AI in Manufacturing: Ready for Impact Driving AI

AI in manufacturing: Ready for impact Driving AI: Connected Cars’ highway to the future AI helps mass media get up close and personal Smart cities: Applying intelligence to urban growth

Special Issue: October 2018

Produced by

(E) BrandConnect, a commercial arm of The Economist Group. (E) BrandConnect operates separately from the editorial staffs of The Economist and The Economist Intelligence Unit TABLE OF CONTENTS

2 Dawning of the age of AI 6 AI in manufacturing: Ready for impact 8 Driving AI: Connected Cars’ highway to the future 10 Smart cities: Applying intelligence to urban growth 12 AI helps mass media get up close and personal 14 Healthcare: AI that saves lives 16 Financial services: Taking AI to the next level 18 Logistics’ extra hidden hand

1 DAWNING OF THE AGE OF AI ne of the biggest strategy considerations for business leaders today is how Oartificial intelligence (AI) will impact their industry, the economy—and hence their profitability—in the future. They have reason to be optimistic. Expert projections promise significant economic gains from the use of AI. One consultancy, PwC, estimates AI developments will add $15.7 trillion to the global economy by 2030. Another, Accenture, looks further ahead, and projects the economic value added by AI in 2035 to be $8.3 trillion in the US alone.

2 3 Some industries, such as fi nancial services, health and life yield and optimise their supply chains. In China, for example, To perform its magic, AI relies on two commodities: computing operations with the next three years, but only 28% have sciences, and automotive, where AI has already been deployed to a survey conducted recently by Forrester for leading global power and data. The growth of cloud computing in the past half- devised a roadmap for implementation. AI’s benefi ts will good effect in several areas, are likely to be the fi rst to generate technology company, Huawei, found that 51% of large decade has provided the mass of server capacity that many AI come not from technology deployment but from the follow- demonstrable benefi ts from its use in the coming years. manufacturers plan to boost investments in production applications need for their number-crunching. Different sorts of on adaptation—or in some cases wholesale re-thinking—of line automation, and 45% plan to increase spending on AI power will also soon be available. One is represented by “edge production, back-offi ce and front-offi ce processes. Such INTELLIGENT CHANGE in inventory forecasting. The same share of respondents computing”, which brings greater power to the outer edge of endeavours require detailed planning. In fi nancial services, banks, wealth managers and insurers reported plans to use AI to help improve quality control. networks or devices themselves, so that the latter can perform Companies also need to invest in and develop expertise. will build a wider variety of innovative, algorithm-driven analysis and take decisions that are time-sensitive. Few organisations believe they have adequate internal customer services, but will also use AI to greater effect in THE FUTURE BENEFITS Computer chips that are specifi c to the needs of AI are knowledge today to develop and work with AI-based the back offi ce, automating payments, fraud detection, risk The long-term impacts of AI on the economy and society may also coming to market. “The new AI chips coming into use applications. Over one-third (36%) of respondents to a recent management and other critical processes. In healthcare, be diffi cult to predict, but it does not require a crystal ball closely mimic the neuron process inside the brain, much Economist Intelligence Unit survey cite a lack of requisite Accenture believes that AI use will generate $150 billion to see the benefi ts it will generate in the next 5-10 years. more so than the all-purpose chips that AI has relied on until people or tools as a major risk of adopting AI, second only in annual savings for US health organisations by 2026, Elements of AI, such as machine learning, are already being now,” says Andrei Kirilenko, Director of the Centre for Global to its costs. Expertise gaps will not be fi lled overnight, with applications in surgery, nursing and administrative used to good effect in all of these sectors in several ways. Finance and Technology at Imperial College London. They will but companies can start to address them by creating an workfl ow the biggest contributors. And among automotive Banks use chatbots to address customer queries and more make more effi cient use of the available computing power, in inventory of existing skills and identifying missing skill sets. producers—20% of whom have fully implemented multiple generally to improve customer service. Insurers now use the cloud and within devices. The result is likely to be greater Data fragmentation also needs to be eliminated. Many AI use cases already, according to the Boston Consulting algorithms to underwrite automotive and health insurance overall precision of AI applications—and the decisions they AI algorithms need to crunch large amounts of data to be Group (BCG)—AI will bring autonomous vehicles to market premiums. AI is widely used by hospitals and medical science and deliver ever more intelligent in-car services. labs to help doctors diagnose illnesses and prescribe Companies must press ahead to plan for AI’s implementation In manufacturing, AI will come into use well beyond the appropriate treatment. Semiconductor manufacturers are automotive and semiconductor industries. Heavy equipment, using AI to take automation to higher levels. Auto producers in their businesses. It has moved well beyond the science labs, consumer goods and other discrete manufacturers, as are leveraging Internet of Things (IoT) sensors and AI and is already a competitive tool for a handful of fi rms that are developing well as those in some process industries, will leverage AI to algorithms to deliver real-time traffi c, safety and other new services and even business models based upon it. improve asset productivity, boost product quality, increase information to cars. make—and a reduced cost of hardware carrying AI chips. effective. Organisations across industries generate data today AI’s capabilities will also grow with improvements in in abundance, but far from all of it can be used by AI software as “machine vision”, which allows software to inspect and evaluate it resides in disparate repositories that are diffi cult to integrate images of static or moving objects. Such advances will widen and aggregate. Greater interoperability of data sets and systems the variety of data sources that algorithms can reliably analyse. that exist within organisations is required, as well as those that The technology will be used not only in autonomous vehicles link organisations on external platforms. AI will bloom when the and transportation systems but also on the factory fl oor. There, use of open, standardised data sources is the norm. manufacturers will use it in conjunction with IoT sensors to enable predictive maintenance of capital-intensive equipment, COMPETITIVE EDGE thus avoiding expensive downtime. “AI-enhanced predictive Importantly, AI needs to become more transparent. maintenance will be a game-changer for parts of the industrial Consumers as well as regulators will demand that companies sector,” says Harald Bauer, a Senior Partner with consulting fi rm that provide AI-based products and services are able to see McKinsey, based in Frankfurt, Germany. into the “black box” where algorithms make decisions. Brian Related applications will emerge in the healthcare sector, Kalis, Managing Director of Digital Health with Accenture, in the form of advanced medical imaging technologies. AI- says “explainable AI” in healthcare is critical but elusive: “As based imaging will be used, for example, to screen different a clinician or administrator, you need to be able to show and forms of cancer. The aforementioned Forrester survey found explain the rationale for a machine-based decision, especially that 33% of large Chinese health organisations intend to if it impacts people’s lives,” he says. “This is a major challenge adopt such smart imaging technologies in the next 3 years. with advanced AI algorithms.” The challenges notwithstanding, companies must press BRINGING AI TO SCALE ahead to plan for AI’s implementation in their businesses. As bright as AI’s potential is, organisations will not realise It has moved well beyond the science labs, and is already a it unless they can scale its applications across a much competitive tool for a handful of fi rms that are developing greater swathe of their operations. In order to do this, several new services and even business models based upon it. challenges need to be addressed. One of the most pressing There remain unknowns about how AI will develop in the of these is strategy development. In a survey conducted longer term, and certainly business risks as attendant in any by the Boston Consulting Group, a consulting fi rm, 87% of major technology undertaking. The bigger risk, however, is executives said their fi rms intend to implement AI in their remaining on the sidelines, as the AI future is now.

