Could you fall in love with a chatbot? That’s exactly what Joaquin Phoenix did in his portrayal of Theodore, a disaffected writer going through a painful divorce in the 2013 movie, Her. Marketed as set in the 'slight future', the film explores themes of love and relationships among people and Conversational AI (CAI) chatbot technology.
The film’s flaw is that this level of interaction between human and bot is not likely to happen for decades to come. However, today's conversational AI, as you may have experienced with Amazon Alexa, Google Assistant or Apple's Siri, is now capable of offering tangible benefits to companies and their business functions, including HR Service Delivery. That's because conversational AI chatbots are now capable of answering domain-specific employee questions 24/7, enabling HR Services teams to augment their service delivery capabilities, while eliminating much of the repetitive and mundane work of service management.
To help you get up to speed with this new and exciting technology, that looks set to transform the way HR interacts with its employees, we created The Ultimate Guide to Conversational AI Chatbots for HR. In this all-encompassing guide you will learn:
- what a Conversational AI chatbot is, how it developed over time, and its key benefits to organizations and HR Service Management
- how AI chatbot technology can be applied to answering employee questions
- how to compare and contrast a rule-based chatbot with a conversational AI chatbot
- how to design and build a conversational AI chatbot experience for your employees
- the metrics you need to have in place in order to measure the effectiveness of your AI chatbot, reveal employee question insights, and continually improve performance
- the key issues around AI ethics, bias and trust, and how you can take steps to mitigate these concerns
Let's start with the many benefits of AI chatbot technology.
Benefits of Conversational AI Chatbots for HR Services Leaders, Their Teams and Employees
“By 2022 70% of white-collar workers will interact with conversational platforms on a daily basis.”
Gartner Top Technology Trends Driving the Digital Workplace, 2019
The benefits of Conversational AI for Contact Support functions like HR Services include resolving queries on the spot and freeing up Call Agents' time. This was seen during the COVID-19 pandemic as Laurence Goasduff, PR Director EMEA and India at Gartner points out in 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020:
“AI is starting to deliver on its potential and its benefits for businesses are becoming a reality. During the pandemic, AI came to the rescue. Chatbots helped answer the flood of pandemic-related questions, computer vision helped maintain social distancing and machine learning (ML) models were indispensable for modeling the effects of reopening economies.”
Other AI chatbot benefits include:
Scalability
If your business is facing a crisis, like the COVID-19 pandemic, you’ll have an increasingly large number of employees who need your help. So either you have the technology and processes in place to absorb the additional workload, or you’ll have to hire more people and spend time and energy on training and managing them. Kane Frisby, Chief Operating Officer at Dovetail Software explains:
“By helping your team cope with a sudden and overwhelming number of employee questions, inquiries and requests brought about by a crisis such as the COVID-19 pandemic, Conversational AI chatbots can augment your HR Services capabilities, helping you form HR Service Delivery “Superteams” that are better able to satisfy your employees’ needs.”
Chatbots can respond to a large number of simple inquiries from your employees and scale indefinitely, unlike human teams. They are available 24/7 every day of the year, which means you can inexpensively scale your HR Services support operations and provide a better service by having immediate answers around the clock for common inquiries.
Increased Operational Efficiency
Greater efficiency in HR service delivery as HR Service teams are able to better respond to sudden workload increases due to an emergency crisis such as the COVID-19 pandemic.
Improved Employee Experience
AI chatbots provide more engaging employee interactions than traditional chatbots, providing the potential for ‘personalized’ conversational experiences.
Improved Service Quality & Availability
AI chatbots provide 24/7 service, meaning employees can get answers to questions anytime of the day or night. This means higher levels of employee satisfaction, as they receive a more intelligent and responsive service from an AI chatbot, when compared with a traditional rule-based chatbot.
Reduced HR Workload Volume
Using natural language processing (NLP) capabilities, an AI virtual assistant chatbot absorbs employee questions, requests and inquiries, rapidly searching a range of data sources, including the HR Knowledge Base, FAQs, and HR policies, providing answers to employees without recourse to HR Advisors.
When employees do contact the contact center, AI-enabled systems empower HR advisors with faster, more accurate information than they could get using traditional systems, better equipping them to respond.
Freeing Up of HR Time
Freeing up of HR time for more strategic activities, as the AI chatbot can help when HR Advisors aren't available or are busy with more difficult to answer complex questions
To understand how these benefits can accrue to HR, it is helpful to gain an understanding of what Artificial Intelligence is. Read our blog post What is Artificial Intelligence? to learn the most important artificial intelligence concepts behind AI chatbot technology.
