themselves can be trained to polish the data that other AI systems may leverage.
Ask Yourself: How could AI make structuring and organizing your organization's data easier?
AI Super Power
Operational Efficiency
Fannie Mae
Last year, the publicly-traded US-government enterprise partnered with Moogsoft to deploy the AIOps platform across their organization for enterprise monitoring. The Wall Street Journal reported that the platform has already reduced IT issues at Fannie Maybe by a third — with plans to reduce the number of incidents even further once the system has had another several iterations self-optimize.
Key Takeaways
Once trained, AI applications can continually improve, which means that your company’s AI ROI will continue to grow as time goes forward. This also partially explains why so many companies are adopting artificial intelligence with such urgency. These applications have a powerful ability to get better over time, which means late entrants may never surpass the capabilities of those who got a head start.
Ask Yourself: Who would need to sign-off of making an AI investment today?
AI Super Power
Robotics
Uses
Manual Labor
Manufacturing
Surgery
Transportation
Phillips 66
Last spring, the oil company deployed an AI robot to automatically inspect oil tanks. Previously, oil tanks had to be drained and cleaned before they could be looked at but now the company could complete the labor-intensive task without having to involve any labor, nor decommission a tank to complete the inspection.
Key Takeaways
There’s a natural alignment between robotics and artificial intelligence. Robots are, of course, designed to automatically complete manual tasks — but by adding a layer of AI, companies can imbue those physical processes with insight, autonomy, and analytics. Moreover, robots can contain sensors whose telemetry can then be fed into the ML system to drive future behavior.
Ask Yourself: Would the robotics you use in your organization benefit from increased intelligence and autonomy?
AI Super Power
Data-driven Decisions
Uses
Retail
Customer Service
Consumer Electronics and Tech Support
Telecoms
Bank of America
Bank of America is turning to artificial intelligence to help reduce its labor force and drive more of its customers to receive help via automated systems and chatbots. In 2018, the company rolled out Erica, an in-app customer service agent. By October of 2019, the digital assistant had handled about 75 million in-app customer service interactions.
Unsurprisingly, 2019 also saw a steep decline in new hires for customer service positions. One study on BOA’s hiring practices found that the conglomerate had reduced branch-related job openings by half. At that same time, the number of AI-and-ML-related BOA job openings had doubled.
Key Takeaways
As artificial intelligence becomes increasingly adept at automating repetitive tasks and interacting with humans, we can expect customer service as we know it to increasingly be handled by machine.
The social implications of this transformation will be profound with as many as 25% of all jobs in the United States being affected. While this number is aligned with “official” estimates, our understanding of AI and its capabilities give rise to the very real possibility that way more than 25% of jobs will be affected. In the case of customer service, the only limiting factor to these changes will be the speed by which companies can roll-out to customer service systems.
Ask Yourself: Does your company have high customer service costs?
AI Super Power
Ongoing Research into AI
Uses
Medical Research
3D Rendering
Genomics
Wildlife Conservation
Recipe Improvement
Microsoft
When it comes to artificial intelligence, Microsoft is literally the thought leader. At the beginning of 2019, the company had filed nearly 700 AI-related patents, beating everyone else, including Google (536 patents), Intel (267), and Amazon (75). Except for a brief time in the 2010s, Microsoft has dominated AI for nearly 30 years.
Today, its artificial intelligence division employs 8,000 engineers — and with that many resources — not to mention multi-billion-dollar investments into other AI startups — there is no limit to the problems they’d like AI to solve.
Some teams are working on healthcare solutions like creating genomic treatment plans or curing ALS. Others are working on 3D renderings built from drone footage. The list goes on.
In addition to developing solutions, their Azure platform also allows companies to deploy ML models at scale.
Key Takeaways
Last summer, Bill Gates said his “biggest mistake ever” was failing to launch an Android-like operating system for mobile. Not to be outdone by the next technology wave, Microsoft has made a big bet that AI will keep them relevant in the tech sector for decades to come.
