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Automotive Industry Gearing towards Digital Transformation with AI by EOS Intelligence
by EOS Intelligence EOS Intelligence No Comments

Automotive Industry Gearing towards Digital Transformation with AI

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Artificial intelligence (AI) has become an integral part of almost every industry, and the automotive sector is no exception. From self-driving cars to predictive maintenance, AI is evolving as a major disruptor in the auto industry, slowly transforming how automobiles are designed, manufactured, and sold. This digital swing is driven mainly by increased competition, consumer preferences for smart mobility, and the benefits of AI. However, AI adoption in the automotive industry is not mainstream yet, with the technology deployed only at the pilot level and in selective business segments. As the world gears toward an era of digital transformation and automation, AI is expected to be part of various business processes in the automotive industry in the coming years.

Artificial intelligence in the auto industry is typically associated with autonomous and self-driving cars. However, the technology has increasingly found its way into other applications over the last few years. Leading auto OEMs are showing an interest in deploying AI-driven innovations across the value chain, investing in tech start-ups, partnering with software providers, and building new business entities.

For instance, a venture capital fund owned by Japanese automaker Toyota, Toyota AI Ventures (rebranded as Toyota Ventures now), with US$200 million in assets under management, invested in almost 35 early-age startups that focus on AI, autonomy, mobility, and robotics between 2017 and 2020. Similarly, in 2022, South Korean automotive manufacturer Hyundai invested US$424 million to build an AI research center in the USA to advance research in AI and robotics. In the same year, CARIAD, a software division of the Germany-based Volkswagen Group, acquired Paragon Semvox GmbH, a Germany-based company that develops AI-based voice control and smart assistance systems, for US$42 million.

Changing consumer preferences, competitive pressures, and various advantages of AI are driving this transformation. According to a 2019 Capgemini research study, nearly 25% of auto manufacturers in the USA implemented AI solutions at scale, followed by the UK (14%) and Germany (12%) by the end of 2019.

There are numerous applications of AI in the automotive industry. Some of the more common and innovative uses of AI include virtual simulation models, inventory management, quality control of parts and finished goods, automated driver assistance systems (ADAS), predictive maintenance, and personalized vehicles, to name a few.

Automotive Industry Gearing towards Digital Transformation with AI by EOS Intelligence

AI-based virtual simulation models used for effective R&D processes

Due to changing customer preferences, increasing regulations concerning safety and fuel emissions, and technological disruption, OEMs are finding it more expensive to make cars nowadays. A 2020 report by PricewaterhouseCoopers says that conceptualization and product development account for 77% of the cost and 65% of the time spent in a typical automotive manufacturing process.

To make R&D cost-effective and more efficient, some auto manufacturers and tier-I suppliers are turning to AI. AI enables the simulation of digital prototypes, eliminating a lot of physical prototypes, thus reducing the costs and time for product development. One interesting concept that is emerging and catching attention in this area is the “digital twin”. The concept employs a virtual model mimicking an entire process or environment and its physical behavior. There are numerous uses of digital twins – in vehicle design and development, factory and supply chain simulations, autonomous driving simulations, etc. In vehicle design and development, digital twins make simulations easier, validate each step of the development in order to predict outcomes, improve performance, and identify possible failures before the product enters the production line.

For instance, in 2019, Continental, a Germany-based automotive parts manufacturing company, entered into a collaboration with a Germany-based start-up, Automotive Artificial Intelligence (AAI), to develop a modular virtual simulation program for its Automated Driver Assistance System (ADAS) application and also invested an undisclosed amount in the company. The virtual simulation program could generate phenomenal vehicle test data of 5,000 miles per hour compared to 6,500 miles of physical test driving per month, reducing both time and costs.

Many leading automotive companies are also looking to utilize this innovative concept in streamlining the entire manufacturing operations. For example, in early 2023, Mercedes-Benz announced that the company is partnering with Nvidia Technologies, a US-based technology company specializing in AI-based hardware and software, to build a digital twin of one of its automotive plants in Germany. Mercedes-Benz is hoping that the digital twin can help them monitor the entire plant and make quick changes in their production processes without interruptions.

General Motors, Volkswagen, and Hyundai use AI for smart manufacturing

Automation processes and industrial robots have been in automotive manufacturing for a long time. However, these systems can perform only programmed routine and repetitive tasks and cannot act on complex real-life scenarios.