Let's define what a chatbot is before moving on to AI chatbots.
What is a Chatbot?
Unlike the image above, a chatbot does not look like a robot. Instead, a chatbot is software that is capable of conversing with users through a chat interface, usually via a desktop or mobile device. In most cases, the chatbot will greet the user and invite them to take some action like asking it a question. When the user replies, the chatbot will parse the question to determine the user’s intent. Finally, it will respond to the user in a consecutive manner, either providing information or asking for further details before ultimately answering the question. The best chatbots are able to continue this back and forth in a natural way, within the scope of what the chatbot is designed to do. In this way, they create rapport with the user, making the user feel understood and helped, without pretending to be human. The most common chatbots are textual chatbots, the kind you would interact with in a chat pop up window on a website, or through a messaging app like Facebook Messenger, Whatsapp or Slack. However, you can interact with some chatbots through your voice, such as with Amazon Alexa, Apple Siri and Google Assistant, which are three examples of audio-based AI chatbots, often referred to as virtual assistants.
There are many other terms that refer to a chatbot, including Virtual Assistants, Chatterbots, Artificial Conversational Entities (ACE) and Talkbots, and they have a history stretching back to the 1960s.
The word chatbot is derived from the words “chat” and “robot”. Google Dictionary defines a chatbot as a computer program designed to simulate conversation with human users, especially over the internet.
A Brief History of the Chatbot
ELIZA, was the first chatbot built in the 1960s by Joseph Weizenbaum, a professor at MIT. Since then there have been chatbots named Parry (1972), Jabberwacky (1988), Dr. Sbaitso (1992), A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) before the latest, most sophisticated AI versions, Siri (2010), Cortana (2014) and Alexa (2014).
Today, there are two types of chatbot:
- rule based
- and AI based conversational chatbots, or digital assistants
With the latest advances in artificial intelligence, companies are moving towards chatbots that are capable of offering conversational experiences that can handle natural, complex sentences from users.
However, rule-based solutions are still relevant to solving HR Services challenges, and should definitely be evaluated along with AI chatbot solutions, so let’s delve a little deeper into rule-based chatbot technology in order to compare and contrast with an AI-powered chatbot.
What is a Rule-based Chatbot?
Rule based chatbots use a series of defined rules to manage and answer user questions. To accomplish this, the Chatbot Designer maps out anticipated employee-HR conversations in a flowchart within a chatbot builder. In an organizational setting, these predefined rules enable the chatbot to resolve employee questions. A rule-based chatbot offers a user experience similar to that of a telephone IVR tree, where the user picks from a hierarchical set of menus to arrive at their topic of interest.
While a rule-based chatbot can answer very simple or complex questions, they are not able to answer any questions outside of the predefined rules, which is one reason why companies are evaluating the use of AI chatbot technology.
What is a Conversational AI Chatbot?
Unlike a rule-based chatbot that follows a series of predefined rules, an AI chatbot is able to respond more intelligently, using Machine Learning techniques such as NLP algorithms that understand the semantic meaning, or intent, of text and voice. While rule-based chatbots are easy to build, and can get basic tasks done efficiently, Conversational AI (CAI) chatbots offer customers, or employees, a more natural communication experience that can answer a broader range of questions.
As previously mentioned, some examples of AI chatbot technologies are virtual assistants like Amazon's Alexa and Google Assistant, and messaging apps, such as WeChat and Facebook messenger, or Apple’s Siri, a virtual assistant that is part of Apple’s iOS, iPadOS, watchOS, macOS, and tvOS operating systems.
Key AI Chatbot Concepts
The building blocks of AI chatbots are Intents, Entities and Dialog. An understanding of how these building blocks fit together to allow chatbots to provide useful answers to the end user is key to creating successful employee conversational chatbot experiences.
Intents
The AI chatbot must first determine the intent of the employee’s question, in order for it to respond correctly. To do this, AI chatbots use Natural Language Processing (NLP) and Natural Language Understanding algorithms to parse the sentence (e.g. the employee question) and understand its meaning. Employee questions such as ‘What type of pension plan does the company offer?’ or ‘Does the company offer health insurance?’ can be interpreted by the chatbot and the employee’s intent identified, making it possible for the chatbot to respond to the question it is being asked by matching the intent to an answer. Two intents are illustrated below for #greetings and #COVID_Vaccine_Treatment.