4 5 conditions rather than a regular schedule. Both should generate taking shape on the production ﬂ oor, with AI robots mainly substantial savings for manufacturers as well as improve asset working alongside engineers rather than replacing them AI IN productivity. According to McKinsey, such use of AI could help outright. Germany’s industrial manufacturers to boost asset productivity by as much as 20% and reduce maintenance costs by up to 10%. LAYING THE FOUNDATIONS Understandably, predictive maintenance is also reshaping the Several elements must be in place before manufacturers are MANUFACTURING service model of many equipment manufacturers. The growing able to scale AI and generate the desired returns from its use. predictive maintenance market drives industry players to move First comes connectivity. Most AI-driven algorithms require a lot Ready for impact towards service providers. As predictive maintenance can reduce of computing power. That—along with the software platforms service downtime to mitigate risks from high-cost equipment and virtual hardware that AI-assisted applications run on—can operations, it is ultimately helping drive a transformation among be found in the cloud, and companies should be working with equipment suppliers from sales to long-term equipment lease one or more cloud providers that offer such resources. The cloud operations or maintenance service models. is also the home of open-source platforms that companies in different sectors are using to innovate with AI. Manufacturers IMPROVING QUALITY AND BOOSTING YIELD must participate more widely in open forms of innovation in order The same catalysts for the growth of predictive maintenance to gain knowledge, expertise and good ideas for AI applications.

Newer generations of robots are becoming much more intelligent—more aware of their environment and increasingly able to train themselves without human intervention. Michael Yost , president of MESA, envisions a highly collaborative human-machine environment taking shape on the production fl oor, with AI robots mainly working alongside engineers rather than replacing them outright. or all the focus manufacturers have been placing on their processes in the next three years. F digitisation, and especially on intelligent automation There are a number of key areas where the AI impact in of equipment will give rise to improved, and automated, quality The other major building block is ample, usable data. technologies, AI has yet to have a signifi cant impact on the manufacturing will be considerable in the next fi ve years. It testing of manufactured goods. Advanced image recognition Manufacturers generally do not suffer from a shortage of factory fl oor. This is about to change, believes Harald Bauer of is likely to become manifest earliest in the automotive and and self-trained systems will help manufacturers to reduce it, but many complain that much of their data is unusable McKinsey. “Until now, AI has been applied in a few niche areas semiconductor industries, where AI has already made some product defect rates, possibly radically in some environments, due to errors, incorrect or absent labels and insuffi cient by some, though by no means all, manufacturers,” he says. “The inroads and where operations are already highly automated. such as semiconductor manufacturing. Mr Bauer believes standardisation across data sets. Companies must do the enablers are in place, however, to allow more manufacturers to But it will also come into use (albeit more gradually) by process, detection rates will be vastly increased compared with human hard work of cleaning and properly integrating the data sets apply AI in a wide range of uses, and at scale.” heavy equipment and FMCG (fast-moving consumer goods) forms of inspection (for German manufacturers by as much they have and continue to amass. Their analytics tools will also Those enablers are high existing levels of digitisation and manufacturers. This shows in the rates of early AI adoption as 90%). Along with improvements in production processes, need to be able to work with unstructured forms of data (such automation, the availability of voluminous data and access among industries. One fi fth of automotive companies are early AI this will help manufacturers (particularly in the semiconductor as images of equipment and products), the analysis of which to the enormous computing power existing in the cloud. To adopters, according to the 2018 BCG report, compared to 15% of industry, Mr Bauer believes) to increase yields considerably. greatly adds to AI’s capabilities. these he might add ubiquitous IoT sensors, which permeate engineered products companies and 13% of process industries Huawei, a leading global provider of information and Manufacturers should not wait before addressing these and most production fl oors and logistics centres in industrialised fi rms. All have much to gain, but there are stiff challenges they communications technology (ICT) infrastructure, has been using other AI-related challenges, including the acquisition of skills economies. will have to address to ensure that AI delivers for them. AI techniques for the past three years to streamline its own and expertise. AI may not yet have made a heavy imprint on the Aside from autonomous vehicles and some consumer complex supply-chain processes. According to Huawei, AI-based sector, but that is certain to change in the foreseeable future. electronics products, AI will make its infl uence felt behind the PREDICTIVE MAINTENANCE TO INCREASE ASSET route optimisation has helped to reduce the number of goods scenes, in production, R&D and supply chain processes. The gains PRODUCTIVITY pick-ups by its logistics service providers and simultaneously that manufacturers make from AI use are unlikely to be headline- Improving asset utilisation—a key determinant of manufacturing maximise the number of full loads. The result, it says, has been grabbing, says Michael Yost, President of the Manufacturing performance—relies on maintaining production equipment a 30% reduction in transportation costs. Shortening routes also Enterprise Solutions Association International (MESA). But in in peak condition, keeping expensive downtime to a minimum reduces carbon emissions, thereby making supply chains greener time, he believes AI will do much to enhance manufacturers’ and maximising its working life. The combination of predictive and more sustainable. operating effi ciency, product quality and innovation capacity. analytics, advanced image recognition technology and, of course, Robots will also play a growing role. Some of the monitoring These longer term effi ciencies could be signifi cant. A voluminous performance data will enable algorithms to predict essential to predictive maintenance will be carried out by robots 2018 report by BCG, a consultancy, found that AI can reduce likely equipment failures. A defi ning attribute of such systems, which are already prevalent in factories. Newer generations of manufacturing conversion costs—the combination of direct says Mr Bauer, is continuous learning—the algorithms’ ability robots, however, are becoming much more intelligent—more labour and overhead costs—by 20%. These cost reductions are to train themselves, based on experience and more data, to aware of their environment (thanks partly to machine vision, of attractive to manufacturers and the same report found that generate more accurate predictions. which image recognition is one important part), and increasingly 80% to 90% of automotive, consumer goods, process industries Not only can preventive action be taken to avoid downtime, but able to train themselves without human intervention. Mr Yost and engineered products companies plan to implement AI in maintenance operations themselves can be based on predicted envisions a highly collaborative human-machine environment

