The increasing power and capability of machines in the digital supply network (DSN) may portend a change in what organizations ask of their workers, in terms of required skills, tasks, and roles. In the coming years, perhaps sooner than later, almost all work will likely involve people working alongside technology or robots they are not currently working with today. Navigating the future of work can be a new and confounding challenge to many supply chain executives who may already be struggling with what their organizations may look like in a novel, more interconnected age. And it can be difficult to identify and prepare for the workforce of the future when the impacts of the DSN on roles and functions are still very much evolving (see the sidebar "A brief look at the digital supply network" to learn more). But with this uncertainty comes the opportunity--and perhaps what many would consider a requirement--to rethink the role of talent in supply chains and discover the potential power of people and machines working together. The addition of advanced technology to a workplace can spur the fear of robots replacing human workers. Certainly, the introduction of advanced technologies could eliminate some tasks and reduce the need for some roles. At the same time, however, it also could lead to the creation of some new tasks and roles. In the United Kingdom, for example, technology has helped to create 3.5 million new jobs between 2001 and 2015, even while it has contributed to the loss of 800,000 other jobs.1

We are continuously hearing about artificial intelligence and machine learning being applied to different fields. IT Ops teams have been building incredibly sophisticated Big Data and Artificial Intelligence systems for business stakeholders, but have found themselves using rudimentary reactive monitoring systems and manual steps to help run the infrastructure required to keep those cutting edge systems up and running. We are finally seeing a growing number of pioneering IT Ops teams applying these principles, and are helping them to leverage their team members into more valuable activities, whilst at the same time they ensure everything is still under control in a more proactive and controlled way.

Smart machines, businesses as platforms, and the gig economy figure into Tom Friedman's riff on where the future of work could take global companies. Tom Friedman is a Pulitzer Prize-winning columnist for The New York Times and author of seven best-selling books. His work covers a broad range of topics, including globalization, the Middle East, and environmental challenges. Friedman is intrigued by the connections among people, businesses, technology, and institutions that shape the evolution of an increasingly complex world. His newest book, "Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations," explores the future of work, a topic also being examined by Deloitte U.S. and Deloitte LLP's Center for the Edge.

Get a Primer in AI for HR from Deloitte's Christa Degnan Manning 685 views 2 weeks ago You can't understand AI until you know the categories: robotic process automation, cognitive augmentation and cognitive automation. Firing Line with Bill Kutik 7:14 Power of One is Power of Many – SAP Group VP David Ludlow - Duration: 7 minutes, 14 seconds. Firing Line with Bill Kutik 6:16 AI, Engagement, SMB Are All In HR's Future – George LaRocque - Duration: 6 minutes, 16 seconds. Firing Line with Bill Kutik This item has been hidden Most Popular Play all 5:38 Interview with Mike Ettling, SAP's President for HCM - Duration: 5 minutes, 38 seconds. Firing Line with Bill Kutik This item has been hidden HCM Analysts & Consultants Play all 5:28 Mercer Global Talent Report Previewed by Katherine Jones - Duration: 5 minutes, 28 seconds.

There is a lot of debate about whether data scientists will or won't be automated out of a job. It turns out that machines are far better at doing rote tasks faster and more reliably than humans, such as data wrangling. "The automation of data science will likely be widely adopted and speak to this issue of the shortage of data scientists, so I think in the near term this could have a lot of impact," said David Schatsky, managing director at Deloitte and one of the authors of Deloitte's new report. Industry analysts are bullish about the prospect of automating data science tasks, since data scientists can spend an inordinate amount of time collecting data and preparing it ready for analysis. For example, Gartner estimates that 40% of a data scientist's job will be automated by 2020.

In the next stage of artificial intelligence adoption, banks will use AI to help understand the intentions and emotions of customers and enable better interactions, according to a new report from Accenture (NYSE: ACN). A new report from Accenture has revealed that banks are set to adopt artificial intelligence (AI) to better understand customers' intentions and emotions, with the aim of enabling better interactions. The report, Accenture Banking Technology Vision 2017, draws on the analysis of an advisory board of more than two dozen individuals, interviews with technology luminaries and industry experts, and results of a survey of 579 bank executives in 31 countries across North America, Europe, Asia Pacific, Africa and South America. The goal of the survey was to identify the key issues and priorities for technology adoption and investment. According to the report, more than three-quarters (78%) of bankers believe that AI will enable simpler user interfaces that will help banks create a more human-like customer experience.

Miniature robots stage a group dance at a smart manufacturing conference in Nanjing, East China's Jiangsu Province on December 7. Photo: VCG Some insiders claim that bubbles are already growing in China's artificial intelligence (AI) sector, following the rapid expansion of the industry. Firms that lack long-term strategies to counteract the emergence of such scenarios may go bankrupt sooner rather than later. Like many attractive industries at their early stages, China's AI domain has been favored by both private capital and the government, Zhu Pinpin, founder of Shanghai Xiaoi Robot Technology Co, told the Global Times on Monday. "To some extent, we expected a bubble, as investors and governments pushed forward expansion of the industry," he said. Opportunities in virtual reality and AI have grown exponentially recently, with both considered good bets on the venture capital (VC) investment circuit, according to a quarterly report that KPMG released in October.

Emerging machine intelligence capabilities and exploding data volumes could enable IT systems to make inferences and predictions, ushering in a new era of cognitive advances. Artificial intelligence (AI) technologies capable of performing tasks normally requiring human intelligence are increasingly being incorporated into enterprise analytics efforts. Yet the bigger story in cognitive computing is machine intelligence, a collection of advances representing machine learning, deep learning, advanced cognitive analytics, robotic process automation (RPA), and bots, to name a few. Spending on various aspects of machine intelligence is projected to reach nearly $31.3 billion in 2019. It is also becoming a priority for CIOs: In Deloitte's 2016 Global CIO Survey, 1,200 IT executives were asked to identify emerging technologies in which they plan to invest significantly in the next two years, and 64 percent intended to invest in cognitive technologies.

Early adopters of artificial intelligence (AI) and cognitive technologies are reporting strong opportunities for economic gains and job creation, according to a study released this week by consulting firm Deloitte. AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms. The overall sentiment of organizations queried by the firm is positive about the current and future role of cognitive technologies in their organizations. Nearly two thirds (63 percent) of the 250 "cognitive aware" US executives surveyed online already have training programs underway for employees to learn how to develop cognitive technologies or work alongside them.

More businesses will be using machine learning to get the job done in 2018, while over 15 million Canadians will be using their smartphones to create augmented reality content. That's according to Deloitte's 17th annual Technology, Media & Telecommunications (TMT) Predictions, which this year reprises 2017's emphasis on AI and machine learning, along with highlighting smartphone tech, digital media and in-flight connectivity, all of which will see advances in 2018. "Deloitte is predicting yet another machine learning big bang," says Duncan Stewart, Director of TMT Research for Deloitte Canada, in conversation with Cantech Letter. Stewart says the visible change will be in the number of companies of all sizes that will be able to take advantage of machine learning, due to an industry shift away from graphics processing units (GPUs) to using more advanced chips to power their massive data-crunching systems. "These new chips are allowing machine learning to be done a lot more cheaply, particularly when it comes to power consumption, and so it expands the market for the product," says Stewart.