2018 AI predictions 8 insights to shape business strategypwc/us/AI2018 2018 AI predictions pwc/us/AI2018 1Here's some actionable advice on artificial intelligence (AI), that you can use today: If someone says they know exactly what AI will look like and do in 10 years, smile politely, then change the subject or walk away. AI is remarkably complex and advancing quickly. It's doing far more in some areas, and far less in others, than anyone would have guessed a decade ago. It's impossible for anyone today to give a precise vision of how the next ten--much less five--years will unfold. That's not to say that it's impossible to make broad predictions about AI's impact in the coming years and decades. We've done that elsewhere. Our aim here is different: to make specific predictions about AI trends for the next 12 months, then draw out key implications for business, government, and society as a whole. We're confident in making nearteam forecasts because these nascent trends are already underway, though they aren't yet attracting the attention they deserve. We've made eight such predictions. They're based not just on insights from AI visionaries and computer scientists. They're also informed by what our leaders in assurance, consulting, and tax see on the ground with clients around the world who are grappling with how to put AI to work in their organizations and prepare their employees for a world in which AI is everywhere. We hope you'll consider how these predictions relate to your own organization.PwC AI predictions for 20181. AI will impact employers before it impacts employment 2. AI will come down to earth--and get to work 3. AI will help answer the big question about data 4. Functional specialists, not techies, will decide the AI talent race 5. Cyberattacks will be more powerful because of AI--but so will cyberdefense 6. Opening AI's black box will become a priority 7. Nations will spar over AI 8. Pressure for responsible AI won't be on tech companies alone2018 AI predictions pwc/us/AI201821AI will impact employers before it impacts employment Everyone has seen the headlines: Robots and AI will destroy jobs. But we don't see it that way. We see a more complex picture coming into focus, with AI encouraging a gradual evolution in the job market that--with the right preparation--will be positive. New jobs will offset those lost. People will still work, but they'll work more efficiently with the help of AI. Most people have heard that AI beat the world's greatest grandmaster in chess. But not everyone knows what can usually beat an AI chess master: a centaur, or human and AI playing chess as a team. The human receives advice from an AI partner but is also free to override it, and it's the established process between the two that is the real key to success. This unparalleled combination will become the new normal in the workforce of the future. Consider how AI is enhancing the product design process: A human engineer defines a part's materials, desired features, and various constraints, and inputs it into an AI system, which generates a number of simulations. Engineers then either choose one of the options, or refine their inputs and ask the AI to try again. This paradigm is one reason why AI will strengthen the economy. At the same time, however, there's no denying that in some industries, economies, and roles--especially those that involve repetitive tasks--jobs will change or be eliminated. Yet in the next two years, the impact will be relatively modest: PwC's forthcoming international jobs automation study, due in February 2018, estimates that across 29 countries analyzed, the share of jobs at potential high risk of automation is only 3 percent by 2020.Why organizations will succeed or fail The upshot In 2018, organizations will start realizing they need to change how they work. As they do so, they'll need to be especially mindful of what has come before: failed tech transformations. There are several reasons why this happens, but two in particular are relevant to the way so many organizations are approaching AI. They're pigeon-holing AI talent. And they're thinking and working in silos.2018 AI predictions pwc/us/AI20183#1. AI will impact employers before it impacts employmentAI-savvy employees won't just need to know how to choose the right algorithm and feed data into an AI model. They'll also have to know how to interpret the results. They'll need to know when to let the algorithm decide, and when to step in themselves. At the same time, effective use of AI will demand collaboration among different teams. Consider an AI system that helps hospital staff decide which medical procedures to authorize. It will need input not just from medical and AI specialists, but also from legal, HR, financial, cybersecurity, and compliance teams. Most organizations like to set boundaries by putting specific teams in charge of certain domains or projects and assigning a budget accordingly. But AI requires multidisciplinary teams to come together to solve a problem. Afterward, team members then move on to other challenges but continue to monitor and perfect the first. With AI, as with many other digital technologies, organizations and educational institutions will have to think less about job titles, and more about tasks, skills, and mindset. That means embracing new ways of working.67%of executives say AI will help humans and machines work together to be stronger using both artificial and human intelligence Source: PwC Consumer Intelligence Series: Bot.Me, 2017 Base: 500 business executives2018 AI predictions pwc/us/AI20184#1. AI will impact employers before it impacts employmentImplications Popular acceptance of AI may occur quickly As signs grow this year that the great AI jobs disruption will be a false alarm, people are likely to more readily accept AI in the workplace and society. We may hear less about robots taking our jobs, and more about robots making our jobs (and lives) easier. That in turn may lead to a faster uptake of AI than some organizations are expecting.The organizational retooling will commence It will be a lengthy process, but some forward-thinking organizations are already breaking down the silos that separate