Introduction: What is Robotic Process Automation (RPA)?
Digital transformation and automation go hand in hand. Companies can leverage the benefits of both these futuristic trends with the help of RPA services. Robotic Process Automation or RPA is a revolutionary concept that automates repetitive business tasks and thereby increasing the efficiency of your teams.
In this post, we are going to discuss the impact of implementing AI in RPA solutions. Let’s start with the benefits of adding AI to RPA solutions.
As per Mckinsey’s survey 65% of small businesses gained success with automation as compared to 55% of large and established businesses.
What are the Benefits of Using AI in RPA?
AI technology is getting mainstreamed quickly in the corporate world. A recent Fortune Business Insights report has predicted that the global AI market will reach USD 1394 billion by the year 2029. When it comes to making RPA ready to provide the benefits of hyperautomation, AI needs to be added to RPA solutions. It can improve the agility and productivity of your business. In other words, AI can add value to RPA technology by promoting hyperautomation and innovation.
AI implementation enables RPA solutions to automate complex processes and users can make improved decisions. Also, AI and RPA combination can enhance the user experience while offering them valuable insights. For example, AI-powered RPA platforms use NLP (Natural Language Processing) to respond in a more human-like way. AI can improve communication and collaboration between RPA solutions and human users.
However, it is fair to mention that AI implementation is challenging. It requires continuous access to large amounts of data along with high-end algorithms. Also, privacy and data security concerns loom large when AI is used in RPA. Biased decision-making and other trivial issues are also possible with the implementation of AI in RPA. But, a reputed RPA services provider company can deal with these challenges effectively.
How Does AI Enhance RPA?
AI can take business process automation to a new level by combining it with RPA (Robotic Process Automation). Many companies have already started gaining an advantage of advanced and intelligent business process automation with this combination.
AI and RPA combination, aka AI Automation, is aimed at generating greater business benefits for the company. Intelligent automation can address the shortcomings of RPA and AI when these technologies work separately.
Here is an example to understand how the combination works for enterprises. Suppose a business process has the need to read details from an invoice at any stage. Now, the details or data are written in an image. In this case, RPA is unable to handle this task because of the lack of image recognition functionality. With AI-powered RPA solutions, you can eliminate this issue as AI can easily analyze and give the necessary response to any changes in the image.
Data is the fuel for AI, and therefore, it is essential for companies to ensure that the AI system can access the data continuously and effortlessly. RPA makes it possible by performing all the manual steps on a 24×7 basis, and then passing the data to the AI-based model. The resultant effect will be passed on to either the end users or other systems for further processing.
Major Use Cases of Using AI in RPA
Gartner has revealed that global spending on RPA software has reached a market value of over $2 billion in recent years. We expect that globally, enterprises could increase their RPA capabilities by three times through 2024. Simply put, the RPA market is expanding and AI has fueled its growth by introducing intelligence.
Here are some of the top use cases of AI in RPA solutions.
Product Lifecycle Management
Manufacturing, utility, and engineering sectors can leverage the benefit of product lifecycle management. However, it is possible that the legacy or individual systems of their different departments including finance and sales are incompatible. Here, IA and RPA combination offers cross-platform support for maintaining consistency in product lifecycle management.
CRM (Customer Relationship Management)
One of the most popular use cases of AI and RPA combination or IA is in customer relationship management. CRM systems need to manage large datasets and information related to the process as well as customers’ interests. An AI-powered tool can help sales professionals get better insights into this data and save their time significantly. An Intelligent Automation tool can provide users with vital customer trends and key behavior insights as well.
The HR departments can simplify long and paper-based processes with the help of RPA technology. However, an RPA solutions company can integrate AI into the solution for automating these processes and ensure that the employee onboarding and other related processes comply with the prevalent regulations.
What are the Challenges & Drawbacks of Implementing AI in RPA?
