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Cognitive: These technologies can mine unstructured or semi-structured data and attempt to understand patterns in the underlying data and processes—very different from mimicking the methodical interactions of a person doing repetitive tasks.
Terms such as cognitive computing, intelligent automation or even artificial intelligence (AI) are often mistakenly included under the RPA umbrella.

AI: AI is about machines simulating human intelligence.

The machine intelligence arena holds secure growth prospects, attributable to which, the key players are focusing on developing an integrated solution including hardware and software. Furthermore, to expand their customer reach, several vendors have collaborated with distributors and end users for product distribution. The increasing prominence of parallel processing applications is leading to increased adoption of the technology in scientific disciplines such as artificial intelligence and data science. Organizations are utilizing artificial intelligence to extract valuable insights from data for providing innovative products and improving customer experience; thereby, increasing revenue opportunities.

Artificial Intelligence is gaining prominence due to its complex, data-driven applications such as voice and image recognition. It offers a great investment opportunity, as it can be leveraged over other technologies to overcome the challenges of high data volumes, high computing power, and
improvement in data storage.

The rapid adoption of virtual intelligence/ AI in end-use industries such as retail, healthcare, and automotive is expected to augment market growth over the forecast period. Organizations are making investments to incorporate artificial intelligence capabilities into their product portfolio.
In November 2016, SK Telecom announced that it had signed an agreement with Intel Corporation to develop the vehicle-to-everything (V2X) technology and video recognition based on artificial intelligence

It’s a no-code or low-code approach

Many organizations have a hard time applying machine learning in business processes, as there is no proper way to integrate machine learning algorithms with the rest of the business logic. With Process Studio, we are providing a solution to combine these two seamlessly and easily create artificially intelligent automated business and IT processes.


Connecting the silos of Machine Learning and RPA Workflows

It is achieved using the following steps

Assimilating data from various channels and sources

Collecting and formatting data from different channels, file types, and source systems have always been a challenging and time-consuming task. Process Studio solves this issue for you by providing you with plugins for various source systems and file formats. Using Process Studio, one can easily collect data from the web, social networks, spreadsheets and business applications amongst others hence processing or formatting it as per requirements.

Integrating ML Algorithms and Models

Once the data from various sources is collected, it is important to properly integrate your ML models or algorithms in your process at the right stage in your workflows. Process Studio comes bundled with plugins for Python and R which facilitate you to integrate your models right into your business process seamlessly. Furthermore, advanced steps for ticket or case classification comes bundled with the product which helps in areas like tickets created from unstructured emails and handling of complaint tickets. Also, you can use RPA and Machine Learning to build complex machine learning algorithms like Ranking & Scoring, Data Extraction & Matching, Approvals & Reviews to name a few.

Going live with the processes

Once we are ready with the processes post-rigorous training and testing of the ML models, it is as easy to go live with them as the seamless integration in the earlier steps. After completing this step and pushing it to the relevant systems via Agents, you will be able to see your Artificially Intelligent Automated Process in action immediately. This not only significantly reduces the time for deployment but also helps you in further maintaining and managing the processes.
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