{"id":922636,"date":"2019-01-30T09:04:13","date_gmt":"2019-01-30T17:04:13","guid":{"rendered":"http:\/\/customerthink.com\/?p=922636"},"modified":"2019-01-30T23:37:47","modified_gmt":"2019-01-31T07:37:47","slug":"getting-ready-for-ai-the-next-digital-frontier","status":"publish","type":"post","link":"https:\/\/customerthink.com\/getting-ready-for-ai-the-next-digital-frontier\/","title":{"rendered":"Getting Ready for AI – The Next Digital Frontier!"},"content":{"rendered":"
Artificial Intelligence (AI)<\/strong> is predicted to be one of the next big digital disruptions. According to a PWC report, the big question is how to secure the talent, technology, and access to data to make the most of this disruption.<\/p>\n Let\u2019s look at the growth of AI using some facts. In a discussion paper<\/a> on AI published by McKinsey Global Institute, it has been highlighted that\u00a0in 2016, companies invested $26 billion to $39 billion in artificial intelligence with tech giants and startups leading the pack. While 20% of the AI firms say they\u2019re adopters, 41% of the firms are not very sure about the benefits of AI. The sectors that are strong adopters are high tech, automotive, and financial services sectors. These are followed by retail, media, and CPG, which are medium adopters. Finally, education, healthcare and tourism are sectors in which the adoption is pretty low.<\/p>\n AI implementation can directly impact the value chain of a company by impacting various functions such as R&D, production, marketing, and user experience. AI promises benefits but also poses urgent challenges that cut across firms, developers, governments, and workers. Let\u2019s analyse the scenario of the practical implementation of artificial intelligence across industries and how companies can strike a fair balance between technology and human interaction.<\/p>\n The history of AI proliferates with multiple boom and bust cycles, rosy promises of a better future, and irksome disappointments. So, what is it this time? Is AI going to revamp the way business processes are executed or will it be another disaster?<\/p>\n Research suggests that AI is finally ready to deliver real-life business results. With more than 74% of customers making their purchasing decisions online, algorithms getting more precise and sophisticated, and the availability of enormous amounts of data, the right recipe is almost here.<\/p>\n Under such circumstances, it\u2019s important to understand how users are adopting AI technologies. To delve deep into the analysis, McKinsey conducted thorough research that offers a snapshot of the current state of the rapidly changing AI industry, looking through the lens of both suppliers and users to come up with a more robust view of the economic potential of AI and how it will unfold.<\/p>\n The idea of artificial intelligence dates back to 1950, when Alan Turing, an English mathematician proposed the prodigal Turing test to analyze whether a computer can communicate well enough to convince a human that, it too, is a human. This was swiftly followed by the development of the first AI program at the Carnegie Mellon University. From 1970 onwards, the growth slumped due to a lack of practical applications of AI. However, it again picked up in the 2000s thanks to faster computers and deep learning algorithms. Advances in the field of Graphics Processing Units have facilitated faster training of deep learning algorithms and have boosted the functional capabilities of AI, like image and pattern recognition.<\/p>\n Though tech giants are the pioneers in investing in AI technologies, commercial adoption is still lagging. Indeed, internal investment by large corporations dominates – it amounted to approximately $18 billion to $27 billion in 2016. Despite all this, the overall adoption of AI has been pretty low so far. One primary reason is that most of the R&D work is largely focused on increasing the internal performance of the firm. According to a McKinsey survey, most business leaders are unsure about how AI can benefit the processes of their organization, where to obtain AI-powered applications, how to integrate them into their companies, and how to assess the RoI in the technology.<\/p>\n The bigger companies, on the other hand, are playing larger cards. Toyota, for example, has allocated $1 billion for new research. Similarly, IBM has pledged to invest $3 billion to make its Watson cognitive computing service a leader in the internet of things.<\/p>\n A significant challenge organizations face is the dearth of true experts in the field of AI. Companies are actively buying AI startups and using M&As to acquire not only promising technologies but also rare resources. This trend has also encouraged more venture capitals and private equity funds to invest in AI startups. However, investors are yet to recognize their RoI as only 10% of the AI based startups are generating good revenue.<\/p>\n Only 20% of businesses have adopted one or more AI technologies into their core business processes, while only 12% of the industries have moved past the experimental stage. Like in every new wave of technology, AI has some early and late adopters. Coincidentally, the companies that were the early adopters in the previous digital wave are maintaining the same pace in this one as well. Typical characteristics of early adopters are:<\/p>\n Also, early AI adopters tend to become serial adopters going forward.<\/p>\n Adoption is indeed a challenge when it comes to AI in business. Most companies have their own apprehensions in this regard. But what about the firms that have already adopted AI in their core business processes? Let\u2019s have a look at how AI can shape different functions across the whole value chain and in diverse sectors.<\/p>\n AI can help organizations gain a competitive advantage by helping them estimate demand. It allows businesses to optimize their supply chain and design better offerings by finding meaning in disparate information and adjusting to random variations to discern trends that can be acted upon.<\/p>\n If we talk about the retail sector, in some cases, AI-based approaches to demand forecasting are expected to reduce forecasting errors by 30 to 50% from conventional approaches. Lost sales due to unavailability of a product can be reduced up to 65% and there can be a reduction of 5-10% and 25-40% respectively in costs related to transport & warehousing, and supply chain administration.<\/p>\n When it comes to matching supply and demand, electric utilities are a special case – they need to make predictions on a real-time basis. National Grid in the United Kingdom in collaboration with DeepMind<\/a>, an AI startup bought by Google in 2014, is predicting supply and demand variations based on weather-related variables. The ultimate objective is to increase the use of renewable power and simultaneously decrease energy consumption by 10%.<\/p>\n Another crucial manner in which AI can impact firms is in the R&D departments, partly helping researchers quickly assess whether a prototype would be likely to succeed or fail in the market, and why AI-based approaches can increase the time to market by almost 10-15% and can further result in productivity gains of 10%.<\/p>\nAre businesses ready for AI?<\/h2>\n
Upheaval of AI<\/h3>\n
Growth and lag<\/h4>\n
Ready to adopt but not to act<\/h4>\n
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Impact<\/strong><\/h3>\n
AI implemented at scale provides diverse returns<\/h4>\n