Year: 2021

These are the problems that conversational AI face.

Conversational artificial intelligence confronts challenges that expect better progressive technology to endure.

Here, we will catch a glimpse of some of the challenges in conversational AI.

Security and Privacy

At the point when users demand a voice assistant, the information sent must be safely prepared and put away. Voice assistance and chatbots must be paid attention in organizations and the high-security norms that organizations characterize for these channels must be conveyed to their clients to make the vital premise of trust. Particularly while performing sensitive individual data analytics that can be taken, Conversational AI applications must be planned with security in mind to guarantee that protection is regarded and all personal details are kept private or redacted depending on the channel being utilized.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Conversations in Native Languages

With only a limited section of the world population speaking English, it is a challenge for a voice assistant to converse in a language other than English. As a result, the choice of chatting to a voice assistant in your mother language is critical to winning more people and building faith more skillfully. The languages of varied regions as well as cultural discrepancies are required to be considered.

Discovery and Adoption

Although conversational AI applications are getting progressively simple to use and standardized for everybody, there are still difficulties that can be defeated to expand the number of individuals who are open to using technology for a more extensive variety of use cases. So, instructing your user based on opportunities can enable the technology to be all the more generally welcomed and make a better experience for the individuals who are not friendly with it.

Language Input

Language input can be a difficult area for conversational AI, regardless of whether the input is text or voice. Dialects and background noises can affect the AI’s comprehension of the raw input. Also, slang and unscripted language can create issues with handling the information. Nevertheless, the greatest challenge for conversational AI is the human factor in language input. Feelings and sarcasm make it hard for conversational AI to understand properly and react appropriately.

Simultaneous Conversations

We all know that most of us keep our smart speakers in our living room and most of us use voice assistance in our smartphones. So, in these situations, we can realize that in these places numerous people could probably have a discussion or deliver instructions. In this situation, the voice assistant might get confused. Hence, it requires skill to differentiate identical voices from each other and not distract user accounts, therefore revealing sensitive user information.

Don’t forget to give us your 👏 !

---

Top Challenges Faced by Conversational AI that You Should be Aware of was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this blog I’ll be trying to explain in detail the types of chatbots, their development, and how they’re made.

Before we start properly, let’s get a few basics down pat. A chatbot (Conversational AI) is an automated program that simulates human conversation through text messages, voice chats, or both. It learns to do that based on a lot of inputs, and Natural Language Processing (NLP).

Chatbots today are integral in many industries and companies for Social Media, E-Commerce, Customer Services and even Healthcare. Some popular examples are Microsoft’s Zo, IBM Watson or Rasa (A tool used to build chatbots for commercial use).

Let’s get to the real meat then 😤

Table of Contents

• Types of Chatbots (Rule-based, Intent-based, Open-Domain)
• Chatbot Architecture
• Natural Language Processing
• Industry Examples
• Further Research and References

Types of Chatbots

Rule-Based Chatbots

Rule-based chatbots are also referred to as decision-tree bots. As the name suggests, they use a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for.

Like a flowchart, rule-based chatbots map out conversations. They do this in anticipation of what a customer might ask, and how the chatbot should respond.

Rule-based chatbots can use very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions. Also, they only perform and work with the scenarios you train them for. Opting for his type of chatbot comes with several benefits, like:

• The chatbot doesn’t need extensive training which makes the implementation process faster and less complicated.
• By pre-defining the structures and answers, you can better control the behavior and responses of the chatbot.

However, rule-based chatbots have their limitations, with a few of their disadvantages being:

• A rule-based chatbot can’t capture typos which means that in some cases it won’t understand the guest, which can cause frustrations.
• The interactions with a simple chatbot feel robotic rather than conversational.
• They cannot learn on their own which means that any improvements need to be made manually.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Intent-Based Chatbots

In comparison, AI chatbots that use machine learning understand the context and intent of a question before formulating a response.

An AI chatbot is a chatbot powered by Natural Language Processing. So, unlike with a rule-based chatbot, it won’t use keywords to answer, but it will try to understand the intent of the guest, meaning what is it that the guest wants. The more it interacts with guests, the better it will become at understanding the intent, and the better it will become at answering guest requests.

The way they work is that they capture the intent behind a question or response. For example, a user would ask something like ‘Hello! How are you doing?’ or even something like ‘Heyoooo’. Despite these being wildly different, the bot captures the intent as `greeting` so it responds with a comment corresponding to that.