Greetings intent
Employee questions that have a COVID-19 treatment intent
Entities
Entities capture specific values in the user input. In the example below, the chatbot is able to detect that the employee’s intent is to find out more information about treatments for COVID-19 and that specifically, the employee wants to know whether there is a cure. By predefining cure as an Entity called ‘@Cure’ in the AI system, the AI Chabot has been able to interpret the employee question correctly.
Dialog
The dialog component enables the chatbot to issue a response to the user based on understanding their intent and the specifics of their request which have been captured through predefined entities.
Context variables (enables a chatbot to memorize information during a conversation)
In addition to Intents, Entities and Dialog, there is an important function called context variables that enables a chatbot to memorize the important parts of an employee’s question, in order to continue to respond to the employee during a conversation.
When follow up questions are asked, the chatbot can use a context variable as a way of memorizing information that it requires to answer the next question, without asking the employee to repeat the same information.
Let’s illustrate how Intents, Entities, Dialog and context variables work together to respond correctly during an employee-HR conversation.
Example Employee-HR AI Chatbot Conversation
Domain: Stock Options
Employee Question: Does the company offer the opportunity to buy some of its shares?
Chatbot Answer: The company provides a share option scheme. Employees have the right to buy a certain number of shares at a fixed price, some period of time in the future.
(Employee question intent: employee wants to find out more about buying company shares. Place the “shares” entity into memory)
Employee Question: Is there a reduced price for employees?
Chatbot Answer: Share pricing options for employees can be found by clicking on this link…
(Employee question intent: employee wants to pay a reduction on company shares compared to the market price)
In the above conversation the chatbot has understood the intent of the employee’s question and, from the first question to follow up question, has ‘remembered’ the employee is talking about “shares”. In so doing, it has been able to recognize the intent of and answer the second question. The chatbot has simulated a human who has listened, understood and answered a question during a conversation with another human.
During a conversation with a chatbot, an employee may wish to speak with a human. The idea of AI and humans working together in this way, and in so doing, maximizing their strengths and minimizing their weaknesses, is known as Intelligence Augmentation. HR Advisers working with AI chatbots to provide services to employees, is an example of Intelligence Augmentation.
Intelligence Augmentation (IA): Human and AI Working Together
Combining the strengths of humans and AI is known as Intelligence Automation (IA) or augmented artificial intelligence. The Institute for Electrical Engineers, a professional body with the mission to advance technology for humanity, provides a definition.
“Augmented intelligence is a subsection of AI machine learning developed to enhance human intelligence rather than operate independently of or outright replace it. It’s designed to do so by improving human decision-making and, by extension, actions taken in response to improved decisions.”
IEEE (Institute of Electrical and Electronics Engineers)
While Gartner, in Top Trends on the Gartner Hype Cycle for Artificial Intelligence (2019), define augmented intelligence as follows:
“Augmented intelligence is a human-centered partnership model of people and AI working together to enhance cognitive performance. It focuses on AI’s assistive role in advancing human capabilities.”
Both definitions support the idea of using AI technology to enhance and strengthen the capabilities of humans, rather than replace them. This is exactly what Conversational AI chatbot technology aims to do for HR Services teams, as it supports HR Advisers by answering the repetitive, banal and oftentimes boring questions, freeing up their time for more complex employee questions and inquiries that are more satisfying to resolve.
AI Ethics, Bias and Trust
To develop and use AI systems responsibly, ethical issues inherent in AI must be considered. That's why it is important to select an AI Chatbot partner company that has a realistic view of their systems and their capabilities and are aware of the different forms of bias that could potentially be present in those systems.
AI & Bias
We have already stated that the quality of AI decision making is only as good as the data it is fed. We therefore have to be aware of systematic bias when building AI systems. This means making sure that the data that we input into our AI systems does not contain bias, or that we are able to adjust for that bias to ensure we’re not misrepresenting a population by using unrepresentative data. For example, if the AI chatbot is trained on data that does not fully represent your employee audience, such as only using data from white males over 40, negative aspects of bias are likely to occur. In this particular case, the AI model would be trained to answer questions for a white male group, neglecting audiences, such as women, Millennials, certain ethnic groups and the transgender population. This could lead to the AI chatbot preferring certain groups over others in its responses.
However, it’s important to remember that Machine Learning techniques are inherently biased. It is due to this bias that the AI system is able to use mathematical algorithms to search for patterns in the data to draw the most probable conclusions from the data. One solution is to modify the data we feed into the AI, through a technique called ‘data augmentation’ to enable less biased data. We still need to be careful as we know that the humans who input the data into the AI system are subconsciously biased.