6 7 will spawn business opportunities we haven’t dreamed of. moving vehicles to communicate with a range of entities Connected vehicles, says Rajkumar, may quickly become the around them. IoT’s biggest revenue generator. “5G enables cars to transfer great amounts of data in near The market for the hardware and services alone that bring real-time,” explains Juri Deuter, IBM Industry Consultant and this functionality to cars is expected to reach $156 billion by a member of its Global Automotive Center of Competence. 2020, according to a 2017 report from consultancy BCG. The “Also, it allows for direct peer-to-peer communication. number of active fl eet management systems—a measure Countries that will adapt the 5G standard quickly will have a of the demand for connected commercial vehicles—is also great advantage in the whole mobility space.” growing, with more than 10.6 million systems covering light and heavy commercial vehicles expected in Europe by 2020, THE FINAL DESTINATION up from just 2 million in 2010. Of course, the holy grail is for cars themselves to become This means a lot of data passing to and from the car. autonomous vehicles, or AVs. The industry divides the Some will be through on-the-fl y connections made between advancement of automotive autonomy into fi ve levels. At the vehicles, pedestrians and infrastructure as they pass. Others fi rst two levels, AI assists cars, but humans are the ultimate will be over cellular networks. authority. Think antilock brakes and adaptive cruise control, The network demands are expected to be massive: a where humans are guided by automation, but must be prepared DRIVING AI connected car, according to Dekra, a company focusing to intervene. The industry reached these fi rst two levels on the safety of human interaction with technology, will comfortably and is now nearing level 3, says Tom Koulopoulos, Connected Cars’ highway to the future generate 25 gigabytes of data per hour. This will rise, according to chip maker Intel, to about 4 terabytes per day as cars “The network is the essential foundation for the n the past decade, the automotive industry has made example, are already being installed with such technology. become autonomous. That’s the equivalent communication between vehicles, infrastructure, Ihuge strides in connecting cars to the outside world. The Colorado Department of Transportation plans to equip data traffi c of almost 3,000 people. and humans. Not only will the network need to Developments like live traffi c information, music streaming, 2,500 of its vehicles with C-V2X and DSRC connectivity by “The network is the essential foundation support billions of newly connected devices, cars remote starting, vehicle tracking, and navigation services the end of the year. Within 10 years, it’s hoped that up to 4 for the communication between vehicles, and infrastructure, it will also need to be fast and have all become commonplace. But the biggest changes— million vehicles in Colorado will be “talking” to each other and infrastructure, and humans,” says Alastair and challenges—for the connected car lie ahead. to the roadway infrastructure. MacLeod, CEO of Teralytics, which reliable.” Alastair MacLeod, CEO, Teralytics. By 2023, worldwide sales of connected cars will reach 72.5 In China, the wide coverage of cellular networks and uses mobile phone data to optimise million units, up from 24 million units in 2015, according to technical advances has also seen C-V2X become widely transportation. “Not only will the network need to support a futurist and founder of the Delphi Group. “We are approaching IHS Markit, an industry consultancy. That means, in just over adopted by the government and it is now a key enabler of billions of newly connected devices, cars and infrastructure, what is one of the most critical infl ection points,” he says. eight years, more than two thirds of passenger vehicles sold the technology. Nine provinces have embarked upon the it will also need to be fast and reliable.” This level is where the autonomous vehicle can, where will be exchanging data with external sources. digitization of their highways, and China aims to have 90% of While some of this can be handled by existing LTE necessary, seamlessly hand off control to the human. “The The connected vehicle is nothing without communication the nation’s highways connected using C-V2X by 2020. networks, much of the promise of connected cars will only challenge with Level 3 is that we hold the AV to a much higher technologies. By leveraging that connection, information Huawei is also working with Chinese and European be realized when 5G is rolled out. This could be soon. The 5G standard than we do human drivers,” says Koulopoulos. “For can fl ow within the vehicle, between vehicles, between the cities—including Barcelona in Spain, Hanover in Germany standard was completed in mid-2018 and some networks that we need better AV, which means we need better AI. And vehicle and the road it’s travelling on and between the vehicle and London in England—to implement C-V2X traffi c and devices are set to be rolled out by the end of the year. in the case of AVs, AI is still somewhat embryonic.” and the cloud—covering a range of scenarios from changing solutions globally to facilitate autonomous driving. 5G will reduce latency and improve reliability—key for Driving the changes required to advance AI to this level is traffi c lights to sudden changes in driving conditions. But this is just a start. Once connected cars become a a focus of machine learning and its subset, deep learning, This connected vehicle future, where cars can interact reality, cars, pedestrians and infrastructure will be better according to Huawei. “Advances in AI, especially deep with the automobiles and infrastructure around them, will connected, roads will become safer, engines will be more learning, have propelled the automotive industry towards be a major shift, says Raj Rajkumar, George Westinghouse effi cient, and drivers and passengers will have a better autonomous driving, giving new impetus to the traditional Professor in the Department of Electrical and Computer experience. As these interactions get smarter, so will AI play a industry,” the company says. Once these advances are Engineering at Carnegie Mellon University. “The most exciting greater role. Carmakers will be able to monitor their vehicles achieved, Huawei believes the industry will reach levels 4 and piece would be when vehicles can communicate with traffi c in a real-world environment, tweaking the driving experience 5, where responsibility for the car rests with the computer, lights...and with other surrounding vehicles.” and coupling it with machine learning. and it connects with its surrounding environment seamlessly. Even if that holy grail is some ways off, the car’s DIGITAL HIGHWAYS DRIVING INTERACTION dependency on data, AI and connectivity has already been Some of this is already happening. Using ad-hoc mesh Wi-Fi AI will improve the interaction between person and vehicle, established, says IBM’s Deuter. “With the adoption of IoT and or technologies like Cellular Vehicle-to-Everything (C-V2X), understanding and anticipating the driver’s needs and 5G, more data will be available and actionable. Most data cars can communicate their intentions with one another, movements. Complex, data-heavy scenarios involving loses its value within seconds. Therefore, if you are able to feed better avoid collisions, and learn when traffi c lights are going multiple vehicles, roads and other elements can be assessed the AI application with more accurate and precise data faster, to change. Government vehicles and roads in Colorado, for using AI. This new branch of the Internet of Things, or IoT, the application will improve and more use cases are possible.”

