Previously I explored the value of eminence and thought leadership to consulting firms, and how unfortunately the power of inbound content marketing has a dark side that forms part of a three-pronged attack on the consulting industry. Meanwhile, the tireless invention and innovation efforts of research teams in companies around the world have helped to keep the pace of technological advancement in computer processing power at or above Moore's Law for several decades. This has given technology companies the ability to put more computing power than the entire Apollo space program into the pockets of more than a billion people around the world. It seems like everything has become digital, including music, books, and even movies. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn.

Previously I explored the value of eminence and thought leadership to consulting firms, and how unfortunately the power of inbound content marketing has a dark side that forms part of a three-pronged attack on the consulting industry. Meanwhile, the tireless invention and innovation efforts of research teams in companies around the world have helped to keep the pace of technological advancement in computer processing power at or above Moore's Law for several decades. This has given technology companies the ability to put more computing power than the entire Apollo space program into the pockets of more than a billion people around the world. It seems like everything has become digital, including music, books, and even movies. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn.

While humans may be ahead of computers in the ability to create strategy today, we shouldn't be complacent about our dominance. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This is the seventh in a special series of commissioned essays MIT Sloan Management Review will be publishing in Frontiers over the Spring and Summer of 2016. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" As a society, we are becoming increasingly comfortable with the idea that machines can make decisions and take actions on their own.

Assigning people the tedious task of manually reviewing and manipulating documents is a sure-fire way to miss errors and introduce inaccuracies as blurry-eyed employees move between programs and cut-and-paste data. Although this is standard practice at many companies, it's a problem that Robotic Process Automation (RPA) technology is now able to solve. RPA can mimic a human's ability to gather data and put data into Excel spreadsheets. It can access disparate systems (ERP, sales, tax, etc.), review tens of thousands of documents in an hour or two (instead of several days), do so without error, and put the data in a standardized format. But RPA's advantages go far beyond imitating keystrokes and avoiding errors; RPA's speed, accuracy and formatting abilities make it an essential foundational technology for a more automated, intelligent enterprise.

Artificial Intelligence is rapidly coming of age, as business leaders increasingly grasp the immense potential of "smart" machines and other innovations as catalysts for greater efficiency and competitiveness. The dollars tell a compelling story. By 2019, the global market for content analytics, discovery and cognitive systems software is projected to reach 9.2 billion, according to IDC, more than double that of 2014.* We see tremendous opportunities ahead for our clients. However, potential rewards will only become reality through substantial research and development, and we've got a lot of work ahead.

Hadoop distributor MapR has raised a new round of funding and may be preparing for an IPO next year, Salesforce acquires analytics startup BeyondCore, Coursera releases a new data analytics course together with PwC, and Apple CEO Tim Cook provided some illumination on how his company regards artificial intelligence (AI). We've got all the highlights in this Big Data Roundup for the week ending Aug. 21, 2016. Let's start with the news from Hadoop distributor MapR. The company recently announced that it has raised a round of equity financing worth 50 million, and provided a few select details about its financial performance. MapR is still a privately held company, so it can choose what to disclose and what not to disclose.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.

Estimates suggest that by the year 2020 we will have 20 billion devices connected to the Internet. Industries are shifting, lines are blurring, and markets are changing all as a result of a technology and digital-centric approach to commerce and business. In this podcast, Cliff Justice, Partner in KPMG's Innovation and Enterprise Solutions group, sits down with Stan Lepeak to discuss: The convergence of robotic process automation (RPA), machine learning, cognitive computing, artificial intelligence, and advanced analytics are driving unparalleled business model transformation. To learn more visit KPMG's Digital Labor website: www.kpmg.com/us/digitallabor. To discover more, read Embracing the Cognitive Era.

We employ some of the brightest and best in professional services, but too much of their time and talent is taken up in research tasks that fail to inspire them. Take our Financial Services Risk and Regulation team – their role is to provide accurate, up-to-date advice on this crucial and increasingly complex area, but the sheer volume of information that needs to be assimilated to do that is growing exponentially. This is one area where AI comes into play – we can now use an AI powered assistant to do the first stage of information processing, using technology tools that can cope with both structured and more importantly unstructured data. That helps speed up research tasks, and frees up time to focus on the intellectual challenges of an engagement turning the gathered information into deep insights. And the best bit is that the tools get cleverer over time: thanks to the inherent machine learning capabilities, the more you use them, the more precise results will be delivered at the first pass.

Today, companies compete for analysts, engineers and data scientists who have hard technical skills and knowledge of distributed computing systems and analytical tools.3 But in the world of machine-reengineering, workers will also need other skills that could be as unconventional as the processes they support.4 These include the ability to program "belief spaces"--advanced probabilistic models that help robots deal with uncertainty--and to work well with intelligent machines.5 Mercedes-Benz, for example, has replaced large, inflexible factory robots with people and smaller, more flexible robots. Now Mercedes-Benz needs a workforce that can teach robots how to collaborate closely with employees on the factory floor.