The bias problem is complex and it is still an active area of research. However, by being aware of bias during the AI system building process, developers can avoid unintentionally creating AI systems that have a negative rather than positive impact. A developer that uses effective training data and performs regular tests and audits, can guard against introducing bias into the results of AI applications. In doing so, an AI chatbot can be built that is trusted by employees and HR.
AI & Trust
A person feels they are entitled to know when they are speaking to a human and when they are speaking to a chatbot. As many conversational chatbots today are indistinguishable from human beings (especially during short conversations), a lack of trust in AI systems can be exacerbated by lack of transparency. Discovering you are speaking to a virtual assistant instead of a human being can be unsettling. Trust is key in developing useful, successful AI systems.
That's why you should ensure that your AI chatbot partner company uses the following guidelines in order to develop an AI solution that instills trust in your end users:
- Transparency - people should be aware when they are interacting with an AI system and understand what their expectations for the interaction should be
- Accountability - developers should create AI systems with algorithmic accountability, so that any unexpected results can be traced and undone if required
- Privacy - personal information should always be protected
- Lack of bias - developers should use training data that is representative in order to avoid bias, while carrying out regular audits to detect any bias that may be creeping in to the AI application. Remember that every piece of information you consume whether it be news articles, photos or videos shared on social media, or advertisements has been created by someone, and their motivations (and biases) may be different from yours.
How to Design and Build a Conversational AI Chatbot for HR Services?
The following is a 5 step guide to designing and building AI chatbots that are capable of answering employee questions effectively while providing a great conversational experience.
1. Define Your Objectives
What is your AI chatbot purpose in virtual life? Is your main aim to use AI chatbot technology to answer your most frequent employer questions? Or do you want to take the drudge work of answering repetitive, dull questions away from your HR Services team, improving their experience at work? Will you go narrow and focus on one area of HR Services, for example, employee benefits questions, or go wide, and use the AI technology to answer questions across a broad range of topic areas?
The answers to these questions will determine how to proceed with the introduction of AI to your HR Services team.
2. Understand Your Audience
Never has there been a more diverse workforce in terms of age range, education and employee behavior. A newly onboarded Gen Z employee will have a far different outlook on life and work to that of a Gen Y. Millennials now make up the largest part of the workforce and their expectation is that workplace technology matches that of the consumer technology they interface with, for example, Facebook, Google Search and Amazon Alexa.
By researching and defining the needs of each segment of your workforce, you will be able to feed this into the design of the AI chatbot experience. For example, if your workforce is made up largely of Gen Z and Millennials, your chatbot may have a more informal tone and personality.
3. Design the Conversational Experience
If it looks like a robot, sounds like a robot and responds like a robot, then it most probably is a robot. A chatbot that sounds like a robot will not offer the kind of human conversational experience that will engage your employees.
That’s why you need to design great conversations. So what does a great conversation look like? Well, in the real world, a conversation needs a balance between talking and listening. When you walk away from a great conversation you feel engaged and inspired, or that you’ve made a real connection with someone, or you’ve been perfectly understood and that you haven’t wasted your time or become bored, and of course, you haven’t been offended. Let’s look at how a great conversation is achieved.
Listen
“Most of us don’t listen with the intent to understand. We listen with the intent to reply.”
Stephen Covey, author of The 7 Habits of Highly Effective People
Listening is important. Stephen Covey, one of the most successful business thought leaders of the 20th century, understood the importance of listening if you want to be effective. And it was Calvin Coolidge, president of the United State (1923 - 1929) who said “No man ever listened his way out of a job.”
As mentioned above, by using context variables in your AI chatbot conversation design you can mimic the human ability to listen.
Learn
“Everyone you will ever meet knows something that you don’t know.”
Bill Nye, popularly known as the Science Guy on American Television
Every conversation with an employee is an opportunity for your HR team to learn. Undoubtedly, employees will ask questions you’ve never thought of before, or even have the answer to. That’s OK, as humans we take new information onboard and learn how to solve new problems. An AI chatbot can learn too, through the process of ‘training’, a key concept of AI covered in our blog post What is Artificial Intelligence?
Ask Open-ended Questions
Ask open questions and avoid Yes/No answers. This is because a Yes or No answer to two questions that have the same intent could be problematic. For example:
Employee: Are the COVID-19 masks provided free of charge?
Chatbot: Yes they are
Employee: Do I have to pay for COVID-19 masks?
