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2024 Guide to Conversational AI for the Employee Experience
Artificial Intelligence
Employee Experience

2024 Guide to Conversational AI for the Employee Experience

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Published on Mar 8th, 2024

What is Conversational AI?

Conversational AI is a type of artificial intelligence (AI) that can simulate human conversation. It is made possible by natural language processing (NLP), a field of AI that allows computers to understand and process human language. NLP is used to analyze the meaning of text and speech and generate responses appropriate and relevant to the conversation. 

The enterprise conversational AI platform market is defined by Gartner® as “the market for software platforms used to build, orchestrate and maintain multiple use cases and modalities of conversational automation.” 

How Does Conversational AI Work?

Conversational AI systems are trained on large amounts of data such as text and speech. This data is used to teach the system how to understand and process human language. The system then uses this knowledge to interact with humans in a natural way. It is constantly learning from its interactions and improving its response quality over time. 

Conversational AI as a Customer

Conversational AI has revolutionized the way we interact with technology, providing countless benefits for both customers and employees. When it comes to the consumer experience, conversational AI enables seamless and personalized interactions. Used frequently to manage customer service inquiries, consumers have long been accustomed to using chat to find information, get their queries resolved, receive product recommendations, and even make purchases. 

Conversational AI for Employees

The power of AI is not limited to the customer experience, however. Employees' needs are not dissimilar to those of consumers, particularly when finding and accessing information.

Employee experience (EX) is defined as the sum of all an employee's interactions with an organization, from the moment they schedule their first interview until they conclude their exit interview. Experts such as Josh Bersin consider employee experience to be a “company-wide initiative to help employees stay productive, healthy, engaged, and on track.” His definition goes on to explain that employee experience has multiple layers, goes well beyond IT and HR and is now an “active strategy” that should be defined and designed not just monitored. 

There are several tools across the enterprise technology landscape that can help improve the employee experience including intranets, communication platforms, and more recently AI Assistants, sometimes referred to as Copilots, that leverage conversational AI technology to achieve outcomes that address common employee frustrations. 

Using conversational AI at scale can have many benefits for the workplace such as: 

  • Reduced costs and increased productivity due to operational efficiency through automation  

  • A better employee experience with personalized and targeted content specific to each employee   

  • Seamless knowledge discovery by providing a 24/7 single source of truth for FAQs (i.e. what are the company holidays? When is benefit enrollment?)  

  • Increased adoption of enterprise technology by simplifying access to systems through integrations and conversation-based triggers  

  • Reduction of shadow AI as employees are looking for AI solutions and may resort to unsanctioned or ad-hoc use outside of the organization’s governance. Implementing an enterprise AI solution will deliver employees the experiences they’re looking for under the guidance of IT regulations.  

With such an extensive range of benefits, AI can support the workplace across departments from HR and IT to Sales and Customer Success. 

Conversational AI for Employee Support

Solving Digital Friction

The influx of enterprise technology in the modern workplace introduces a significant cognitive challenge: the cost of constant context switching. As workers navigate a myriad of tech interactions, the brain will struggle with rapid and frequent digital multitasking that leads to a scattered focus. This is just one example of the digital friction that hinders the digital employee experience.  

Digital friction, commonly described as the unnecessary effort an employee must exert to use technology, encompasses all the extra steps to complete routine tasks, toggling between disparate applications to find information, and the complicated workflows that take time away from meaningful work.  

Digital transformation has contributed to the digital friction problem. The host of new apps and collaboration platforms that were deployed to enable remote work caused some negative side effects no one could have seen coming. 

While each individual solution launched was well-suited for helping workers be more engaged and productive, their cumulative effect was a frustrating, disjointed experience that dragged down productivity. There were suddenly too many places to go to find information and complete tasks, and communications were coming from more sources than ever before.  

As a result of the overload of apps, information, and noise, digital friction also began a domino effect that has affected business: 

  • Missing important communications – Employees frequently miss important announcements and time-sensitive reminders. 

