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ARTIFICIAL INTELLIGENCE

by EOS Intelligence EOS Intelligence No Comments

Beauty Tech Giving Beauty Industry a Facelift

In recent years, artificial intelligence and virtual reality have been adding an additional dimension to the beauty industry, quite literally. With consumers increasingly embracing and demanding personalized offerings and precise results, leading brands, such as L’Oréal and Shiseido are investing heavily in the space. Just as in many other industries, AI is revolutionizing beauty products and how they are conceptualized, created, and sold. However, it is a long road from being perceived as gimmicky promotions to improving customer engagement to becoming commercial go-to solutions.

Artificial intelligence (AI) has been greatly integrated in our lives through different sectors and now the beauty industry is no exception. The use of AI, augmented reality (AR), virtual reality (VR) as well as complex beauty devices has revolutionized the way consumers perceive, apply, and select beauty products. Moreover, in the age of online retail, it enables companies to maintain a similar personalized level of service that would otherwise require a physical interaction with a beauty consultant. Technology is creating new experiences for the consumer, both in terms of beauty products’ features as well as purchasing process.

Beauty industry is also one of the most competitive sectors, with consumers always being on the lookout for new products and having low brand loyalty. Beauty tech seems to address this issue as well, as it elevates consumer engagement through enhanced personalized offerings, which in turn is a trend that has been driving the beauty industry for several years now.

The three main aspects of beauty tech encompass personalization through AI, virtual makeup using AR and VR, and smart skincare tools/beauty gadgets.

Personalization through AI

Across the retail sector, the key to consumer’s heart and pockets for a long time has been personalization of products and sales experience. Beauty industry is no exception. Consumers have been looking for the perfect skincare product that work best for them or the lipstick shade that goes perfectly with their skin tone. Moreover, consumers want this all from the comfort of their home. This is where AI comes in.

Through retail kiosks and mobile apps, AI enables companies to offer personalized shade offerings that are especially curated for the individual user. A number of companies is investing and capitalizing on this technology to differentiate themselves in the eyes of the consumer. One of the leading market players in the beauty industry, L’Oréal, has been one of the first companies to invest in AI- and VR-based beauty tech and acquired Toronto-based, ModiFace, in 2018. There are several different ways companies, such as L’Oréal, have incorporated AI into their product offerings.

Beauty Tech Giving Beauty Industry a Facelift by EOS Intelligence

Beauty Tech Giving Beauty Industry a Facelift by EOS Intelligence

Lancôme (a subsidiary of L’Oréal) has placed an AI-powered machine, called Le Teint Particulier, at Harrods and Selfridges in the UK, which creates custom-made foundation for the customer. The machine first identifies ones facial color using a handheld scanner, post which it uses a proprietary algorithm to select a foundation shade from 20,000 combinations. Following this, the machine creates the personalized shade for the user, which can then be bottled and purchased.

In addition to physical store solutions, AI-powered apps and websites also offer consumers personalized recommendations. In 2019, L’Oréal applied ModiFace’s AI technology to introduce a new digital skin diagnostic tool, called SkinConsult, for its brand, Vichy. The AI-powered tool uses more than 6,000 clinical images in order to deliver accurate skin assessment for all skin types. It analyzes selfies uploaded by users to identify fine lines, dark spots, wrinkles, and other issues, and then provides tailored product and routine recommendations to the user to address the skin concerns.

My Beauty Matches, a UK-based company, offers AI-based personalized and impartial beauty product recommendations and price comparisons. The website asks consumers diagnostic-style questions about their skin and hair type, concerns, and preferences, and uses AI to analyze the data and recommend products from 400,000 products (from about 3,500 brands) listed on its website. Alongside, the company runs Beauty Matches Engine (BME), which is a solution for beauty retailers using consumer data and AI algorithms to identify consumer purchasing and browsing patterns as well as their preferred products by age and skin or hair concerns. This helps retailers predict and stock, which product the consumer is likely to purchase, improving sales, increasing upsells, and providing a personalized solution to customers.

On similar lines, another app, Reflexion, uses AI to measure the shininess of skin through pictures and offers personalized product recommendations. The app claims to provide much deeper analysis than regular image analysis apps and provides additional features such as testing if products such as foundation are evenly applied. The app works by measuring a face surface’s Bidirectional Scatter Distribution Function (BSDF), which is a measure of light reflected on the user’s face.

