Conversational AI is the way in which machines are built to understand and conduct conversations with people. It is a method for automating conversations to a degree where people get useful information from machines in a manner that resembles conversations between people. It is used for a variety of cases such as virtual assistants for customer service automation and voice automated bots. Conversational intelligence gathers useful information from automated conversations such as topics discussed, things uttered, information requested. Such items are useful for finding various types of data points to give more insight into the nature and capabilities of the conversation conducted.
The goal of Conversational AI is to be similar if not indistinguishable from human conversations. What this means is that users can talk to machines in a manner that is exactly the same or very closely resembles human-to-human communication. Next we will go through the various forms of such systems.
The simplest form is a chatbot. A chatbot is an interface through which the user can obtain information from the machine. The interface is usually in written form (chat) and in many cases the chatbot presents the user information with simple Yes/No type of options. These if-else statements are essentially decision trees where the user selects a certain answer. Upon the selection of that answer the user is given a follow-up question with a choice of answers again.
Such solutions are useful because they are usually operated through a clickable interface. The user is presented with buttons and by clicking on the buttons the user confirms its choices and receives information. It is a quick way of showing info to users. The challenge, however, is that while the user is directed towards the answer, the chatbots at times do not provide a specific answer to the question that the user is looking for.
An improvement upon a chatbot is what's called a Virtual Customer Assistant (VCA). These intelligent agents are not only capable of presenting a multiple choice selection of answers to the user but also understand user intent from free text. Understanding natural language is a significant leap. Many times users are looking to articulate their specific concern to the machine in a similar manner they would do to a human. User has a question and asks that specific question from the machine e.g. “When will I receive my payment from Bank ABC?”. The main drive behind this is that users are looking for a quickest way to get an answer to their specific question.
In our example, the user is looking to understand in hours or minutes how long it takes for the payment to arrive from Bank ABC. Not Bank XYZ or GFD but Bank ABC. And the VCA has to provide this answer in the form of “It takes 6 hours during weekdays to receive a payment from Bank ABC”. In such a situation only the most relevant answer matters and for the users it does not matter if the answer comes from a machine or a human. As long as it is accurate and relevant. Bank of America has built such a VCA called Erica for their customers. In addition Conversational intelligence in the VCA can highlight insightful data about what was discussed in the conversation to improve the customer experience.
One can see how a traditional chatbot falls short in this example. The chatbot might show an illustration of transfer times from other banks or give a link to a self-help article. However, the user needs a concrete answer. Hence, Virtual Customer Assistants with their ability to understand specific intent from free text, are helpful here.
The third Conversational AI category can be defined as an agent assist system. These solutions do not directly interact with users. Rather they help customer service agents in giving accurate and timely answers to users. As an example, such agent-assist systems listen in on what users are telling customer service agents. The system is not responding to users directly. Instead, the system is telling things to the customer service agent that she should tell the user.
One can think of these systems as smart advisors that give advice on what to tell users. It is up to the agent whether to take the advice or not. An example of this system can be a so-called co-pilot mode in live chat products.
The system listens that the user is asking about the location of their package. It immediately makes a query that the user at hand with email XYZ has one package to be received and the estimated time of delivery is 3 days. The system presents this information to the agent and the agent can deliver the information to the user in one click.
The technology that powers Virtual Customer Assistants is called Natural Language Processing (also known as NLP). It’s aim is to teach machines to understand and make sense of human language. NLP is composed of several parts and the parts that are most often used in relation to VCAs is called Natural Language Understanding (NLU) and Natural Language Generation (NLG).
NLU deals with understanding meaning from what users have said. Essentially it is an ensemble of learning algorithms that try to identify, learn various patterns and make decisions or predictions on their own, relying purely on data instances and at times on human input.
We can categorize learning Conversational AI algorithms into two types: classifiers and language models. In order for the classifiers to work (i.e. learn and establish connections), there needs to be an existing compilation of phrases, which are already categorized into groups or classifications. This method requires human input. A common classifier for example is a spam detection system inside your mailbox. There are 2 classes, spam and not spam, which are regularly filled with new inputs either labeled as „spam“ or „not spam“ by the users.
Language models, on the other hand, can do its work of learning and predicting possible next words on a raw text without the assistance of humans. When starting out with a particular machine learning project, there are usually not that many categorized phrases (classifications) available but plenty of raw text data. Unless you are using products with Conversational Intelligence functionality to distill raw text data into insight, you need language models to proceed.