  • Delayed approvals – Managers lose approvals amongst other emails, blocking important work or requests from being completed. 

  • Time wasted from context switching – Employees lose valuable time jumping from one system to another to complete tasks or look up common information. 

Guide Attention with an Enterprise Copilot

A solution to digital friction has arisen in the form of guided attention technology which includes solutions that rethink how employees interact with enterprise technology. While digital assistants are not new to the workplace, a new wave of assistants have taken shape with smarter technology, adding a layer of proactivity to the experience.  

By intelligently nudging, surfacing, promoting, and granting access to the right information and actions, AI Assistants act as a copilot for the digital workplace. This is the future of conversational AI; a symphony of proactivity and guided focus that shifts the relationship between the worker and technology. From schedules and priorities to backend workflows, intelligent AI Assistants nudge employees with timely insights, tasks, and suggestions.  

AI for Knowledge Management

Microsoft’s New Future of Work Report summarizes the shift in how conversational AI supports knowledge retrieval explaining, “Knowledge is no longer only embedded in documents, spreadsheets, and text – it is now embedded in conversation and can be served up dynamically through that same medium.”  

With the help of an AI Assistant, employees no longer need to waste time searching through files, systems, and documents to find the answers to their questions. Instead, they can opt for a natural language conversation for inquiries such as “what are the company holidays?” or “what is the maternity leave policy?” In just seconds, knowledge bases, documents, and apps are combed through and summarized to deliver the answer. As described by Microsoft, “LLMs can draw on knowledge generated through, and stored within, different tools and formats, as and when the user needs it. Such interactions may tackle key challenges associated with fragmentation, by enabling users to focus on their activity rather than having to navigate tools and file stores, a behavior that can easily introduce distractions (see e.g., Bardram et al. 2019).” 

Tasks, Workflows, and Employee Support Automation

Implementing conversational AI at work can have significant impacts on the way employees' complete tasks and workflows. While it may seem initially daunting to get started, there are a few steps that may help you with a seamless kickoff. The following outlines key elements to evaluate prior to launching an AI employee bot for the digital workplace. 

  1. Addressing Digital Friction – In order to maximize the benefits of conversational AI the first step requires an understanding of the pain points and challenges within the organization. There are several areas where the symptoms of friction will rise to the level of awareness. Many orgs will go deeper to really understand where they can start to create a better workday and they will look to qualitative areas such as employee journeys, onboarding, line of business functional improvements, knowledge search and so on. They will also look at qualitative metrics that provide tangible ROI on where they can begin to create savings/generate revenue – incident management, ticketing, expenses, workflows, and redundant processes   

  2. Mapping Digital Friction to Systems, Processes, & Tasks Understanding which systems and processes are creating friction, and the direct impact to employees, shows you where to begin. It is easy to start where there is pain, but it may be more impactful to identify low hanging fruit for successful initial deployments – identify and further measure out what will bring the most benefit to employees. This will help measure initial impact and propel employee adoption as they may see tangible benefits sooner.  

  3. Assessing Benefits and Technical Lift – Map the economic benefit, time to value, ownership, risk potential first and then evaluate what data and application access is needed to begin to introduce use cases to employees 

  4. Selecting Use Cases After the first few steps, you can create a real map of use cases. Create a list of potential that describes the actions and activities and what the corresponding downstream effect will be. For example, approvals and reminders are a common use case for conversational AI where we’ve seen the ability to take approval cycles that previously could have taken days to just mere hours. It addresses the impact of the delay, the productivity sink, the time waiting, and decision making.  

Conversational AI For IT

AI redefines IT agility by making it easier than ever before to perform tasks and retrieve information from across the organization. With conversational AI, businesses can deliver novel experiences across the workplace that simplify processes for employees from accelerating ticket deflection with self-service access to policies and FAQS to streamlining IT ticket management by allowing employees to submit service tickets, add comments, close, or check for status updates.  