Nudemeter is another such product, which uses AI to personalize makeup choices and foundation shades for a full spectrum of skin tones, including darker skins. The app uses color analysis and digital image processing along with its AI algorithms that ensure accurate color measurement irrespective of background lighting, pixels, etc. The app is currently being used by Spktrm Beauty, a US-based niche beauty company targeting shoppers with dark skin.

Virtual makeup through AR and VR

In today’s world where consumers prefer to shop from the comfort of their homes, AR and VR are enabling beauty companies to provide experience similar to that of physical retail to their consumers. AR and VR technologies-based apps let users experiment virtually with a range of cosmetics by allowing them to try several different shades, all within minutes and through their smartphone. This elevates the users shopping experience and improves sales conversion.

Sephora’s Virtual Makeup Artist enables customers to try on thousands of shades of lipsticks and eyeshadows through their smartphones or at kiosks at Sephora stores. While many such apps and filters have been in use for some time now, they are increasingly becoming more sophisticated, providing accurate color match to the skin and ensuring the virtual makeup does not move when the user shakes their face, changes to a side angle, etc. In addition, such apps also provide digital makeup tutorials to engage customers.

On similar lines, L’Oréal uses ModiFace’s AR and AI technology to provide virtual makeup try-on on Amazon and Facebook. The technology enables customers using these two platforms to try on different shades of lipsticks and other make-up products through a live video or a selfie from an array of L’Oréal brands such as Maybelline, L’Oréal Paris, NYX Professional Makeup, Lancôme, Giorgio Armani, Yves Saint Laurent, Urban Decay, and Shu Uemura.

Moreover, AR-based try-on apps helped brands connect with their customers during the previous year when most customers were stuck home and could not physically try on make-up. LVMH-owned Benefit Cosmetics has been investing in AR tech, and launched Benefit’s Brow Try-On Experience program (along with Taiwanese beauty-tech company, Perfect Corporation), which helps online shoppers identify the right eyebrow shape and style for them and then choose products accordingly. The company uses facial point detector technology for the program. The app witnessed a 43% surge in its daily users during April and May of 2020 (as compared with January and March 2020), when people were confined to their homes owing to the COVID outbreak. This helped connect with consumers in a fresh manner and increased brand loyalty. Moreover, Benefit claims that brows products have been their strongest category post-COVID outbreak.

One of China’s leading e-commerce players, Alibaba, also partnered with Perfect Corporation to integrate the latter’s ‘YouCam Makeup’ (an AR-based virtual makeup try-on technology) into Alibaba’s Taobao and Tmall online shopping experience.

Smart devices

In addition to AI and AR based apps and solutions, smart devices is another category in the beauty tech space that is gaining momentum. A certain section of premium consumers are increasingly open to invest heavily into smart beauty gadgets that not only improve skin and hair quality but also help them quantitatively measure the results from using a certain product. While these products are currently expensive and for a niche audience, they have been gaining popularity, especially across the USA and China.

One such smart skincare device is L’Oréal’s Perso, which is based on ModiFace’s AI-powered skin diagnostics and analysis technology. Perso uses AI, location data, and consumer preferences to formulate personalized moisturizer for the consumer. The product is further expected to extend into foundations and lip shades. Perso is expected to be launched in 2021.

On similar lines, in July 2019, Japan-based Shiseido, launched its smart skincare device called Optune, which measures a user’s location-based weather and air pollution data, sleep data, stress levels, and menstrual cycles to create a custom moisturizer. Optune is available on a subscription basis and costs about US$92 per month.

In 2020, P&G also launched a premium skincare system, called Opte Precision. The skincare device uses blue LED light to scan one’s skin and applies a patented precision algorithm to detect problem areas and analyze complexion. Post this, the device releases an optimizing serum that is applied to spots to instantly cover age spots, pigmentation, etc., and to fade their appearance over time. The device has 120 nozzles and works on a technology similar to that of a thermal inkjet printer. The device targets a premium niche audience and costs US$599 with refill cartridge costing US$100.

In 2018, Johnson & Johnson’s drugstore skincare brand, Neutrogena, also launched a smart skincare device – a skin scanner, called Skin360 and SkinScanner, which uses technology from FitSkin (a US-based technology company). The scanner comes in the form of a magnifying camera that gets attached to a smartphone. The camera, which has a 30-time magnifying power helps scan the size and appearance of one’s pores, size and depth of fine lines and wrinkles, the skin’s moisture level, and also provides a score to the skin’s hydration level. The data is processed in a mobile app, which in turn provides a complete skin analysis and offers expert advice and product recommendations. While most smart skin devices are relatively expensive, this one retails at around US$50.

