How to Implement Knowledge AI for Automating Repetitive Employee Queries

‘What’s the status of my request for a new laptop?’

A help desk agent who has just joined a call is as clueless as you are.

It’s a long wait time for the requester until the agent searches for ticket history and returns with the correct status info.

What else?

• Employees are less predictive about when they can have the right device to work at their best.

• Employee value is reduced.

• Disengaged employees cost organizations lost productivity.

Instantaneous, real-time, yet factual information is a tough challenge for organizations, forcing them to struggle to meet employee expectations.

When thinking about employee resilience, tech-enabled capability building is the key to creating value for employees and businesses.

It’s a win-win for organizations when they focus on performance and people through tech investment. McKinsey & Co. predicts $1 billion in economic profit for organizations with a perspective of employee resilience and development.

However, organizations have long struggled to deploy the right tech-enabled capability and resorted to a predefined or rule-based chatbot to facilitate employee support.

A chatbot with default replies can help with problems with known challenges 一 but with lots of effort from the users and help desk agents.

Knowledge AI and conversational AI can come together to speed up employee support, reduce the time to search for information and improve CSAT (employee satisfaction).

In this article, we will talk about why it is time to switch to Knowledge AI for employee support rather than relying on regular chatbots for repetitive employee support and value addition.

How often do you look at the downsides of enterprise search systems or default reply-based chatbots?

Maybe never.

Information is scattered across various intrasystem and external databases. So, data lying in silos can be complex to find quickly and, more often, result in workspace productivity issues.

The pain points with an enterprise search system, such as chatbots, are strikingly significant.

• Incomplete insights into employee questions

KB articles are often limited in providing complete insights into employee questions.

• New information is often available but requires manual effort to update the knowledge bases, a challenge for most organizations with limited resources.

• Information surfaced in a question-answering window has a list of suggestions in the form of articles, requiring users to dedicate extended time for knowledge discovery.

• More time to build KBs

Building knowledge bases for search systems or FAQs takes a huge chunk of time, bypassing the need to make changes to the existing content in KB articles and leading users to struggle to resolve problems, even with self-service functionality.

• Less integration flexibility with content systems

Traditional chatbots or enterprise search systems offer limited integration capabilities with content resources spread across enterprise-wide systems, hence limited knowledge discovery to offer the help employees need to resolve problems autonomously.

• Limited problem-solving capability with built-in VAs

Virtual agents or chatbots built within the service desk or help desk systems can offer service desk assistance but are limited to ticket creation and escalation to the various stages to tier agents. There is no straightforward or real-time autonomous help with many built-in chatbots.

• Lack of personalized conversational experience

Though employees want independent problem-solving capabilities, the human touch adds to a great conversational experience, most miss out on this opportunity with rudimentary chatbots or default-reply-based chatbots. With end-to-end conversations sounding interactive, users tend to adapt fast and connect more to solve the workspace problems autonomously.

Large language models are AI-powered natural language models with huge corpora of datasets.

Over-the-shelf LLMs such as ChatGPT or GPT-4 contain generic data to answer generic questions.

For example, when you combine your enterprise search systems or chatbots with LLMs, your employees can retrieve answers that everyone else can find for issues such as headphone sound troubleshooting, and application installation, to name a few.

Similar IT or employee support problems other enterprises have can fetch solutions to some length from LLM-powered chatbots.

Regarding specificity, meaning if users need a domain-specific solution to problems, LLMs alone cannot offer help.

For example, An organization using a project management tool like Notion has specific data related to various sales operations, marketing, etc.

An employee wants an update about a client’s project, say, a data migration project. Over-the-shelf LLM-powered chatbots can limit this expectation. On the other hand, LLMs integrate generic information, which can be wrongful, causing hallucinations and misinformation.

Knowledge AI integration can be highly efficient to improve search functionalities and knowledge discovery to help with effective employee support.

Knowledge AI, as the name suggests, refers to an information discovery capability built on top of generative artificial intelligence and conversational AI technologies to improve search performance through personalized and summarized knowledge discovery to enhance employee productivity.

Often, structured FAQ-based knowledge falls short of employee support expectations to deliver help with common and repetitive questions.

Knowledge AI can fetch data from unstructured data grounded in KB articles within the platform and offer NLP responses to queries through intent detection, context, and entity extraction.

Knowledge AI offers custom NLP responses beyond pre-defined rules or keyword-based search results compared to traditional search platforms or chatbots.

As a result, Knowledge AI offers more personalized and conversational responses that help employees resolve their problems at scale.

Workativ brings Knowledge AI search integrations for its conversational AI platform to allow users to leverage the benefits of large language model properties or generative AI capability to enhance the speed of capturing information, producing personalized and intent-based information for the repetitive and common user queries.

Workativ provides a ChatGPT-like search experience that is more personalized and conversational for autonomous problem-solving in the enterprise setting.

“Retrieving information from several knowledge bases or KB sources through connector-based integrations across disparate systems, such as SharePoint, Dropbox, Google Drive, Notion, etc., helps employees quickly find context-aware information and resolve common issues more efficiently”.

From a ChatGPT or LLM point of view, finding answers to queries is best for learning new skills or knowing something to solve a problem in an ongoing process.

As we said earlier, domain-specific issues need custom answers employees can use to automate and resolve problems independently.

Knowledge AI removes friction in the self-service capabilities, found with rule-based search systems or chatbots by tapping into various knowledge articles uploaded through Workativ or Internal KB, External KB, and Website KB.

It means knowledge search is not restricted to more than just a few article bases. Instead, it has an extensive language model database to augment the semantic search and deliver accurate and coherent generative answers to NLP-based common queries.

With Workativ Knowledge AI, users can remove the need to train the model as needed with custom solutions to be built with LLM models or GPT models from scratch.

Neither does it need fine-tuning nor prompt-engineering. All it takes is upload content to train, modify, or delete content so that users can always get up-to-date information and accelerate search performance via Knowledge AI.

Also, Knowledge AI provides a closed domain to upload content and build your specific large language model to offer domain-specific answers with more robust security checks to avoid misinformation like the open-source training method, making it less vulnerable to hallucination and delivering biased and wrong information.

Take one example here. For security reasons, a user needs to know the VPN settings of his device while working from home. It could hamper his work if he doesn’t get the information in time.

A traditional search system could only provide articles about VPN setting issues. Unaware of which piece could provide an apt and accurate answer for the self-service, he might comb through lengthy articles and handle information in a silo.

However, Knowledge AI provides straightforward, accurate, summarized, and personalized versions of responses for any common and repetitive queries.

Using Knowledge AI-powered self-service built in a conversational AI chatbot, a user can capture only necessary information presented in a summarized manner to help resolve the VPN setting issues.

As McKinsey says, tech-enabled employee development and resilience can add to organizational growth. It is right that giving your employees the ability to handle queries steadily through Knowledge AI is a phenomenon.

Implementing Knowledge AI is a frictionless way to nurture employee experience, reduce friction from the self-service platform, and give more flexibility to allow them to address repetitive and common queries more independently and conveniently.

Workativ conversational AI is a cost-effective solution for organizations to harness the power of Generative AI and large language models, helping users fetch summarized, accurate, and relevant answers in no time.

To gain the ability to level up your employees and optimize your bottom-line expenses, Workativ Knowledge AI is the right tool to reach your business objectives.

Schedule a demo today.