Language model based learning algorithms can start to make connections on how the text is constructed in the specified language, which in turn can be useful later when the classifications need to be formed. Because of this, most modern learning algorithms today are language models. In a way, language models behave like grammar. The available data directs the model to choose the most fitting next word. Those kinds of models are implemented in a wide variety of use cases, for example in intent prediction, machine translation, speech recognition, sentiment analysis and many others.
NLG refers to the ability of the computers to generate text on their own. In the contexts of a Virtual Customer Assistant it means that the computer generates the answer to the question that the user is asking.
For example, if the user asks “Where is my payment?” then the algorithms generate an answer on their own such as “Your payment will arrive in 4 hours”. In most AI solutions, the answers are pre-written. Meaning once the intent match is made, the answer is pulled from the database and presented to the user. NLG makes things more open-ended as the algorithms themselves generate text that will be used as an answer.
Usually NLG algorithms are trained on a vast set of text. The algorithms create answers that are served to users’ intent based questions. The drawback of using NLG in Conversational AI solutions is that the answer can be inaccurate or poorly worded. As most companies want to use their own language and message when communicating with customers, pre-written answers dominate over computer generated answers. One of the recent best known algorithms is GPT-3 developed by OpenAI.
The main differences between chatbots and Virtual Customer Assistants mostly deal with the way in which they have skills to comprehend humans. Chatbots are more rudimentary in their approach.They mostly act like condensed menus of vast amounts of information. They are more like navigational elements that direct the user to a collection of information for the user to choose from.
VCAs in contrast are more like intelligent search engines. The users are approaching them with open ended questions framed in a similar manner as if the users would be asking their friend. The questions may contain slang, there can be typos, emotions, ambiguous terms - essentially all things that describe also a real human to human conversation. As such a VCA brings more comprehensive understanding to conversation than chatbots also because of its ability to analyse data within conversations through its Conversational Intelligence capabilities.
The main problems that Conversational AI solves are related to finding relevant information. It is meant for companies seeking to deploy something that reflects their practical and honest guide to customer experience. Some of the most common use cases are highlighted below. In many areas the solutions can be deployed through text-based mediums (chat, messaging apps, email) or through voice (phone-based automated support, voice assistants) provided by various vendors and startups.
This area is perfect for AI because there are large numbers of users that are looking for answers to similar types of problems. Users are looking for companies to take ownership of their products and services they offer. This repeatability of similar queries makes customer service one of the perfect fits for chatbots and virtual customer assistants.
Siri, Alexa, Google Assistant - all these utilize intent detection, speech-to-text and text-to-speech. These voice operated bots answer general everyday questions for users, deliver them information and allow for performing everyday tasks such connecting to Internet of Things devices (e.g. turning on lights) and ordering food.
Chabots and Intelligent Virtual Assistants are used for booking trips. Many hotel or flight bookings are related to running a specific query and bots are used to gather data automatically and then present the user with a variety of answers. Customers today are increasingly mission-driven and their purchases often are less about the what and more about the who. This applies to purchase decisions across categories beyond travel.
Whenever you use autocomplete in search engines then it uses a selection of algorithms that allow it to predict most common search terms. Spell-checkers also utilize Natural Language Processing algorithms to determine spelling errors and mistakes.
Automated processes run questionnaires on job applicants, ask for feedback on the work experience and provide informational assistants to answer FAQs. These process automation tools enable to onboard and serve a digital workforce with more efficiency and speed.
Next let's dive into the way it actually works. It is composed of several steps.
First there is what we can call the input. It is information that the system needs to have to start processing. For example in the case of a chatbot it can be the button that the user clicked. In the case of VCAs it can be the sentence or the question that the user asked in text format. In the case of a voice operated bot it can be the audio file recording of what the user said.
After obtaining the input, the machine needs to analyze it, to make sense of it. Here Natural Language Understanding is used to make sense of what the machine was asked. In the case of a chatbot it can be relatively simple. We just take the input button and then through a database search determine what we should reply if this button is selected.
In the case of VCAs it is a bit trickier. The challenge lies that we need to understand what the user meant i.e. what is their intent. The difficulty is because users can say the same intent in literally a million different ways. For example if the user is asking a VCA a question like “Where is my money” or “I haven't received my salary” then what they actually mean (i.e. what is their intent) is “How long does it take for the transfer of money from one bank to another”. In such cases the VCA needs to be able to deduct from such two different questions the true intent behind those questions, which is the same.