The use of AI in automotive manufacturing makes these production processes smarter and more efficient. Some of the applications of AI in manufacturing include forecasting component failures, predicting demand for components and managing inventory, using collaborative robots for heavy material handling, etc.

For instance, General Motors, a US-based automotive manufacturing company, has been using AI-based design strategies since 2018 to manufacture lightweight vehicles. In 2019, the company also deployed an AI-based image classification tool in its robots to detect equipment failures on pilot-level experimentation.

Similarly, a Germany-based luxury car manufacturer, Audi, has been using AI to monitor the quality of spot welds since 2021 and is also planning to use AI in its wheel design process starting in 2023. In 2021, Audi’s parent company, Volkswagen, also invested about US$1 billion to bring technologies such as cloud-based industrial software, intelligent robotics, and AI into its factory operations. With this, the company aims to drive a 30% increase in manufacturing performance in its plants in the USA and Mexico by 2025.

In another instance, South Korean automotive manufacturer Hyundai uses AI to improve the well-being of its employees. In 2018, the company developed wearable robots for its workers, who spend most of their time in assembly lines. These robots can sense the type of work of employees, adjust their motions, and boost load support and mobility, preventing work-related musculoskeletal disorders. Thus, AI is transforming every facet of automobile manufacturing, from designing to improving the well-being of employees.

Companies provide more ADAS features amidst increasing competition

Automated Driver Assistance System (ADAS) is one of the powerful applications of AI in the automotive industry. ADAS are intelligent systems that aim to make driving safer and more efficient. ADAS primarily uses cameras and Lidar (Light Detection and Ranging) sensors to generate a high-resolution 360-degree view of the car and assists the driver or enables cars to take autonomous actions. Demand for ADAS is growing globally due to consumers’ rising preference for luxury, better safety, and comfort. It is estimated that by 2025, ADAS will become a default feature of nearly every new vehicle sold worldwide. ADAS is classified into 6 levels:

Level 0 No automation
Level 1 Driver assistance: the vehicle has at least a single automation system
Level 2 Partial driving automation: the vehicle has more than one automated system; the driver has to be on alert at all times
Level 3 Conditional driving automation: the vehicle has multiple driver assistance functions that control most driving tasks; the driver has to be present to take over if anything goes wrong
Level 4 High driving automation: the vehicle can make decisions itself in most circumstances; the driver has the option to manually control the car
Level 5 Full driving automation: the vehicle can do everything on its own without the presence of a driver

At present, cars from level 0 to level 2 are on the market. To meet the growing competitive edge, several auto manufacturers are adding more automation features to the level 2 type. Companies have also been making significant strides toward developing autonomous vehicles. For instance, auto manufacturers such as Mercedes, BMW, and Hyundai are testing level 3 autonomous vehicles, and Toyota and Honda are testing and trialing level 4 vehicles. This indicates that the future of mobility will be highly automated relying upon technologies such as AI.

Volkswagen and Porsche use AI in automotive marketing and sales

There are various applications of AI in marketing and sales operations – in sales forecasting and planning, personalized marketing, AI-assisted virtual assistants, etc. According to a May 2022 Boston Consulting Group (BCG) report, auto OEMs can gain faster returns with lower investments by deploying AI in their marketing and sales operations.

Some automotive companies have already started to deploy AI in sales and marketing. For instance, since 2019, Volkswagen has been leveraging AI to create precise market forecasts based on certain variables and uses the data for its sales planning. Similarly, in 2021, a Germany-based luxury car manufacturer, Porsche, launched an AI tool that suggests various vehicle options and their prices based on the customer’s preferences.

Automakers integrate AI-assisted voice assistants into cars

Cars nowadays are not only perceived as a means of transportation, but consumers also expect sophisticated features, convenience, comfort, and an enriching experience during their journey. AI enhances every aspect of the cockpit and deploys personalized infotainment systems that learn from user preferences and habits over time. Many automakers are integrating AI-based voice assistants to help drivers navigate through traffic, change the temperature, make calls, play their favorite music, and more.

For instance, in 2018, Mercedes-Benz introduced the Mercedes Benz User Experience (MBUX) voice-assisted infotainment system, which gets activated with the keyword “Hey Mercedes”. Amazon, Apple, and Google are also planning to get carmakers to integrate their technologies into in-car infotainment systems. It is expected that 90% of new vehicles sold globally will have voice assistants by 2028.