data into cartels and employees into isolated units. Some will also start on the massive workforce upskilling that AI and other digital technologies require. This upskilling won't just teach new skills. It will teach a new mindset that emphasizes collaboration with co-workers--and with AI.How workers think about human-machine AI centaurs 78% Would work with AI manager if meant more balanced workload65% Would free employees from menial tasks64% Would offer employees new work opportunities50% Would follow AI system if predicted most efficient way to manage projectSource: PwC Consumer Intelligence Series: Bot.Me, 2017 Source: PwC Consumer Intelligence Series: Bot.Me, 2017 Base:500 business executives; percent agreeing with statement Base: 500 business executives; percent agreeing with statement2018 AI predictions pwc/us/AI201852 AI will come down to earth--and get to work There are plenty of publications promising an AI-powered future that will look like magic: fleets of autonomous cars that never crash or encounter traffic jams, robot doctors that diagnose illness in milliseconds, and smart infrastructure that optimizes flows of people and goods and maintains itself before repairs are ever needed. All that may come--but not in 2018. Executives think that AI will be crucial for their success: 72% believe it will be the business advantage of the future. The question is: What can it do for me today And the answer is here.Augmenting human productivity If AI sounds far-fetched, what about a tool to perform repetitive white-collar tasks, so managers can spend their time on analysis How about one that detects fraud and increases supply chain resilience This is the value of AI in 2018: it lies not in creating entire new industries (that's for the next decade), but rather in empowering current employees to add more value to existing enterprises. That empowerment is coming in three main ways: Automating processes too complex for older technologies Identifying trends in historical data to create business value Providing forward-looking intelligence to strengthen human decisionsValue from tedious tasks Consider how most companies' finance functions spend a large portion of their time: wading through data coming from ERP, payment processing, business intelligence, and other systems. Many staff members also spend hours each day poring through legal contracts and emails, or performing mundane transactional tasks. The result is that value-adding analysis is what many finance professionals only do when they have time left over from their other, routine tasks.2018 AI predictions pwc/us/AI20186#2. AI will come down to earth--and get to workNow imagine an AI system scanning all the function's data, identifying trends and anomalies, performing many transactions automatically, and flagging relevant issues for further attention. Imagine AI also identifying and explaining likely risks and offering data-driven forecasts to support managers' analysis and decisions. It may not be as sexy as a smart city, but this kind of practical AI is ready right now. And it's often sneaking in through the backdoor. Enterprise application suites from Salesforce, SAP, Workday, and others are increasingly incorporating AI.Where industries will put practical AI to work Ranking of AI impact by its potential to free up time, enhance quality, and enhance personalizationRanking 1Industry HealthcareHigh-potential use cases Supporting diagnosis by detecting variations in patient data Early identification of potential pandemics Imaging diagnostics Autonomous fleets for ride sharing Semi-autonomous features such as driver assist Engine monitoring and predictive, autonomous maintenance Personalized financial planning Fraud detection and anti-money laundering Automation of customer operations Autonomous trucking and delivery Traffic control and reduced congestion Enhanced security Media archiving, search, and recommendations Customized content creation Personalized marketing and advertising Personalized design and production Anticipating customer demand Inventory and delivery management Smart metering More efficient grid operation and storage Predictive infrastructure maintenance Enhanced monitoring and auto-correction of processes Supply chain and production optimization On-demand production1Automotive 3Financial services 4Transportation and logistics Technology, media, and telecommunications Retail and consumer 56 7Energy 8Manufacturing Source: PwC Global AI Impact Index, 20172018 AI predictions pwc/us/AI20187#2. AI will come down to earth--and get to workImplications Business problems will open the door to AI Leaders don't need to adopt AI for AI's sake. Instead, when they look for the best solution to a business need, AI will increasingly play a role. Does the organization want to automate billing, general accounting and budgeting, and many compliance functions How about automating parts of procurement, logistics, and customer care AI will likely be a part of the solution, whether or not users even perceive it.New kinds of ROI measures are needed Sometimes the best approach to gauge AI's value is to use the same measures you'd apply to any other business investment: metrics such as increased revenue or reduced costs. But AI's most powerful benefits are often indirect, so organizations will want to explore other measures of ROI. Automated full-time equivalents can capture how AI is freeing human workers from mundane tasks. Other metrics can show how AI is improving human decision-making and forecasts.54%of business executives say AI solutions implemented in their businesses have already increased productivity Source: PwC Consumer Intelligence Series: Bot.Me, 2017 Base: 500 business executives2018 AI predictions pwc/us/AI201883 AI will help answer the big question about data Many companies haven't seen the payoff from their big data investments. There was a disconnect. Business and tech executives thought they could do a lot more with their data, but the learning curve was steep, tools were immature, and they faced considerable organizational challenges.Few businesses get value from their data--but AI could change that Public sector Power & utilities Industrial products