The third element is dialogue management. Here there are essentially two options. One is to use pre-made answers that are served to the users as a response to their questions. The benefits of this is that the users are getting the same kind of answers when they ask the same questions. Meaning there is no variability in answers to similar questions. In the case of customer service the brand communicates its answers according to a certain playbook or set of instructions.
It is also possible to use NLG for forming answers. In such a way the machines themselves put together words and sentences to provide an answer for the user. There have been advancements made in NLG but it is up to each use case and builder of Conversational AI solutions to determine whether NLG is accurate enough to provide a sufficient level of answer quality to their users.
The fourth element is learning. The way these systems learn can be essentially two-fold. On the one hand we can use reinforcement learning which means that the system gets either a positive or a negative comment to the answer that they provided (e.g. user votes helpful or not helpful to the answer). This negative or positive feedback indicates to the system whether the given answer was correct or not.
The other way is to use humans as AI trainers. In this case human trainers go through the answers that the system gave to users. Human trainer determines for each answer whether it was correct or not. If the answer was correct, the system gets an upvote. If the answer was incorrect, the system gets a down vote and the correct answer is chosen. by the human trainer.
Conversational Intelligence is to a large degree the analytics part. Here insightful conclusions are made from vast troves of conversations between users and machines. Everything about such conversations can be essentially measured.
At the simplest these can be statistics on the pure amount of conversations that the users have had with a Conversational AI solution. This can be broken down into number of users who chatted with the system, number of users who interacted via buttons or number of users who did both. Also in analytics it is important to measure whether conversations were handed over to human agents or not.
In addition it is possible to measure a wide variety of topics such as the general user satisfaction levels, the number of topics that were triggered by the users, the amount of specific keywords used inside conversations and the satisfaction with specific answers.
Such information is helpful for product managers to find out what users are telling about their products or for customer service managers to learn what are the topics that currently the virtual customer assistance are incapable of answering. In addition sentiment analysis can also be measured to determine emotions from the text to indicate whether users were happy with the answers or not.
When it comes to building Conversational AI solutions there are a variety of options available. Customer experience is going through a phase of rapid development and there are frequent updates on how companies are using tools available for improving their offering. For those that are more technically minded there are numerous open source tools out there These range from single snippets of code to full solutions.
For those looking to use more ready-made tools for building their own AI solution then there are various options out there. One of the most reliable sources for finding software tools are on software comparison sites where reviews and detailed specifics of products are available. Users can be directed to find information on Intelligent Virtual Assistants (here and here), Natural Language Processing solutions and simpler Bot Platforms Software.
The capabilities of each of the solutions are different and the users are recommended to go through reviews and product features to determine which software solution is the best fit for them. Each data professional has her own tools for analyzing data and users are recommended to use tools they get value from.
Some of the suggestions for finding adequate Solutions are to look at the capabilities that the software has. For building text based comprehensive Intelligence Virtual Assistants it is advisable that the selected platform has capabilities of Natural Language Understanding, training the Virtual Assistants, understanding a variety of different languages and the ability to connect the Virtual Assistants to live chat products.
Also make sure there are sufficient Conversational Intelligence features available in the product to understand insight from free text conversations. These requirements are advisable for those seeking to build out more comprehensive intelligent virtual assistants.
For click-based chatbots simpler platforms should suffice. So you should be really mindful of the use case you want to have for your Conversational AI solution. If there is a need for using voice operated solutions then one should also look at the text-to-speech and speech-to-text capabilities of the platform or whether the platforms have APIs available that can connect to third-party systems.
With a myriad of products available, it can be difficult to decide which platforms are the top tools to use. We spent 30 hours going through dozens of products and put together a carefully curated list of top 17 Conversational AI software platforms. You can see the top 5 here and see the full list of top 17 Conversational AI software platforms here.
1. AlphaChat
AlphaChat is a no-code end-to-end Conversational AI platform allowing anyone to build Natural Language Understanding Intelligent Virtual Assistants. The platform also offers advanced features for enterprise customers such as authentication, SSO, APIs, agent co-pilot mode and intelligent routing.
Top Features:
What’s special about this tool: Standard Package offers everything for building your own AI. The product also has insights into customer conversations (topics discussed, customer satisfaction) and statistics on AI performance. Enables training the NLU chatbot in one language and have it automatically chat in any language. AlphaOS with DIY custom code writing available for advanced users.
Pricing: 10-day free trial. Paid plans from €399/month.
2. Meya
Meya is a Conversational AI chatbot program for developing customizable virtual assistants.
Top Features:
What’s special about this tool: Meya offers its own Bot Flow Markup Language for developers in building their chatbots.