Integration and technological challenges hamper the adoption of AI

The adoption of AI in the automotive industry is still at a nascent stage. Several OEM manufacturers in the automotive industry are leveraging various AI solutions only at the pilot level, and scaling up is slow due to the various challenges associated with AI.

At the technology level, the creation of AI algorithms remains the main challenge, requiring extensive training of neural networks that rely on large data sets. Organizations lack the skills and expertise in AI-related tools to successfully build and test AI models, which is time-consuming and expensive. AI technology also uses a variety of high-priced advanced sensors and microprocessors, thus hindering the technology from being economically feasible.

Moreover, AI acts more or less like a black box, and it remains difficult to determine how AI models make decisions. This obscurity remains a big problem, especially for autonomous vehicles.

At the organizational level, integration challenges make it difficult to implement the technology with existing infrastructure, tools, and systems. Lack of knowledge of selecting and investing in the right AI application and lack of information on potential economic returns are other biggest organizational hurdles.

EOS Perspective

The applications of AI in the automotive industry are broad, and many are yet to be envisioned. There has been an upswing in the number of automotive AI patents since 2015, with an average of 3,700 patents granted every year. It is evident that many disrupting high-value automotive applications of AI are likely to be deployed in the coming decade. Automotive organizations are bolstering their AI skills and capabilities by investing in AI-led start-ups. These companies together already invested about US$11.2 billion in these startups from 2014 to 2019.

There is also an increase in the hiring pattern of AI-related roles in the industry. Many automotive industry leaders are optimistic that AI technology can bring significant economic and operational benefits to their businesses. AI can turn out to be a powerful steering wheel to drive growth in the industry. The future of many industries will be digital, and so will be for the automotive sector. Hence, for automotive businesses that are yet to make strides toward this digital transformation, it is better to get into this trend before it gets too late to keep up with the competition.

by EOS Intelligence EOS Intelligence No Comments

Slowly but Surely – Insurance Realizes AI’s Value

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Several sectors, such as banking, F&B, automotive, and healthcare have seen major transformations at the hands of artificial intelligence (AI) ‒ we discussed benefits of AI in fast food industry in our previous article – Artificial Intelligence Finds its Way into Your Favorite Fast Food Chain in November 2017. AI has become an integral part of a large number of industries, providing new solutions and facilitating greater back-end efficiency as well as customer engagement and management. Insurance sector, on the other hand, has been largely slow to react to this disruptive trend. In 2017, only about 1.3% of insurance companies invested in AI (as compared with 32% insurance companies that invested in software and information technologies). However, this is expected to change as insurance companies have begun to realize the untapped potential that AI unearths in all aspects of their business, i.e. policy pricing, customer purchase experience, application processing and underwriting, and claim settlement.

Insurance industry has been one of the sectors that have operated in their traditional form for several decades, without undergoing much of substantial transformation. This is also one of the reasons why the insurance sector has been relatively late in jumping on the AI bandwagon.

Artificial intelligence, which has significantly transformed the way several industries such as automotive, healthcare, and manufacturing operate, also presents a host of benefits to the insurance sector. Moreover, it is expected to drive savings not only for insurance companies but also brokers and policy holders.

Streamlining internal processes

AI has the ability to streamline several internal processes within insurance companies. There is a host of duplicating business operations in the insurance sector. Automation and digitization can result in about 40% cost cutting, and this can be achieved by automating about 30% of the operations.

This can be seen in the case of Fukoku Mutual Life Insurance. In 2017, this Tokyo-based insurance company replaced 34 employees with IBM’s Watson Explorer AI system that can calculate payouts to policyholders in faster and more precise manner. The company expects to boost productivity by 30% and is expected to save close to US$1.26 million (JPY 140 million) in the first year of operations. To put this in a perspective, the AI system cost the company, US$1.8 million (JPY 200 million), and its maintenance is expected to cost US$130,000 (JPY 15 million) per year. Therefore, Fukoku seems optimistic about achieving its return on investment within less than two years of installing the AI system.

In addition to providing automation of processes, AI can bring out disruptive transformation throughout the insurance value chain. Some of the most substantial benefits of using AI in the insurance sector are expected to be seen in policy pricing, offering of personalized insurance plans, as well as claim management.

Policy pricing

Traditionally, insurance companies used to price their policies by creating risk pools based on statistical sampling, thereby all insurance policies were based on proxy data.

AI is transforming this by moving policy pricing analysis from proxy data to real-time source data. Internet of Things (IoT) device sensors, such as telematics and wearable sensor data, enable insurance firms to price coverage based on real events and real-time data of the individuals that they are insuring.