Open-Domain Chatbots

Arguably, Intent-Based chatbots are also AI-Based Bots. These chatbots operate on a semi or completely Artificial Intelligence backed algorithm. A bot built on GPT-3 would be a perfect example of this, where it understands the context and responds completely on it’s own.

This type of bot is much more suited for complex kinds and a larger number of queries. Choosing an AI chatbot comes with several advantages, like:
The chatbot can understand typos and grammatical mistakes, so it is still able to respond to the question.
It will continuously improve without much further assistance.
Conversing with an AI chatbot feels much more natural and human-like.

Although Artificial Intelligence is an advanced technology, this bot also has its limitations:

• AI chatbots go through a learning process, which makes their implementation process more complicated and longer.
• By not using pre-defined structures, the conversations led by an AI chatbot are less predictable.
• When a chatbot was wrongly taught something, it takes a while before it “unlearns” and can learn the correct behavior.

Chatbot Architecture

NLP Engine

The Engine is the core component that interprets what users say at any given time and converts the language to structured inputs that the system can further process. Even if a chatbot is doamin specific, it needs to contain and harness a vast amount of information. The NLP Engine helps with that.

It contains advanced machine learning algorithms to identify the user’s intent and further matches them to the list of available intents the bot supports. It can be understood to have two components:

• Intent Classifier: Intent classifier takes user’s input identifies its meaning and relates back to one of the intents that the chatbot supports.
• Entity Extractor: Entity extractor is what extracts key information from the user’s query.

Knowledge Base

This is the key component in answering users’ frequently asked questions. Q&A systems interpret the question and respond with relevant answers from the knowledge base. It can be trained manually or trained to do so itself

• Manual Training involves the domain expert creating the list of frequently asked users queries and map its answers. This helps the bot quickly identify the answers to the most important questions.
• Automated Training involves submitting the company’s documents like policy documents and other Q&A type of documents to the bot and asking it to train itself. The engine comes up with a list of questions and answers from these documents. The bot then can answer with confidence.

Data Storage

The data storage is simply a store of previous interactions to feed to the nlp engine so that the bot retains a semblance of context during a chat. This is especially critical to remember knowledge about the user for further interactions.

Natural Language Processing

Natural Language Processing in Chatbots finds a way to convert the user’s speech or text into structured data. Which is then utilized to choose a relevant answer. Natural Language Processing includes the following steps;

• Tokenization: The NLP separates a series of words into tokens or pieces that are linguistically representative, with a different value in the application.
• Sentiment Analysis: It will study and learn the user’s experience, and transfer the inquiry to a human when necessary
• Normalization: This program model processes the text to find out the typographical errors and common spelling mistakes that might alter the intended meaning of the user’s request.
• Named Entity Recognition: The program model of chatbot looks for different categories of words, similar to the name of the particular product, the user’s address or name, whichever information is required.
• Dependency Parsing: The Chatbot searches for the subjects, verbs, objects, common phrases and nouns in the user’s text to discover related phrases that what users want to convey.

Industry Examples

Meena by Google

Meena is an end-to-end, neural conversational model that learns to respond sensibly to a given conversational context. The training objective is to minimize perplexity, the uncertainty of predicting the next token (in this case, the next word in a conversation).

At its heart lies the Evolved Transformer seq2seq architecture, a Transformer architecture discovered by evolutionary neural architecture search to improve perplexity. Find out more about it in Google’s AI blog.

Replika

Replika was founded by Eugenia Kuyda with the idea to create a personal AI that would help you express and witness yourself by offering a helpful conversation. It’s a space where you can safely share your thoughts, feelings, beliefs, experiences, memories, dreams — your “private perceptual world.”

Essentially, Replika is a chatbot that while talking to you, gradually learns to imitate you till it becomes you.

Tay by Microsoft

Tay was an artificial intelligence chatter bot that was originally released by Microsoft Corporation via Twitter on March 23, 2016; it caused subsequent controversy when the bot began to post inflammatory and offensive tweets through its Twitter account, causing Microsoft to shut down the service only 16 hours after its launch. It has since been seen as a case study on how user interaction can ruin chatbots.

Further Research and References

For the purpose of creating this blog, we referred to several sources which can be found below. Apart from those, if you are interested in learning about or developing chatbots, we’d encourage you to check out Rasa, a popular open-source library used to develop chatbots. And there’s also the Chatbotslife Magazine right here on Medium!