Automotive Energy & mining Tech, media, & communications Healthcare Financial services Consumer markets Hospitality & leisure Source: PwC 2017 Global Digital IQ Survey Source: PwC 2017 Global Digital IQ Survey Q: To what extent do you agree with the following statement (strongly agree): We effectively Q: To what extent do you agree with the following statement (strongly agree): We effectively utilize all the data we capture to drive business value utilize all the data we capture to375, 131, 156, 433 Bases: 72, 217, 135, 322, 237, 75, drive business value Bases: 72, 217, 135, 322, 237, 75, 375, 131, 156, 43343% 37% 33% 33% 30% 30% 27% 26% 24% 13%Now, some are rethinking their data strategy as the landscape matures and AI itself becomes more real and practical. They're starting to ask the right questions, like: How can we make our processes more efficient and What do we need to do to automate data extraction 2018 AI predictions pwc/us/AI2018 9#3. AI will help answer the big question about dataAt the same time, organizations are now able to take advantage of new tools and technical advancements, including: Easier methods for mining less-structured data, including natural language processing for text indexing and classification Enterprise application suites that incorporate more AI Emerging data lake-as-a-service platforms Public clouds that can take advantage of different kinds of data Automated machine learning and data managementFeeding the AI beast Despite these advances, many organizations still face a challenge. Many kinds of AI, such as supervised machine learning and deep learning, need an enormous amount of data that is standardized, labeled, and cleansed of bias and anomalies. Otherwise, following the ancient rule-- garbage in, garbage out--incomplete or biased data sets will lead to flawed results. The data must also be specific enough to be useful, yet protect individuals' privacy. Consider a typical bank. Its various divisions (such as retail, credit card, and brokerage) have their own sets of client data. In each division, the different departments (such as marketing, account creation, and customer service) also have their own data in their own formats. An AI system could, for example, identify the bank's most profitable clients and offer suggestions on how to find and win more clients like them. But to do that, the system needs access to the various divisions' and departments' data in standardized, bias-free form.The right approach to data It's rarely a good idea to start with a decision to clean up data. It's almost always better to start with a business case and then evaluate options for how to achieve success in that specific case. A healthcare provider, for example, might aim to improve patient outcomes. Before beginning to develop the system, the provider would quantify the benefits that AI can bring. The provider would next look at what data was needed--electronic medical records, relevant journal 2018 AI predictions pwc/us/AI2018 10#3. AI will help answer the big question about dataarticles, and clinical trials data, among others--and the costs of acquiring and cleansing this data. Only if the benefits--including measures of indirect benefits and how future applications can use this data--exceed the costs should this provider move forward. That's how many organizations will ultimately reform data architecture and governance: with AI and other technologies offering value propositions that require it.Implications Success will lead to success Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They'll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries.Third-party data providers will prosper There's no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.More synthetics are coming As data becomes more valuable, advances in synthetic data and other lean and augmented data learning techniques will accelerate. We may not need, for example, a whole fleet of autonomous cars on the road to generate data about how they'll act. A few cars, plus sophisticated mathematics, will be sufficient.59% 2018 AI predictions pwc/us/AI2018of executives say big data at their company would be improved through the use of AI Source: PwC Consumer Intelligence Series: Bot.Me, 2017 Base: 500 business executives114 Functional specialists, not techies, will decide the AI talent race As AI spreads into more specific areas, it will require knowledge and skill sets that data scientists and AI specialists usually lack. Consider a team of computer scientists creating an AI application to support asset management decisions. The AI specialists probably aren't experts on the markets. They'll need economists, analysts, and traders working at their side to identify where the AI can best support the human asset manager, help design and train the AI to provide that support, and be willing and able to use the AI effectively. And since the financial world is in constant flux, once the AI is up and running, it will need continual customizing and tweaking. For that too, functional specialists--not programmers--will have to lead the way. The same is true not just throughout financial services, but in healthcare, retail, manufacturing, and every sector that AI touches.Citizen data scientists wanted AI is becoming more user friendly. Users no longer need to know how to write code in order to work with some AI applications. But most still demand far more technical knowledge than a spreadsheet or word processing program does. For example, many AI tools require users to formulate their needs into machine learning problem sets. They also require an understanding of which algorithms will work best for a particular problem and a particular data set. The exact level of knowledge required will vary, but we can broadly divide AI's demands on human knowledge into three categories. First, most members of an AI-enabled enterprise will need some basic knowledge of AI's value as well as what it can and can't do with data. Second, even the most mature AI program will always need a small team of computer scientists. The third group, which many organizations aren't yet paying attention to, are AI-savvy functional specialists.2018 AI predictions pwc/us/AI201812。。。。。。