An example of this is usage-based or pay-per-mile auto insurance, wherein a telematics sensor box (a black box for a car), is installed into a car to track information such as speed, driving distances, breaking habits, and other qualitative and quantitative driving data. Using this data, insurance companies can offer a customized policy to the car owner, charging lower premium from safe drivers or offering less-used cars the pay-per-mile option. It also helps insurance companies charge suitable premium from reckless drivers and long-distance drivers.

In February 2017, UK-based mobile network brand, O2, expanded into the auto insurance space with a telematics product called the O2 Drive. The device tracks different aspects of a user’s driving habits and offers discounts and personalized insurance policies based on it. The company is positioning its products to attract teen and young drivers as they are most likely to be open to sharing their driving data.

In addition to auto insurance, IoT devices such as wearable devices and smart home solutions also help in setting policy pricing in health and home insurance. US-based Beam Insurance Services uses a smart toothbrush to offer dental insurance. The company uses data accrued from the smart toothbrush, such as number of times a person brushes their teeth, duration of brushing, etc., to offer a personalized insurance policy. It claims to offer up to 25% lower rates in comparison with its competitors.

In another example, UK-based Neos Ventures offers IoT-powered home insurance based on a smart home monitoring and emergency assistance device. The device and its accompanying app helps users reduce instances of fire and water-based damages as well as break-ins and thefts. The premise of the company is that if they can successfully reduce the chances of any mishaps, they can offer cheaper premiums to the insured.

While IoT devices can greatly personalize insurance pricing, the largest caveat to the success of this pricing mechanism remains that customers must be willing to share their personal data with insurance providers to attain savings in the form of lower premium. As per Deloitte – EMEA Insurance Data Analytics Study 2017, about 40% of customers surveyed seemed open to track their behavior and share the data with insurers for more accurate premiums for health insurance, while 38% and 48% customers were open to tracking and sharing data in case of home and auto insurance, respectively.

Slowly but Surely – Insurance Realizes AI's Value

Customer purchase experience and underwriting of applications

The relationship between an insurance agent and the customer is an extremely important one for insurance companies. Many times the customer is dissatisfied with its interaction/experience with the insurance agent as they feel that the agent does not have their best interest at heart or the agent is not available for them as and when required.

This issue is effectively addressed with the use of AI-powered chatbots or virtual assistants. Advanced chatbots use image recognition and social data to personalize sales conversations and provide a better customer experience. Thus, agents and insurance representatives are being replaced by chatbots, which deliver faster and more efficient customer experience.

ZhongAn, a China-based pure online insurance company uses chatbots for 97% of its customer queries without any human involvement. It also uses AI to offer innovative insurance products, such as cracked mobile screen insurance. It uses image recognition technology to detect whether the image shows the mobile screen is cracked or intact. It can also decipher if the picture has been photoshopped or altered to ensure the claim is genuine. Since its inception in 2013, the company has sold about 8 billion policies to 500 million customers (these include cracked mobile insurance as well as the company’s other popular products).

To blend the human experience with chatbots, companies have started branding their chatbots with human names. New York-based P2P insurance company, Lemonade, uses exclusively chatbots named Maya and Jim to interact with customers and create personalized insurance options in less than a minute within the Lemonade app. The chatbots Maya and Jim are alter-egos of the company’s real-life employees with the same names.

Similarly, in December 2016, ICICI Lombard General Insurance launched a chatbot called MyRA. Within six months of operations the virtual assistance platform sold 750 policies without any human intervention, while it was used by 60,000 consumers for queries.

In addition to elevating customer’s purchase experience, AI also helps in reducing insurance underwriting/processing time and ensuring higher quality. The underwriting process traditionally has a range of manual tasks that make the process slow and also prone to human errors. However, AI helps achieve quicker and more reliable data analysis. AI tools such as Machine Learning and Natural Language Processing (NLP) help underwriters scan a customer’s social profile to gather important data, trends, and behavioral patterns that can result in more accurate assessment of the application.

New-York based Haven Life (a subsidiary of MassMutual), leverages AI technology to underwrite its life insurance policies. It requires its customers to submit a 30-question application (which is more conversational in nature as compared with the detailed traditional life insurance forms) and upload few documents such as medical records, motor vehicle driving records, etc. The AI technology analyzes the provided information along with historical life insurance data and asks additional questions if required. In several cases, it also offers coverage without the mandated medical test. Through AI, the company has reduced its underwriting time from the typical 1-2 weeks to as low as 20 minutes.