Chatbot: Yes they are
The intent of both questions above is the same, however the ‘Yes they are’ answer looks bad in the second instance. The following answer is far better:
Employee: Do I have to pay for COVID-19 masks?
Chatbot: COVID-19 masks are free
Incorporate Part of the Employee’s Question into the AI Chatbot's Response
Try to incorporate part of the user’s question in the chatbot response. The employee will feel more understood and the chatbot will be perceived as more intelligence as a result, while giving the impression of showing empathy.
Provide Succinct and Accurate Answers
Rather than responding to an employee with the entire terms and conditions of a healthcare policy in the chat window, linking to the right page with an HTML clickable link would be more appropriate.
Remember ‘It’s Not About You’
Remember, ‘it’s not about you’, so focusing on your chatbot's listening and understanding capabilities and showing empathy with the employee are key. After all, great conversationalists are interested in other people.
Give the AI Chatbot the Right Tone and a Personality
Whenever you design a chatbot you should take into account its tone and personality. You can do this by considering the audience and purpose of the chatbot and then adjusting the chatbot conversational skills accordingly. For example, if you are targeting a younger demographic, jokes and emojis might be appropriate. Much like you would if you were to train a human HR Adviser, you want to ensure the chatbot issues responses that are appropriate for the circumstances, whether a formal or informal tone is in order. Consider the welcome message, “Hello. How can I help you?”, this can be improved by saying something like:
Hello, My name is Helen and I’m a chatbot here to assist you with questions, inquiries or requests, you may have about benefits, healthcare and COVID-19.
This is a much more user friendly welcome message for the employee. Let’s see why.
First, we have given the chatbot a bit of personality by giving it a name. It’s no longer a random piece of software. It’s Helen.
Next, we are transparent with the employee that it is a chatbot. This is important because it sets the right expectation with the employee. We want the chatbot to be human-like, but we don’t want to falsely pretend that it’s actually a human, as this may lead to disappointment.
Finally, we define the scope of the chatbot by guiding the user on what they might be able to ask. The worst thing a chatbot can say is a wide open, “What can I do for you?”. It’s okay as a reply later on in the conversation, after you already declared the chatbot’s scope. But a “Hello, What can I do for you?” is an invitation to ask the chatbot anything, and that will inevitably lead to disappointments. No matter how thoroughly we design the chatbot, we are not going to make it capable of answering every question it will receive. The same is true if we were training a human HR Adviser. But even more so for a chatbot.
4. Determine the Conversational AI Chatbot Metrics & Analytics
When introducing an AI chatbot to your HR Services team and employees, you will have to remember it will need ongoing attention. Like any new employee it needs support and ongoing training. That’s why you will need metrics in place that will help you fine-tune your chatbots performance.
By analyzing these metrics by individual conversation, you can check the chatbot’s performance. As you correct mistakes made in each conversation, the model (behind the chatbot) learns, ensuring chatbot performance improves over time.
Metrics include:
- Clusters supported: The number of employee questions and answer pairs your AI chatbot is capable of responding to.
- Questions per cluster: For each cluster or topic, how many variations of questions is your chatbot able to answer?
- Accuracy rate: Within the clusters and questions, how often is the AI chatbot giving a correct answer? Over 80% accuracy would indicate high performance.
- Total number of users: How many employees talked to the chatbot?
- Daily usage: Measures how frequently your chatbot is being used.
- Total number of engaged users: Engaged users who have repeated conversations with your chatbot on a daily, weekly or monthly basis. The engaged user sees the value in using the chatbot and are happy to keep returning to using it.
- Goal Completion Rate (GCR): Has the employee obtained an answer to their question that has sufficiently satisfied their needs.
- Fallback Rate (FBR): Like a human HR Adviser, an AI chatbot may not know the answer to the employee’s question. The FBR shows you whether the chatbot is able to understand the employee’s request and provide a relevant answer. The higher the Fallback Rate, the lower employee satisfaction will be, and it signifies the bot needs more training.
- Chatbot to Human Transfer: At some point in the conversation, the employee may wish to be transferred to a human HR Adviser, if they feel they need a comprehensive discussion. The key is optimizing the conversation experience so that the handover occurs at the optimum point of the chat.
- Conversation duration: The length of a chatbot conversation should be long enough to solve the employee’s problem, but short enough to prevent them from giving up.
- Hours in HR Services Time Saved per Month: As the chatbot frees up HR Adviser time, you may find that you can reallocate an Adviser to higher value activities.
