Bring the work to the man, so that the man does not have to go to the work.
This was the driving principle behind the invention of the moving assembly line, first introduced by Henry Ford in October 1913 at the Ford Piquette Avenue plant in Detroit. The production line was not a foreign concept, even in Ford’s time, but it was limited to the manufacture of smaller, simpler goods like canned food. Putting hundreds of parts together into a car, however, was a different matter altogether.
Ford was already obsessed with driving efficiency in his production line. His factory was laid out according to the sequence of production (i.e., chassis then body then paint), and parts were delivered to each substation as needed. Each substation featured assembly stands on which the cars would sit, in a row, waiting for workers to add on a part. Much productivity and efficiency were derived from this arrangement and standardization of tasks. But nothing was automated.
This changed with the moving assembly line. It automated one seemingly trivial thing—walking—but this made all the difference. What used to be a complex assembly process, done mostly by specialized teams in different substations, was streamlined into one unbroken chain made up of 84 simple tasks that almost anyone could do. Workers no longer wasted time moving around the factory floor, and they no longer needed much training.
Productivity skyrocketed. Costs plummeted. The assembly time for the Model T fell from more than 12 man-hours to fewer than three per car. The moving assembly line became the industry benchmark and was adopted by every industrial manufacturer. 100 years later, Ford’s factories would collectively churn out a vehicle every 3.75 seconds—all thanks to automation.
A similar revolution is underway in the digital age.
Like Ford’s factories, organizations today are slowed by all sorts of processes, regulations, and legacy systems. For example, it’s not unusual for IT support staff to spend a significant amount of time installing software or to have customer service agents bogged down by data entry tasks. This is changing with intelligent automation (IA), the next phase of automation.
Using cognitive computing technologies such as machine learning, natural language processing, and computer vision, organizations can now automate low-value cognitive tasks and empower employees to focus on more high-value work. Some of the more popular use cases today include using predictive analytics to reduce downtime of a fleet of trucks, using machine learning to quickly spot problems in contracts, and using robotic process automation (RPA) to process a bank account opening in seconds. To say that IA is disruptive would be an understatement.
A recent study of 153 business leaders involved in decision making around machine learning, data, and analytics confirms this. 86% of respondents believe machine learning is already impacting business, with 52% expecting it to affect the majority of business operations within the next two years. However, most respondents have yet to make significant investments, with 84% investing under $1 million to date. And 42% recognize they have skill deficiencies, particularly in the shift from traditional IT to machine learning and data science.
Despite the apparent benefits, the adoption of intelligent automation is still relatively limited. Few IT leaders have a grasp of what it really is, let alone the ability to weigh the pros and cons. They may not have the scale or a suitable business problem to justify the investment. And procuring the right expertise, namely data scientists and IA-experienced software engineers, is especially challenging. Some IT leaders are still struggling with the perception that IT is just a cost center, which makes it an uphill battle to secure more funding.
That said, intelligent automation can be an opportunity for organizations to leapfrog the competition—if you act now. Our new strategy brief, The Intelligent Automation Edge, has all you need to know to launch your first IA initiative. We’ll explore the five areas that IA can make the biggest impact, and essential principles derived from our experience in the field to improve your chances of success.
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