“SAP, CEO Bill McDermott has predicted over the next five to 10 years the hype will be around machine learning, artificial intelligence, and augmented reality,” writes Aimee Chanthadavong (@). It’s not the hype surrounding those technologies that is going to be important but their implementation. McDermott is certainly aware of that. He told participants at the 2016 SAP Sapphire Now conference, “I think very strongly that intelligent applications will fundamentally change the way you do work in the enterprise and the way you collaborate with your trading partners outside of the enterprise.” One of the uses of artificial intelligence that is already making a big impact in the business world is robotic process automation (RPA). “According to [Professor Mary Lacity of the University of Missouri-St Louis and Professor Leslie Willcocks of Oxford],” reports Sean Keyes (@), “the returns on investments in RPA are off the charts. In the fourteen cases they’ve studied, the one-year return on the investment in RPA was between 30% and 350%.”
As I argued in a previous article, companies should be thinking in terms of Cognitive Process Automation™ rather than Robotic Process Automation, because process automation can be much more agile than most people realize. That’s the real beauty of artificial intelligence, cognitive computing, and machine learning — agility and flexibility. We are only limited by our imaginations when it comes to deciding how to use these technologies. Dorian Pyle and Cristina San Jose explain that this flexibility comes from the fact that “machine learning is based on algorithms that can learn from data without relying on rules-based programming.” They add, “Machine learning is nothing like learning in the human sense (yet). But what it already does extraordinarily well — and will get better at — is relentlessly chewing through any amount of data and every combination of variables.” Data, of course, is the fuel that powers the information age. It is no coincidence that machine learning and other forms of artificial intelligence started blossoming at the same time oceans of data were being generated by computers, smartphone users, loyalty programs, and so forth. That symbiotic relationship will only grow more important as the Internet of Things (IoT) matures. Pyle and San Jose note, “GE already makes hundreds of millions of dollars by crunching the data it collects from deep-sea oil wells or jet engines to optimize performance, anticipate breakdowns, and streamline maintenance.”
What we have seen accomplished by machine learning is just the tip of the iceberg. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group earlier this year, Joseph Sirosh (@), Corporate Vice President of the Data Group at Microsoft, told participants, “We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built. Even if you are on the right track, if you don’t hop on this bandwagon and actually build things and deploy them and take advantage of their strength, you won’t be very effective.” Taylor Soper (@) recapped some of the trends that Sirosh discussed:
“Sirosh said that the manual management of business processes will become ‘antiquated’ as technology starts managing this at scale. ‘Everything at scale in this world will be managed by algorithms and data,’ he said. Sirosh added that there’s a need for effective platforms for managing these algorithmic businesses.”
“‘When the effort to build and deploy machine learning models becomes a lot less — when you can “mass manufacture” it — then the data to do that becomes widely available in the cloud. We’re going to have a cloud platform that’s like a department store.’ Sirosh said that as the effort to build and deploy machine learning models becomes easier, we’ll have huge app store-like marketplaces — to his analogy, ‘department stores’ — for APIs and applications that can be used to build software to help automate more processes.”
Mission-critical intelligent apps
“Sirosh used DocuSign as a good example of how machine learning and algorithms are being used to develop ‘mission-critical intelligent apps.’ He said DocuSign’s customers have seen a 93 percent reduction in the time it takes to close a contract.”
Industrial Internet of Things
Sirosh asked, “What happens when 50 billion machines become connected? … Everything from hospitals to factories to highways can be improved with IoT technology.”
“Sirosh talked about the concept of ‘data lakes.’ ‘You can ingest information from multiple data streams in a lake,’ he said. ‘But who are the swimmers?’ The swimmers, he said, are tools like Hadoop, Apache Spark, NoSQL, and other applications that help crunch numbers at scale and provide actionable insights.”
Pyle and San Jose conclude, “C-level executives will best exploit machine learning if they see it as a tool to craft and implement a strategic vision. But that means putting strategy first. Without strategy as a starting point, machine learning risks becoming a tool buried inside a company’s routine operations: it will provide a useful service, but its long-term value will probably be limited to an endless repetition of ‘cookie cutter’ applications such as models for acquiring, stimulating, and retaining customers.” McDermott agrees. He told Sapphire Now participants, “We need the system to tell us what to do. Based on algorithms of that data and inputs that are in that data bank, we need to be able to advise you on what the next step for your sale cycle, who you should meet with, and what the expected outcomes are and what the level of probability would be on you striking a deal.” My prediction is that cognitive computing, which is a combination of artificial intelligence (including machine learning), advanced mathematics, and natural language processing, will provide the foundation for most successful businesses in the future.
 Aimee Chanthadavong, “Machine learning, A.I to follow on the priority list for businesses: SAP,” ZDNet, 17 May 2016.
 Sean Keyes, “Companies Are Making 350% On Their Investments In Machine Learning,” Daily Reckoning UK, 17 May 2016.
 Stephen F. DeAngelis, “Cognitive Computing and Business Process Automation,” Enterra Insights, 11 May 2016.
 Dorian Pyle and Cristina San Jose, “An executive’s guide to machine learning,” McKinsey Quarterly, June 2015.
 Taylor Soper, “The future of machine learning: 5 trends to watch around algorithms, cloud, IoT, and big data,” GeekWire, 11 May 2016.