EOS Perspective

While AI and AR have been embraced by a lot of industries in the past, beauty tech is still in its infancy. That being said, there is a lot of potential in the space, especially with the consumer becoming increasingly comfortable with technology. While till recently, most technology-based products in the beauty sector were gimmicky and more for fun and consumer engagement, brands have started taking this space seriously, and started launching products that offer real sales growth opportunity.

Moreover, while AI and AR-based technologies have been accepted fairly easily by the consumers and industry players alike, smart devices is still a very niche category, with most products focused on a niche affluent clientele, who are willing to spend more than US$100 on products that may help improve their skin. There is a lot of potential for this segment to innovate, collaborate, and launch products at a more affordable price point in order to reach the masses.

Over the next couple of years, we can expect new niche players, exploring the benefits of beauty tech to enter the market in addition to greater number of partnerships between traditional beauty giants and technology companies. As personalization continues to be the mantra for consumers, beauty companies cannot look to ignore the space in the coming future.

by EOS Intelligence EOS Intelligence No Comments

Agritech in Africa: How Blockchain Can Help Revolutionize Agriculture

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In the first part of our series on agritech in Africa, we took a look into how IT and other technology investments are helping small farmers in Africa. In the second part, we are exploring the impact that potential application of advanced technologies such as blockchain can have on the African agriculture sector.

Blockchain, or distributed ledger technology, is already finding utility across several business sectors including financial, banking, retail, automotive, and aviation industries (click here to read our previous Perspectives on blockchain technology). The technology is finding its way in agriculture too, and has the potential to revolutionize the way farming is done.


This article is the second part of a two-piece coverage focusing on technological advancements in agriculture across the African continent.

Read part one here: Agritech in Africa: Cultivating Opportunities for ICT in Agriculture


State of blockchain implementation in agriculture in Africa

Agricultural sector in Africa has already witnessed the onset of blockchain based solutions being introduced in the market. Existing tech players and emerging start-ups have developed blockchain solutions, such as eMarketplaces, agricultural credit/financing platforms, and crop insurance services. Companies, globally as well as within Africa, are harnessing applications of blockchain to develop innovative solutions targeted at key stakeholders across the food value chain.

Blockchain to promote transparency across agriculture sector

The most common application of blockchain in any industry sector (and not only agriculture) is creating an immutable record of transactions or events, which is particularly helpful in creating a trusted record of land ownership for farmers, who are traditionally dependent on senior village officials to prove their ownership of land.

Since 2017, a Kenyan start-up, Land LayBy has been using an Ethereum-based shared ledger to keep records of land transactions. This offers farmers a trusted and transparent medium to establish land ownership, which can then further be used to obtain credit from banks or alternative financing companies. BanQu and BitLand are other examples of blockchain being used as a proof of land ownership.

This feature of blockchain also enables creation of a transparent environment where companies can trace the production and journey of agricultural products across their supply chain. Transparency across the supply chain helps create trust between farmers and buyers, and the improved visibility of prices further down the value chain also enables farmers to get better value for their produce.

In 2017, US-based Bext360 started a pilot project with US-based Coda Coffee and its Uganda-based coffee export partner, ​​Great​ ​Lakes​ ​Coffee. The company developed a machine to grade and weigh coffee beans deposited to Great Lakes by individual farmers in East Uganda. The device uploads the data on a blockchain-based SaaS solution, which enables users to trace the coffee from its origin to end consumer. The blockchain solution is also used to make payments to the farmers based on the grade of their produce in form of tokens.

In 2017, Amsterdam-based Moyee Coffee also partnered with KrypC, a global blockchain, to create a fully blockchain-traceable coffee. The coffee beans are sourced from individual farmers in Ethiopia, and then roasted within the country, before being exported to the Netherlands.

This transparency can help food companies to isolate the cause of any disease outbreak impacting the food value chain. This also allows consumers can be aware of the source of the ingredients used in their food products.

Agritech in Africa: How Blockchain Can Help Revolutionize Agriculture by EOS Intelligence

Blockchain-based platforms to improve farmer and buyer collaboration

Blockchain can also act as a platform to connect farmers with vendors, food processing, and packaging companies, providing a secure and trusted environment to both buyers and suppliers to transact without the need of a middleman. This also results in elimination of margins that need to be paid to these intermediaries, and helps improve the margins for buyers.