And here are our sources:

• “Towards a Conversational Agent that Can Chat About…Anything”, Google Research: Brain Team, 2020
• “Understanding the Architecture of Conversational Chatbots”, VSoftConsulting Blog, 2019
• “The Rise of Social Bots”, Ferrara Emilio, Varol Onur, Davis Clayton, Communications of the ACM, July 2016
• “Why Microsoft’s ‘Tay’ AI bot went wrong”, Hope Reese, Tech Republic, March 2016

Don’t forget to give us your 👏 !

---

Machine Learning in Chatbot Development was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Have you ever conversed with a chatbot before?
Well if you haven’t, you may consider trying out chatting with one.

Unless, if you don’t even realize that you have done it before and thought it was a human operator~😮

It’s alright, we’ve been there too and we’re here to tell you that chatbots are here to stay, and we’ll give you a heads up on what to expect when you’re chatting with one!

So.. first things first,

1. How do you differentiate a person and a chatbot? 🤔

Differentiating a chatbot from a human can be tricky at times,
a chatbot that was designed and built well will give anyone a difficult time distinguishing them.

So the most obvious thing that you can observe is how fast do they reply to you.

Yes, a human service center may reply to you fast but a chatbot will reply to you lightning-fast ⚡⚡. Seriously, they reply so fast that they can solve problems or give recommendations to you in a matter of seconds!

Take Intumit SmartRobot for example, it can reply based on the keywords that you typed in, and identify your question or inquiry, word by word. It is all thanks to the power of NLP (natural language processing) which helps it to communicate based on its machine learning algorithm.

For the sake of your time, let’s not get too technical here and focus more on how they can solve your problem. 😉

2. How can Intumit SmartRobot solve your problem?

Imagine a scenario in which you’re about to return a defective product and you’re looking for a quick answer on how to return it (assuming that the company doesn’t have a FAQ page), and you needed to wait for 5 minutes before getting a reply.

Not time-friendly at all, right? 🥺

Here’s where Intumit SmartRobot comes in!

A chatbot can reply and give you the most relevant answer in 5 seconds!
That reduces your waiting time by 60 fold! ⌛️

Aside from giving you a definitive answer, Intumit SmartRobot can even provide you with several suggestions that you might need or be interested in. 😄

Such as cashback policy, product swap policy, and many more. Even if a company does have a FAQ page, navigating through the website may be considered a hassle to some.

This is why a chatbot is useful for both customers and service agents. Customers will get what they need quickly and service agents can focus on resolving a more complex issue simultaneously.

In addition, there are several features that a chatbot can be equipped with. For instance, rich menu contents, carousel contents, events and data collection features, etc. Not to mention, chatbots can be found on websites, social and messaging platforms, and even in-app messaging too. 💻⌚️📱

Those are just a few of the things that Intumit SmartRobot can do.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

However, as perfect and time-saving as it sounds,
a chatbot can never replace a human agent.

That is why the coexistence between a human agent and a chatbot needs to exist. They complement each other while resolving and providing the best service to customers. 🤝

A chatbot can help with the basics, while the human agent will back them up in case a more complex issue arises.

In this case, 🤖 we have developed Intumit SmartRobot as a conversational AI that can switch between sessions with a 👥 human agent anytime a customer needs to. The human agent can also take over a conversation when sensitive words are detected, immensely reducing customer friction and improving their customer experience.

We hope that the information helps!

Share 🙌, clap 👏 and follow us 👍 to learn more about Intumit SmartRobot and how it can be applied to different industries! 😎

Do you have questions?
Visit our website at www.intumit.com
or email us at sales@intumit.com

Talk to you soon! 😉
FB | LinkedIn | Twitter | YouTube

Don’t forget to give us your 👏 !

---

How’s it like to Coexist with a Conversational AI, aka a Chatbot? 🙂🤖 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are eLearning chatbots worth it? Do they really help your students? Should your institute invest in one? Well, if you really care about your students and want to help them learn in the most effective manner during these unprecedented times, you should consider investing in an intelligent eLearning chatbot. Here’s why!

Why should you use eLearning chatbots?

With remote learning becoming the norm and students taking courses from all over the world, Edtech has become extremely important. Student engagement tools like eLearning chatbots help students find all the answers they seek, wherever they are at any time.‍

What are the benefits of eLearning chatbots?

1. Instant answers

Artificial intelligence chatbots will allow your students to get answers to their questions instantly. They won’t need to wait for ages, hoping to get an answer to an email. They won’t even need to bother getting on a phone call with your institution and waiting on hold till they manage to reach your representatives.

An eLearning chatbot will be able to answer around 80% of your students’ queries without any hiccups. And guess what, if your bot can’t answer a query, you can train the bot with great ease so that it is able to answer questions like that whenever they are asked in the future.