Pricing: Paid plans starting from $799/month.
3. Hyro
Hyro is platform that analyzes conversational data to create a basis for conversational interfacese.
Top Features:
What’s special about this tool: Focus on the healthcare, government and real estate sector.
Pricing: Paid plans price available on request.
4. Rulai
Rulai is a Conversational AI platform allowing to create Virtual Assistants.
Top Features:
What’s special about this tool: Rulai enables training chatbots to understand natural language and integrate with various 3rd party tools.
Pricing: Free trial available on request. Price available on request.
5. Ebi
Ebi is a Conversational AI platform for building chatbots utilizing IBM Watson machine learning models.
Top Features:
What’s special about this tool: Ebi offers custom virtual assistant development projects to build an MVP in 30 days.
Pricing: Price available on request.
Chatbots can be helpful tools for automating customer service and capturing leads. That’s why it is important to pick the right tool to suit your needs. With so many products available, it is tough to decide which ones are the top chatbot tools. To make your life easier we spent hours going through dozens of products and putting together a carefully curated list of top 30 chatbot software platforms - from simple bot builders to more comprehensive no-code and low-code AI chatbot products. Here are the top 5 and you can see the full list of 30 chatbot software platforms here.
1. AlphaChat
AlphaChat is a no-code Conversational AI platform allowing anyone to build Natural Language Understanding (NLU) chatbots and Virtual Assistants.
Top Features:
What’s special about this tool: Powerful tool with insights into customer conversations (topics discussed, customer satisfaction) and statistics on bot performance. SLA and Alpha Operating System with custom code available for enterprise chatbots.
Perfect for: Companies with 500+ chats / month seeking a more advanced Conversational AI solution.
Pricing: 10-day free trial. Paid plans from €399/month.
2. Qualified
Qualified allows building chatbots with their no-code product for automating your support and sales.
Top Features:
What’s special about this tool: Qualified provides live chat, chatbots and voice calls.
Perfect for: Companies seeking clickable chatbot without Natural Language Understanding.
Pricing: Paid plans from $1200/month.
3. Chatfuel
Chatfuel is a no-code chatbot development platform for Facebook, Instagram and Messenger for increasing sales, reducing cost and automating support. One of the top chatbot companies in terms of popularity.
Top Features:
What’s special about this tool: Chatfuel works with Facebook products and allows to create chatbots to engage with users on their platforms.
Perfect for: Companies seeking clickable chatbot without Natural Language Understanding.
Pricing: Free plan limited to 50 users, paid plans from $15/month.
4. Tars
Tars is a chatbot platform allowing you to create conversational landing pages to improve your PPC conversion rate. It provides a suite of templates for conversation-based lead generation.
Top Features:
What’s special about this tool: Tars is a good option for those seeking to convert leads better from their advertising campaigns.
Perfect for: Companies seeking clickable chatbot without Natural Language Understanding.
Pricing: Free 14-day trial available, paid plans from $499/month.
5. ManyChat
ManyChat is one of the top chatbot companies building chatbots for Facebook Messenger and SMS. The product is geared towards driving sales, getting more leads and engaging with customers.
Top Features:
What’s special about this tool: Manychat is one of chatbot software companies with a large community and specializes in sales and marketing chatbots in Facebook.
Perfect for: Companies seeking clickable chatbot without Natural Language Understanding.
Pricing: Free up to 1,000 contacts. Paid plans starting from $10/month.
AI Customer Service Software enables companies to automate their processes so that customers get accurate information 24/7. AI helps to unlock the benefits of efficient support by serving customers information that they want, when they want it. This can be done for example through chatbots and Intelligent Virtual Assistants.
To build AI into your customer service it is important to pick the right tools. With a wide variety of products available, it can be overwhelming to decide which platforms are the best ones to use. We spent 25 hours going through dozens of products and put together a carefully curated list of top 10 AI Customer Service Software Companies. You can see the top 5 here and see here for the full list of top 10 AI Customer Service Software Companies.
1. AlphaChat
AlphaChat is a no-code end-to-end Conversational AI platform allowing anyone to build Natural Language Understanding Intelligent Virtual Assistants. The platform also offers advanced features for enterprise customers such as authentication, SSO, APIs, agent co-pilot mode and intelligent routing.
Top Features:
What’s special about this tool: Standard Package offers everything from to build your own AI. insights into customer conversations (topics discussed, customer satisfaction) and statistics on AI value performance. Enables to train the NLU chatbot in one language and have it automatically chat in any language. SLAs and AlphaOS with DIY custom code writing available for enterprise accounts.