Farmshine, a Kenyan start-up, created a blockchain-based platform to auger trade collaboration among farmers, buyers, and service providers in Kenya. In January 2020, the company also raised USD$250,000 from Gray Matters Capital, to finance its planned future expansion to Malawi.

These blockchain platforms can also be used to connect farmers to other farmers, for activities such as asset or land sharing, resulting in more efficiency in economical farming operations. Blockchain platform can also enable small farmers to lease idle farms from their peers, thereby providing them with access to additional revenue sources, which they would not be able to do traditionally.

AgUnity, an Australian-start-up established in 2016, developed a mobile application which enables farmers to record their produce and transactions over a distributed ledger, offering a trusted and transparent platform to work with co-operatives and third-party buyers. The platform also enables farmers to share farming equipment as per a set schedule to improve overall operational and cost efficiency. In Africa, AgUnity has launched pilot projects in Kenya and Ethiopia, targeted at helping farmers achieve better income for their produce.

A Nigerian start-up, Hello Tractor uses IBM’s blockchain technology to help small farmers in Nigeria, which cannot afford tractors on their own, to lease idle tractors from owners and contractors at affordable prices through a mobile application.

Smart contracts to transform agriculture finance and insurance

Less than 3% of small farmers in sub-Saharan Africa have adequate access to agricultural insurance coverage, which leaves them vulnerable to adverse climatic situations such as droughts.

Smart contracts based on blockchain can also be used to provide crop-insurance, which can be triggered given certain set conditions are met, enabling farmers to secure their farms and family livelihood in case of extreme climatic events such as floods or droughts.

SmartCrop, an Android-based mobile platform, provides affordable crop insurance to more than 20,000 small farms in Ghana, Kenya, and Uganda through blockchain-based smart contracts, which are triggered based on intelligent weather predictions.

Netherlands-based ICS, parent company of Agrics East Africa (which provides farm inputs on credit to small farmers in Kenya and Tanzania) is also exploring a blockchain-wallet based saving product, “drought coins”, which can be encashed by farmers depending on the weather conditions and forecasts.

Tracking of assets (such as land registries) and transactions on the blockchain can also be used to verify the farmers’ history, which can be used by alternative financing companies to offer loans or credits to farmers – e.g. in cases when farmers are not able to get such financing from traditional banks – transforming the banking and financial services available to farmers.

Several African start-ups such as Twiga Foods and Cellulant have tried to explore the use of blockchain technology to offer agriculture financing solutions to small farmers in Africa.

In late 2018, Africa’s leading mobile wallet company, Cellulant, launched Agrikore, a blockchain-based digital-payment, contracting, and marketplace system that connects small farmers with large commercial customers. The company started its operations in Nigeria and is exploring expansion of its business to Kenya.

In 2018, Kenya-based Twiga Foods (that connects farmers to urban retailers in an informal market) partnered with IBM to launch a blockchain-based lending platform which offered loans to small retailers in Kenya to purchase food products from suppliers listed on Twiga platform.


Read our previous Perspective Africa’s Fintech Market Striding into New Product Segments to find out more about innovative fintech products for agriculture and other sectors financing in Africa


And last, but not the least, blockchain or cryptocurrencies can simply be used as a mode of payment with a much lower transaction fee offered by traditional banking institutions.

Improving mobile internet access to boost blockchain implementation

While blockchain has shown potential to transform agriculture in Africa, its implementation is limited by the lack of mobile/internet access and technical know-how among small farmers. As of 2018, mobile internet had penetrated only 23% of the total population in Sub-Saharan Africa.

However, the GSM Association predicts mobile internet penetration to improve significantly over the next five years, to ~39% by 2025. Improved access to internet services is expected to boost the farmers’ ability to interact with the blockchain solutions, thereby increasing development and deployment of more blockchain-based solutions for farmers.

EOS Perspective

Agritech offers an immense opportunity in Africa, and blockchain is likely to be an integral part of this opportunity. Blockchain has already started witnessing implementation in systems providing proof of ownership, platforms for farmer cooperation, and agricultural financing tools.

Unlike Asian and Latin American countries, African markets have shown a relatively positive attitude towards adoption of blockchain, a fact that promises positive environment for development of such solutions.

