Annunziata highlights three new job growth areas: skills that cut across traditional lines of engineering and software development, creating a new role like a "digital-mechanical engineer"; data scientists, who specialize in fields from cybersecurity to pattern recognition to data visualization; and user interface experts, who can design human-machine interfaces that make a job easier and people more productive. We recently wrote about how Ford is recruiting more electrical and software engineers to do this kind of engineering-plus-design work for its cars, as software becomes a bigger part of why people buy a vehicle. As cars get more connected, such as sharing data car-to-car to know if there's an accident or traffic jam ahead, these skills get more important. Companies such as GE and Ford need universities to start training such specialists.
Policymakers And The Public Must Be Convinced
How much machine automation should people allow? We see this debate beginning around Google's self-driving car. In financial markets, automated high-speed trading creates controversy at times such as the "flash crash," when markets seem to overreact.
The risks are real, Annunziata says, so we need "transparent public debate on how much control we are giving to machines." Cybersecurity also becomes of vital public importance when it relates to networked power plants, jet engines and healthcare equipment. GE argues for cybersecurity regulation that's less fragmented across states and countries. Again, it points to why GE needs to make a case that the risks and economic disruption of a more networked and automated business world will pay off in economic growth.
Companies Must Invest
GE's report focuses mostly on macroeconomics. Getting the U.S. economy back to the 3.1% productivity growth rate of the 1995 to 2005 Internet boom, rather than the 1.6% rate since then, drives its prediction of $10 trillion to $15 trillion in economic growth from the industrial Internet.
But investing in the industrial Internet is a microeconomic issue -- companies make this decision one by one, project by project. The New York Times, in writing about GE's industrial Internet concept, cited an example of a wind farm operator upgrading the sensors and optimization software -- and netting a modest 3% energy output gain. InformationWeek wrote about Union Pacific's system to monitor train wheels and use analytics to predict failures, leading to a 75% drop in wheel-related derailments, but the next level of investment and innovation hinges on more effective sensors, better predictive analytics and better data sharing to predict the effects on the entire rail network.
The decision by companies whether to invest in this technology brings us back to the need for better analytics, more automation and new skills, factors that will drive the ROI of these Internet of things projects. We are seeing companies take these steps and make incremental gains with networked machines. But we need an innovation ecosystem to kick in to get anywhere near GE's $15 trillion vision.