8 9 over the next three years. Also part of its long-term transport agenda is the use of autonomous buses and airborne drones.

SMART STREETS Traffi c control systems that incorporate AI will also come into wide-scale use on city streets in the next fi ve years. Globally, the traffi c management system market is expected to show average annual growth of 18.2% a year over the next decade, according to a 2018 report from market research fi rm FMI. Traffi c lights are already automated in many cities and many make extensive use of IoT sensors. AI will add new levels of intelligence in the form of image-mapping devices observation programme. It provides public, commercial and powerful predictive analytics to view and analyse nearby and other users with geo-spatial data relating to six broad traffi c, and respond by opening and closing traffi c lines as the spheres of activity: land management, water management, conditions require. Shenzhen’s “Traffi c Brain”, launched with atmosphere monitoring, marine resources management, the help of Huawei in September 2017, incorporates several climate change, emergency response and security. of these capabilities and the city’s traffi c police expect an More broadly, AI will fi nd other uses, such as interactive 80% improvement in traffi c fl ow after all traffi c lights are citizen services. Chatbots already respond to citizen queries SMART CITIES integrated into the system. and in healthcare, data is being used to forecast hospital Cities will also need intelligent platforms incorporating peak times and adjust staffi ng needs accordingly. In energy Applying intelligence to urban growth AI capabilities to integrate and act on data fl ows from the utility, AI enables engineers to forecast infrastructure failures, growing array of smart municipal services they provide. The plan predictive maintenance and design more effi cient and city of Columbus, in the US state of Ohio, has launched one environmentally sustainable systems. such platform—called a “Smart City Operating System”— “AI must be used by cities to help them become more n futuristic visions of smart cities, advanced technology is accrue overnight, and lengthy periods of trial and error lie ahead that aggregates and analyses mobility-related data from human-centric,” says Dr Khanna. As one example, the type of Ioften portrayed as an enabler of clean, comfortable, safe and before the benefi ts become visible. several different sources, with the aim of developing smart mobility platforms she envisions will not only be automated pleasant urban environments. As attractive as such visions transport solutions for city residents. Dubai is deploying and integrated, but will be personalised to each individual’s are, today’s city authorities hope such technology will alleviate INTEGRATED URBAN TECHNOLOGY different platforms to manage automated health, mobility transport needs at any given time. Similarly, she says, AI will some of the more diffi cult problems they face as growing urban AI needs to be used in conjunction with existing and emerging and citizen information services. The city of Weifang, in China’s help cities, in partnership with healthcare providers, to deliver populations put greater pressure on public services and often technology capabilities—such as the legions of IoT sensors Shandong Province, is pursuing a wide-ranging platform personalised health information and services to their citizens, ageing infrastructure. Existing infrastructure is creaking in being embedded across different types of cities and predictive for example through chatbots. many of the world’s large cities, says Ayesha Khanna, CEO and data analytics—and relies on the continued growth of “London’s population will expand by 3 million However, for AI to deliver the desired co-founder of ADDO AI, a Singapore-based AI consultancy. “AI computing power, both in the cloud and in devices themselves. benefi ts at scale, city administrations must provides city authorities a chance to make their infrastructure As these technologies grow and improve, so does the power people between now and 2040. Building more challenge the relevant agencies through smarter and more sustainable,” she says. of urban AI. But whether it be in infrastructure planning, utilities infrastructure will grow more diffi cult and more medium and long-term planning to map out Globally, 55% of the world’s population now lives in urban provision or managing the fl ow of people and traffi c, AI is fast expensive. We will need AI and other advanced the deployment of such technologies. There areas and it’s projected to rise to two thirds by the middle of becoming part of the fabric of the technology-based solutions to technologies to help move and serve our residents are three other areas in which city authorities the century, according to the World Bank. Meeting the needs of some of the most pressing challenges faced by the world’s cities. can bring direct and indirect infl uence to these growing urban populations is a universal challenge and “Transportation and traffi c issues are the biggest headaches more effi ciently.” Theo Blackwell, Chief Digital bear to maximise AI’s impact. One is ensuring applies equally to cities in the developed and developing world. facing rapidly urbanising cities,” according to Dr Khanna. One Offi cer, the City of London. that network operators provide suffi cient “London’s population will expand by 3 million people between way in which AI will help to alleviate congestion and reduce access to the computing power—both in now and 2040,” says Theo Blackwell, the Chief Digital Offi cer vehicle pollution, she says, will be the use of shared mobility strategy to integrate data from, and support the management the cloud and in or close to local devices—that AI-powered for the City of London. “Building more infrastructure will grow platforms and apps. These will help citizens to plan multi- of, environmental protection, parking, lighting, elderly care and analysis requires. Another is priority attention to data—creating more diffi cult and more expensive. We will need AI and other modal journeys in and around cities, selecting the most optimal other municipal services. or supporting open platforms where ever greater volumes of advanced technologies to help move and serve our residents forms of shared transport expected to be available at given useful data for AI are surfaced and exchanged, and cleaning and more effi ciently.” times, whether bus, shared bikes or cars, rail or walking. HUMAN-CENTRIC AI standardising the voluminous data sets that cities already hold. Need fuels investment, and worldwide spending on smart Using AI can enhance operational and predictive capabilities, To encourage ongoing innovation in municipal services by Lastly, fi nancial and other forms of support for innovative city technologies is expected to be US$81 billion in 2018 and prevent traffi c accidents and injuries, eliminate peak-time city agencies, businesses and other organisations, data tech start-ups by cities can fuel a rich and continuous fl ow of nearly double that fi gure—$158 billion—in 2022, according traffi c congestion and facilitate the future planning of traffi c platforms should ideally be wide-ranging, integrated with AI-based applications and services to be used in cities. London’s to IDC, a technology research fi rm. But while AI solutions are infrastructure. Indeed, Mr Blackwell says that the development maximum data standardisation and widely accessible to experience, says Mr Blackwell, shows that government support being applied by city planners, public service providers and of apps and platforms that can turn such data into actionable users. European cities are well served in this respect by for such innovative ideas can help them reach scale right across infrastructure companies alike, large-scale benefi ts do not information is among the top priorities for Transport for London Copernicus, the European Union’s satellite-based earth the city, to the benefi t of all its citizens.