Claim management

AI can play a significant role in two of the most critical aspects of claim management, i.e. the time to settle a claim and fraud detection.

The time to settle a claim is one of the performance metrics that customers care most about. Using AI, companies can expedite the claim process. Chatbots are used to address the First Notice of Loss (FNOL), wherein customers submit their claims by sending pictures of the damaged goods along with answering few questions. The chatbot then processes the claim and assesses the extent of loss and its authenticity, to determine the correct amount for claim settlement.

Lemonade set a world record in December 2016 by settling a claim using its AI bot, Jim, in only three seconds. The AI bot reviewed the claim, cross-referenced it against the policy, ran several anti-fraud algorithms, approved the claim, sent wiring instructions to the bank, and informed the customer in the three-second window.

Another interesting area of application is in agriculture, where machine learning can also help quickly analyze claims (pertaining to loss spread over a wide area) using satellite imaging, which would otherwise take humans significantly greater time and costs to ascertain.

As mentioned earlier, AI can bring massive savings to insurance firms by reducing fraudulent claims. As per US-based Coalition Against Insurance Fraud (CAIF) estimates, insurance carriers lose about US$80 billion annually in fraudulent claims. AI technologies provide insurance firms with real-time data to identify duplicate and inflated claims as well as fake diagnoses.

In addition, many companies use AI to run algorithms on historical data to identify sequences and patterns of fraudulent claims to identify traits and trends that may be missed by the human eye during the initial stages of claim processing.

According to CAIF, in November 2016, about 75% of insurance firms used automated fraud detection systems to detect false claims. Paris-based Shift Technologies is one of the leading players in this domain, claiming to have a 250% better fraud identification rate as compared with the market average. The company had analyzed more than 100 million claims from its inception in 2013 up till October 2017.

EOS Perspective

There is no denying that AI has the capability to transform the insurance industry (as it has transformed many other industries). Although, initially slow at reacting to the AI trend, insurance companies have realized its potential.

As per an April 2017 Accenture survey, about 79% of the insurance executives believed that AI will revolutionize the way insurers gain information from and interact with their customers. This is also visible in the recent level of investments made in AI by the insurance sector. TCS’s Global Trend Study on AI 2017 stated that the insurance sector outspent all the other 12 sectors surveyed (including travel, consumer packaged goods, hospitality, media, etc.) by investing an average of US$124 million annually in AI systems. The cross industry average of the 13 sectors stood at US$70 million.

Thus, it is very important for insurance players to get on board the AI trend now. Since they are already late (in comparison to some other industries) in reacting to the trend, it is critical that they adapt to it to remain relevant and competitive.

However, the key barrier to AI implementation are the complex and outdated legacy systems that hold back innovation and digitization. The companies that do not manage to implement tech innovations in their legacy systems due to high cost might just be acting penny wise, pound foolish.

by EOS Intelligence EOS Intelligence No Comments

Artificial Intelligence Finds its Way into Your Favorite Fast Food Chain

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The idea of robots replacing humans has always seemed like talks of the future, however, it is not as distant as it seems, especially when it comes to the fast food industry. The fast food market, which is characterized by cut-throat competition and high share of low-skilled jobs, has recently been swept by a technology wave. Leading players, such as Domino’s, Starbucks, or KFC, are investing heavily in artificial intelligence (AI) to increase efficiencies and differentiate themselves in this overly crowded industry – some are integrating it with their back-end operations while others with the consumer interface. However, with investments in technology increasingly becoming an industry trend, the question remains if AI will provide the competitive edge to these players or are consumers yet not quite ready to lose the human touch.

Artificial intelligence has been the buzz word for some time and the fast food industry is also catching on the wave. With some market leaders claiming to be as much a technology company (owing to huge technology budgets) as a food business, these players are incorporating AI in several verticals to improve operational efficiencies and elevate consumer experience.

The wave of AI adoption is particularly prominent in the US market, where labor costs are increasing significantly, hence AI is being seen as a tool to reduce costs in the long run. Just recently, in the beginning of 2017, minimum wages have been increased in 19 states and will reach US$13.50/hour in Washington state and even US$15/hour in California by 2022 (the minimum wages in 2017 stood at US$11 and US$10.50 for Washington state and California, respectively).