At the moment, most development in blockchain agritech space is concentrated in Kenya, Nigeria, Uganda, and Ghana. However, there is potential to scale up operations in other countries across Africa as well, and some start-ups have already proved this (e.g. Farmshine was able to secure the necessary financing to expand its presence in Malawi). Other companies can follow suit, however, that would only be possible with the help of further private sector investments.

Still in the nascent stages of development, blockchain solutions face an uncertain future, at least in the short term, and are dependent on external influences to pick up growth they need to impact the agriculture sector significantly. However, once such solutions achieve certain scalability, and become increasingly integrated with other technologies, such as Internet of Things and artificial intelligence, blockchain has the capability of completely transform the way farming is done in Africa.

by EOS Intelligence EOS Intelligence No Comments

Agritech in Africa: Cultivating Opportunities for ICT in Agriculture

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Agriculture technologies in Africa have been undergoing significant development over the years, with many tech start-ups innovating information and communications technologies to support agriculture at all levels. While some technologies have been successfully launched, some are in initial stages of becoming a success. Private sector investments have been the key driving factor supporting the development of agriculture technologies in Africa. In the first part of our series on agritech in Africa, we are examine what impact and opportunities arise from the use of these technologies in Africa.

Agriculture plays a significant role in Africa’s economy, contributing 32% to the continent’s GDP and employing 65% of the total work force (as per the World Bank estimates). Nearly 70% of the continent’s population directly depends on agribusiness. Vast majority of farmers work on small scale farms that produce nearly 90% of all agricultural output.


This article is the first part of a two-piece coverage focusing on technological advancements in agriculture across the African continent.

Read part two here: Agritech in Africa: How Blockchain Can Help Revolutionize Agriculture


Agriculture in Africa has been under the pressure of many challenges such as low productivity, lack of knowledge and exposure to new farming techniques, and lack of access to financial support, especially for the small-scale farmers. These challenges are prompting investments in newer technologies to enhance the productivity through smart agriculture techniques.

Lately, there have been an increased use of various technologies in agriculture in Africa, such as Internet of Things (IoT), Open Source Software, Cloud Computing, Artificial Intelligence, Drones/Unmanned Aerial Vehicles (UAVs), and Big Data Analytics. Many tech start-ups have developed solutions targeting various aspects of agriculture, including finance, supply chain, retailing, and even delivering information related to crops and weeds. These solutions are accessible to farmers through front-end devices such as smart phones and tablets, or even SMS.

Agritech in Africa - Cultivating Opportunities for ICT in Agriculture by EOS Intelligence

Start-ups lead agritech development in Africa

Many agritech start-ups in Africa have come up with solutions that have led to a rise in productivity of the farms. Drones have been a breakthrough technology, helping farmers oversee their crops, and manage their farms effectively. Drones use highly focused cameras to capture picture of crops, soil or weeds. This, coupled with big data analytics and Artificial Intelligence (AI), provides insights to farmers, saving their time and effort, while also helping them find potential issues which could impact the productivity of their farms.

There are various agritech start-ups that are developing such drones, and providing them to farmers for rent or lease to analyse their crops and farms. A South African agritech start-up, Aerobotics, offers an end-to-end solution to help farmers manage their farms using drones, through early detection of any crop-related problems, and offering curative measures for the problems using an AI-based analytics platform. The company partners with drone manufacturing companies such as DJI and Micasense to deliver these solutions.

Acquahmeyer, another start-up based in Ghana, also provides drones to its farming customers to help them use a comprehensive approach to apply crop pest control and plant nutrition management for their farms.

Advent of advanced technologies such as IoT is also helping farmers to adopt smart farm management through the use of smart sensors connected in a network. This helps every farmer to get granular details of the crops, soil, farming equipment, or livestock, enabling the farmers to devise appropriate farming approaches.

Kenya-based UjuziKilimo provides solution for analyzing soil characteristics using electronic sensor placed in the ground. This helps farmers with useful real-time insights into soil conditions. The solution further utilizes big data analytics to guide the farmers, by offering insights through SMS on their connected mobile phones or tablets.

Hello Tractor, a Kenyan start-up, provides an IoT solution, through which farmers can have access to affordable tractors which are monitored virtually through a remote asset tracking device on the tractor, sharing data over the Hello Tractor Cloud. Farmers, booking agents, dealers, and tractor owners are connected via IoT. The company is also collaborating with IBM to incorporate artificial intelligence and blockchain to their solutions.