2. 24/7 support

Some students prefer studying in the still of the night or early in the morning when no-one can disturb them (speaking from experience here). It’s an effective way to study without distractions, but there is one tiny issue — it’s quite hard to get answers to their questions at those hours because professors and administrative staff would not be available to help them at that time. And that gets frustrating. It could also make students feel anxious.

This problem is even more pronounced because, due to the pandemic, your students are studying remotely, spread all over the world. Their timings might not match your staff’s schedule.

The good news is that you can fix this issue without forcing your staff to pull all-nighters throughout the year.

Those instant answers that your chatbot provides won’t be limited to your working hours. Your students could ask a question at 4:45 in the morning and still get an answer in less than a second.‍

3. Increased student satisfaction

When you students get answers instantly around the clock, their level of frustration goes down and their satisfaction levels start to rise. The anxiety of not having the answers they seek simply disappears.

Students feel better about your educational institution or eLearning platform. They’ll spread the word about you in a positive way, becoming evangelists for your institution and providing you with free word of mouth marketing.

The accessibility of information and the reduction of anxiety would also help your students learn in a more effective manner and perform better in their evaluations, thus creating powerful social proof for your institution and attracting more prospective students.‍

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

4. Staff gets to focus on higher value work

As I mentioned earlier, intelligent chatbots can handle about 80% of the queries coming in from your students. Only the most complex ones would be passed on to your staff.

This means that your staff would not have to waste their time monotonously and mechanically answering the same repetitive questions over and over again.

This would allow your staff to focus their attention and energy on tasks that are more fulfilling and matter more. It would even give your staff the opportunity to take the time to prepare more thoroughly and deliver lectures that are more insightful and engaging than ever before.‍‍

7 eLearning chatbot usecases

1. Admissions

Chatbots are very powerful and effective tools for driving admissions and enrollment campaigns. They allow you to reach prospective students on the channels they would prefer engaging with you over, and deliver information about courses, the institution, etc.

In fact, the bot could make the admissions and enrollment process much easier for your students by allowing them to sign up and apply for courses directly over the bot. Instead of having to navigate through your website or even downloading your app, your prospective students could fill forms within the chatbot or just provide their information in a conversational format by answering the questions that the chatbot asks.

Your chatbot could even be integrated with your database, CRM, or Google Sheets to store the information collected from prospective students.

Your prospective students could even submit necessary documents by uploading them to your chatbot.‍

2. Administrative queries

Have the lectures been rescheduled? Is the college open or is it a holiday? When will the next examination cycle start?

Your students have a lot of administrative queries on their minds and you should seek to resolve them as fast as possible. Chatbots can help here, since most of these questions are repetitive in nature, have answers that have been pre-decided, and your chatbot can be trained in advance to answer them.‍

3. Notifications and reminders

Yes, it’s normal for your students to forget about assignment submissions and other deadlines. No, it’s not excusable for your institute to say, “We told you once 3 months ago and expected you to remember the submission deadline.”

You can use your chatbot to broadcast notifications and reminders to your students about assignments, exams, events, etc. And to make it even better, you get to broadcast these messages on the channels your students prefer using, like WhatsApp.

Pro tip:Use WhatsApp message templates to broadcast messages to your students.‍

4. Assignment submissions

Your chatbot does not need to stop at simply informing your students that they have an assignment to submit. Your intelligent Engati chatbot can actually make it possible for your students to submit their assignments directly over the chatbot.

To make that possible, you’d just have to add a file upload node in your chatbot flow.‍

5. Notifications and reminders

Go back to a student studying late in the night or before daybreak. There’s no way they’ll be able to get in touch with a professor for a quick doubt-solving session at that time. And if they’re cramming right before an exam, not getting the answers they need could frustrate and worry them to the point where they might not be able to study effectively and the anxiety could hamper their performance in the actual exam.

An eLearning chatbot could fill in for the professor here, clarifying doubts and providing students with all the answers and information they need.‍

6. Sharing additional information

Got extra reading material that could be helpful to your students? Share it with them over an intelligent chatbot. Deploying your chatbot on channels like WhatsApp and Facebook Messenger makes this even more effective because of the very overt notifications that your students will receive when they get a message from your bot.

You can use your education chatbot to send documents as well as links for further reading.‍

7. Doubling as an HR chatbot for your institute’s staff

Your bot does not need to be limited just to your students. You could even use it as an HR chatbot to help your staff. It could help them apply for leave, check your policies, request payslips and do much more with minimum effort.‍

eLearning chatbot best practices

1. Speak the language your students are comfortable with

You’ve got students from all over the world and you got to make them feel at home. Forcing them to only engage with your chatbot in English is not the best idea, especially because only 26% of netizens even understand English.