Their timing could not be more perfect. Right now, companies everywhere are still experimenting with AI’s possibilities. By putting thousands of tech’s top minds on these problems, Microsoft hopes that it will continue to expand upon its suite of AI use cases for almost limitless applications.
Ask Yourself: Would investing in AI now, lead to competitive advantages in the future?
AI Super Power
Prescriptive Algorithms
Uses
Retail
Inspections
Employee Training
Quality Control
The Home Depot
Home Depot has deployed a monitoring system that uses prescriptive analytics to reduce shoplifting, employee theft, or other errors (known collectively in the retail industry as “shrink”). According to the 2019 National Retail Security Survey, shrink costs retailers about 1.38% of sales. In the case of Home Depot, ending shrinkage could save the company $1.4b in losses.
Using machine learning, the company has begun automatically flagging anomalies across the retail experience, which could be an indication of “fraud, wrong pricing, a training issue”, etc. Once the system identifies an issue, it sends out a guide to give employees and managers more insight into which action to take.
Key Takeaways
Artificial intelligence is giving managers the ability to monitor employee activity at scale and with much more precision. Prescriptive algorithms not only use data to identify anomalies but can also surface additional training materials to help employees better perform their duties — or perform their duties in a way that’s standardized across the entire enterprise.
In addition to seeing prescriptive algorithms in a retail context, we are also seeing this in the transportation space. Fleet managers are using prescriptive analytics to identify weak drivers and train them to drive more safely and/or with less wear-and-tear on vehicle parts like brakes or tires.
Ask Yourself: How big is your current investment in employee training?
AI Super Power
AI Audits
Uses
Cybersecurity
Model validation
De-bias Certification
Boeing
With the crash of two Boeing 737 Max aircrafts in 2019, the company is, unfortunately, having to take a hard look at how the AI used in its in-flight software may have contributed to the deaths of hundreds of people. According to Vox, the company began expanding its automated system, MCAS, in the early teens to derive more performance from their planes to better compete against foreign airline manufacturers.
Unfortunately, the organization appears to have insufficiently managed this expansion of MCAS’s capabilities, which meant that many departments didn’t realize how aggressive and riskier the system had become, thereby allowing key safety redundancies to fall through the cracks.
Key Takeaways
Artificial intelligence is powerful but it is by no means flawless. Whether or not the mistakes of AI lead to fatalities, companies must ensure that they have good protocols in place to ensure that any errors or biases that come from machine-learning algorithms can be properly identified and preempted.
Doing so may require engaging artificial intelligence security experts to conduct audits of your AI system to ensure that it’s working within acceptable safety parameters.
Ask Yourself: Have your existing AI deployments been audited for security and reliability?
AI Super Power
In-house AI Team
Uses
Fraud Detection
Customer Service
Customer Personalization
Wells Fargo
Wells Fargo’s foray into artificial intelligence echoes that of the other financial firms on this list. For example, the company is making a multi-billion-dollar investment in data analytics and has engaged some of the industry’s top minds to develop machine learning algorithms for their financial products.
In 2018, Wells Fargo rolled out machine learning and artificial intelligence to their customer-facing app, which allowed the organization to give customers automatically-generated suggestions and financial advice. If, for example, a customer had more money in their checking account than expected, the system would surface a suggestion to move some of that money into savings.
Wells Fargo is also looking for novel ways to deploy machine learning for fraud protection. For example, the company is experimenting with creating a system that would specifically detect elder abuse.
Key Takeaways
The biggest companies on earth have spent the last decade getting their AI house in order. In the early teens, that effort was focused on data analytics but now, they are, of course, focused on layering those datasets with intelligence to derive even more insights and opportunities from them.
We expect in the coming decade for AI to become much more sophisticated across enterprise. The majority of companies have dabbled and deployed AI solutions. Now, armed with a track record of success and a well-curated in-house AI team, we can expect these companies to shift from using artificial intelligence to automate and scale manual tasks to using artificial intelligence to accomplish things never before thought possible.