AI has also witnessed a rapid growth in adoption across agriculture sector in Africa. Agrix Tech, based in Cameroon, has developed a mobile application that requires the farmers to capture the picture of diseased crop, which is then analyzed via AI to detect crop diseases, and helps the farmers with treatment solution to save their crops.

AI is also helping Kenyan farmers with the knowledge on planting the right crops at the right time. Tech giant, Capgemini, has teamed up with a Kenyan social enterprise in Kakamega region in Western Kenya to use artificial intelligence to analyze farming data, and then send insights about right time and technique of planting crops to the farmers’ cell phones.

There are other agritech solutions that include mobile applications which use digital platforms such as cloud computing to reach out to farmers, and provide them with apt agriculture solutions. Ghana-based CowTribe offers a mobile USSD-based subscription service which enables livestock farmers to connect with veterinarians for animal vaccines and other livestock healthcare services using cloud-based logistics management system. The company focuses on managing the schedules, and delivering the right service to the livestock farmers, to help them safeguard their animals from any health-related problems.

Several agritech investments are also impacting the financial side of agriculture. Kenya-based Apollo Agriculture provides solutions related to financing, farm inputs, advice insurance and market access through the use of agronomic machine learning, remote sensing, and mobile technology using satellite data and cloud computing.

Another Nigerian start-up Farmcrowdy has developed Nigeria’s first digital agriculture platform that provides financial support to the farmers by allowing those outside the agriculture industry to sponsor individual farms.

Several other agritech start-ups across the continent, such as Ghana-based Farmerline and AgroCenta, and Nigeria-based Kitovu have also launched data-driven mobile application for farmers. These technology solutions are proving to be a boon for agriculture sector in Africa, helping improve the overall efficiency and productivity.

Agritech in Africa - Cultivating Opportunities for ICT in Agriculture by EOS Intelligence

Agritech development is concentrated in Kenya and Nigeria

But, when it comes to first adopting the newest technologies and starting an agritech business in agriculture, Kenya and Nigeria have been leading in the adoption of new agritech solutions, accounting for a significant share of agritech start-up across Africa. Kenya has played a pioneering role in bringing agritech in Africa since 2010-2011, when the first wave of agritech start-ups began to bring new niche innovations. Currently, Kenya accounts for 25% of all the agritech start-ups in Africa, and the development is progressing rapidly, thanks to the country’s advancement in technology, high smartphone penetration, and relatively widespread internet access.

Similarly, Nigeria too has sailed the boat of success in agritech start-ups since 2015, and now it accounts for 23.2% of total agritech start-ups in Africa, with include major players such as Twiga Foods, Apollo Agriculture, Agrikore, and Tulaa. The growing inclination amongst Nigerian farmers towards using digital tools in agriculture sector has further pushed the rapid development in agritech sector in the country.

Other countries have also shown potential for agritech development, though it is still in the initial stages of becoming mainstream in their agriculture sectors. Ghana has encouraged several start-ups to launch different technology innovations for making agriculture more sustainable, while South Africa, Uganda, and Zimbabwe have also witnessed the rise in agritech start-ups over the years with newer technologies for agriculture sector.

Recent investments highlight the agritech potential

The agriculture technologies in Africa got the boost from the increased private funding. According to a report by Disrupt-Africa released in 2018, there has been a total investment of US$19 million in agritech sector since 2016. These investments have largely focused on funding agritech start-ups working on bringing innovative agriculture technologies. Also, according to the same report, the number of agritech start-ups rose by 110% from 2016 to 2018.

Some of the recent investments in the agritech sector include Kenya’s Twiga Foods, a B2B food distribution company, which raised US$30 million from investors led by Goldman Sachs in October 2019. The company aims to set-up a distribution centre in Nairobi to offer better supply chain services, while also expanding to more cities in Kenya, including Mombasa.

In December 2019, Kenya-based agritech start-up Farmshine, also raised US$25 million in funding from US-based Gray Matter’s Capital coLabs (GMC coLabs), to expand its operations in Malawi. GMC coLabs also invested US$1 million in another Kenyan B2B agritech start-up Taimba in July 2019. Taimba provides a mobile-based cashless platform connecting smallholder farmers to urban retailers. The investment was focused on strengthening Taimba’s infrastructure and increase the delivery logistics to cater to new markets.

