Role of AI in Mitigating RPA Challenges

Robotic Process Automation bots (RPA) have been aiding the business world for over twenty years, all the while standing tall as the star of the efficiency show. But can it think? Can it be deduced and optimized? Not exactly.

You can think of an RPA software robot as a digital assistant. A digital assistant that can handle boring, repetitive tasks for you. These tasks could look like sending a mass email, generating reports, uploading a file, or, if you'd like, downloading one. A task could look like anything, so long as the steps to execute it are sequential, pre-programmed, and repeatable.

How this has worked is you would show the bot what you would like it to do by recording your actions on a computer. The bot would then be able to play this recording back and repeat your same actions over and over again. You could schedule this bot to run at a specific time or even trigger it by an event. But you can't make it adapt to any sort of change without completely rewriting its steps. Its strength lies in rule-based automation, making it perfect for predictable work, but quite terrible if we venture into uncharted territory. This turns out to be quite the issue since we use RPA bots to hold up our businesses, and businesses operate in the real world—a huge steaming vat of entropy. That's where Artificial Intelligence (AI), the brain of the tech world, enters the scene.

Did you know the concept of AI has existed for centuries and that it has even popped up in Greek mythology? AI hasn't been put into practice for centuries, but it has been transforming businesses since the early 2010s. One area this transformation shows is how rigid RPA processes can now be made adaptive. Less stiff and more shapeshifting.

While RPA solutions can follow a set of pre-programmed rules to automate repetitive tasks, AI can learn from data, make informed decisions, and adapt to changing circumstances. RPA perfectly executes instructions, and AI is the creative problem solver, constantly learning and constantly adapting. RPA prepares data, delivering clean and structured data that works as fuel for AI algorithms. This lets AI learn and make accurate predictions.

One unexpected curveball could send an RPA bot's workflow crashing down. Enterprises are now witnessing the fruits of this pairing. Who wouldn't jump at the opportunity to sweeten their customers' experience, obtain faster service, enhance internal operations, improve security, boost employee satisfaction, and even guarantee a high ROI? The thought of possessing such a huge competitive advantage is seductive. Rightfully so. AI and RPA are a match made in automation heaven.

RPA Challenges

It wasn't always sunshine and algorithms. RPA's inflexibility in adapting to change resulted in cumbersome and redundant processes. RPA works best with well-defined and structured processes, but many business processes are complex and dynamic. It cannot handle tasks that require understanding unstructured data, natural language, or exceptions. AI can augment RPA with capabilities like natural language processing, unstructured content extraction, and exception handling, enabling more complex and intelligent automation.

Needless to say, RPA hit a few roadblocks in its day.

• Manual identification of where to apply automation was time-consuming and prone to errors. AI now helps automate the identification process by using "click mining" technology, which can observe a person and recommend where to apply automation, thus saving time and improving accuracy.
• RPA lacked a common approach or framework for automation across different vendors and platforms, which created compatibility and scalability issues. AI helped by standardizing the automation strategy and creating more business value.
• RPA needed a clear vision and roadmap for automation, as well as the right tools and software to execute it. AI now helps RPA bots by using tools such as SaaS, PaaS, or systems integrators to select the automation software and solution for the organization.

RPA's meticulous workflows, infused with AI intelligence, create an automation world that's both efficient and adaptable. From marketing companies that adapt to their customers preferences in real-time to personalised healthcare plans generated by AI analysis, the results are staggering.

RPA and AI Use Cases

Many industries have harnessed the best of both high-tech worlds.

1. RPA and AI in the Manufacturing Industry

The manufacturing industry now automates tasks like data entry, transaction processing, and inventory management. The industry basks in delight as it now has faster turnover, reduced costs, and significantly fewer errors.

2. RPA and AI in Finance and Banking

RPA and AI, having streamlined customer service, fraud detection, and predictive analytics in the world of finance and banking, have boosted customer satisfaction, risk mitigation, and data-driven decision-making.

3. RPA and AI in the Insurance Industry

Processing claims, issuing policies, and underwriting are all tedious tasks in the insurance industry. RPA and AI swoop in to elevate efficiency and accuracy.

4. RPA and AI in Public Administration

In Public Administration, automating processes such as task assessment, document analysis, and service delivery with AI and RPA enhances transparency, accountability, and citizen satisfaction.

5. RPA and AI in the Retail Industry

In the retail industry, the combined use of RPA and AI can optimize inventory management, stock replenishment, competitive pricing, and dynamic pricing have greatly benefited the industry.

Quiet blessings in the form of automated data entry and fine-tuning processes allow employees to save time and energy for more valuable tasks. RPA and AI reduce a lot of errors that these processes are prone to if done manually. Customer service and support flourished by integrating the dream team. Chatbots and virtual assistants are tasked with handling common queries, resolving issues, and escalating complex cases to human experts. Mortgage applications are easier now with automated data collection, verification, and entry (the RPA wheelhouse), analysis of credit risk, suggesting mortgage options and ensuring compliance (AI's domain).