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NSPL SOlution Advisor

RPA and AI

RPA Blue Prism
RPA Blue Prism:
 EdurekaBlue Prism is an RPA Tool that contains the ability of a virtual workforce powered by software robots. RPA helps companies to automate the business processes in an agile and cost-effective way. The tool is based on Java Programming Language and gives a visual designer with drag and drop functionalities.


Automation Anywhere
Automation Anywhere is a sturdy enterprise-grade Robotic Process Automation tool that gives RPA, AI, ML, and analytics capabilities. Automation Anywhere gives its users scalable, secure, and resilient assistance.

Any methods which are mundane, repetitive, whose volume is high can be automated.

Automation Anywhere allows you to create automated tasks with ease.

Its user-friendly interface gives you the essential features to record, change and run tasks that range from simple to more complex ones.



UiPath is an advanced RPA tool that enables you to design automation processes visually, through diagrams. UiPath provides complete end-to-end automation, calling it “hyperautomation” Here are some of the advantages of UiPath –

Activity library: UiPath has an extensive activity library with pre-built drag and drops actions.
Security– UiPath offers high-level security since we can store and encrypt the credentials on a centralized server
Recording– UiPath has recorders for desktop apps and emulators for the quick creation of automation

Third-party integration:
 We can plug in various technologies from IBM Watson & Google.
Powerful Debugging– UiPath offers intuitive and flexible debugging options.

Artificial intelligence(AI)

Artificial intelligence (AI) is an extensive branch of computer science concerned with intelligent structure machines proficient in executing tasks that usually require human intelligence. AI is an interdisciplinary science with various approaches, but advancement in machine learning and deep learning produces a paradigm shift in almost every sector of the technology industry.

The AI Advantage in ITSM is:

Previously, we set the phase for our AI discussion with part one, “AI at employment in ITSM.” Now, in part two, “Use Cases and Features,” we will look at specific, AI-based use case and features scenarios across many ITSM modules that describe how AI-based features and models can change the way IT service desk works. Let us start with the chatbot.

Chatbots: Chatbots handle a particular category of incidents and requests, provide there is proper documentation of the past request history and relevant knowledge articles. Here, we will discuss two plans in which chatbots could help service desks. The first is an application of narrow artificial intelligence, which is available now. The second one is based on false general information, which is more efficient but might take longer to develop.

Remote user benefit request. An end-user in the field reports that their laptop is slow and wants to replace it. They seek to discover the right asset upgrade form but cannot. Then they next try calling the service desk but don’t get through to anyone. And in the last, they reach out to the chatbot.

Annotations, comments, or add notes to a request. IT tech is working remotely to identify an issue with a workspace, so they cannot contact the service desk portal to update the request information. Instead, they use the tech supporter chatbot that gets the thing to happen.

Knowledge Management: Artificial Intelligence chatbots and algorithms are only helpful as their accessible knowledge-based. Fortunately, AI can also help to build a study knowledge base. We will discuss these two use cases to understand how AI can contribute to Knowledge Management in IT service desks.

An automatically rating solution to reject and approve of them. There may be multiple solutions and knowledge-based articles that use for an extended period. Specific ML-based models can aim to identify the achievement rate for each solution based on significant performance. It can achieve by considering multiple factors such as acknowledgment from end-users, end-user and technician rating of articles, and the reopen rate of tickets.

An ML-based model can even advise which sections must be retired and what parts could be enhanced. The rating of solutions is based on their presentation over time, which helps the IT service desks provide the exact answers to users at the time of ticket making and assists chatbots during a chat meeting.

Service Request Management: Today, complicated service requests like employee onboarding are either manually coordinate on predefined automation based or technicians. Manually performing these jobs can be cumbersome and inefficient. For existing automation, all processes are static and need intelligence. This automation doesn’t fit the possible situation and requires human involvement periodically to wait on a path. 

 IT Change Management: IT Change Management is a process that can break or make a company IT infrastructures. Many setting up and risk evaluation go into altering before implementing; despite all these attempts, changes will fail due to personal error. When it appears to analyze changes, humans can also resist mining insights from the vast volume of data generated on IT change management and implementation. AI can help minimize change management risk by preventing human error and improving the analysis.

IT Asset Management. IT Asset Management and CMDB serve as the basis on which every ITSM process functions. AI can also assist IT service desk members to monitor and manage IT software and hardware assets better. ML systems can continuously monitor a CI’s performance or go beyond the available CI performance information and predict breakdown, saving both end-users and IT people from a pile of difficulty. AI can assist IT service desk software difference and produce critical warnings by involving the dots across several areas, which is approximately impossible to do manually. Some ITSM tools vendor has already in progress, offering both these capabilities to their end-users. Multiple third-party vendors provide plug-and-play solutions that can execute this operation.

Getting Ready for the AI Wave in ITSM AI redefines how IT service desk and IT service desk members work; the service desk prepares for the future AI wave. The AI model’s efficiency or application depends on the data train and the accessible knowledge from items like documented solutions.

IT service desk team has to record all their needs correctly, problems and alters; maintain a correct IT service desk database; and construct a well-equipped knowledge -base. As ITSM tool vendors try to add AI into their product, the service desk team must prepare themselves to gather AI benefits in ITSM.