Robotic Process Automation (RPA) and Artificial Intelligence

In its most basic form, it refers to a machine’s ability to learn from previous experiences by analyzing historical data (supervised and semi-supervised instances) to solve a specific problem. As it consumes the input variables, it applies multiple algorithms to generate some or all (mathematical) mathematical models that serve as the foundation for the intended outcome set (dimensions). This is the key difference between all computer programmes in which the model is trained to generate the output rather than implementing a programmatic version of a logic or an algorithm.

Businesses may automate higher-order operations that formerly required human judgement and perceptual abilities by integrating RPA with cognitive technology such as machine learning speech recognition and natural language processing.

1. Process Automation

Businesses are aware that improving efficiency in the workplace and productivity of employees is crucial to be successful in a highly competitive digital world. Every process is able to be automated as it has an operational procedure that is clearly defined.

“Going from manual to auto-drive” —

There was a time that software scripts to assist in (semi-automatically) performing routine tasks like system administration, network provisioning and so on. were extensively sought after to improve the efficiency of operations. Although there was a person in charge of these tasks, the advantages of a software-based approach were evident very quickly, and it began to gain traction in the world. With the advancement of design of systems, specifically the shift to service-oriented architecture (SOA) the systems become more and more complex. Different ways of integrating with internal and external services began to appear and resulting in a greater demands for orchestrationand automationfor seamless execution.

Utilizing low-level understanding of engineering for applications and access to various applications and tools Software modules were developed for task blueprints, ensuring that tasks could be scheduled on demand or on a time-scheduled intervals as a software routine, without the need for human intervention.

Benefits of Process Automation

Businesses are starting to realize that increasing the efficiency of their businesses and productivity of employees is essential to succeed in today’s highly competitive digital marketplace.

Automation by software is essential for companies to remain in the game and attain efficiency by being in compliance with (or over) Service Level Agreements (SLAs). Making use of the power of robotic software, the companies have created new opportunities for process engineering within the IT sector. Information processing systems are being developed with the intention of RPA as a feasible and more accessible alternative than previously.

Incorporating AI in Process Automation

To ensure the success of the program, it is crucial to comprehend the principles that are behind the success of RPA when paired with the AI powered strategy. RPA systems in which AI is considered to be the main focus requires an understanding of how the two technologies work together in a cooperative manner. Design concepts from both perspectives should be clearly identified before moving into the stage of implementation.

Looking at the whole picture, historic data used for model training is essential. In addition, other factors like trigger points, actors boundary of the subsystem and domain information, API/hooks for interfacing, rules, corners where human intervention might be required, handling exceptions and more. are all likely to be vital.

2. Inducing AI/ML in Process Automation Strategy

If you live in the cave, everybody knows the meanings of AI or ML. Terms like supervised/unsupervised learning and deep learning don’t sound foreign.

Description of AI/ML is outside the topic for this piece. If you wish, you can read these sources to learn more about the topic .

There must be an in-depth grasp of the subject. In addition, understanding of system architecture and a thorough understanding of the details that relate to functions, processes, sub-systems interconnect logic, interfaces and so on. are crucial to use as a guideline for the design plan.

To maximize ROI and cost efficiency You’ll also have to be aware of what solutions are currently available commercially, or open source. For instance, an off-the-shelf solution that works well is more preferred than re-inventing a wheel. More time-consuming cycles and waste can cause harm to the overall project. Making use of the technology and solutions available (where feasible) can help reduce risk and allows you to concentrate on other areas in order to reach your goal system earlier.

3. Design and Implementation Techniques

Figure 1 illustrates a typical end-to-end design for a component that illustrates the implementation of an full-featured AI controlled Robotic Process Automation (RPA).

Process Description

Beginning from the top, we will have an interface for users which can be used to collect user requests. The user’s intentionis recognized through categorizinginto categorical categories that are pre-defined using NLU pipelines. These pipelines are usually made by a variety of NLP libraries like SpaCyor NLTK which supports word vectors/embeddings in multiple languages, tokenization, and so on. Another essential task that is also handled by this layer is the extraction of entities(contextual data) from user requests. Entities are parts of text that is important to data scientists or business, for example, names of people, addresses location, account numbers, and so on. They are then sent through the downstream process to be processed further. Modern algorithms and techniques such as CRF (Conditional Random Fields), Stemming and others. are employed to build models to perform Named Entity Recognition (NER). It is possible to support NER is included in libraries such as SpaCy that allow entity extraction based on their pattern as well as other properties of statistical nature. In addition, specific bits of information such as dates and times can be extracted by using libraries such as the duckling.

