For some years now, intelligent process automation, or IPA, has been helping to improve productivity and efficiency, as well as reduce operational risks and improve the customer experience. In this post I tell you what it is, as well as how you can start using it. Are you interested in improving? Then you are interested in this post. Do not miss it, keep reading.
IPA, or Intelligence Process Automation , is an emerging set of new technologies that combines fundamental process redesign, robotic process automation, and machine learning. In short, it is a set of business process improvements and next-generation tools, which help the worker to be able to dedicate themselves to knowledge and creativity, eliminating repetitive, replicable and routine tasks. Plus, it can radically improve the customer journey by simplifying interactions and speeding up processes.
According to the consulting firm McKinsey , the companies that are using IPA are obtaining very interesting results. They have achieved an average of 50-70% automation of tasks, which has translated into 20-35% annual run rate cost efficiencies, and a 50-60% reduction in direct process time, with a very high return on investment.
But how does IPA work? It mainly uses five core technologies. These are:
- Robotic Process Automation (RPA).
It is software that automates routine tasks such as data extraction and cleansing through existing user interfaces. The robot has a user ID as a person and can perform rule-based tasks such as accessing email and systems, performing calculations, creating documents and reports, and checking files. - Smart workflow.
It is a process management software tool that integrates tasks performed by groups of humans and machines. This allows users to launch and track the status of a process end-to-end in real time. The software will manage transfers between different groups, including between robots and human users, and provide statistical data on bottlenecks. - Machine learning / advanced analytics.
They are algorithms that identify patterns in structured data, such as daily performance data, using “supervised” and “unsupervised” learning. Supervised algorithmslearn from the structured data sets of inputs and outputs before starting to make predictions based on new inputs themselves. Unsupervised algorithms look at structured data and begin to provide information about recognized patterns. - Natural Language Generation (NLG).
They are software engines that create seamless interactions between humans and technology, following rules to translate observations from data into prose. Broadcasters have been using natural language generation to compose gaming stories in real time. Structured performance data can be piped to a natural language engine to automatically write internal and external management reports. - cognitive agents.
They are technologies that combine machine learning and natural language generation to build a fully virtual workforce (or “agent”) that is capable of executing tasks, communicating, learning from data sets, and even making decisions based on “sensing.” of emotions”. Cognitive agents can be used to help employees and customers over the phone or via chat.
- Robotic Process Automation (RPA).
Fine, but what real applications can it have?
According to several reports, the area within companies that is getting the most benefit from implementing this type of technology is finance and accounting . Others would be those specific to production or IT, as well as the human resources area.
3 examples of how it is being used effectively, could be these:
EXAMPLE 1. Commercial insurance underwriting.
These processes are usually quite tedious and require a large amount of resources and a large investment of time. IPA can drastically improve that entire process by creating a seamlessly analyzable signup process, allowing employees to qualify their customers efficiently. That results in a reduction in overall response time and greater accuracy from the organization.
EXAMPLE #2. Incorporation of clients in the Banking sector.
The banking sector has also benefited a lot after the adoption of smart automation technology. Today, banks are actively leveraging IPA technology to classify and extract unstructured information from customer onboarding documents to make them useful to the bank’s management system. This implies greater customer satisfaction and faster income generation for the bank.
EXAMPLE #3. Automation of trade processing in investment management.
In the case of investment management, IPA can be of great help. It is often seen that the investment company receives the trading processing information through emails and in PDF formats. In such a case, extracting the vital and relevant information becomes a tedious task. However, that is not the case with the API. While using intelligent automation technology, you can seamlessly extract relevant data from unstructured content and can even integrate that data with investment management systems. All of that eliminates extra time that would otherwise have been wasted on manual data processing.
Now that you have gotten used to the idea of what IPA or intelligent process automation is , we are going to see how you can begin to implement it in your company in order to get the most out of it.
How to get started with an Intelligent Process Automation or IPA system
Getting IPA up and running doesn’t require a massive investment in stratospheric infrastructure. For example, implementing an RPA is not too expensive, since it uses the existing IT systems and back-end of your company, quickly offering a return.
A good way to start getting these processes going could be this:
#1. Quickly align APIs with the operating model.
The first thing to do, without a doubt, is for the application to go in the same direction as the company’s general strategy. To do this, they must share objectives and the route to achieve them. In many cases, the API has an important, even dominant, role in driving change, but its greatest value is realized when companies understand how they can work with the other capabilities and approaches in the operating model.
#two. Design around the full portfolio of IPA solutions to maximize impact.
Organizations need to envision and implement optimization programs to maximize return on investment. Although it is easier and faster to implement siled automation projects, this approach is not the most appropriate. By themselves, individual technologies are insufficient to achieve overall value as sought with IPA. Instead, to transform the way a group works requires a fundamental redesign of the process.
Therefore, a detailed implementation roadmap should be created to identify all automation improvement opportunities and allow the company to sequence IPA initiatives, balancing their impact with the feasibility of scaling solutions from initial use cases.
The IPA journey should be started by quickly creating an overview of current tasks and the resources and capabilities needed to carry them out. Then deploy a team of experienced incubators to redesign group processes and workflows based on in-depth knowledge of IPA lines of business and capabilities.
#3. Build a minimum viable product (MVP) fast.
It is usually quite overwhelming to start working directly with everything that IPA has to offer. As with other digitization efforts, it’s best to select, based on speed and impact, an end-to-end process or customer journey to redesign and improve with IPA, and then work to release the most streamlined version of the product that can perform the task. This way you can quickly test what works and what doesn’t and make changes accordingly.
#4. Build momentum and capture value .
Any IPA implementation must combine quick wins with larger, longer-term developments. The detailed roadmap must be based on a fundamental process redesign that sequences automated modules to production and reinvents the way groups must work to capture value.
#5. It incorporates lasting capabilities to achieve sustainability.
A successful way to sustain value creation is by creating a Center of Excellence (CoE) to govern the transformation and support the rapid implementation of IPA solutions through capability development, certification and standards, vendor management, and the creation of a library. of reusable solution patterns.
Systematic controls need to be in place and organizations need to embed critical business analytics and digital skills across lines of business, so they can take ownership of the process. They also need to redesign organizational structures to capture value, establish a future-state operating model to scale their IPA initiatives, create blueprints for future structures, and thus capture impact.
In this way, the processes will free teams to focus on more creative activities.
It is critical to involve the business and functional teams in the process. The most successful way to develop lasting IPA capabilities is through a learning-by-doing approach that combines coaching, on-the-job training, and knowledge sharing.
#6. Carefully coordinate change management and communications.
As with any large transformation program, a strong communication plan will be required to help manage the redeployment, generate excitement, and align the change with corporate strategy. Success in establishing the new execution model will depend on how aligned it is with the organization’s culture and how well people are able to adapt to agile practices.
