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ChatGPT turns out to be a sudden buzzword, but can we disregard its amazing abilities to create and automate NLP-based tasks?
While everyone observes the surge of ChatGPT, which has been quite significant, Generative AI owes its popularity, industry interests, and expansion to GPT architecture.
A Generative Pre-trained Transformer is a core framework that underpins ChatGPT and helps the LLM-powered chat interface unleash the unique potential to redefine the existing state of automation in everything that takes much more time to perform and is susceptible to errors.
Business leaders across industries are already using some form of automation to streamline their day-to-day jobs and optimize employee productivity. They want to expand the existing automation capabilities of their business processes and enhance current business processes.
Looking back at the state of isolation during COVID-19, it has been pragmatic for every business to innovate a way to survive, turning work-from-home or telecommuting into a new normal thing.
For today’s business, the challenge isn’t to support the new-age necessity for a work-from-home ecosystem but to align with strategic needs. To this end, a mechanism must be built to facilitate IT or HR support virtually and provide necessary employee support to help enable workspace productivity and performance.
From that standpoint, virtual enterprise employee service desks demonstrate capabilities insufficient to automate response and service delivery, making finding information difficult and impacting productivity.
With Generative AI redefining natural language processing tasks,virtual service desks can harness the power of deep learning technology to expand the current state of agent efficiency and employee support in a unique way.
There are risks with Generative AI. But that isn’t stopping industry leaders from investing more and more in this emergent technology.
With the massive potential to transform the existing business processes, Generative AI seems mainstream now, forcing leaders to adapt to this new industry trend lest they lose many prospects in the ‘wait-and-see’ queue.
According to the Gartner Poll, overhalf of organizations have doubled their Generative AI investment in the last ten months. Their primary focus is on customer-facing services.
“Executives consider Generative AI for it can drive innovation, optimization, and disruption across various business functions,” said Karamouzis, Distinguished VP Analyst at Gartner.
47% of businesses are increasing GenAI's investment in customer-facing solutions such as software development, marketing, customer service, or chatbots.
Gartner predicts that customer service or chatbots will see a 16% investment increase in GenAI.
Businesses want to change how customer service, or if we are right, employee support is managed. With Generative AI, the lever of customer or employee support ─ chatbots can gain extended automation capabilities tostreamline communications and problem-solving.
Your tryst with ChatGPT so far is quite impressive. You can code fast to build an essential website with some necessary menus or draw inspiration to create graphics for marketing initiatives.
Generative AI or GPT interface can be good enough to automate effort-intensive tasks by deciphering NLP queries.
The ability to generate content, especially summarizing lengthy articles or notes, translating languages to different languages, classifying human responses, and creating new writeups, which generative AI is known for, can be extremely useful to augment and enhance how virtual employee service desks work.
Combining all of these potentials within service desks, Generative AI can help automate virtual employee support more efficiently than what users were supposed to leverage with existing virtual employee service desk tools.
As a result, Generative AI can expand and enhance the chatbot automation capability or remove friction from theself-service portal from a service desk.
A virtual enterprise employee service desk may have automation tools.
However, the changing or unique needs require more advanced automation capabilities to manage and streamline service desk operations or queries for virtual employees.
In day-to-day employee support, existing levers are enough to support known but not unique cases.
Let’s learn about the existing challenges faced by service desks for virtual employees and the Generative AI solutions to these problems.
Your traditional service desk can handle a small number of tickets and provide support for what a virtual employee needs for a common problem.
Generative AI makes it easy to keep service desk support available 24/7 for your employees.
With a large language model working behind the scenes, Generative AI can gain massive potential to automate service desk NLP tasks, such as responding to natural language queries in an automated way.
Traditionally, a service desk isn’t flexible enough to receive answers to common employee queries. Though it has automated response templates for common queries, they can be less predictive at specific points and provide repetitive answers.
With Generative AI, service desk managers or subject matter experts can save time creating common problem FAQs.
Each common IT or HR issue requires service desks to create a huge volume of FAQs. This is time-consuming and labor-intensive, too.
But, Generative AI provides an excellent way to upload any length of data in the forms of PDFs, documents, images, Excel, or just about anything to train the large language models underpinning genAI.
The data can be comprehensive, containing IT or HR scenarios and the history of cases an organization handled. As a result, GenAI no longer needs to depend on keyword-based search or FAQ-based pre-defined responses. Instead, the technology can apply semantic search to find a match for NLP queries across documents and produce coherent responses.
Using a genAI-based service desk for common and related queries, employees can expect a resolution and reduce downtime in an ongoing conversation.
The problem is such for a service desk that it takes time for agents to triage a ticket and escalate it to the right team.
One of many reasons is that a logged report to the service desk contains a vague explanation. Someone sending an incident note may need more knowledge to describe the incident and provide proper insights.
As a result, they pose a risk to the service desk when triaging incident notes, categorizing tickets based on urgency and escalating to the right team.
With that, if an expert incident manager is out of the office, it is tough to get expert help in real time and comprehend the incident message, leading to a delayed incident triage and routing of the ticket to the right team member at the service desk.
For example, an employee’s desktop crashes down, and he writes an incident note to the service desk. If the note is unclear, the service desk can have difficulty routing the ticket to the right team among the desktop, application, and network teams.
Generative AI eliminates the need for expert help to decipher what an incident message contains for the service desk. Using its pre-trained language model, GenAI can draw existing knowledge from large language models and apply semantics and context to classify text and categorize incidents.
For example, if a user sends out a message that reads, ‘screen jumps.’
The word is not an appropriate technical term to state a problem. If messages are unclear, your team needs to communicate with the requester via email, voice, or whatever to clarify.
A GenAI-powered service desk helps classify the message and understand which team can handle the issue appropriately.