You need to be able to communicate with your students at scale in their own language. Your Engati chatbot can help you reach, engage, and educate your students in 50+ languages.‍

2. Reach students where they want to be reached

Do your students have to keep visiting your website and navigating through it to reach you? Maybe they have to email you and wait for a response. Or are you forcing them to call your up and deal with their worst nightmare — an IVR?

Do better.

You know that your students are spending around hours on social media (28 minutes a day on WhatsApp itself). Why aren’t you reaching them there?

Use your Engati chatbot to engage and educate your students over WhatsApp, Facebook Messenger, Telegram, and 10+ other channels, delivering an omnichannel experience.‍

3. Use a Oneview inbox

You can’t have an omnichannel experience without context flowing across all these channels. When you use Engati’s chatbot + live chat solution, you get a Oneview inbox for all your customer conversations, even the ones coming in via email.

Our conversation stitching feature allows your representatives to look at past conversations that they, other agents, or even your chatbot had with particular students, so that they always have context across all channels and never have to repeat questions and frustrate customers.

4. Pay attention to analytics

Your chatbot is a goldmine of data and you need to use it right. Explore the analytics dashboards and even integrate your bot with Google Analytics to understand what your students are asking questions about what what their main concerns are.‍

5. Keep training

You’ll even be able to see the amount of student queries that your chatbot was actually able to answer and which ones it could not answer. If you see a pattern and find out that a lot of students are asking a certain question, you might want to train your chatbot to answer it.

Training becomes even easier when you don’t have to upload answers to individual questions. With our DocuSense technology, you could just upload a document to the bot and it will parse through it and pull all the answers that your students need — extremely useful for training your bots to answer doubts regarding course material.‍

6. Use chatbots and live chat

Your bot’s are not here to replace your representatives, they’re here to make their lives easier. Your bots can answer the simple, repetitive questions, but you’d want to transfer the complex, situational ones over to your live agents using live chat. And the best part is that with Engati Live Chat, your bot can route these queries directly to the agent most equipped to handle them, instead of sending your students hopping across departments.‍

University of Rochester’s student wellness chatbot

The University of Rochester Medical Center is one of the best academic medical centers in New York. They wanted to make OBGYN and mental well-being information more accessible for students as well as faculty.

They built an intelligent chatbot with a go-to-market time of just 14 days using Engati’s no code chatbot platform.

Their bot helps students take a break during the pandemic and travel (virtually) to museums and other attractions. It even helps them with guided meditations and much more.

This article about “Why do you need to deploy an eLearning chatbot” was originally published in Engati blogs.

Don’t forget to give us your 👏 !

---

Why do you need to deploy an eLearning chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Insurance is one of the most critical industries there is; it is what saves people and organizations from falling into financial trouble after a crisis or an unforeseen event. However, it is often deemed as the least innovative industry with outdated processes, time-taking procedures and delays, legacy IT systems, and many dissatisfied and even disgruntled customers.

With the change in demographics and a rapidly growing population of millennials who are tech-savvy and like to perform their banking and finance-related transactions online, it has also become indispensable for every organization in the banking and finance sector to cater to their needs. Evidence has it that millennials are more likely to purchase insurance online if the time and hassle costs are lower, unlike the middle-aged and older demographic that prefers purchasing insurance after meeting and discussing options with an insurer. So, the insurance industry is slowly and steadily working its way out of this reputation of being technologically outdated and of providing inconsistent and friction-filled customer experiences. How? Through a digital transformation lead by Artificial Intelligence (AI) and Machine Learning (ML). To effectively understand how technology and AI have disrupted and revolutionized the insurance industry, we must first look at how the industry is leveraging AI.

How is AI used in the insurance industry?

Risk evaluation: The core of the insurance industry is risk, and its effective evaluation/assessment. Using AI and ML, insurers can leverage the data deluge to assess risks with higher accuracy, understand new correlations and patterns, get more in-depth insights, make better predictions, and accordingly, customize their plans and adjust their premiums. For instance — through AI and ML, if the firm finds that a particular group of policyholders are at a higher risk of actually facing a fire accident, then the firm can adjust the premium accordingly.