10 11 than half the consumer content interactions stem from Researchers from the Massachusetts Institute of results calculated and presented by its own AI-powered Technology, for example, have developed AI that watches recommendation engine, according to the company’s chief videos and gives them a positive or negative emotional score technology offi cer, Emmanuel Frenehard. But he says that for each second. The idea is to predict the likely audience AI goes beyond personalisation, and ensures the pressure reaction to a script prior to its production. Meanwhile, on mobile networks is reduced so experiences don’t appear scientists from NVIDIA, a chip maker, have formulated a way glitchy. “Personalisation becomes more sophisticated when to train generative adversarial networks (GANs)—a class of we have the ability to recognise the device form factor, the AI algorithm—to create virtual celebrities from a database of type of connectivity, the speed of the network,” Mr Frenehard actual celebrities. explains. “Our algorithms are able to take into account the state of the device and deliver a video that is appropriate to THE FUTURE OF CONTENT CREATION? the device.” Such approaches may not be that far off from commercialisation. AI will be able to write high school essays by 2026, generate a top META-DATA DEVELOPMENTS 40 hit song by 2028, and even write a New York Times bestseller But this is only the start. When AI is used to trawl through by 2049, according to predictions in the joint World Economic MEDIA less structured data, even more interesting use cases are Forum and McKinsey study. predicted to arise. Nearly 36,000 exabytes of data will be But by then, terms such as ‘song’ and ‘book’ might AI helps mass media get up generated in 2020, according to a joint study released in themselves seem a little outdated, as AI will increasingly join February by the World Economic Forum close and personal and McKinsey, with all but 7,700 exabytes of this data being unstructured—data that is not organised in any predefi ned ver the past ten years, the entertainment and media media & entertainment sector leader at EY, a consulting model. O(E&M) industry has dealt with a period of intense and fi rm. “Content discovery and recommendation is expected to NBCUniversal, for example, has started sustained disruption as technology has completely altered improve signifi cantly in the future as streaming services and leveraging AI technologies like computer the fundamentals of the delivery and production of mass networks are able to access richer and more granular data vision to dig deeper into the content, media. Over the next decade, artifi cial intelligence (AI) is set sets, and machine learning is applied to better understand unearthing data on who is in a scene, to further this change, transforming not only the industry preferences across key customer segments,” he adds. “This what is happening, what is being said and but also what the terms ‘media’ and ‘entertainment’ mean. will not only enhance how micro audiences are targeted with even the underlying sentiment, according A 2018 survey by consulting company PwC of the impact of relevant content but also guide investments in new content.” to Entefy, an AI software company. This 150 emerging technologies on the global E&M industry from metadata is then used to generate 2018 to 2022 concluded that AI “will dominate... AI will have a DISCOVERY AND ENGAGEMENT customised clips or better search tools pervasive impact on all types of companies involved in E&M Surprisingly, the AI revolution in the E&M industry is quite and, importantly, can help with content and will become the industry’s new battleground.” far progressed. AI can be used in many areas of content analysis and copyright identifi cation, It’s a sizeable front. Total global spending on the E&M identifi cation, but only accurate recognition can lead to which is a large issue for E&M businesses.

AI is fundamentally changing what was a mass medium into A PERFECT MATCH forces with other technologies to create entirely new forms of a highly bespoke one, enabling the creation of highly personalised Just as AI is used to better match content with the consumer, entertainment. Virtual reality and augmented reality (VR and it will also be used to create content that is better suited to AR) were already a US$6.1bn business in 2016, according content that depends on the tastes of the individual, the devices the user. This will start with relatively simple approaches. to a 2018 PwC report released earlier this year, and are they’re using and their location. Twentieth Century Fox, for example, worked with IBM’s AI projected to grow to US$143.3bn by 2020, in part by working in 2016 to create a trailer for its movie Morgan. This helped together with AI to customise the experience of the user. industry was US$1.9trn in 2017, according to PwC estimates, successful consumer discovery of new media assets and reduce the time spent on creating the trailer from the Mr Frenehard from ifl ix points to customising ads, product and is expected to rise to US$2.4trn in 2022. The fi ght for videos. PwC’s survey points to Netfl ix’s recommendation standard 30-odd days to just 24 hours. It will also make placements or other features in videos for viewers as AI these industry revenues is likely to be fi erce, and will centre algorithms as an example of how AI is used to build content retrieval easier, enabling consumers and companies features in the pipeline: “Real-time localisation of content around one key issue—personalisation. AI is fundamentally customer engagement and satisfaction, a now commonplace to search using images, videos and audio and changing within the video, such as substituting signs in a movie, street changing what was a mass medium into a highly bespoke development. A survey by Ooyala, which provides software businesses in this area, such as media monitoring. names or ads on a car,” he explains. But that’s just the start. one, enabling the creation of highly personalised content that and services for producing, streaming and monetising video, But this too, will soon seem outdated. “We expect media Eventually, expect every kind of content, be it movie, video depends on the tastes of the individual, the devices they’re showed that 21% of the industry was already using AI, and companies to also explore what benefi ts they can achieve game, or VR and AR, to be tailored slightly differently for each using and their location. about half of those were using it to automate the creation of by leveraging AI in certain elements of the creative process, consumer. As Jacopo Bracco, former president of DIRECTV “AI is transforming content discovery and personalising metadata that is key to this kind of process. including script writing, casting and even character creation,” PanAmericana, puts it: “The nirvana is being able to deliver the viewing experience,” explains John Harrison, global At ifl ix, an Asia-based video on demand service, more says Mr Harrison. the right content to the right person.”