Conversational AI For HR

Bringing conversational AI to the human resources team helps employees more effectively engage with HR technology, while helping offload the burden on HR staff to navigate questions, so all employees can spend time on more meaningful work that drives fulfillment. An AI-powered chat experience transforms the digital workplace by extracting and summarizing information for employees, returning results in modern rich UI experiences, forms, and more. In addition to finding information, conversational AI also helps employees perform tasks and submit requests using natural language making it easier for employees to quickly access things like their latest pay slip, calendar events, and employee directory information. 

Conversational AI For Comms

Reduce distractions and time-wasting context switching by guiding employees’ attention to important tasks, information, and communications through a conversational interface. Not only can AI be leveraged in conversation to retrieve information, but it can also be used to generate content. While obvious communications use cases include supporting content creation from blog posts to email subject lines, workplaces can also use generative AI for content insights such as summarization, key points, categorization, and sentiment analysis.  

Conversational AI Challenges


Leveraging LLMs at work opens the door to questions around security with the most prominent being: what happens to our employees’ data?  

In today’s fast-past business landscape, organizations stand at a crossroads – to buy, build, or bring their own LLM. It’s a decision fraught with complexity. On one side, the specter of security concerns and the looming challenge of shadow AI demand cautious navigation. On the other hand, the promise of empowering your workforce with groundbreaking tools and access to the forefront of AI innovation beckons irresistibly. Embracing this opportunity means not just staying in the race but leading the charge towards a future where employees are equipped with the most advanced solutions to excel. 

For organizations that opt to buy it is critical to consider how vendors manage data. Some store user data and use it to train their models. Workgrid, for example, does not store data nor use customer’s data to train models. In the case of the Workgrid Assistant, users bring their own LLM, APIs, and data-sources to the Workgrid platform which ensures that your propriety data remains under your control, safeguarding your valuable information and intellectual property while enabling you to implement an intuitive copilot without the costly time to build and manage in-house. 

Language Translation

For large enterprises looking for a global conversational AI solution, language translation remains a challenge. According to Microsoft, “Lack of relevant linguistic and societal context in languages and cultures will impact task level performance for LLMs, for example in handling dialects within the same language family (Hada et al. 2023).”   

Up-to-date Training Models & Misinformation

A challenge amongst those leverage LLMs is ensuring the information generated is accurate. There are a few reasons why an LLM might deliver misinformation, including the age of the model itself. For example, a model trained in 2020 will not know things like who won the 2023 Super Bowl. Should a user ask a timely question, the model will not have the knowledge to answer effectively. In the context of the workplace, AI is only as smart as the information it’s given. If employee information is not organized or properly maintained in the database, and that information is then ingested by an LLM, it’s possible that the results will also be incorrect. 

In other cases of misinformation, prompt behavior can sometimes spur different results. Microsoft reports, “Sophisticated prompt behavior - Seemingly minor changes, including capitalization and spacing can result in dramatically different LLM outputs (Holtzman 2021; Arora et al. 2023)”  

In addition to prompts and training models, there have been cases of AI unintentionally generating false information, referred to as hallucination AI.  When a user prompts AI for information, models use the data they are trained on. But in some cases, AI creates its own sources referring to facts that simply do not exist.

Workgrid’s AI Assistant helps organizations implement AI for employees with added guardrails built in. A series of generative AI apps called “Genie Apps” include built-in prompts to help users understand what can be created with this powerful technology while providing constraints to keep output professional.

