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The 3 Essentials of AI Bots for IT Help Desk

Dec 14, 2017 1:04:47 PM


IT Help Desk meets Artificial Intelligence

One of the areas within an enterprise ripe for disruption is the traditional IT Help Desk. Over the past decade, enterprises have become more cost-efficient through the outsourcing of Help Desk operations. However, in order to continue driving efficiencies across the enterprise, internal workforce productivity must also rise. This is where Artificial Intelligence (AI) enters the picture.

With recent advancements in infinite computing, natural language understanding (NLU), and deep learning, the application of enterprise AI solutions is more practical than ever before. In fact, with roughly 30-50% of Level 1 Help Desk support cases being repetitive, you can leverage the power of NLU and cognitive automation in the form of AI bots.

AI bots are self-learning software systems that understand the human language without requiring human assistance. They can supercharge your enterprise IT Help Desk causing your team’s productivity to skyrocket, and ultimately drive increased enterprise efficiency.

For starters, we’ve laid out the three essentials of an AI bot for your enterprise IT Help Desk needs:

1. Enterprise Language Understanding

Similar to how IT Help Desk professionals are trained to provide consistent and relevant support handling service requests, AI bots must be trained to understand language specific to your enterprise. AI bots must utilize an Enterprise Language Model (ELM) which resembles an IT service handbook or knowledge base specific to your enterprise.

An ELM represents the language (colloquialism, acronyms, cryptic notations, jargon, company terms, and domain-specific vocabulary) and intentions (or intents for short) which are expressed all the time in IT Help Desk requests. Therefore, having an ELM to understand conversations is the first step to any enterprise application of AI.

Sparse Data vs Dense Data

Sparse data refers to a low volume of data usually in the thousands that can be analyzed by simply using a spreadsheet. If you have sparse data, you should identify high volume issues and create and train intents manually. Any enterprise jargon or internal documents should also be used to quickly build your ELM.

Dense data refers to a large volume of data usually going to hundreds of thousands and even millions of records.  If you have dense data, an AI bot should identify high volume issues and surface intents automatically from your data sets to build your ELM.



The Parlo Broca NLU service is built for enterprises with both sparse and dense data collected from chat logs, CRM, documents, emails, and knowledge bases. Using a combination of machine learning and linguistic engineering, Parlo builds an ELM which accurately detects and classifies IT Help Desk intents of high volume.

With minimal training involved, Parlo can build your ELM and get your Help Desk bot up and running in less than 4 weeks.




 2. Interactions with Users

Once you’ve built a robust ELM, you must decide how your AI bot should interact within your enterprise IT Help Desk environment. AI bots can operate as an AI worker or an AI assistant.

AI Worker vs AI Assistant

An AI Worker does not involve turn-by-turn conversations with users. In fact, it is invisible to the users as the bot is deployed directly on the IT Help Desk software (ServiceNow, Ivanti, Remedy, or even an email server) which is used to capture incidents. AI Workers can be trained to completely resolve an incident/service request, or simply do some pre-processing to help a human agent resolve the ticket.  If the AI Worker is trained to resolve an incident it will act on it. Otherwise, it puts the ticket back in the queue for a human agent to take action.

For example, if a service agent requests IT Help Desk bot to “Increase disk space”, “Unlock my account”, or “Reset password” for a user, the AI Worker will automatically execute those commands on the back-end and update the ticket for the end customer (as shown below).


The advantages of this model are the following:

  • There is no disruption in user behavior, and
  • Works very well with the outsourcing models an enterprise might have as it just involves adding an AI Worker to the workforce.

An AI Assistant indulges in turn-by-turn conversation with users. Think of it functioning as a Level 1 support assistant that interacts with users and solves trained issues right away. If it is not trained for a particular service request, it logs a ticket and assigns it to a human support assistant for follow up (as shown below).



The advantages of this model are the following:

  • Simple issues are immediately resolved directly within the conversation channel (website, mobile app, Slack, Skype, etc), and
  • It helps in reducing MTTR by gathering mandatory information normally done by a human worker

3. Ability to Fulfill Service Requests

Underneath the covers of an AI bot, workflows must be in place to execute relevant tasks and business processes. There are two ways to create these workflows for AI Workers to fulfill back-office requests:

  1. Use an FAQ Knowledge Base
  2. Use Robotic Process Automation(RPA) and APIs

FAQ Knowledge Base vs RPA/API

In order for an AI Bot to truly understand a service request, the ELM alone will not suffice. Many IT Help Desk tickets involve extracting multiple complex entities (parameters associated with the request) from the ticket and calling RPA/APIs to do a backend function.

Here is a service request involving an FAQ knowledge base:
“I need to exchange my phone”

An AI Bot would extract the intent “exchange phone” and provide the relevant support link from the enterprise's knowledge base. The AI Bot can go one step further by extracting phone entities (iPhone, Galaxy S6, Pixel 2, etc) in order to guide the user to more specific support articles or higher level support engineers.

Here is a service request involving RPA:
Please give me access to the channel scrum in slack”

This involves extracting entities "scrum" and "slack" from the request, the username from the ticket, and then calling an RPA to fulfill the request.

Parlo can seamlessly connect with enterprise knowledge bases, RPAs, and APIs to fulfill routine IT Help Desk requests and create the appropriate workflows within our chatbot platform.


AI-powered bots and cognitive automation are quickly becoming the driving force for digital transformation across all enterprises. Now is the time to embrace this technology and use AI bots for IT Help Desk automation.

Getting started is always a challenge, so be sure your AI bot solution covers the three essentials:

  1. Enterprise language understanding using both sparse and dense data
  2. Interactions with users through an independent AI Worker or dialogue with an AI assistant
  3. Ability to fulfill back-office requests using FAQ knowledge bases, RPAs, or APIs

If done correctly, your users will see a quicker resolution of their incidents and you can significantly decrease your operational costs. So what are you waiting for! The time to create your AI Workforce has arrived. Talk to us to learn why Parlo is the preferred AI chatbot platform for our largest enterprise clients and technical service partners.

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If you found this article useful, please let us know your thoughts below. We regularly publish content like this on our blog in our mission to bring smarter AI to smarter enterprises.

Parlo builds smarter bots for smarter enterprises. We build AI chatbots that employ cutting-edge machine learning to seamlessly integrate with your business. They’ll support your human workforce, delight your customers, and save you time and money.