Cellulant, a leading pan-African digital payments service provider that offers a real-time payment platform to farmers, also raised US$47.5 million from a consortium of investors in May 2018, which is the largest investment in the African tech industry till date. Cellulant also plans to channel a significant portion of funds into its Agrikore subsidiary, an agritech start-up dealing with blockchain based smart-contracting, payments, and marketplace system.

EOS Perspective

African agritech is expected to witness high growth in future. According to a CTA report on Digitalization for Agriculture (D4Ag) published in 2018, digital agriculture solutions are likely to reach 60-100 million smallholder famers, while generating annual revenues of nearly US$320- US$470 million by the end of 2020.

Adoption and use of innovative technologies such as remote sensing, diagnostics, IoT sensors for digitalization of agriculture is steadily moving from experimental stage to full-scale deployment, contributing to the data revolution in agriculture, while also unlocking new business models and opportunities.

Apart from these, blockchain is gaining prominence, and finding applications in the agriculture sector in Africa. This technology has the potential to significantly impact the agriculture sector, which we will discuss in the second part of our series on Agritech in Africa.

However, lack of affordability and knowledge to access such technologies, especially by small-scale farmers, has restricted the growth and reachability of these solutions. With the need to educate farmers and make such technology affordable and viable, it is likely that it may take at least 5-7 years before these technologies become truly mainstream in the continent.

A disparity of investments has been observed among the countries in the region. Over the years, countries such as Kenya, Nigeria, and Ghana have experienced a strong growth in terms of private investments, while other countries are left wanting. Investors have prioritized easy-to-reach markets in Africa, leaving behind the lower-income markets, resulting in agritech becoming less sustainable and scalable in these markets. However, several other African countries have shown the appetite to adopt agritech solutions, and offer significant potential.

This requires an intervention and participation from both governments and private investors, which can help improve scalability of agriculture technologies in the region. Implementation of farming digital literacy, public-private partnerships, and increased private sector investments in agritech enterprises can help the agritech industry experience a consistent and higher success rate, thus bringing the agriculture technology to a mainstream at faster pace.

by EOS Intelligence EOS Intelligence No Comments

Infographic: Google’s Tech Initiatives Transforming Industries

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Google, beyond being the leading search engine worldwide, is also one of the largest and most innovative companies. Through its innovations, Google along with other Alphabet companies (parent company of Google and its subsidiaries) is transforming various industries by empowering them with technology. Its solutions have reached diverse industries such as agriculture, manufacturing, healthcare, energy, and fishing, among others.

Innovation has always been at the core of Google’s strategy and it is bringing artificial intelligence (AI), machine learning, augmented reality, robotics, among others to shape various industries. It has introduced surgical robots to medicine, Google glass to manufacturing, AI-enabled programs to energy, among various other solutions that are revolutionizing these industries. We are taking a look at where Google has already left its innovative footprint.

Google’s Tech Initiatives Transforming Industries - EOS Intelligence


Alphabet companies included in the infographic:
Verily – Alphabet’s key research organization dedicated to the study of life sciences
Verb Surgical – A joint venture between Johnson & Johnson and Verily
DeepMind – Alphabet’s artificial intelligence company
Global Fishing Watch – An organization founded by Google in partnership with Oceana and SkyTruth
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

Infographic: Four Digital Trends in Aviation that Will Fly High in 2018

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Emerging technologies are sprawling over the aviation sector making travel seamless, convenient, automated, and personalized. Airports and airlines are adopting technologies that simplify the passengers’ travel experience by digitalizing baggage and boarding processes, making wayfinding in busy airports efficient, and making check-ins more rapid, among many others. Digitalization is not only helping to deliver greater customer satisfaction, but also minimizing costs, increasing revenue, and improving efficiency – for instance, within six months of chatbot usage, Aeromexcio was able to reduce average customer service resolution time via chat to two minutes from 16 minutes.

Some of the key technologies to flourish in aviation in 2018 include biometrics, artificial intelligence-powered chatbots, robotics, and Internet of Things. With emerging technologies set to redefine the travel experience, it is essential that the airports and airlines take action now to ascertain they are well-placed to tap the opportunity.

digital trends in aviation

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Note: Mexico-based airline:Volaris; Germany-based airline:Lufthansa; Netherlands-based airline:KLM; UK-based airline:Virgin Atlantic; USA-based airlines:Delta, JetBlue; Taiwan-based airline:EVA Air; New Zealand-based airline:Air New Zealand

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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.

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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