Outlining the route to successfully combine AI with automation technologies, to achieve hyper-automation. Enlisting the aspects to ponder on before embarking on the process to ensure a successful project.
A number of organizations, these days implement automation to address various needs. Automation handles seemingly mundane tasks with high speed and great accuracy. Combining AI with automation leads to Hyper-automation. In this article, we shall journal the possible way an organization can achieve hyper-automation.
Revisiting the AI
Artificial Intelligence refers to the idea of robots emulating human behavior on a cognitive level. This involves setting about pre-defined rules based on the lengths of possibilities and thereby simulating normal human behavior of response and decision making.
These days robots are taught to study a pattern and detect inconsistencies and thereby come to a decision. It is a highly complex task to feed this kind of ‘intelligence’ to the machine, but the invention has come a long way and it becomes a powerful tool if it were to be integrated with automation technologies. In many ways, AI suitably complements automation.
Artificial Intelligence may be integrated with automation by means of specific types designed to extend the normal capabilities of an automated system.
Optical Character Recognition ranks among the most utilized varieties of AI. It is an essential tool for companies transitioning from manual to digital, as it recognizes handwritten characters and converts them to a typed document.
For example, in the case of a form filled manually, the OCR serves to identify the characters correctly. Once the text has been read, it can transfer the content via an automation tool to be recorded in the required system.
To complete the series of tasks OCR employs computer vision which is a component to scan the document. Next, it identifies a field for instance NAME and the data corresponding to it. It then maps the data to the appropriate field name in the system to store the record.
The figure below displays an instance of an HR document from which the hand-scribbled name of the signee is to be extracted. The OCR fetches this data against the field name ‘NAME’ and saves it.
Natural Language Processing (NLP)
Yet another frequently used type of AI is NLP. NLP is used to understand human language and also generate the same. NLP, however, doesn’t actually understand human language but captures a few basic properties.
For example in the query, ‘ What is the weather forecast for tomorrow?’, the NLP picks up on the subject of ‘weather forecast’ with the intent ‘what is and the context of time and direction as ‘tomorrow’.
This is the technology used by Alexa and Siri. in both cases there exists another layer of technology that converts speech to text and text back to speech.
Getting Started With Hyper-automation
With the businesses revving with automation and AI, it seems only natural for some to attempt to combine both. HashCash aims to experiment with the prospect of a combination of technologies.
Identifying The Use Cases
It is not a correct approach to seek hyper-automation just for the sake of it. Organizations need to figure out what cannot be solved or maybe enhanced to attain a higher quality of results. Customer feedbacks may be studied to gain an insight on how to better cater to their needs. Once the bottlenecks have been pinpointed should you move forth with advancing our technologies.
Research On Available Technologies
It is not a good idea to take a plunge into implementing a novel technology, without first querying if the available technologies work as well. This may reduce the cost of building a new model from scratch.
If you must make a shift, research in-depth into the AI technologies to focus on. You might need help with AI technologies, in which case it is advisable to hire a consultant.
Selection Of Your Tool
Once the technology is finalized you move towards the tool vendor. It is advisable to select a tool that enables you to work across technologies and integrate any AI capability in spite of your existing tool landscape.
Finally, in this phase, it is advisable to test your new technologies internally first; Starting with the small problem areas and learn to resolve those efficiently. Once you’ve perfected this, the system is ready to be scaled up.
With charted goals and expertise, HashCash is ready with its resources to delve into the process and also share the expertise with intense partnership exercises.