The introduction of automated processes became the foundation for the industrial revolution starting in the late 1700s. As innovation is applied to automation strategies, productivity increases. According to research published by McKinsey & Company – by as much as 0.8 to 1.4 percent per year (source). Conversely, the rise in automation includes the concern of job loss and workforce obsolescence. This trend continues today. Regardless of what your perspective is on Artificial Intelligence, the push to improve productivity is relentless. Today, intelligent automation is now leading the drive to the next level of productivity improvement.
What is Automation?
Automation has historically been defined as “the use of largely automatic equipment in a system of manufacturing or other production processes.” This is a very narrow description and misses much of the growth occurring today. Not only can tasks be automated by equipment, but entire business processes and systems can be programmed to respond to change based on the receipt of new inputs. The investment in machine learning and Artificial Intelligence technologies are making this advancement possible, driving huge growth across many industries.
As each industry explores new opportunities to automate, it is important to keep a perspective – simply repeating a poor process quickly will not deliver a positive impact on the bottom line. Automation must be “smart” and focus on ways to continuously improve over time, or just accelerate the creation of even bigger problems!
Being smart about how you apply an automation strategy helps to ensure the time, resources, and cost investment will result in a positive impact on your business. Here is where the concept of “intelligent” automation comes into play.
What is Intelligent Automation?
When we talk about intelligent automation, this is not a reference to a computer programmer being smart about what tasks or processes are being automated – although this is certainly a best practice to follow! Rather, a process can be defined as “intelligent” when data is collected that can help improve the likely output of activity to be aligned with the desired end state.
For example, if you were to play a game of Blackjack and automated the process of when to draw a new card or hold your existing hand, you could automate the decision of whether to draw on a 16, a soft 17 (with an Ace), or a hard 17 and above. Statistics tell us that over time, the best move is to draw on the soft 17 and hold on to the hard 17. Adding intelligence to this automated task, such as what cards have already been played and how many decks are being used on the table, would result in improved performance.
Robotic Process Automation (RPA)
RPA is a form of intelligent automation that is based on symbolic software robots or artificial intelligence /digital workers. By combining the automation of physical tasks with artificial intelligence, it is possible to establish intelligent process automation systems that can drive enormous productivity improvement and performance gains.
It should come as no surprise that the RPA market is white-hot today. Industry researchers suggest that the overall RPA software market grew 62.9% in 2019 to $1.4 billion and is expected to reach $3 billion by year-end 2023. This growth is continuing at such a fast pace that it is leading to the emergence of an entirely new field of automation – Hyperautomation – a key trend enabled by multiple types of technologies for content ingestion, integration, and support for the increasingly automated workplace.
The Quest for Greater Productivity
There is much interest in the automation of tasks and processes to achieve higher performance. What can be done to best harness this productivity improvement? Two steps can be done to help position your organization, systems infrastructure, and overall business strategy to capture the most upside of this megatrend.
- Move away from paper-based, manual business operations. Organizations must remove manual paper entry processes whereby data is entered into your business systems automatically – either at the source of input or by intelligently automating the scanning of this information into your systems. The more data that is digitally collected and available to be used in its original form as part of a business process, the greater productivity improvement that is possible.
- Learn how to better manage unstructured data. Organizations can overcome the challenge of collecting disparate data with natural language processing (NLP) tools, voice recognition, chatbots, or more robust rule engines to capture complex decision-making. Increasing access to this information by your machine learning and AI processing tools will add new intelligence to your automation process, helping you to drive new productivity.
Fortunately, there are many new options available today, especially for those businesses in the financial services, real estate, title, healthcare, and other industries where paper still a considerable role in how the business operates. Service providers that offer proprietary AI systems designed to work within your specific industry can deliver a faster time-to-value, helping to justify the business case and ROI to make the investment, which seems like an intelligent decision and a great place to start.