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According to a recent McKinsey survey, just under a third of organizations have engaged intelligent technologies across multiple businesses or functions. Scaling Intelligent Automation (IA) transformation appears to be a crucial challenge. Based on my experience, the key limiting factor here is that IA projects are typically human-workload intensive, resulting in lengthy and expensive projects.
But what if technology could help organizations implementing intelligent automation?
New technologies and concepts have recently come to the market to help accelerate and improve the IA implementation process. While most of these technologies are still maturing, they already deliver significant benefits to organizations using them.
Intelligent Automation implementation projects typically include:
1. Identification and assessment of intelligent automation opportunities.
2. Design and implementation (including coding) of the IA programs.
3. Program maintenance
Let’s take a deeper look at each of these steps and the supplemental technologies available to support the enterprise implementation of IA programs.
Intelligent automation opportunities can be identified at two levels: process or data. At the process level, two technologies are available—process discovery and process mining. At the data level, we call this data discovery.
Selecting the appropriate IA opportunity to implement is critical. Nevertheless, process and data analysis, documentation, assessment, and prioritization are workload-intensive. They consist of interviewing, observing, collecting, and analyzing data. As a result, this phase often needs two to six months of work.
The first process discovery technology was launched in June 2018 by Kryon Systems. Here are the key steps it uses:
In my experience, this type of solution can help accelerate intelligent automation implementations three to five times faster than normal while increasing the number of use cases discovered by about two.
Launched by Celonis in 2016, modern process-mining solutions serve the same objectives as process discovery tools. Their difference lies in the way they analyze the process data. Unlike process discovery solutions, which use computer vision and user-interface object recording, process-mining solutions use the logs extracted from systems like ERPs.
Process mining and process discovery solutions are used in conjunction to improve an outcome. Process discovery is usually less accurate but offers a more comprehensive view of the potential across all processes. In contrast, process mining provides the precise detail of each process execution but only on the systems generating structured logs. Processes performed on other applications like email, Excel, or PowerPoint cannot be recorded.
Finding relationships between data that can drive business value consumes resources and time. Instead of manually testing a hypothetical outcome against a dataset, data discovery solutions scan massive amounts of data to discover thousands of hidden drivers behind strategic business challenges. These solutions also combine companies' information with external sources (e.g., economy, weather, demographics) to reveal hidden patterns and deeper insights.
For example, let’s look at a data discovery solution implemented by a global payment company. It improved fraud detection by 7 percent in just five weeks with cost savings of $140 million.
When enterprises combine different technologies to automate end-to-end processes with artificial intelligence, the failure of any one of these single components often causes the entire process to collapse.
Technology vendors are creating programs that generate robotic process automation code directly by using the outcome from process discovery or mining solutions. These programs are so exciting because they automatically create and add automation workflows directly into the automation design studio. Developers can then further refine the code. Based on my experience, about 60 to 70 percent of the code for most IA projects can be pre-generated, doubling the speed of implementation.
While data discovery platforms help data scientists create value by identifying relations between data, AutoML solutions support data scientists in building their models. In a typical machine learning application, data scientists have a dataset consisting of input data points for training. Typically, the raw data is not in a suitable format to feed into algorithms. Instead, a data scientist has to apply data pre-processing, features engineering, and selection methods that make the dataset suitable for machine learning applications.
After these pre-processing steps, data scientists then select algorithms and optimize their parameters to maximize the predictive performance of their machine learning model. Each of these steps has its challenges and involves significant time and resources. AutoML systems help automate these steps.
When organizations deal with hundreds of IA programs, managing the changes and failures is challenging. When enterprises combine different technologies to automate end-to-end processes with AI, the failure of any one of these components often causes the entire process to collapse.
One effective way to mitigate this issue is using a system that predicts and identifies the changes in the program's environment. Such systems can proactively adjust the environment (if the change is due to an environment failure) or the automation program (if the program needs to be adjusted). If the change cannot be performed automatically by the system, it alerts a person to address the issue.
To get started, meet with your intelligent implementation team to identify where they spend most of their workload or have the most pain points. These are indeed, the areas where you can generate the maximum benefits from using the above levers.
The content of this article is inspired by the Amazon bestseller book “Intelligent Automation.”
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