AI in Manufacturing: Ready for Impact Driving AI
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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.