The rising popularity of Generative AI applications, allows business leaders to tap into Generative AI benefits in an unexpected way. Customer experience and cost optimations are among the top business expectations from Gen AI, as per the Gartner Poll results.

While 70% of organizations showed interest in exploring Gen AI technology, 45% of survey participants want to increase AI investment 一 the Gartner report further suggested.

The growing interest in Generative AI isn’t just for improving customer experience, but business executives want to leverage this AI technology to implement use cases across employee IT support. By improving internal functions through employee engagement and productivity, business leaders seek to drive cost optimization.

Further down the line in the employee IT support context, Generative Artificial Intelligence can enable organizations to streamline IT processes for their employees in a more humanized way than rule-based chatbots by unleashing the power of natural language processing.

Since more and more people want to solve a problem independently, rule-based chatbots may have limited capability to create custom responses and help proceed with a conversation session.

If your IT support relies on rule-based chatbots, it is time to explore the enormous capability of Generative AI chatbots to facilitate real-world applications in employee IT support and drive growth.

The most fundamental definition of a chatbot is a computer program to facilitate a human-machine conversation without assistance from a human agent yet delivering a real-life communication experience.

A chatbot can be as simple as a FAQ-based digital assistant to help users get answers to a simple query, such as a chatbot for an apparel brand that answers a query ‘How to return a dress’ would suggest from its predefined sets of rules or templates to ‘choose the appropriate order number of the dress and select the RETURN option.’’

A more advanced chatbot that uses natural language processes can be sophisticated in responding to users and helping them with a live agent when it cannot find more data to solve a problem.

For example, when an employee wants to solve a bluescreen of death, a custom response may not be sufficient. In such a scenario, a service request would be escalated to a live agent, and a solution would be offered.

Regardless of where chatbots are used, they generally have two versions 一

• Rule-based chatbots

• Conversational AI

Be it customer support or user support, organizations can use both of these types of chatbots to facilitate a problem-solving task. Unlike customer support, rule-based chatbots, conversational AI, or AI-powered chatbots can implement different use cases to alleviate user problems and help them accomplish their tasks for end-to-end customer service delivery.

A rule-based chatbot for IT support is rule-based when designed with if/then conditions or pre-defined answer templates.

In a rule-based chatbot, a conversation goes back and forth to deliver a human-like conversation experience. To follow up a dialog in the next stage, it offers several conversation card options for users to choose from and offer an appropriate response by matching the user’s input to the if/then condition trees.

For example, a chatbot for IT support can help an employee with‘’How do I reset my password’’ automation with rule-based templates.

A rule-based chatbot lacking natural language processing capability can be restrictive in understanding user intent and providing expected responses. A chatbot must be pre-trained with use-case questionnaires and associated responses in this scenario.

However, the problem is if there is a question in a slightly different manner, ‘Why is my internet very slow when connected to VPN,’ a simple chatbot may not understand the intent of the query, which is intended to solve a problem with internet connectivity and VPN both, but a chatbot is likely to offer suggestions only related to VPN.

Conversation Flow in a rule-based chatbot

As you can see, with the limited capability of NLP, a rule-based chatbot does not offer desired help and impact user experience.

AI-powered chatbot for IT support

AI-powered chatbot simply refers to conversational AI that leverages machine learning, deep learning, and neural networks to use natural language processing better to understand users’ intent, extract contexts, and deliver accurate responses for a user query.

Let’s say an account assistant wants to perform a budgeting solution for a technology stack. But, he lacks access to the appropriate tool. A conversational AI bot can understand the user’s intent and approve his access to Microsoft Excel by raising a service request to the manager.

Conversation Flow in an AI-powered chatbot

The above dialog flow displays how efficiently an AI-powered chatbot detects a user’s intent by taking advantage of natural language processing even though a query does not include the keyword ‘Microsoft Excel.’

Going forward, AI-powered chatbots consist of two kinds of machine learning-based chatbots.

• Retrieval chatbots

A retrieval chatbot searches a response for an input query by matching metrics across the pre-collected data repository.

