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february 03 2021
, predicting it is also one of the few technologies to gain significant traction during the pandemic and rapidly continues to expand in 2021. a key contributor to its rapid growth could be its convergence with another powerful technology: artificial intelligence (ai). this simple addition has been revolutionary for businesses; the added ai element has advanced enterprises to unprecedented levels of productivity by enabling rpa to operate at a considerably faster pace, deliver greater results, and perform more complex tasks. it is predicted that pure rpa tools will soon be replaced by those with ai functionality, and the latter will eventually become the new normal.
what are the exact ai features that dramatically boost rpa functionality? why is ai seen as the perfect match for rpa? how does ai add value to rpa?
pure robotic process automation
pure rpa tools can automate strictly ruled-based and repetitive processes that involve structured data. for an rpa bot to sufficiently automate, the task in question must involve predictable, straightforward steps with a clear direction. a pure rpa can, amongst many things, do the following:
copy & pasting
accessing emails and opening attachments
extracting and processing structured data
automation of the above process alone is impressive and has proved to be highly useful for organizations. afterall, the despised mundane and repetitive tasks could be assigned to bots that can operate 24/7 while employees can focus on more meaningful areas.
pure rpa tools, however, are no longer satisfactory for many businesses in an increasingly digital environment where processes are regularly complexifying with varied steps and rules. to automate many of the business tasks today, there is one crucial element needed: adaptability. and this is exactly how ai enters the picture. artificial intelligence provides rpa bots the ability to learn and adapt to data in real-time. an enormous benefit!
ai has many traits which make it perfect for rpa, including:
optical character recognition (ocr): likened to the “eyes of a robot”, ocr allows the bot to electronically extract text from visual stimuli such as images and documents. this can be extremely convenient in areas such as finance, insurance, or any other industry that is heavily document-oriented.
natural language processing (nlp): this refers to the ability to read the language to analyze data, whether it's structured, semi-structured, or unstructured, and process it further. this can be used to, for example, extract the necessary text from one source to correctly fill the information in a form, or correct errors in a document, or analyze a phone transcript to update relevant customer details in the organization database.
machine learning (ml): this allows the rpa bot to learn from past data and infer meaning. through pattern generation, an ai-powered rpa bot can be more "intelligent" to understand and draw conclusions from both structured and unstructured data. as a result, the rpa bot can be more adaptable, thereby, minimizing the level of human intervention needed.
the combination of rpa and ai enables the bot to perform sophisticated tasks including, but not limited to:
data search: rpa ai robots can swiftly access both internal and external networks to search for data based on keywords and extract the necessary information. moreover, an ai-powered rpa can go beyond searching by keywords and identify any related pattens which provide the words meaning in context; they can look for any text whether it's a relevant word, phrase, or even an entire paragraph to provide useful information.
predictive analysis: in addition to merely searching, rpa ai bots can use machine learning models to generate new data to predict future market behavior and assess potential outcomes. this can be useful for sales and marketing departments which can evaluate the data to plan strategically and generate more leads.
contract analysis: contracts often involve key sections that cannot be skipped at any cost. unlike the human eye (which is prone to tiredness and distractibility), an rpa ai bot can utilize nlp and ocr technology to review and analyze multiple contracts at a pace unmatched by humans. rpa ai bots can assist organizations to remain compliant through speedy yet accurate reading and extraction of relevant information to be inputted into the appropriate forms, allowing subsequent generation of reports.
invoice processing: it's common for organizations to receive invoices in a wide variety of formats: fax, pdf, image, or even a paper copy. this often leads to staff having to manually extract and input the data into the financial database software used by the company. luckily, the rpa ai bot can use its ocr functionality to process all types of invoices, irrespective of the format, and automatically transfer the organization's financial database without requiring human intervention.
the fusion of rpa and ai has massively boosted rpa capabilities, allowing what was once only possible in science fiction to now be reality. by leveraging rpa and ai capabilities, businesses can now reach new heights through assigning many tasks – even extremely sophisticated ones – to rpa bots and focus instead on strategy development and business growth. it can be argued that ai is now no longer just a 'premium function' of rpa bots, but rather an essential part which facilitates a fully end-to-end automation service, bringing businesses closer to their full potential.
founded in 2015, laiye helps businesses achieve more, reach their full potential, and transform into smarter, more effective, efficient, productive enterprises. laiye helps businesses and people realize their full potential by optimizing the human-machine collaborative alliance with sophisticated, dynamic, productivity-enhancing digital “workforce solutions”. laiye delivers the deepest, most advanced ai/rpa/nlp solutions to help businesses become more efficient, effective, agile, and successful. and it frees people to focus on meaningful, innovative, mission-critical initiatives.
core technologies include robotic process automation (rpa), process mining, natural language processing (nlp), conversational intelligence, text recognition, and image recognition.
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