12 13 intend to adopt smart nursing technologies, and 17% will with greater levels of precision. Other medical technology adopt medical robots, in the next 3 years. firms, for example China’s MGI Tech, are developing genetic sequencers that promise to significantly reduce the cost BENEFITS FOR PATIENTS AND DOCTORS of sequencing to medical institutions. As a result, gene Advances in diagnostics will be partly driven by the growth sequencing is likely to take a major leap forward in the of AI-based image recognition technologies in screening for next few years, with beneficial effects for doctors and diseases. The use of AI techniques can reveal much more patients alike. detail from MRI and other scans than human clinicians can possibly identify, and can sift through extremely large REMOTE CONTROL complementary data sets to aid diagnosis. One application The impact of AI will be felt in both developed and will be cervical cancer screening. Researchers at Lehigh developing countries, especially where primary care University in the US state of Pennsylvania, for example, physicians are in increasingly short supply, notes Mr Kalis. have recently developed an AI-based screening technique “How do we create new primary care delivery models where that, in addition to offering greater screening accuracy and machines and humans work together to save labour and effi ciency, can be used at relatively low cost in developing produce better patient outcomes?” he asks. Health-tech countries, where incidence of the disease is highest. Global innovators that are using AI to deliver automated medical Market Insights predicts that the medical imaging and advice may soon have an answer. In the UK, Babylon diagnosis segment of the global AI healthcare market will Health and Your.MD are digital health assistants that

“How do we create new primary care delivery models where machines and HEALTHCARE humans work together to save labour and produce better patient outcomes?” AI that saves lives Brian Kalis, Managing Director of Digital Health and Innovation, Accenture.

grow by an average 40% each year between now and 2024. perform triage and diagnosis of patient illnesses based he contours of AI’s future imprint on health and life compound annual rate of 38% from its 2016 level of about Genome sequencing will also advance thanks to AI. The on data input into an app. Both work with the National Tsciences are visible today. Technologies that use AI, $320 million. Europe’s market size is projected to grow at ability of medical science labs to better understand the Health Service in different ways but both companies say such as the advanced analytics tools that help medical a similar rate during the same period to reach around $3.8 genetic causes of diseases, and to design therapies to their apps are helping make GPs’ lives easier by handling practitioners in hospitals to diagnose cancer and other billion. The fi rm believes global AI market healthcare will treat them, will eventually result in much more effective simpler cases and giving the GPs time to focus on more diseases, have already made a distinct mark in the sector. surpass $10 billion by 2024. prevention strategies and care approaches. Technology complex ones. Rather than a job destroyer, in many areas of Automated online tools help diagnose individuals’ minor Take for example automation. Robots will be a common companies such as Google are bringing AI techniques to healthcare such as this AI is augmenting existing roles and medical complaints before they visit the doctor’s offi ce. sight in operating theatres of the future. The use of AI- bear that analyse ever larger sets of sequence data but freeing up experts to be more effi cient and effective. Algorithms crunch data from wearable devices that alert driven robots to augment the role of humans surgeons will patients (and their doctors) to potential health issues. become widespread in US hospitals over the next fi ve years, according to Brian Kalis, the Managing Director of Digital AI BECOMES MEDICAL MAINSTREAM Health and Innovation at Accenture, a consulting company. AI’s power to understand disease patterns and diagnose His fi rm estimates that the use of such robots will generate illnesses, and to suggest preventive and curative $40 billion of value annually for the US healthcare industry measures, will continue to grow. But its use in health and by 2026. “Reducing complications and errors that can occur life sciences is expanding beyond these applications. during unaided human surgery will lead to shorter hospital Thanks to continuous innovation by health-tech start-ups stays,” says Mr Kalis. “This will contribute much of that and established players, AI will make its appearance in projected value.” operating theatres, and will provide clinical science labs AI-assisted nursing tools will also boost effi ciency, with tools of unprecedented power to identify the root generating $20 billion in value for US hospitals over the causes of disease. In the next fi ve to seven years, AI will same period, according to Accenture. Another consultancy, come to the aid of medical professionals in these and other McKinsey, estimates that using such tools to perform routine ways, saving lives in the process. nursing functions will eventually boost nurses’ productivity This also represents signifi cant growth opportunity for by between 30% and 50%. China’s healthcare sector is organisations in the health sector. According to research also likely to see brisk growth in adoption of AI-assisted fi rm Global Market Insights, the US AI market in healthcare technologies in these areas. According to new research from is expected to exceed US$4 billion in 2024, growing at a Forrester, 20% of large health industry organisations in China