Underwriting: For reinsurers and underwriters to effectively, and seamlessly perform their tasks, AI efficiently gathers data from different internal and external sources and provides deep and intelligent insights. By taking control of the data deluge and leveraging it, such insurance executives can move from the detect-and-repair mindset to a predict-and-prevent philosophy, reduce guesswork in insurance, make accurate, data-driven decisions and develop a competitive edge for the organization.

24×7 assistance to insurance shoppers: AI in the form of conversational bots are capable of handling much larger volumes of insurance shoppers. They effectively and efficiently handle large volumes of queries, provide automated advisory and personalized recommendations to clients through multi-turn, natural, and goal-oriented conversations. Conversational bots, thereby, enable organizations to automate the query management process to a great extent. So, the human agents can be free from the drudgery and sheer monotony of handling regular inquiries and step in when required to customize, convince, and convert shoppers. Such automated advisory saves the time to give a quotation to the client, enabling them to make quick, data-driven insurance shopping decisions.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Increase conversions by human agents with chatbots as live agent assistants: It is impossible for human agents to effectively and productively handle large volumes of data on a daily basis and make conversions and upsell products. Chatbots who have ML capabilities can be programmed and taught to act as live sales assistants to agents. The chatbots collect, collate, and analyze voluminous data from various sources, making key insights available to the policy agents while they are making pitches to the potential clients. These conversational bots make relevant information and high-quality insights available to human agents in a timely fashion and recommend the right products based on the client’s profile. This way, the human agents not only convert clients but also upsell and cross-sell the right products and increase the lifetime value of the client.

Onboarding assistant: It is a long and cumbersome process from preparing and giving quotes to clients, getting them to choose a policy, and prepare the policy papers to make them pay and onboard themselves on the policy. Known as the quote-to-cash (QTC) process, the time and hassle costs to the human agents from this process contribute to their lower sales figures and inability to generate adequate additional revenue. With the help of conversational bots that can be programmed and taught to act as onboarding assistants, it is possible to automate a significant part of the process, reduce the time and effort required and enable human agents to focus on converting new clients. These onboarding assistants can solve any queries that clients have, help them find in-network physicians/ hospitals, understand the plan coverage, etc.

Claims management: Filing for claims and getting it is often an exhausting and emotional task that is high on time and hassle costs because clients file for claims after a major event like an accident or natural calamity or illness/ death of a family member. Like with personalized assistance to policy shoppers, conversational bots automate to handle voluminous calls in the claims process, which very often involves repetitive questions. Human agents can step in when required and use the full conversation history to handle more complex questions and issues. With chatbots and AI, the claim adjudication time is reduced, and the fraud detection percentage has increased.

High-quality and consistent customer experiences: Dynamic and meaningful conversations in natural language through conversational bots have enabled insurance organizations to provide high-quality, seamless, and consistent customer experiences by lessening the wait time and frustration levels of the clients. They have enabled clients to engage in self-service, understand insurance jargon better, and uncomplicate the claims process. For instance, the provisions for car insurance customers to file for claims through the mobile app by clicking pictures and submitting the claim instantly has helped reduce the time, money, and hassle costs for the clients.

The impact of Conversational AI in the insurance industry

As mentioned earlier, the digital transformation and AI adoption in the insurance industry is slow yet steady. In some respects, such as using chatbots for onboarding assistance and personalized sales assistance, many players have already adopted or are adopting AI technology, while in other respects, there is yet to be full-fledged AI adoption. In the coming years, the insurance industry as a whole will benefit from the operationalizing AI.

At SmartBots, we help organizations adopt Conversational AI (chatbots/virtual assistants) to improve employee productivity and provide great customer experience. Reach out to me at stallin@smartbots.ai to explore more on Conversational AI.

Don’t forget to give us your 👏 !

---

How the Insurance industry is leveraging AI (Artificial Intelligence)! was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

submitted by /u/Elegant_Dot_7075
[link] [comments]

As it incresingly cooperates with integrated rescue system

There are various ideas about what artificial intelligence is capable of and how people can use it for their good. In connection with the term “AI,” people often imagine a variety of abstract IT industries. Some even see AI as a potential threat to the future of humankind. But the opposite is closer to the truth so far since artificial intelligence quickly advances in protecting the safety and health of humans.

AI already keeps people safe in a variety of industries. For example, it cooperates with healthcare professionals helping to determine a patient’s diagnosis and possible treatment. During that, it relies on an enormous amount of data that the human brain wouldn’t be able to process. Artificial intelligence also protects human lives in criminology. It helps during investigations of human trafficking thanks to facial recognition tools, behavior prediction, and the ability to quickly process large clusters of information.