Conversational AI vs Chatbot

Proactive vs Reactive

The difference between conversational AI and chatbots is mainly tied to how they handle inputs. Chatbots are a type of conversational AI but not all chatbots are conversational AI. Rule based chatbots use keywords and other language identifiers to trigger pre-written responses and are not built on conversational AI technology. This means these types of chatbots are limited in response to what has been added into the chatbot’s rules. Non-conversational AI chatbots require continual maintenance with text-only commands and inputs to remain up to date and effective, while conversational AI uses inputs and sources such as websites, databases, and APIs to learn. There are a few application examples of conversational AI technology:  

  • Chatbots: often used in customer service applications to answer questions and provide support  

  • Virtual assistants: often voice-activated and can be used on mobile devices and smart speakers  

  • Speech recognition software: used to transcribe lectures, create transcripts of phone calls, or generate automatic captions for videos 

A reactive digital assistant is merely a chatbot; which is most digital assistants. These bots wait for user input, responding to questions only when prompted. In fact, these tools often end up annoying the end user. Think of these simple bots as a basic search functionality from 1999, with a chat interface. Sure, they could be useful, but this type of bot is hardly transformative. In the modern work environment, these deployments end up being another place to go – adding to mounting digital friction. Thus, reactive chats end up failing to improve the employee experience and instead adding to the digital friction already burdening many organizations. 

Another reason for failed chatbot deployment is scope is often limited – and costly. Use cases are often narrowed down tightly to a slim margin of pre-defined functionality, like simple Q&A or order taking. What is even more disheartening is that to make these simple chatbots function properly, a tremendous number of hours are spent on professional services attempting to wire up the bot to operate properly. 

On the other hand a proactive chatbot, increasingly referred to as an "enterprise copilot," is a paradigm shift in the relationship between the worker and technology. These digital virtuosos actively read the room—your schedules, priorities, and backend workflows—and nudge you with timely insights, tasks, and suggestions.

They don't merely sit around waiting for you to come to them to ask questions; they foresee your needs. They guide your attention where it matters most, streamlining your tasks and preventing potential bottlenecks. In doing so, this is an evolution of being a simple tool to becoming intelligent collaborators.

Learning vs Functional

When evaluating types of digital assistant’s another key differentiator between chatbot and conversational AI is the method in which they share knowledge. 

Learning refers to a chatbot that is designed to learn and improve performance over time. It is programmed with machine learning algorithms that allow it to analyze and understand user input and adapt its responses accordingly. Learning chatbots use natural language processing techniques to understand and generate meaningful responses. They continuously gather data from user interactions and use it to enhance their knowledge and conversational abilities.

On the other hand, a functional chatbot is focused on completing specific tasks or achieving a particular goal. This type of chatbot often operates off decision-tree logic and is designed with predefined algorithms and rules to handle user queries and provide accurate, relevant answers or perform specific tasks. Functional chatbots are typically used for customer support, order tracking, FAQs, or any task-based interaction. They may not be able to learn or adapt their responses based on user interactions and typically require more manual management from the product owner. 

Choosing an AI Assistant

Choosing an AI Work Assistant for your digital workplace comes down to a few things but technology is certainly a key factor. There must be a seamless marriage between strategy, culture, and technology. The assistant must work and operate within your tech stack but according to your business and organizational goals. Here are a few key features to look for: 

  • Must be ubiquitous – The ideal AI Assistant lives where employees work and supports use cases across domains and departments. Employees do not need another place to go, and digital workplace leaders do not need more technology to manage across teams. Interactions and activity should flow into employee's work. The best assistants are the ever-present copilots – Batman and Alfred, Iron Man and Jarvis, your employees and their AI Work Assistant – it operates where they are and provides them with a universal access point, so they do not need to log into another application or hunt for information portals.

  • Deliver guided information and value where the employee is – Employees should not need to engage with the bot to receive utility. The AI Assistant should include proactive notifications that deliver “to knows” and “to dos” versus the reactive chatbots of yesteryear. 

  • Understand the user's intent – Conversational AI work assistants differ from classic chatbots by leveraging advanced NLP capabilities and artificial intelligence to create a better relationship between user intent and results.  