14 15 fi rms will generate from AI use in the next fi ve years. products and advice to fi t customers’ health profi les. Stricter Robo-advisors, algorithm-driven tools used by wealth data privacy rules in some markets may impede sharing of FINANCIAL management fi rms to provide automated fi nancial such data in the short term, but cross-sector data fl ows will planning services to customers, were among the earliest inevitably increase, he believes, as customers come to expect manifestations of AI in the fi nancial sector. They continue to more joined-up fi nancial, health and other lifestyle products. gain in sophistication and still have considerable room for Dr Kirilenko believes there will also be change in the SERVICES growth. According to one source, robo-advisors will manage mix of players able to offer fi nancial services. Combining $1 trillion in assets by 2020 and close to $4.6 trillion by 2022. their AI and broader digital expertise with their unique Taking AI to the next level That fi gure is around $200 billion today. ability to glean customer behaviour and preferences, large technology companies will come to compete more directly FRONT AND BACK OFFICE APPLICATIONS with traditional fi nancial institutions. “Regulators in many Banks and non-bank lenders will use AI to accelerate loan markets are more open today to the idea of replacing parts of approvals, which are already being automated today. Real the fi nancial systems with technologies that work,” he says. time lending decisions will become widespread, as credit To thrive in such a landscape, established players will scoring and risk appraisals are similarly automated. need to embrace more active partnerships with fi ntechs, Insurance providers, according to Mr Ahmed, will use according to experts at Huawei. In banking, for example, facial recognition in combination with AI techniques to speed innovation will increasingly originate in platform-based the settlement of customer claims. One Chinese insurer is ecosystems, rather than within banks themselves. already able to analyse and settle accident claims within a The admonition that organisations’ data assets must arts of the fi nancial sector, notably wealth management personalised products. “New fi nancial products that no one minute today using these techniques. “That’s dramatically be readied for AI uses applies to all industries, but the P and equity trading, have been among the fi rst movers heard of before will proliferate,” he says. changing the customer-fi nancial institution interaction while challenge may be greater for established fi nancial industry in the commercial use of AI. The scope of AI-led innovation increasing effi ciency for everybody in the process,” he says. is now widening: banks and insurers are actively applying AI BIG DATA DRIVERS AI will also come into use in the back offi ce, as banks push techniques in the front and back offi ce, developing innovative What directions will AI-led innovation take? One will be ahead with the automation of payments, fraud detection, customer-facing services and automating operations such marked improvement in capabilities that exist today, as compliance and other operations. Much of this is currently as payments, risk modelling and fraud detection. machine learning tools analyse ever larger amounts of data driven by robotic process automation (RPA), but predictive One of the key drivers of such swift adoption of AI in the in ever wider varieties. For example, trading fi rms such as analytics and machine learning will begin to play a larger role fi nancial industry is cost savings. By 2030, traditional fi nance hedge funds, notes Syed Musheer Ahmed, General Manager in these areas within the next couple of years. sector organisations could reduce their costs by 22%, of the Fintech Association of Hong Kong, have pioneered As an example, banks are actively exploring the use of AI according to fi ntech research company Autonomous Next, in the use of genetic algorithms to generate trading ideas. algorithms to automate risk management processes with what would amount to more than $1 trillion in effi ciencies. “They narrow them down and mix them with evolutionary the dual objectives of improving regulatory compliance and These savings would be found across front, middle and algorithms and then invest them, which generates revenue reducing costs. Know your customer (KYC) and anti-money back offi ce operations with, for example, a reduction in growth,” he says. Such trading algorithms should gain in laundering processes, for instance, are largely rules-based retail branches and bank tellers, the application of AI to predictive accuracy, something which has considerable room today, with human developers establishing and updating the compliance and data processing, and the automation of for improvement, according to some analysts. rules software programmes must use. AI-based systems, by contrast, are trained over time to identify patterns and players. For example, research conducted by the consultancy One of the key drivers of such swift adoption of AI in the update rules accordingly, increasing the systems’ speed and PwC in early 2018 found that fi nancial services fi rms were fi nancial industry is cost savings. By 2030, traditional fi nance sector reducing the likelihood of errors. among the least effective in their use of data in a cross- organisations could reduce their costs by 22%, according to fi ntech sector comparison. Only 26% of fi nancial sector executives research company Autonomous Next, in what would amount to PARTNERSHIPS FOR NEW BUSINESS said their fi rms use data effectively, the third lowest of ten New payment models and services are likely to emerge as industries covered. There is much to do to redress this, but more than $1 trillion in effi ciencies. fi nancial service providers gain wider access to data from industry fi rms will need to integrate as many siloed data sets underwriting and collections systems. The chatbots that banks are now using for customer other sectors. The insurance industry’s access to automotive and systems as regulation allows, as to be effective AI needs Technology companies—notably small fi ntechs—have interaction will similarly use wider data access and data, generated by in-car IoT sensors, is leading to the growth access to wide varieties of data, and in different formats. provided the impetus for much of the AI-led innovation. continuous learning to make more targeted offers to of usage-based car insurance, for example, in which AI- Banks and insurers both will also need to deploy analytics Some established fi nancial sector players have responded customers and provide more accurate remedies to resolve based assessments of driver behaviour factor into “pay-how- tools that are adept at working with unstructured data. energetically themselves, co-opting fi ntechs’ AI expertise and issues. In fi ve years, such virtual assistants may rival you-drive” premiums. This is not entirely uncharted territory for established innovations. But Andrei Kirilenko of Imperial College London websites and the physical branch in importance as banking Dr Kirilenko similarly expects the ability of insurers and fi nancial industry players, and many are moving ahead maintains that the variety of services traditional banks and channels for customers. In a global survey conducted by The banks to apply AI techniques to the analysis of customers’ aggressively to build AI capabilities. But if the borders insurers offered remains limited. He expects a new wave of Economist Intelligence Unit in early 2018, banking executives health data—generated, for example, from personal health separating the fi nancial and other sectors erode, as Dr innovation as technology companies exert more pressure pointed to “improvement of the customer experience” and monitoring devices—to result in new types of fi nancial Kirilenko predicts, some fi nancial institutions will fi nd it on the industry to meet customer demand for smarter, more “greater customer engagement” as the main benefi ts their products. Wealth managers will also craft investment diffi cult to keep pace.

16 17 “This is really the power of using artifi cial intelligence to amounts of data it generated and apply AI to that to improve pull live information and relate it to a customer’s specifi c its internal operations and business effi ciency. By analysing supply chain and shipments,” Data Analytics Innovation the company’s order history, for example, it was able to Leader at DHL’s Asia Pacifi c Innovation Center, Timothy Kooi provide optimal pickup paths and reduce the number of says. “Of course it’s not smart enough yet to make decisions vehicles required to fulfi l the same number of orders. AI also for you, but it can give you detailed decision advice. That’s a enabled it to optimise its inventory management across its step in the right direction.” warehouse storage locations. Paperwork was digitised and However, to effectively train machine learning algorithms, the time for re-inspections was reduced to 10 seconds. From data must be collected and stored well, which is an area placing the order to loading the goods, the entire process that’s being addressed through advances in cloud computing now takes less than two hours. and networks. “We are just starting to scratch the surface “Jointown didn’t feel that the traditional business-driven of the use cases for AI. Companies have an unfathomable IT model was effi cient enough,” Wan Yougang, Jointown amount of data at their disposal, but if this data is bad, Deputy Director of Operations and IT Management, says. then the output will also be bad,” says Kevin McMahon, “Instead, we prefer a strategy-driven IT model in which Executive Director of Mobile and Emerging Technologies at enterprises prioritise their future. Huawei Cloud has helped SPR, a digital technology consulting fi rm. “It doesn’t matter us become a driving force for business innovation, and how sophisticated the AI algorithms are. Improvements specifi cally IT innovation.” in technologies like cloud computing, edge computing, networking, IoT and 5G are creating more effective ways to FUTURE FORECASTS collect and store data, allowing AI to one day provide more For all the present advances, however, it’s the future that accurate and actionable insights.” offers the greatest promise for AI in logistics. As paperwork gets digitised, there’s hope that it will not only speed up AI ENABLERS processing but be mined for intelligence far beyond its LOGISTICS’ Jointown Pharmaceutical Group, a large Chinese drug present use. DHL’s Kooi believes AI could seek to predict distribution company, has drastically changed its operations the future based on the knowledge gathered from logistics using AI based cloud technology. data. For example, using a machine-learning based tool EXTRA HIDDEN HAND Jointown was able to refi ne the way it collected the vast developed by DHL, it could predict air freight transit delays An industry inﬂ ection point

