The picture is actually even worse than those numbers alone suggest, says Mark Muro, a senior fellow at the Brookings Institution. Existing federal “readjustment programs,” he says, include a collection of small initiatives—some dating back to the 1960s—addressing everything from military-base closings to the needs of Appalachian coal-mining communities. But none are specifically designed to help people whose jobs have disappeared because of automation. Not only is the overall funding limited, he says, but the help is too piecemeal to take on a broad labor-force disruption like automation.
Let’s assume that computers are going to make their mark in your line of work. Indeed, let’s posit that software will soon perform most of the cognitive heavy lifting you do in your job and, as far as the essential day-to-day operation of the enterprise is concerned, make decisions as good as (probably better than) those made by 90% of the people who currently hold it. What should your strategy be to remain gainfully employed? From an augmentation perspective, people might renegotiate their relationship to machines and realign their contributions in five ways.
Implementation: During this phase, set up and customize the technology. If necessary, extend the current IT systems with specialized plugins and add-ons. At this time, documentation is critical, and you should record each and every functionality. You should also implement administrator and select end-user training, followed by end-to-end and user-acceptance testing to determine feasibility before the next phase.
The IT industry depends on similar Agile practices of different names to meet the market’s demand for their products and services. Test automation is vital to Agile and the companies using Continuous Integration and Delivery, TDD, and BDD. For the titans of technology and the IT industry at large to reap the benefits of test automation, they must rely on automation frameworks.
Clearly this is a realm in which knowledge workers need strong skills in computer science, artificial intelligence, and analytics. In his book Data-ism, Steve Lohr offers stories of some of the people doing this work. For example, at the E. & J. Gallo Winery, an executive named Nick Dokoozlian teams up with Hendrik Hamann, a member of IBM’s research staff, to find a way to harness the data required for “precision agriculture” at scale. In other words, they want to automate the painstaking craft of giving each grapevine exactly the care and feeding it needs to thrive. This isn’t amateur hour. Hamann is a physicist with a thorough knowledge of IBM’s prior application of networked sensors. Dokoozlian earned his doctorate in plant physiology at what Lohr informs us is the MIT of wine science—the University of California at Davis—and then taught there for 15 years. We’re tempted to say that this team knows wine the way some French people know paper.
Within BPM, automated business processes are managed collectively to improve an organization’s overall workflow in terms of achieving greater efficiency, adapting to changing business needs, reducing human error and clarifying job roles and responsibilities. BPM is itself a subset of infrastructure management, which maintains and optimizes an organization's core operational components such as processes, equipment and data.