Apart from the need to control costs, the interest in AI is driven by the fact that it provides food business with great advantage – the use of AI helps companies gather valuable data about customer choices, flavor trends, etc., and use this information effectively.

Leading players, such as Domino’s, Starbucks, KFC, and CaliBurger, have already started using AI is different verticals of their businesses to not only reduce costs but also to remain one step ahead of the changing consumer expectations.

Domino’s

Domino’s can be easily slated as one of the most aggressive fast food players when it comes to adoption of technology. The company has embraced AI in several aspects of its operations aiming to smoothen both the ordering and the delivery sides of the business.

In early 2017, Domino’s launched an AI-based technology called the DRU (Domino’s Robotics Unit) Assist, which enables consumers to order a pizza on the app using their voice. The in-app AI assist, which was built in partnership with natural language company, Nuance, converses with customers in a human-like manner and discusses orders, menus, ingredients, store locations, and operating hours.

Along similar lines, the company has also launched its Facebook messenger bot, wherein customers can converse with the bot on the messenger app to learn about menu options, discount offers, and also order food. In addition, Domino’s is in the process of launching its ‘Domino’s Anywhere’ feature, through which customers can drop an exact location pin using GPS (as in case of Uber) when ordering pizza thereby facilitating delivery at various locations, such as parks, and other public places without providing an exact address.

Simultaneously, the company is also using AI to automate the delivery process. In November 2016, in New Zealand, Domino’s partnered with Flirtey, a drone company, to undertake the first commercial delivery of food by a flying drone. While this technology is largely futuristic for mass adaptation, the company is focusing on land-based autonomous delivery vehicles to deliver pizza to customers’ doorsteps. This technology went to trial in June 2016 in Australia and in 2017 in Germany, while the company plans to roll it out in the Netherlands for customers within the one-mile radius by the end of 2017. The technology, which is provided by Starship Technologies (a European start-up), has GPS tracking, computer vision and object detection capabilities, and can travel within a three mile radius, carrying up to 10kg weight for a cost as low as US$1.32 (£1).

McDonald’s

McDonald’s is one of the recent players to blend fast food with technology. The company stated that its investments in technology are to be one of its key strategies in 2017, calling it the ‘Experience of the Future’ strategy. As per its plans, McDonald’s aims to replace cashiers with self-ordering kiosks in 2,500 of its restaurants by the end of 2017 and in another 3,000 restaurants by the end of 2018. The cost of each kiosk is estimated at about US$50,000-60,000.

In addition to this, the company plans to roll out mobile ordering across 14,000 US locations by the end of 2017. Mobile ordering will not only ease the ordering process but also help the company gain access to valuable customer data, which in turn can be used to recommend additional dishes and personalized deals. McDonald’s has already launched mobile ordering in Japan and received a positive response with customers using the app ordering about 35% more than usual.

Since 2015, the company has also been rolling out digital menu displays across its stores in the USA as well as globally. They use AI to highlight weather-appropriate options. This feature has resulted in increased sales by 3-3.5% in Canada.

Starbucks

Starbucks has also developed an artificial intelligence program to improve customer ordering experience. The program, which is known as the Digital Flywheel program, links itself with the accounts of Starbuck Reward members and makes order suggestions based on order history, weather conditions, time of day, weekend/workday, and other such factors. In addition, it brings additional convenience to the ordering process for the Reward program members, who can order directly from push notifications or text message and collect their ready order from a nearby Starbucks.

Moreover, embracing the voice computing trend, the company has launched an AI-based ordering system on its app that allows customers to order and pay for their orders using voice. The company has also launched a ‘Starbucks Reorder Skill’ for users of the Amazon Alexa app, wherein users who have linked their Starbucks account to their Amazon Alexa account can re-order their usual drink (at one of the last 10 visited Starbucks stores) by simply saying “Alexa, order my Starbucks”. However, this service is currently limited to the order of the users’ usual designated drink instead of ordering anything off the menu.

Starbucks has made significant investments in technology on a continuous basis, having invested close to US$275-300 million in its partners and digital initiatives globally in 2016, an increase from an investment of US$145 million in 2015.

KFC

While most quick service restaurants players are using technology to elevate their app-based ordering experience, KFC in China is taking a different route to join the AI bandwagon. In April 2016, KFC (in collaboration with Baidu, China’s leading search engine) launched a robot-run restaurant in Shanghai called Original+. The restaurant is run by a robot named Dumi, which takes customer orders and is smart enough to handle order changes and substitutes. While the robot can understand the three main dialects of Mandarin spoken in China, it cannot distinguish other dialects and accents. The payment at the outlet is made through smartphones via mobile payment systems.