AI will also be used by firefighters in the future, even in the Czech Republic, where a research project is currently underway to create a digital voice assistant for the contact center of the Fire and Rescue Service.

A robot firefighter? Not quite

Czech startup Born Digital, which focuses on digitizing human conversation and develops voice and chat assistants armed with artificial intelligence, is one of the subjects involved in developing new voice technology to assist firefighters when their human operators are overloaded.

“For example, large-scale fires are a major strain on emergency services. We are developing a technology that can receive calls, obtain and evaluate important information and pass it on to the fire brigade,” describes Born Digital director Tomáš Malovec.

As the number of calls increases, the voicebot receives them and records what happened, where it happened, how many people are on the spot, and whether they are in danger or injured. Then he passes the obtained information to the intervening firefighters.

“Our current voicebot technologies are already at a very high level. But in this case, we have to go one level higher and minimize the error rate because real human lives are at stake. In the future, the solution will not help only during fires, but also during cases of various natural disasters,” says one of the Born Digital project leader Ondřej Ťupa.

During the process of creation, artificial intelligence has many things to learn. The solution has to be able to classify the object of fire, ask meaningful additional questions and respond adequately to the situation.

During the process of creation, artificial intelligence has many things to learn. The solution has to be able to classify the object of fire, ask meaningful additional questions and respond adequately to the situation. “A forest fire, a car explosion after an accident, or flames spreading through the apartment after a mishap in the kitchen are different situations and require different scenarios and additional questions,” explains Born Digital linguists Klára Vostřelová.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Quickly and without misunderstandings

The Voice assistant already recognizes official Czech Republic addresses from the speech. The great advantage of artificial intelligence is its speed, with which it manages to check if the specified address exists. For example, if a caller from Brno reports a fire in Barunčina Street, the system quickly recognizes that there is no such street in the city, and asks another question regarding the address. On the contrary, for a caller from Prague, where Barunčina Street exists, the voicebot only makes sure with a confirmation question.

The speed of data verification is not the only advantage of involving artificial intelligence in the work of emergency operators. In addition to being able to handle several calls at the same time, the voicebot continuously assigns reports of the same event to each other and reduces duplicates. He is, therefore, able to constantly refine and update information for firefighters and other rescue services.

The whole project is financed within the grant program “Security Research Program of the Czech Republic 2015–2022“. On the research, Born Digital participates with the Technical University of Ostrava, Brno University of Technology, and SpeechTech and Phonexia companies.

Artificial intelligence vs. fire

The Czech digital voice assistant is unique, but there are more AI solutions to assist firefighters around the globe. In the USA, under the supervision of NASA, the Digital Assistant AUDREY (The Assistant for Understanding Data through Reasoning, Extraction, and sYnthesis) is also gaining shape. His task will be to help those firefighters who are already in action at the scene of the fire.

In the future, AUDREY should be able to obtain data from the immediate vicinity of intervening firefighters, compare them from its extensive database of knowledge about fires and then advise firefighters on how to proceed with firefighting. AUDREY learns to work with data about temperature, the presence of gases, the flammability of various materials, and many other types of information. Together with an assistant from the Czech Republic, these two technologies equipped with artificial intelligence could be an unbeatable pair that will save a significant number of lives daily.

Don’t forget to give us your 👏 !

---

Artificial intelligence helps to save human lives was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Let’s face it: tracking time sucks. It’s boring, demanding, and something we don’t feel as productive as the work itself.

We treat it like something we can skip or do poorly, right? Well, it’s quite the contrary.
Especially for professional services and agencies, time is money. Literally.

In 2015, a study from AffinityLive estimated that the U.S. economy is losing around $7.4 billion a day due to employees bad time tracking.

Over the last few years, a lot of tools and apps have been released to ease this task and enable more quality tracking: Harvest, Toggl, Clockify, to name a few. They all aim there.

So that’s it, right? Nope.
The majority of us still hate it.

Our own struggle

We were having similar issues in xtream, the startup I co-founded.
Ok, we weren’t losing billions a day 🙂. Yet the poor quality of our timesheets impeded us from trusting the value of some mission critical quantitative metrics.

How can you compare projects profitability when you have only a rough idea of the time spent on them? How can you bill customers accurately, without risking underbilling because you didn’t account for some extra work you did?

It wasn’t a lack of tracking: we were all using Clockify apps, filling timesheets with worked hours on projects and tasks.

It was a lack of accuracy: we were filling our timesheets only when due (like end of month reporting) or once a week top.
So either we spent our entire Friday thinking about what the hell we had done over the past few days, or our timesheet was pretty much a guess.