However, if a query has no match for a user response, the large repository cannot generate new content or response.

• Generative AI chatbots

Based on massive large language models, Generative AI can produce semantic search results in a sequence-to-sequence generation manager. A chatbot powered by Generative AI is more powerful to create new content and response even if there is a limited repository by using natural language understanding and deep neural network in a sequence-to-sequence manager. It means a bot constantly reads through previous and current conversations back and forth and understands the next sequence of a word to automatically produce a more natural and contextually accurate conversation in follow-up questions.

However, Generative AI’s limitation lies in over-generating or under-generating responses, which may not be legitimate due to its unsupervised learning capabilities.

The right solution is to merge retrieval-based capability withGenerative AI to produce more authentic and real-time responses in the ITSM setting. We’ll explore more about the power of large language model properties of Generative AI in combination with Conversational AI.

An IT support chatbot aims to streamline mundane employee support tasks, automate repetitive tasks to free up agents, and help them dedicate more time to critical IT support issues. And, of course, it aims to improve user experience.

Since the objective is the same for both types of chatbots, rule-based and AI-powered chatbots offer similar kinds of assistance to help a user solve an IT-related problem.

Similarities between Rule-based Chatbots and Generative AI

• Streamline mundane IT requests

• Automate workflows for routine tasks

• Offer seamless employee support

• Free up IT agents to solve more critical IT service requests, for example, handling network outages by addressing network configuration or hardware issues

• Save time and effort, and enable cost reduction

When it comes to improving user experience and helping them solve a problem in the workplace at scale, Generative AI chatbots certainly are way ahead of rule-based chatbots.

By leveraging the power of natural language processing, conversational AI can empower Generative AI and offer to improve search results that further deliver accurate IT responses.

A conversational AI platform powered by a large language model can conveniently deliver a response to an ambiguous or unclear user request in real-time.

For example, if a user seeks an answer to a query, ‘When is my laptop arriving,’ a Generative AI chatbot can well understand that it is related to the ‘order status’ of the laptop. By searching across its internal knowledge base or CRM data, it can fetch the details and tell a specified arrival date.

In this scenario, Generative AI is able to reduce disambiguation from a service request yet offers a contextual response to the user.

Conversely, a rule-based chatbot does not have the extended knowledge of AI search capability, the power of natural language processing, and natural language understanding that help synthesize a complicated user request and offer an appropriate suggestion.

Say, a user asks for the status of a laptop, a rule-based chatbot may ask for more details, such as the order number, the product name, etc. In this specific scenario, since the responsibility rests with the company, the purchase details also rest with them.

Hence, the user cannot pass appropriate information and thus gets no help.

Generative AI can produce new content using its large language models. But, LLMs are not always correct and may produce hallucinations, which impact user experience.

Let’s say a user wants to know about the internal policy for leave management, and it generates a wrong response, he/she may apply for leaves for situations that are not covered by the leave policy, and as a result, the user may suffer deductions in remuneration.

That’s why Generative AI or large language model is always better and more efficient when combined with conversation context and knowledge AI.

With conversational AI and knowledgeAI underpinning the capability of large language models, Generative AI can generate enriched user responses.

Generative AI sequentially tracks previous and current conversations during an interaction session, learns within the conversational session, and keeps responding to a user. In a continuous interaction session, Generative AI applies KnowledgeAI competency to extract conversation contexts and try to retrieve the most appropriate answer from the internal database.

Further, it applies the power of LLM properties to enrich response. This is how Generative AI works to generate a response.

The service request flow contains several steps until the Generative AI generates an accurate and appropriate response.

Steps for Generative AI to generate a response

• A user sends a service request

• Generative AI searches for intent from KnowledgeAI for a request and finds a response

• Generative AI delivers a response if there is an intent matching of query

• A fallback topic is triggered if there an unknown intent arrives

• The query moves to the large language model to retrieve an answer

• LLM offers a response to an IT request\

• If there comes a ‘no answer found’ response, a fallback topic would be triggered again for agent help

• Generative AI captures all interactions in history and provides contexts to agents

• IT agent solves the query

• The ticket is closed

Continuing further, Generative AI’s outstanding capability also helps enrich a response by applying LLM properties.