Ask Yourself: What new business units could be powered by artificial intelligence?
AI Super Power
Regulatory compliance
Uses
Legal
Government
Finance
Health Care
Citigroup
Last year, Citigroup announced it was launching an initiative to bring machine learning to the time-consuming, highly manual process of reviewing global trade transactions and ensuring their compliance.
The new platform will provide:
1. In-depth analysis of global trade transactions.
2. Advanced analytics and natural language processing to better understand networks of related parties, unstructured data, and customer activity over time.
3. Process automation that combines analytic results and trade-related bank policies to help focus on trade transactions activities that may need further investigation under Citi’s escalation process for them.
Citigroup operates in more than 160 countries, which means it must dedicate considerable resources to ensure financial transactions are completed within the confines of the law. This work is highly complex and requires a veritable army of lawyers and financial experts to keep everything above board. By developing AI-powered solutions to monitor these processes, Citigroup is making a big leap forward.
Key Takeaways
This use case is one of the most complex we’ve seen. In addition to automatically processing the dataset of real-time financial transactions, the platform will also rely on Natural Language Processing to extract additional insights from every transaction.
Eventually, we can expect that Citigroup will use AI to automatically interpret new regulations and adapt the system as the legal financial framework on the ground starts to change.
Ask Yourself: Does your organization spend heavily to ensure that you are in compliance with local and/or industry-specific regulations?
AI Super Power
Digitized Decision-making
Uses
Management
Predictive Insights
Institutional knowledge
Marathon Petroleum
Marathon has recently rolled out a suite of tools in its drive to automate its oil-drilling sites. At an FT Digital Energy Summit, Bruce McCullough, SVP of technology and innovation and CIO of Marathon Oil, mentioned his company’s focus on several initiatives: IoT consolidation, consistent data, data cleansing, and real-time access. As he explained, he hopes these priorities will derive more advanced analytics and insights.
The company’s ultimate goal is to digitize decision-making. Rick O’Brien, Marathon Oil digital oilfield project manager, said the company would like to “take all the knowledge that a really, really strong production supervisor has and turn it into an AI tool that will think on behalf of that person and that asset”.
Key Takeaways
Marathon understands that as organizations drive toward automation, they must put systems in place to digitize the decision-making process, making it easier for stakeholders to make the right calls and help the company move forward. This effort isn’t limited to natural resources. In medicine, artificial intelligence is helping doctors develop treatment plans. In finance, AI is driving investment strategy.
In all of these domains, we can see an ongoing effort to retain employee experience as proprietary insider information. Fortunately, with AI, your best performer can leave your organization but you will still retain their strategy as a series of ML models.
Ask Yourself: How would your business change if your top-performing employees chose to leave?
AI Super Power
AI-powered Customer Service
Uses
Technical Support
Cybersecurity
Comcast
Compared to other telecoms on this list, the company’s (publicly available) deployment of artificial intelligence has been on the thin side. The biggest way we’re seeing AI is via their customer service. The company is looking to completely automate the process by which customers (a) receive phone support and (b) get technicians sent to their homes.
In the case of the latter, the company is can now predict with almost 90% accuracy whether (or not) a technician can fix a problem by being on-site. To develop the application, the company collated a large dataset including information taken from incoming customer service calls and data from its network operations, respectively. The company then built a model to integrate this data and make a prediction.
In addition to automating the customer service process, Comcast is also making key investments in AI startups. In 2019, it acquired AI cybersecurity company BluVector. The company uses machine learning to combat cyber threats, including zero-day malware, fileless maleware, and ransomware.
Key Takeaways
In 2015, after the collapse of a proposed merger between Comcast and AOL Time Warner, the company rebranded itself and promised to invest $300 million to improve its famously reviled customer service. At the time, Comcast indicated it would invest much of that into scaling up their operation with more hires. However, five years later, we see that Comcast appears to have leveled up its CS abilities through artificial intelligence. Presumably, the hires were for AI experts and data scientists.