Point taken: you simply cannot remember all that stuff for much longer than a day.

We know, we know. You can use timers, yay!
No, we don’t want to use timers. You forget to start them, you forget to stop them, you get a bunch of meaningless entries in your sheet… bleah 🤮

I mean, if you’re happy with timers or with manually inserting entries in your sheet once every few weeks in a frustrating guessing game, good for you.

If you hope of a better way, read on.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Enter the power of chatbots

What was really blocking us?

Reacting is easier than acting: we had nothing to assist us and to remind us of our daily duty. When we tried setting up just a simple notification broadcasted to everyone, our accuracy improved substantially.

Boring tasks cannot steal from your best time: it was not only a matter of effort (let’s be honest, it really isn’t that hard to press a couple of buttons), but of platform. Productivity apps are better used on your laptop, or at least with a working mindset. Even when you use them on your phone… it just feels like work — you know what I mean?

That’s when we started experimenting with a chatbot. Something easy and fun to play with, proactively reminding you to be diligent in your timesheet, and most importantly available on a platform where we all are at any time, where we feel relaxed and comfy. Which for us was Telegram.

This was 2020. Truth be told, pandemic had left us with quite a few extra hours with nothing to do. Which is not ideal for an agency, but for R&D projects is gold material: we were able to dig in deeper than we would have normally. What came out of the cylinder was a chatbot powered by AI technologies for NLP, like Microsoft LUIS, developed with Microsoft Bot Framework and released on our Azure cloud.

It’s quite mouthful, but it truly isn’t much. In a couple of weeks we had a running chatbot and we started using it.

We kept a KISS approach: keep it simple, stupid! We didn’t develop any of the time tracking stuff and none of the features of any of those products. We just took our Clockify workspace and we integrated it, via API, with our chatbot.

One year later, we look back and we feel like we solved time tracking.
No, not for everyone and not every time: late and inaccurate timesheets still occur, and we try our best to avoid them. But 76% of our time entries are filled via chatbot, which means that are recorded the same day they are worked. Remember the chart above? This translates into substantial improvement in accuracy.

That’s why we chose to make it publicly available. If it helped us, it could help someone else. Everyone with a Clockify account can start using our chatbot for free right away.

As of now we support Telegram and Teams. Just click on the links and you should be able to start chatting in a matter of seconds.

Slack may follow easily, if we see there’s a need to. If you’d like it to be on other platforms as well, just let us know in the comment section down below 🙃.

Similarly, we don’t need to stop at Clockify. If you think you’d need this as we do, but your favourite tool is different, let us know. It might be easy to integrate.

How does it look like?

I won’t bore you with the technicalities of how it’s done.
Just know that if you want to have a look at its internals, you can check out the source code yourself, there’s no black magic: we wanted it to be completely transparent in the way it is written.

I do want to show you how it looks though.
Here I am, at 5.30 in the afternoon, finishing up this article. What happens?

The bot reminds me that I’m approaching end of day, and therefore I realistically know what I did today. If I ignore it, which I do a lot, it reminds me once every hour until I’ve done my duty or shut it (pure evil).

I can fill my timesheet in natural language: it may not seem like much, but it means I can go quick and drop partial words and misspellings, without any hard feeling. Note: I can only fill my hours for the current day, so that it encourages entry accuracy by design.

It asks for a task, but because it knows I use them: for projects where I never use tasks, where I’m ok with project-level, it doesn’t bother asking.

And that’s it guys. That’s how we solved our problem with time tracking.
Since then we found ourselves diligently filling our timesheets much more frequently and in a variety of situations that don’t relate to work: on the subway, while watching our night Netflix, or when we take a ☕️ before bed.

Hope it can help you too!

Wrapping up

You can start using our bot as you like, it’s free of charge 👌
If you’re curious or skeptical about its inner parts, don’t hesitate having a look at its codebase: it’s open source and you can drop us an issue for any bug or feature you may want. Write us at oss@xtreamers.io if you don’t know what GitHub is but you still have questions for us.

If you want to know more about who is behind this bot madness, check out our company website. We are a small startup, and we love this stuff.

If you have better ideas on how to solve the time tracking problem, or you think our solution sucks, let us know in the comments.

Don’t be too harsh though.
We’re engineers, but we have feelings too.

Happy tracking! 👋

Don’t forget to give us your 👏 !

---

Time tracking for the busy and lazy: can chatbots help you? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

submitted by /u/Horiizon_o6
[link] [comments]

submitted by /u/techcouncilglobal
[link] [comments]