After the intent and data objects are retrieved, they move on to the next stage that is called an Machine interface. Machine interface. In this stage, the robot software can be connected to control the orchestration process and (mainly) automatization aspects. For instance, ‘Create and transmit a report “provision or configure a network device’ or use a PayPal to buy tickets for movies’.

I’m reducing the amount of detail to show the sub-systems that require but, you can observe how the various parts can be joined with a certain amount of precision to form an integrated system.

Policies and Best Practices

The above diagram could over simplify the work involved in creating this structure. There are many moving parts every component (sub-system) requires a substantial effort and a thorough understanding of the domain to ensure efficient deployment and coordination. It is recommended to take a comprehensive review of the development phase and work out details such as protocols and data structure as well as access needs, performance and efficiency availability, logging and monitoring, handling of exceptions and the technology’s leverage.

If possible, externalizing as well as decoupling control logic may bring benefits in terms of the ability to design with flexibility and update system functions.

How do you handle the issue of any exceptions?

There will instances where one-off situations can arise, such as an insufficient balance to buy grocery or other items’, insufficient delivery address ‘incomplete or insufficient data’ unfavorable (weather) weather conditions’ and other such. To handle these situations properly there are a variety of policies that can be developed and incorporated into the system via an independent exceptions control system.

The ability to rely on corrective actions based on the error message can enhance your user’s experience. In obscuring the details, the error message that you present to the user should provide an the correct description of the problem and offer suggestions for the best next action(s). This also presents a chance to use Machine Learning which, when trained, a model is able to predict the follow-up action based upon the variables as well as the contextual information.

4.An Example Case — Healthcare

Let’s look at an illustration. News are going around that the healthcare organizations are increasingly embracing artificial intelligence in order to enhance patient care and efficiency. So, let’s take a look at how RPA could be helpful in this case.

Effective RPA will result in better healthcare management, with dramatic improvements in the quality of coordinating health care, population wellness remote monitoring, and utilization management. If healthcare professionals are no being slowed down by the complexities of their day-to-day tasks they are able to concentrate on more crucial actions like having a one-on one conversation with patients.

How RPA is applicable here? Manually intensive tasks that companies are tangled with. They can be streamlined by shifting the workload to robots. For instance, think about billing or coding. An appropriately defined process could be further enhanced with automated processes that allows multiple entries from different forms are analyzed and the bill will be generated and then displayed at the kiosk for checkout without human intervention. In fact, collecting the payment from the patient would be an additional step. Additionally, the report/claim coded can be delivered an insurer (payer) without making a fuss.

In the same way, instances in which AI supported image recognition employed to detect malignant traces could be integrated into an alert system and notification system, which means that sensitive topics such as cancer, tumors, and so on can go to a final evaluation by domain experts prior to making a decision.

Apart from cost savings providers are able to enforce an adaptable system that can be quickly changed as and as needed to maximize efficiency.

“Design by Data” approach — What’s it got in the interest of us?

Determine areas in which AI can outdo conventional application based on logic in terms of size ability, flexibility and effectiveness. There are situations when the process must decide the next step by analyzing hundreds of factors. The consideration of all these variables (dimensions) during the process of design and construction could be difficult. In addition, the logic of a program needs to be rewritten for any new parameter addition in contrast to ML method, only the model must be updated, while the business logic is left unaffected.

When monitors or sensors produce massive amounts of data it can be an extremely difficult task to decipher and find pertinent data.

Then putting everything together

This  RPA as well as conversational AI particularly at the point of convergence. We examined the major elements that play a major influence on the design and the strategy. Apart from drawing value of industry norms, the business case(s) should be considered for a perfect execution plan that ranges from the initial outline to the finer details.

An understanding of the basics of AI-based conversation is essential to enhance design quality as well as fully establishing practical accuracy. Machine learning pipelines serve as the basic building block to creating precise results.

Robotic Process Automation is considered by many to be a crucial component of digital transformation. When software robots are employed alongside AI and guided by policies and previous data, efficiency improvements is possible, benefiting from automation, AI/ML and generating opportunities for an ideal customer experience, increasing the trust , increasing efficiency and enhancing ROI.