The collaboration opened another AI-enhanced café in Beijing in December 2016, wherein customers take pictures of themselves with a machine, which then recognizes the users face, sex, age, and mood, along with analyzing the time of the day to recommend suitable meal options and completes the ordering process. Moreover, upon revisit, the machine recognizes the user and shows order history as well as dining preferences to quicken the order process. However, unlike the Shanghai restaurant, this restaurant also offers the traditional ordering process. While these may seem futuristic, the company has expressed its plans to open more smart restaurants in the country.

CaliBurger

Apart from market leaders, smaller players, such as CaliBurger, are also investing heavily in technology in both the front and back end of their operations. The California-based burger chain has brought AI into their kitchens through the use of AI-enabled robot, called Flippy, which is capable of cooking/flipping burgers and placing them on the bun. The robot, which was launched in March 2017 in the chain’s Pasadena, California outlet is created by Miso Robotics, a pioneer in the robotics for restaurant business. The concept is currently in test run and if successful, it is expected to be rolled out in early 2018 with expansion plans to more than 50 outlets worldwide by 2019.

EOS Perspective

It remains no secret that most leading and few niche smaller players are turning to AI to elevate their service levels in this competitive industry. Companies which have traditionally not taken the digital route up till now are also joining the technology bandwagon. Pizza Hut, which has always been one step behind Domino’s with regards to technology deployment, has invested US$12 million in technology in Q2 2017 towards improving its digital and delivery services. The chain plans to invest US$180 million in a technology overhaul by the end of 2018.

It can be expected that at this stage of technology development most of the automation will be successfully implemented in the customer-facing side of the business, and will comprise technologies such as bots and voice recognition that can be integrated into apps and other ordering mediums. This not only helps consumers by easing the ordering process but also helps companies gather valuable data about customer preferences and ordering trends, which in turn can be used for providing complementing recommendations and thereby increasing sales. While AI-enhanced ordering and payment may be the path of the future, it will be far-fetched to say that it will eliminate the need for humans in this side of the industry altogether. With increased sales due to AI-based ordering, the need for humans will remain, however, their role may evolve from ordering to management.

The adoption of AI or automation at the food preparation and delivery end, on the other hand, still seems a little futuristic. While several players, such as Domino’s and CaliBurger, have started investing and launching this technology, the wide application of it seems distant. This is especially true in the food preparation tasks, due to an increasing trend towards customization of orders and the growing use of complex ingredients to cater to niche audiences that require dairy-free, vegan, gluten-free, or other such options. Till the time robots that can handle such complexities are developed, these jobs will largely be conducted by humans with maybe automating the easier aspects of the process (such as flipping the burgers). Moreover, with the fast changing consumers’ needs it will be hard for robotics companies to preempt the trends and develop robots that can match the required skill sets both now and in the future.

That being said, the use of AI by the restaurant industry is definitely on the rise and while we may not know the extent to which it will take over the current operations, we can definitely be sure that this is increasingly becoming the point of focus as well as innovation in this highly competitive space.

Driverless Autonomous Vehicles
by EOS Intelligence EOS Intelligence No Comments

Autonomous Vehicles: Moving Closer to the Driverless Future

An Uber self-driving car was reported getting into an accident in Arizona last month. But as the saying goes “any publicity is good publicity”, this also holds true for autonomous vehicles. The news sparked a discussion and shed some light on potential challenges the technology may face before it becomes available for commercial use. At the same time, it spread awareness about the level of safety testing being done to improve the technology before it is rolled out to the public. We are taking a look at what’s potentially in store for users waiting to see streets flooded with driverless vehicles.

Autonomous self-driving vehicles have been the talk of the industry for some time now, with some of the initial attempts to create a modern autonomous car dating back to 1980s. However, major advancements have only been made during the last decade, coinciding with advancements in the supporting technologies, such as advanced sensors, real-time mapping, and cognitive intelligence, which are perhaps the most crucial to the success of any autonomous vehicle.

Early advancements in the segment were led by technology companies which focused on developing software to automate/assist driving of cars. Some prime examples include nuTonomy, which has recently partnered with Grab (a ride-hailing startup rival to Uber) to test its self-driving cars in Singapore, Cruise Automation (acquired by GM in 2016), and Argo AI, which has recently received a US$1 billion investment from Ford. These companies use primarily regular cars/vans that are retrofitted with sensors, as well as high-definition mapping and software systems.