AI implementation in RPA solutions has many challenges and a reputed RPA company in Canada has to address them through careful planning and proper execution. Here are some of these challenges and the ways to overcome them.
Define Clear Strategy
It is one of the biggest challenges of AI implementation in RPA solutions. Companies have to define a clear roadmap in line with their business goals, work culture, and industry trends before implementing AI. Also, as an entrepreneur or owner, you need to identify the processes that can be automated for taking the benefits of the AI-RPA combination.
Nowadays, most companies have some idea of the AI and RPA mechanism, so they can define a clear strategy that aligns with their business models. A team of experienced professionals in the RPA company in Canada can also assist you to make a clear roadmap.
Automation and scalability are other challenges of AI implementation in RPA solutions. AI needs scalability to give better results. Companies can use an interoperable architecture to support the seamless data exchange between automation tools and an existing business system. It is necessary to have a scalable infrastructure that can address the complexity of automation along with the RPA solution.
Ensure Data Security
Ensuring data safety is challenging for every company. While driving automation, companies need to opt for reliable and accurate data sources while following compliance policies. Any unauthorized access to the data should be stopped and regular monitoring can assist companies to improve data quality and enhance its security.
Maintain the Trust
It is imperative for companies to establish and maintain a transparent framework that enhances trust. Also, companies have to consider the potential consequences of automation with the impact of AI integration in decision-making.
The inclusion of all employees and stakeholders in this process and a proper explanation for bringing automation into several processes can build trust among your employees.
How to Overcome the RPA Implementation challenges
Challenges related to AI implementation in RPA can be addressed by applying the following practices-
Define proper strategy containing long-term goals and vision
Establish effective communication between your teams and the IT department
Define roles and responsibilities related to RPA implementation
Make sure that every employee has taken a necessary training
Choose the right process and ensure that the process is executed properly
Identify the tasks that require automation accurately
Keep some amount aside for regular maintenance and upgrade of the RPA solution
Apart from this, it is always beneficial to consult an RPA solutions company with a proven track record of RPA implementation to overcome these challenges.
An RPA solutions company can integrate AI into RPA in a seamless way. You can consult a reputed RPA company in Canada to get the best of both worlds while dealing with the challenges of implementing AI in RPA solutions. When it comes to offering more personalized, human-like experiences to your customers, you can get the benefit of AI and RPA combination.
Silver Touch Technologies Canada is your preferred RPA solutions company. As a robust and reliable digital transformation partner, we assist you at every step of taking your business to the next level. We integrate technological advancements for streamlining workflows and increasing the productivity of modern enterprises.
Do you want to know more about our RPA services? Simply drop us a line at email@example.com and one of our expert business consultants will get back to you soon.
AI in RPA refers to the use of Artificial Intelligence (AI) technologies and techniques within Robotic Process Automation (RPA) systems. RPA is a type of automation technology that allows software robots (often referred to as “bots”) to emulate human tasks and interact with software applications to perform repetitive, rule-based tasks in a business process. AI in RPA can enhance the capabilities of RPA systems by enabling bots to perform tasks that require cognitive abilities such as natural language processing, image recognition, and decision-making.
AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making.RPA, on the other hand, refers to the use of software robots or bots to automate repetitive, rule-based tasks that involve structured data. RPA bots can interact with applications and systems just like humans, performing tasks such as data entry, data validation, and transaction processing, among others.
RPA (Robotic Process Automation) is a technology that is often used alongside AI (Artificial Intelligence) but is not considered to be a part of AI. RPA and AI are distinct technologies, although they can be complementary and used together in automation solutions.
AI (Artificial Intelligence) can be used in conjunction with RPA (Robotic Process Automation) to enhance the capabilities of RPA bots and enable more advanced automation solutions.
Here are some ways in which AI can be used in RPA:
1. Data extraction and validation
2. Decision-making and exception handling
3. Cognitive automation
4. Machine learning-based automation
5. Chatbots and virtual assistants