  • Rapid building There must be a way to quickly quell friction and add value to users. Oftentimes those are exercises that can classically take months. There are several AI Assistants that are simply chatbots under the cover and require more explicit keyword mapping and pro-code, which takes time. The ideal scenario is that an organization can quickly identify friction points and stand-up rich experiences that become conversational. For example, developing experiences 10x faster using a low code drag and drop editor connected into their individual tech stack. With the Workgrid builder every workflow feature in an app has an intuitive drag and drop builder to customize the functionality of your app. Multiple app owners can collaborate to build and customize each step of the way. 

  • Templates and Data Sources – Conversational AI Assistants should have the ability to quickly connect to the common data sources the enterprise deems critical. It also should be quick to develop and deploy, having the ability to connect to those data sources but also to start from a template that allows technologists to quickly create experiences such as a procurement approval app, travel reminder app, ServiceNow knowledge app, and more to allow you to deliver value to your organization incredibly fast  

Conversational AI Terms You Should Know

LLM – A large language model (LLM) is a powerful language model known for its remarkable capability to comprehend and generate language in a general sense. LLMs achieve this by leveraging extensive data to learn billions of parameters during training, as well as utilizing substantial computational resources throughout their training and operation. The functioning of large language models involves taking an input text and iteratively predicting the subsequent token or word.   

NLP Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. NLP techniques enable computers to understand, interpret, and generate human language, enabling tasks such as language translation, sentiment analysis, and chatbot interactions. 

Machine Learning Machine Learning (ML) is a subset of AI that involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. It is based on the idea that systems can learn from and adapt to data, improving their performance over time. 

Model In the context of AI, a model refers to a representation or a mathematical expression that can mimic or simulate a complex real-world phenomenon or process. It can be considered as an algorithm or a set of rules, trained on data, which aims to make accurate predictions or classifications. Models in AI are created using techniques like machine learning or statistical analysis and are used to solve specific tasks or problems. 

The Future of Conversational AI

The conversational AI Market is projected to reach USD 32.51 Billion by 2028, exhibiting a CAGR of 21.6% over the forecast period. This growth is driven by the increasing adoption of Conversational AI technologies in various industries. 

According to a study conducted by Brynjolfsson et al. (2023), the implementation of a generative AI-based conversational assistant proved to be particularly beneficial for novice and low-skilled workers. The study also found that experienced and highly skilled workers could share their tacit knowledge more effectively with the tool. 

Furthermore, users were found to be 2x faster at solving simulated decision-making problems when using LLM-based search, compared to traditional search methods. This highlights the efficiency and effectiveness of Conversational AI in problem-solving scenarios. 

These early studies suggest that Conversational AI can make a lasting impact in the workplace, enabling expedited problem solving and improved knowledge discovery. Investing in an AI Assistant can be a great starting point for organizations looking to leverage the benefits of Conversational AI. AI Assistants serve as copilots for the digital workplace, providing organizations with conversational AI features that cater to the diverse needs of employees across job roles and departments. 

Workgrid's Conversational AI Assistant

Workgrid's AI Work Assistant connects employees with the information they need, when they need it, through a conversational interface. With robust intent understanding, exception handling, and contextualized answers, the assistant can do far more than a rule-based chatbot. 

The assistant searches across multiple data sources - including enterprise systems, documents, and knowledgebases - to answer employee questions. In addition to answers, the assistant integrates with business applications to surface real-time notifications, tasks, and alerts, allowing employees to take action right within the conversational interface. The AI Work Assistant supports information discovery for employees by: 

  • Delivering answers with relevant recommendations and suggestions using advanced query analyzer and semantic similarity analysis 

  • Ranking algorithms to showcase results based on relevance 

  • Summarizing data points from multiple sources into one cohesive answer using Retrieval Augmented Generation (RAG) 

  • Prompting employees for more information if an intent is understood but the question cannot be fulfilled 

To see the Workgrid in action take a virtual product tour of the AI Assistant or schedule a personalized product demo now.

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