Pricing: 10-day free trial. Paid plans from €399/month.
2. Bold360
Bold360 is a customer service AI software company offering live chat and messaging automation for customer support.
Top Features:
What’s special about this tool: Bold360 offers comprehensive tools for automating live chat for agents and for customers.
Pricing: Price available on request.
3. Amelia
Amelia (from IPSoft) is an AI software company that allows building job-based digital employees for external and internal customer support.
Top Features:
What’s special about this tool: Amelia is a no-code AI platform for creating digital assistants for a variety of use cases including internal IT and support.
Pricing: Price available on request.
4. Twilio Autopilot
Twilio Autopilot is an AI platform from the communications software provider to build conversational IVRs and bots.
Top Features:
What’s special about this tool: Twilio Autopilot integrates with Twilio Flex contact center solutions and can be deployed across multiple channels.
Pricing: Messaging and chat $0.001/message, voice $0.04/minute.
5. Zendesk Answer Bot
Zendesk Answer Bot is a platform from the contact center software provider that allows building chatbots for support automation with the Flow Builder.
Top Features:
What’s special about this tool: Zendesk Answer Bot is a tool for building a quick bot to answer common questions and escalate complex queries to the agents.
Pricing: Paid plans from €49/agent/month includes up to 50 AI-powered automated answers.
There are a variety of benefits and below we have listed some of the most common of them and also indicated some numerical values that could be attached to them.
Intelligent Virtual assistants are often deployed in customer service where customers chat with virtual agents. These virtual agents can provide deflection rates up to 50-80%. This means that of all the customers that chat with such virtual agents, 50 to 80% of them do not need to be directed to human customer support. This brings significant savings for companies as they need to hire less humans to deal with their large customer bases.
When connecting back-end systems and personalized offerings to customer profiles, it is possible to offer customers products based on their actions on the go. Such timely and relevant offers can increase sales. When done right (i.e. not being pushy) it is possible to understand your customers and bring them the most benefit.
In chat based environments customer service agents can usually do 3 simultaneous chats with customers at any given point. Provided that, say on average, they are solving each case in 6 minutes they can solve 30 customer cases per hour. A machine can do essentially an unlimited number of conversations per hour. So when you have tens and hundreds of thousands of conversations on a monthly basis, Conversational AI can definitely bring benefits as you scale.
In today's world of subscription-based businesses customers have a variety of subscriptions that they are subscribed to. Whenever they are going to cancel their subscriptions it is possible to build solutions that specifically ask customers which services they want to cancel. In these cases it can be possible to have the customer only cancel one service and leave the remaining ones uncancelled. This helps companies protect their revenue base and decrease churn. By looking at insight through the Conversational Intelligence dashboard, it is also possible to understand the intents and phrases used for why customers wanted to leave and use this information to make the product better.
The differences between working hours and regular hours have blurred. Especially as 2020 put a burden on customer service staff. As customers are working remotely the expectation is that businesses are reachable 24/7. The impact can be seen in a variety of sectors including banking, telecommunications and healthcare. This adds pressure on companies to be able to have staff on call to connect with customers at all times. And even if real-time conversations are unavailable 24/7 the replies to customer inquiries should be made in a matter of hours not days.
In such added pressure situations these solutions can be used to provide customers information around the clock at the time when they need to. Such immediate answers give customers a sense that the company is easier to reach and has taken the extra steps for customers to reach the company at the time when it is convenient for the customer.
Like any promising advanced technology, Conversationally AI has its own challenges. Conducting intelligent and informational conversations between a machine and a human is difficult. These challenges are closely related to the fact that computers need to comprehend humans the way humans do.
Intent detection is at the heart of Conversational AI. How do you understand what the user wants? The challenge with intent detection lies in the fact that people have literally a million different ways of asking the same thing. This means that they could be asking about a bank transfer from one bank to another in a myriad of different ways such as “Where is my salary”, “Where is my payment” and “I haven't received money”.
All these are different ways of asking the same thing. And computers have to be able to interpret these different things as the same thing. This is a challenge. There are a variety of algorithms that work on enhancing the likelihood of finding the correct intent from user utterances but one should be mindful that understanding human meaning is difficult and approach such solutions with realistic expectations.
With speech-to-text solutions that aim to convert human voice to text there is the challenge with background noise. Users in everyday situations when they are talking on the phone or into their computer are not equipped with the sound systems in studios that professional musicians have.