Ask Yourself: What aspects of your customer service should be automated?
AI Super Power
Federated Systems
Uses
Mobile Internet
Patient Privacy
Chatbots
Proprietary Data
Cybersecurity
Anthem
In 2019, CNBC reported that Anthem had poached several key artificial intelligence resources from Apple, which industry experts saw as the former’s attempt to shore up a competitive disadvantage in the rising tide of tech turning to healthcare.
Beyond building out its internal team, the health care organization has forged highly-publicized partnerships with technology leaders. In 2018, they embarked on a 12-month pilot project with the company, doc.ai to determine whether AI could be used to predict allergies. Doc.ai is a medical records platform that uses artificial intelligence to crowdsource user health information for research purposes.
A year later, they partnered with Stanford University’s Computer Science department by joining its AI for Health Affiliation Program. The research partnership is designed to find new ways to bring artificial intelligence to healthcare — and the healthcare system more broadly. Their first area of focus will be finding ways to use Artificial Intelligence to make insurance prices more transparent.
Key Takeaways
Unlike some of the other health care organizations on this list, Anthem is trying to align itself with Silicon Valley to derive greater value from their internal and external investments in AI. The company clearly predicts encroachment into their space from the big tech behemoths like Google and Amazon.
A security detail we’d like to flag for you is related to doc.ai — particularly that the platform is designed to keep user’s private medical records on their phones while still retaining the ability to use that data in an anonymized, aggregated way to train their AI models. These federated systems are what allows Apple to improve Siri’s voice transcription functions without having to transmit your private messages to the company’s servers.
Ask Yourself: Would your datasets be more powerful by safely integrating private or proprietary data?
AI Super Power
Hardware
Dell
In 2018, Dell partnered with NVIDIA and Intel to launch a suite of hardware solutions designed to facilitate the deployment of artificial intelligence. The “prevalidated designs” combined both hardware and software to make it easier for companies to deploy Artificial Intelligence at the server level.
This product line is just one of many, which indicate that Dell is serious about bringing cognitive intelligence technologies its product portfolio across IT and desktop computers.
Key Takeaways
Through its partnership with Intel, Dell hopes its hardware technology will become the go-to for Artificial Intelligence deployments. There are, however, several other platforms that you can use to deploy AI. Microsoft, Amazon, and Google all offer cloud services in this category.
Ask Yourself: When deploying AI, would you need to use your own servers or would a scalable cloud solution suffice?
AI Super Power
Quality Assurance
Uses
Manufacturing
Product Design
Food Supply
Engineering
Dupont
Dupont has quietly built a digital transformation team that is working to bring technology solutions to the chemistry and materials conglomerate, including solutions in the AI space. Publicly-available information about the team is scarce; however, in one Delaware Business Times article, we got a glimpse of what they were working on.
In one example, Digital IT Scrum Master Ruby Kapoor described a project he was working on to bring machine learning to address issues of complex manufacturing. The essential premise of her project is that perfectly calibrated manufacturing equipment leads to perfectly produced products. By feeding equipment sensor data into their ML models, Ms. Kapoor hopes to identify a predictive relationship between that data and quality control. “If you make the adjustments [to equipment] earlier,” she explained, “you improve quality and then you improve the yield.”
Key Takeaways
Since the rise of the internet of things ecosystem, the world’s best factories have made big investments in digital transformation. Now, oceans of data flow from almost every piece of equipment, giving manufacturers more information than they know what to do with.
Unfortunately, many companies lack the internal resources to translate this information to reduce costs and increase efficiency. For that, companies need AI.
Ask Yourself: Would your company benefit from greater automation as it relates to quality assurance?
AI Super Power
Good Communication
Uses
Change Management
Public Relations
Marketing
Customer Service
State Farm
State Farm recently pushed back against soulless AI-powered customer service with a series of hilarious ads that featured a creepy robo-agent and promised to keep customer interactions human.