- Increase in HR Services Opening Hours: A chatbot doesn't sleep. By turning it on, you can become a 24/7 operation and measure when employees are using the service.
5. Develop, Build & Deploy the AI Chatbot Solution
There are a number of areas that need to be considered when developing the AI chatbot solution:
Development Environment
AI chatbots are built in conversational chatbot development platforms such as Amazon Lex, IBM Watson, Google Dialog Flow, or the Microsoft Azure Bot Framework, in order to take advantage of the computing power and AI capabilities, such as the latest NLP algorithms, that these Big Tech companies offer chatbot developers.
Chatbot UX Conversation Configuration
As detailed above, using your conversation design in conjunction with Intents, Entities and Dialog, you can design the conversational experience you want for your employees.
Thanks to advances in technology you can build AI chatbots without coding. In fact, anyone can build and customize an AI chatbot using intuitive and easy-to-use platforms. This is because ‘under the hood’ of the AI chatbot platform, custom AI using NLP and NLU understands your employee’s intent, enabling you to train an AI model to respond appropriately, which means you can start building your first conversational AI chatbot after a short 2-hour training.
Chatbot Setup options
Options include a standalone chatbot that provides exceptionally fast customer support, answering employee questions with natural language responses.
Alternatively, you can integrate the AI chatbot with your live chat or rule-based chatbot window, helping to deflect frequent issues, freeing up time for your HR Advisers to spend on more complex, interesting and satisfying work.
For Better Results, Integrate the AI Chatbot with Your Knowledge Base
By connecting your AI chatbot to a Knowledge Base you can make it smarter and significantly more useful to your employees. This is because your chatbot will now be able to extract insight and answers to questions from the Knowledge Base.
The diagram below shows the difference in Short head and Long tail questions.
The chatbot still handles the whole interaction with the employee, but it will outsource long-tail questions to the Knowledge Base.
AI technology can be used to create AI enterprise search capabilities that break open data silos and retrieve specific answers to your employee questions while analyzing trends and relationships buried in enterprise data. Using AI search you can extract value from unstructured data and documents and your AI-enabled chatbot will be able to answer long tail questions dynamically, pulling knowledge directly from the Knowledge Base.
Add Voice Recognition (Optional)
You can give a voice to your conversational AI chatbot by integrating it with Speech-to-Text, a service that converts written text into natural-sounding audio in a variety of languages, and Text-to-Speech, which converts audio and voice into written text for quick understanding of content. The combination of these services is commonly known as automatic speech recognition, or ASR.
ASR uses deep learning functionalities to convert speech to text, along with natural language processing (NLP) to recognize the intent of the text, which enables your AI chatbot to offer your employees a highly engaging user experience and life-like conversational interaction. Automatic Speech Recognition uses the same deep learning technologies that power Amazon Alexa, Google Assistant and Apple’s Siri. By adding this service to your chatbot you can quickly and easily build sophisticated, natural language, conversational bots (“chatbots”).
Additionally by training an Automatic Speech Recognition Model on domain-specific employee question language, your AI chatbot will adapt to the terminology of your specific domain. For example, a general question that a base model will comprehend is “What is an HR department?”, while a trained model can learn to interpret a domain specific employee question such as, "What is the company’s Highflyer Rewards Program?".
Deploy the AI Chatbot for Employees
You can integrate your chatbot and deploy it to a channel of your choice, including company website, Facebook Messenger, Slack and Microsoft Teams.
The employee will be able to type in their message in Slack (for example), and that input will be sent to the AI conversation engine, which will provide a response and send it back through Slack, where it will display it to the employee. So you could have the same assistant deployed over multiple channels, allowing employees to interact with the same “engine” from different interfaces.
If you use the Automatic Voice Recognition Service your employees will be able to talk to your chatbot through the phone rather than a website or a messenger.
Conclusion
Today’s AI chatbot might not be advanced enough for you to fall in love with it on a personal level, but recent advances in Machine Learning algorithms that can mine big data, including unstructured data, to capture its meaning, along with the exponential growth in computational power, such as IBM’s Summit or Amazon’s AWS services, mean AI solutions are now capable of delivering real business results for organizations.
In the case of HR, we have seen how Conversational AI chatbot services can be designed and built to answer employee questions in a human-like conversational way that engages employees, while offering HR leaders a cost effective and efficient solution that can augment their HR Services team's ability to respond to employee needs.
If you would like to find out more about how conversational AI chatbot technology can help improve your HR Services' performance, please get in touch.