Challenges such as traffic sounds, user mumbling or speaking out of range of the microphone add an additional layer of complexity to speech-to-text. Speech-to-text algorithms have to be capable of understanding sound input from from the real world where imperfection in sound quality is often the norm.
Human speech has a lot of nuances. As an example slang is often used in ways which are not reflected in general dictionaries or the general vocabulary of a language. As such solutions should be trained to use slang and especially the type of language that the audience of a conversational solution is likely to use. It is advisable that trainers of AI systems include slang into the knowledge base of Virtual Assistants.
Tone is another matter as sarcasm is something that is tough to detect even for humans. So when a user is not satisfied and then gives praise to the company, it could be that the user is actually really angry at the company. In such case detecting emotions or combining user intent with a previous intent could allow to detect the true intent and meaning of the user.
Text-to-speech solutions make voice sounds from text. Human voices differ greatly and the challenge lies in making artificial sounds for machines sound as logical and eloquent as human sounds. The challenge lies in the fact that there is no way to generate all the sound sequences ahead of time because the combination of words and letters is infinite.
As such text to speech systems have to be able to put together words into sentences from little bits and pieces. Combine them so that the tempo and the intonation of speech matches with what the human ear is accustomed to hearing from other humans when they speak.
In the context of Conversational AI, the solutions should in general have privacy protective measures that are similar to those technologies where human-to-human conversations take place (e.g. emails or phone conversations). In addition to privacy one should also be mindful that AI solutions are not perfect in giving their answers. As such when deploying such solutions one should be mindful that the AI at times gives wrong answers or even answers that are inappropriate. The AI is not doing this on purpose but rather because it lacks training data or the human intent detection is simply so difficult that it is impossible to give right answers all the time.
If you are serious about using machine learning based communication with your customers, it is advisable to first sit down and write down some key points. This can complement your customer experience roadmap and they way you build your company's customer centric culture. These things below allow you to keep focus and ultimately achieve your goals. Here is a list of things to do.
First thing you should think of is a use case. This means that whatever Conversational AI solutions you are using, they should be tied to a metric or a Key Performance Indicator for your company. AI is a means to an end. It is a tool that you are deploying to achieve a goal. That can be a business goal, a technical goal or something different.
Whatever the goal might be you should always be mindful that the AI is helping you in achieving that goal. There could be cases where you just want to deploy AI for the sake of AI but such cases do not usually result in actual business value. So if you have interest in serving up maximum ROI for your project be sure to start with the goal or the metric in your mind right from the beginning.
Secondly you should have a realistic deployment timeline for your strategy. This means that you should break down how you are going to achieve your goal. That is the foundation and aim for your strategy. It is advisable in the beginning to start out with a smaller pilot project or a proof-of-concept. This can be a simple 3-4 month project where you use technology at hand that you buy from other companies through their products or you have a team in a house building something.
Whatever the case, you need to have results delivered quickly. The process will be imperfect and there will be shortfalls. But at the end of the day you accomplished something, you deploy the solution and you test it out in the real world. This gives you an understanding of the end-to-end deployment of the full solution you have in mind for your company. From planning, to building, to deployment, to communicating to the stakeholders in your company and to getting actual user feedback.
Thirdly keep in mind that there are things that you do not know and you have to keep an open mind. When you set your use case and metrics at first, seek out solutions that can provide you the basic features for reaching those metrics. Do not make the solution too complex in the beginning. AI adoption takes time and it makes sense to learn from others' experience and gather knowledge on how machine-human interaction can impact the world we live in.
You can always add layers of complexity in the end. But most often it is such that you get a feeling for whether the solution is working at all or whether you need to tweak something that is not even technology-related. Most often when deploying AI solutions, companies need to sync their processes to maximize the value from Conversational AI Solutions.
So be mindful that changes might happen and it is a sign that you are progressing and learning how to best utilize the technology to achieve your goals.
Conversational AI enables the construction of automated communication between machines and users. It is a way through which companies can automated their customer support, provide 24/7 information on products and services, gather leads and enable users to benefits from instantaneous help at scale.
Through utilizing Intelligent Virtual Assistants and Natural Language Understanding it enables building smart machines to help users across messaging and voice channels. Conversational Intelligence brings further benefits through insightful findings in the data.
If you are looking to try this out for your own business or needs, feel free to sign-up for an account with alphachat.ai In just 5 minutes you can get your own natural language understanding Intelligent Virtual Assistant that you can connect with your website.
Pre-trained in all languages and also with template answers you can easily modify. And if you have more specific enterprise needs, reach out and we can accommodate these as well.
