A recent study suggests that the AI market as a whole is expected to grow to nearly $ 2 trillion in the next six years. Another study suggests that over 70% of customers choose an AI bot over a human agent to track their order; 80% are satisfied with the bot services.
RPA and conversational AI both make heads turn. The possibility of their scaling makes them grow at such a spectacular pace, so let’s look at what happens inside the conversational AI and RPA sectors.
Coming from traditional chatbots, Conversational AI is significantly ahead of them. Unlike traditional chatbots, it uses deep learning and can understand queries with spell-errors and short-form queries. Traditional chatbots act as navigators, and conversational AI is more of a problem solver.
While it cuts costs, conversational AI also gives a productivity boost to multiple operations and helps in personalization while keeping its operational versatility alive. Conversational AI handles essential use cases like healthcare, Lead generation and customer experience.
Amazon CEO Andy Jassy stated he is “excited to witness the possibilities of Generative AI” and mentioned that the trend is not new. “Most large, deeply technical companies like ours, have been working on these very large Generative AI models themselves for a long time,” said Jassy in an interview.
RPA, on the other hand, has been increasing its presence around the industrial face of the globe ever since it saw the first use-case. According to a study, more than 50% of Users have begun using RPA. This is expected to increase by more than 70% in the coming years. RPA has improved accuracy and compliance by almost 90% and has pushed for a cost reduction of 60%. Around 80% of the people who have implemented RPA look at a significant increase in RPA Implementation over the next three to four years.
Let’s understand RPA and AI more before we get deeper into understanding RPA and conversational AI integration.
AI And RPA; Use Cases And Capabilities
RPA robots can interact with business applications by mimicking human actions through the user interface. They can perform crucial tasks like data extraction, downloading reports, and other rule-based tasks like humans.
RPA streamlines development, boosting digital transformation by freeing human hours from mundane tasks. This enhances employee satisfaction and productivity.
Artificial Intelligence
Artificial intelligence, being a captivating field, sparks a multitude of interpretations. For some people, it is a scientific discipline; to others, it is futuristic technology, even a standalone realm. But the truth is that AI is a fusion and not a standalone tech. It combines machine learning, deep learning, and natural language processing into a tapestry of innovation. The collaboration of these technologies fosters expert systems that mimic human cognition and excel in decision-making, pattern recognition, linguistic comprehension, and delivering coherent responses.
RPA+ Conversational AI
RPA and conversational AI, their integration and the integration results can change how a business operates and how customers and not just employees look at that business.
Here are a few interesting use cases of the integration of RPA and conversational AI:
Customer Engagement
Conversational AI chatbots excel at elevating customer engagement through personalized services, being one of the best conversational AI use cases. They’re trained to tailor recommendations and exclusive offers and even solicit feedback via surveys, all finely tuned to individual customer preferences and behaviors.
These bots also shine in proactively reaching out and dispatching timely alerts, notifications, and friendly reminders. RPA bots come into play once the customer responds, seamlessly executing tasks like sending confirmation emails, updating CRM records, or swiftly processing orders.
Employee Engagement
Conversational AI chatbots are pivotal in elevating employee engagement by offering self-service access to various HR-related functions. These include easy access to HR policies, quick retrieval of payroll information, and streamlined leave request processing.
Complementing this, RPA bots act to automate the behind-the-scenes tasks associated with these inquiries. This includes keeping employee records up-to-date, promptly dispatching notifications, and efficiently generating reports, pitting another good example of conversational AI use cases.
HR Recruitment / Virtual Assistant
Conversational AI chatbots can quickly become virtual HR assistants, aiding recruiters with candidate screening, interview scheduling, and friendly reminders. In tandem, RPA bots are equipped to manage the administrative intricacies of the hiring process deftly. This includes tasks such as issuing offer letters, gathering necessary documents, and facilitating the smooth onboarding of new team members.
Conclusion
By adding AI capabilities to RPA, organizations can create intelligent solutions and expand the automation scope to back-office processes and front-office services.
Processes that are time-intensive, repetitive, and require human interactions (customer or employee) can now be included and automated, enhancing regular RPA to Intelligent automation. Conversational automation takes automation, as we know it, to another level, saving precious time for employees and improving customer experiences by automating complex processes end-to-end.