However, software alone is not capable enough to offer self-driving driving functionalities, therefore, automotive OEMs are taking the front seat when it comes to driving advancements in autonomous vehicles segment. New cars/vans, which are tuned to work seamlessly with this software, are likely to adapt better with the algorithms and meet stringent performance and safety standards required before they can be rolled out commercially. California-based Navigant Research believes that with its investment in Argo AI, Ford has taken a lead among such automotive OEMs in the race to produce an autonomous, self-driving vehicles.

Advanced levels of autonomy still to be achieved

In a nutshell, there are five levels of autonomous cars. Levels 1 through to 3 require human intervention in some form or other. The most basic level comprises only driver assistance systems, such as steering or acceleration control. Most common form of currently prevalent autonomy is Level 2, which involves the driver being disengaged from physically operating the vehicle for some time, using automation such as cruise control and lane-centering. Tesla’s current Autopilot system can be categorized as Level 2.

Level 3 involves the car completely undertaking the safety-critical functions, under certain traffic or environmental conditions, while requiring a driver to intervene if necessary.

Most OEMs developing autonomous cars target launching their vehicles in the next three to five years. Tesla is probably the closest, with its Model 3 car with Autopilot 3 system expected to be unveiled in 2018 (however, this depends on whether the regulations are in place by then). Nissan, Toyota, Google, and Volvo plan to achieve this by 2020, while BMW and Ford have set a deadline for 2021. Most of these companies are working on achieving cars with Level 3 autonomy, with a driver sitting behind the steering wheel to take over from the car’s programming as and when required.

Level 4 and Level 5 vehicles are deemed as fully autonomous which means they do not require a driver and all driving functions are undertaken by the car. The only difference is that while Level 4 vehicles are limited to most common roads and general traffic conditions, Level 5 vehicles are able to offer performance equivalent to a human driving in every scenario – including extreme environments such as off-roads.

Some OEMs, Ford in particular, are against the practice of using a human as a back-up, based on the understanding that a person sitting idle behind the wheel often loses the situational awareness which is required when he needs to take over from the car’s programming. Ford is planning to skip achieving Level 3 autonomy and target development of Level 4 autonomous vehicles instead.

Google is currently the only company focusing on developing a Level 5 autonomous car (or a robot car). The company already showcased a prototype that has no steering wheel or manual controls – a prototype that in true sense can be the first autonomous car. Tesla also plans to work on achieving the highest level of autonomy and plans to fit its cars with all hardware necessary for a fully-autonomous vehicle.

High costs continue to be challenging

While the plans are in place, one massive roadblock that persists in the development of these cars of future are costs. There are multiple sensors used in these cars, including SONAR and LIDAR. The ongoing research has helped to reduce the costs of sensors – Google’s Waymo has managed to reduce the costs of LIDAR sensors by 90%, from about $75,000 (in 2009) to about $7,000 (in 2016) – but they are still very expensive. The fact that a driverless car requires about four of these sensors, makes the cars largely unaffordable for consumers, and that puts off any discussion of feasibility of commercial production at this stage.

EOS Perspective

The first three months of 2017 have been particularly eventful, with several prototypes launched or tested. This activity is expected to increase further as companies try to meet their ambitious plans to roll out self-driving cars by 2020.

Initial adoption is likely to come from companies investing in commercial fleet, particularly those focusing on on-demand taxi or fleet, similar to what Uber or Lyft offer. Series of investments by large bus manufacturing companies, such as Scania, Iveco, and Yutong, also indicate how this technology will be the flavor of the future in public transport.

It is too soon to comment how and when exactly these autonomous vehicles can be expected to impact the way people choose to travel and how they may redefine the societies’ mobility. It is likely to depend on how the regulatory environment evolves to allow driverless cars in active traffic. Current regulatory environment for driverless cars is still at a nascent stage and allows only for testing of these cars in an isolated environment. Some states in the USA, particularly California, Arizona, and Pennsylvania, have opened up to testing of these cars in general public. However, recent accidents and cases of autonomous cars breaking traffic rules have put pressure on authorities to reconsider their stance until the cars become more advanced and tested to handle the nuances of public traffic. We might need to wait another decade or two before driverless cars are a reality in many markets. As things stand, endless efforts continue to go behind the curtain, as companies strive to win the race to develop highly autonomous and safe vehicles.

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