ogistics—the hidden hand that manages moving goods data and interactions include elements of AI. This is despite L from source to end user—has long been seen as an a recent survey by consulting group EY that predicted the industry ready to reap huge benefi ts from technological global value of goods transported would quadruple by 2050, change. It relies on physical and digital networks to move to be worth US$68.5 trillion. goods safely and securely, and generates vast quantities of This gap leaves plenty of room for innovation and data, all areas ripe for the application of artifi cial intelligence logistics is now at a signifi cant inﬂ ection point—demand (AI). Yet while its uptake may have been slower than some for AI is soaring as companies strive to reap the cost industries, like manufacturing and fi nance, those in the reductions and effi ciencies it promises. A study by industry say AI is starting to have an impact on almost consulting fi rm McKinsey concludes that transport and every element of the business, from demand generation to logistics can derive up to $500 billion of incremental value warehousing through to the transport, transfer, delivery and from the use of AI over other analytical techniques, an acceptance processes. amount second only to the travel industry. At present, the sheer scale of the industry presents a challenge that has only been partly addressed by technology. DATA-DRIVEN EFFICIENCIES Take shipping for example. An estimated 90% of international DHL, a large German logistics fi rm, is testing a wide range non-bulk cargo is moved by sea, yet the majority of the of AI applications across all its businesses, which cover air, shipping industry’s documentation is still recorded on paper land and sea freight. The use of machine learning and natural or sent by fax. And while the broader logistics industry has language processing, for example, is being applied in its embraced digitization, it is still in its early days: According Resilience360 supply chain risk management product, which to IBM researchers, only 10% of current logistics systems, processes millions of variables to assess supplier risk.

18 19 to propose proactive mitigation. Batstone, CEO of contextere, an industrial software company Paperwork is also an area where AI will have wide- focused on AI solutions, believes the key use of AI may be on ranging implications beyond simple effi ciencies. A 2015 what he calls the “last tactical mile, where warm hands touch joint report by the World Bank and the World Trade cold steel.” This AI, he says, would enable human operators to Organisation found that countries with ineffi cient trade have natural language conversations with machines, asking documentation requirements took more time to process questions such as ‘What does this error code mean on the imports and exports, and were more likely to have a higher elevator panel?’ ‘What special tools do I need to repair this poverty rate. And while many documentation requirements landing gear?’. originate from government, some are also a result of force “By combining human ingenuity with AI, we are already of habit or old systems. improving the productivity and safety of the industrial DHL believes much of the human element involved in workforce,” says Batstone. “Technological innovation and job customs paper checking can be removed, detailing in a creation are not mutually exclusive. In fact, the strategy to 2018 paper how an AI platform can be trained to automate address both the opportunities and threats of a globalised, customs declarations using natural language processing connected world should be to combine AI and humans.” and the self-learning capabilities of deep learning. By using How these two visions of the future of AI and logistics new data to continually improve its performance, fi ndings resolve themselves also depends on broader decisions

Logistics is now at a signifi cant infl ection point— demand for AI is soaring as companies strive to reap the cost reductions and effi ciencies it promises. A study by consulting fi rm McKinsey concludes that transport and logistics can derive up to $500 billion of incremental value from the use of AI over other analytical techniques, an amount second only to the travel industry. suggest such a system could ‘ingest’ all the necessary outside of technology. But those in the industry agree that paperwork and process it, fl agging only those anomalies there’s no question that AI’s success will depend on the that require manual intervention. According to the report, quality of the data that it receives, and the quality of the Ernst and Young is already using a similar approach to networks over which the data travels. DHL’s Kooi points detect fraudulent invoices with 97% accuracy. Wherever to a project it rolled out with Huawei last year to integrate there are complex manual processes that require skillful narrowband communications into a truck fl eet in Liuzhou, knowledge of regulations, industries and customers, AI China, using Huawei’s and China Mobile’s NB-IoT chips. The could lend a hand. chips enabled a simple improvement: coordinating trucks to Huawei has also applied AI to its own document arrive and unload their containers in a loading dock, at the processing requirements. It says it used Optical Character right bay, at the right time. Being able to share and coordinate Recognition (OCR) technology, which has a numeric value that locational information in a smart and effi cient way was accuracy of up to 97%, to streamline both the processing and vital, according to Kooi. “Connectivity is absolutely key to the recording of its customs documents. When combined information sharing in a timely manner,” he says. with big data analytics, this increased the company’s Similarly, Contextere CEO Batstone sees AI, IoT and customs effi ciency and signifi cantly reduced its risk connectivity as vital components of the same body. “From an exposure, according to Huawei. infrastructure perspective IoT is like the body, connectivity is the nerves and AI is the brain. To get the full potential THE HUMAN ELEMENT of AI you need to be able to implement it fully at the edge For some in the supply chain, however, the holy grail of AI with links to the cloud,” he said. “When IoT and 5G are fully remains not in automating the human out of the equation but realised, AI will move from being an amazing tool to being the of better integrating the human into the supply chain. Gabe baseline of literally everything we do today.”

20 HUAWEI CONNECT 2018:

Sponsor Corporate Communications Dept. Huawei Technologies Co., Ltd.

E-mail: [email protected] Tel: +86-755-28780808 Address: Huawei Industrial Base, Bantian, Longgang, Shenzhen 518129, China

Copyright © Huawei Technologies Co., Ltd. 2018. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd.

Disclaimer The content of this document is for information purpose only, and provided “as is”. Except as required by applicable laws, no warranties of any kind, either express or implied, including but not limited to, the implied warranties of merchantability and ﬁ tness for a particular purpose, are made in relation to content of this document. To the maximum extent permitted by applicable law, in no case shall Huawei Technologies Co., Ltd. be liable for any special, incidental, indirect, or consequential damages, or lost proﬁ ts, business, revenue, data, goodwill or anticipated savings arising out of or in connection with any use of this document.