For instance, a query goes like this, ‘How can I fix a printer paper jam?’

Just discover further how it is convenient with Generative AI to find a solution to a problem like ‘Printer Paper Jam’ and self-resolve it seamlessly, whereas a rule-based chatbot offers multiple steps just to increase the downtime and add employee frustration.

On top of it, who has time to read through multiple articles for a solution?

IDC already predicted that vendors are more likely to applyGenerative AI to ITSM and ITOps use cases to improve response time and expand ITSM use cases.

Companies can leverage multiple benefits to improve IT support for ITSM managers and internal employees.

• Speedy resolution of a user request

Generative AI is more straightforward than a rule-based chatbot, ensuring users can auto-resolve their issues without human intervention. With the capability to search across knowledge articles, Generative AI produces near-accurate results that help solve a problem.

• Automation of repetitive tasks

IT support tasks such as password resets, printer issues, network issues, or application provision are regular and repetitive affairs for service desk help. Generative AI delivers faster and simpler ways to solve these issues using response enrichment rather than providing article links or folders.

• Efficient ITSM processes

Streamlining all repetitive IT support processes using Generative AI and ensuring that a service request finds a solution at a low-tier stage is easier. As a result, auto-resolution capability saves time for IT agents for critical tasks like a failure in data centers or servers and reduces the downtime impact.

• Cost efficiency

ITSM managers can recommend actionable plans for mitigation efforts for downtime using Generative AI-powered automated incident detection and summary creation capability.

As self-service capability eliminates the dependency on higher tier-level support, it gradually reduces agent salary and incentives costs. Generative AI, therefore, reduces bottom-line costs.

With the advent of large language models, Generative AI is gaining huge traction across every industry. With the ability to expand multiple use cases for various real-world scenarios, Generative AI allows for process efficiency, productivity gains,and cost efficiency.

The above instances explicitly demonstrate the competencies of Generative AI in improving IT support and enhancing the user experience compared to rule-based chatbots.

We do not say rule-based chatbots are useless, but Generative AI is more advanced and sophisticated to solve a user request and save time. For an enterprise with over 3,000 headcounts, an employee IT support chatbot leveraging LLM properties' power is more effective than rule-based chatbots in delivering real-time responses and solving issues.

On the other hand, for a small organization with less user traffic to its service desk, a rule-based chatbot may suffice.

Considering immense capabilities to generate new user responses for IT support issues, Generative AI tends to be a game-changer for ITSM domain-specific use cases.

Workativ’s virtual assistant for IT support allows users to leverage knowledgeAI infused with Generative AI and LLM properties. This capability allows you to upload multiple articles related to IT issues and their solutions to the knowledge base and build a data repository to respond to user queries.

Workativ enables you to create your large language model using a website knowledge base, external KB, or Workativ KB. During training, you can apply conditions to choose a response from any of the Knowledge bases and offer an accurate suggestion.

In addition, you can take advantage of LLMs to create use cases for hundreds of IT issues and offer enriched solutions.

To learn more about Workativ’s IT support chatbot and its Generative AI capabilities for your industry-specific use cases, get in touch with our sales expert.

Want to learn more about what impacts Generative AI has on enterprises?

As per a research study, the chatbot market size is estimated to reach $2,485.7 Million By 2028. A significant advancement in natural language processing has led to this growth. With the growth of Generative AI, industry leaders are more enthusiastic about applying it to a wide variety of industry use cases. IT support is one of the various use cases to go through the transformation.

As rule-based chatbots fall short of user expectations in many instances, Generative Artificial Intelligence meets user demand by enabling faster resolution of IT issues in real-time and in a more user-friendly way.

Our article demonstrates the immense possibilities of Generative AI to ramp up user experience in the IT support ecosystem. If you aim to drive business benefits using Generative AI for your IT support, consider using an IT support chatbot powered by large language model properties.

Schedule a demo today with Workativ’s product team.