As Generative AI offers insights into various types of screen-related problems, the service desk can easily classify that the desktop support team is better at handling these issues than the networking or application team.
This is an impoverished option to apply classification and find escalation suggestions for immediate help.
However, if you have a custom classification model, you can have a more straightforward and automated option to perform ticket triage. To do this, you need specific triage data to train your model.
With a changing shift for agents, the same agent may be unavailable on a service requester call. Employees become frustrated when they need to repeat the case history.
Finding a resolution for an issue takes time and causes a productivity slump for the agent.
Another challenge withagent performance is that they must constantly type messages or provide suggestions on a call.
If an amateur agent handles a particular case, suppose a login issue with a digital attendance system, he may need expert help to provide the right suggestions and resolutions.
In both scenarios, an agent needs insights to help the requester out of the problem. But, if knowledge is not available, resolution will not be timely.
With the ability to generate unique content with prompts and offer insights into the requester's sentiments, Generative AI can immensely help populate answers for an employee's queries even if the agent dealing with the case is unavailable.
Also, genAI makes it easy to find the history of previous cases, understand patterns of resolutions provided, and enable an agent to provide appropriate suggestions in real-time and solve problems instantly.
As a result, for an agent who is not adept enough to write grammatically correct messages or offer suggestions on voice calls, the Generative AI service desk can easily enable it to craft contextual and meaningful messages to offer autonomous help.
Though automation is applied to service desk operations, automation is not fully implemented to offer frictionless help in the self-service portal through a virtual enterprise service desk.
Say password reset is automated. However, an employee experiences constant access issues with a web-based email system.
A virtual employee finds working challenging if he faces login issues, even if the password is reset. A perfect resolution is to connect with an agent in real-time. But if an agent is not available, wait time increases.
Large language models can trained with existing case history, unique cases, and even resolution data.
On top of that, Generative AI constantly learns from experiences and builds a predictive model to suggest a resolution.
Say, automated password reset is done successfully. But, a login access issue persists.
Suppose a service desk is powered by a Generative AI chatbot with large language models and a massive dataset of documents with related issue-resolving tips, such as account permissions, account lockout, browser cache or cookies, etc. In that case, an employee can get summarized answers and resolve the password issue autonomously.
A service desk regularly performs repetitive tasks such as password resets, account unlocks, adding users to a group, and onboarding or offboarding.
Virtual employees can become frustrated if they get resolutions quickly.
Agents and HR executives remain busy with more strategic work manually, so they can rarely offer more personalized help, which can cause friction in the employee experience.
Generative AI can extend the automation capability of existing service desk tasks through NLP-based query-resolving solutions.
Repetitive and mundane tasks such as password resets, PTO inquiries, tax inquiries, IT support help, etc., are automated efficiently without the need to guide the users with article notes.
Instead, Generative AI can offer users summarized and straightforward answers and help them resolve issues autonomously in real-time.
To help enterprise leaders alleviate virtual employee support challenges, Workativ brings to the forefront the best of Generative AI within its conversational AI platform that harnesses the power of a large language model and allows them to leverage this exciting technology cost-effectively.
Workativ has introduced Knowledge AI for service desk operations to transform the virtual employee experience through more meaningful and personalized replies to employee questions.
Knowledge AI allows users to upload massive datasets to the large language model platform embedded inside the Workativ conversational AI platform in any form and train the model with little effort, like foundation models.
As a result, users need not spend time creating a large set of FAQ-based templates. Instead, they can leverage the benefits of custom responses through Knowledge AI search integration inside a conversational AI platform and quickly generate the most accurate, relevant, and coherent response for employees.
In addition, Workativ also uses Hybrid NLU for its chatbot search with ranker and resolver endpoints to derive the most relevant information from the knowledge base and improve search performance.
When searching for information across Wikipedia, the search has random results that may contain something other than what a user is looking for. Hybrid NLU in Workativ can eliminate this challenge and surface relevant answers using ranker or resolver endpoints.
When using a large language model for a service desk, users can get generic responses that are not useful for specific cases.
Workativ’s Knowledge AI allows you to surface domain-specific responses more straightforwardly, helping your virtual employees solve problems effectively and efficiently.
On the other hand, Hybrid NLU provides accuracy in responses that remove confusion and solve problems steadily.
As a result, virtual employees remain calm and less stressed when a sudden downtime happens because they know they can leverage Knowledge AI and get autonomous help, reducing friction and MTTR to help them get back to work in less time.
With Workativ redefining virtual enterprise employee service desks, leaders or IT managers can dedicate more time to strategic responsibilities and ramp up employee productivity that expedites growth and value.
GoTo has more than 50+ applications in its environment, like Adobe, VMware, SolarWinds ITSM, Office 365, SharePoint, NetSuite, Monday.com, Slack, and others.
Workativ helped the GoTo team auto-resolve repetitive IT queries, issues, and requests and improved the experience for over 3,500+ employees using Knowledge AI in the conversational AI platform.
To learn how to implement a cost-effective Generative AI service desk project for your virtual employees, schedule a demo today.
Traditional employee service desks use keyword-based knowledge searches. When question volume increases, or unique questions are asked, businesses can face scalability issues.
Generative AI provides extended automation advantages to streamline workflows and make information search easy and fast for employees to resolve issues. This helps in increasing efficiency and reducing downtime.
Virtual employee service desks receive routine requests such as password resets, software installs, account unlocks, etc. Generative AI uses deep learning algorithms to automate these tasks by extending the automation limit.
Generative AI improves productivity and efficiency for service desk operations. Employees solve routine problems fast, agents can address unique cases efficiently, and service desk see fewer tickets, which apparently improve cost efficiency for businesses.