Voice user interfaces are filled to the brim with human language. Human languages are beautiful; they are geographically diverse and filled to the brim with uncertainties. Some languages have words that derive their semantic meaning from their context; for others, meaning derives from the tone in which the speaker says it or the syntactical placement of the word in the sentence. In linguistics, there are five main categories to human language that help linguists understand how each language harmonizes into a unified whole: syntax, semantics, pragmatics, morphology and phonology. Because of how ambiguous human languages are and how uniquely innate they are, it may be evident that they would not translate smoothly into a computer. While partly true, it is an inevitably complex subject. Computational linguistics is the academic study of computing natural language. On the theoretical and artificially intelligent level, this is referred to as machine language learning. All of the above is used in voice user interfaces.

Machine language learning is a component of deep learning. Deep learning is a core part of artificial intelligence and is critical to an artificially intelligent system’s performance – whether text-based, voice-based, or neither. Machine language learning possesses many layers, with its main one being natural language processing – also commonly referred to as NLP. Like deep learning at the core of artificial intelligence, natural language processing is at the core of machine language learning. It is an essential component of the fundamentals of any computational linguistic algorithm. Natural language processing often goes together with natural language understanding (NLU), and together, they make up the most successful chatbots and other text and voice-based artificial intelligence systems.

The use of computation to store text-based data is not a new concept. Any simple computer programming language will have a text-based database. It thus makes sense to want to apply language analysis, language translation, conversational design, and language databasing to computation. From a computational perspective, computational linguists can draw from their interdisciplinary background and apply it to artificial intelligence in the form of conversational artificial intelligence within chatbots, virtual assistants, and even virtual reality.

Voice User Interfaces

Voice user interfaces such as those seen from Google (Google Home) or those as technologically iconic as Amazon’s Alexa have become commonplace within many people’s day-to-day lives. For example, FitBit Sense now allows one to talk to Amazon’s Alexa right from the watch itself with the press of a button. As visual interfaces begin to intertwine themselves with voice user interfaces, it is critical that we not only understand why they work but how this will impact our lives as humans who already use language to connect on many levels with other people. How this phenomenon will translate into chatbots is an entirely different narrative.

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As mentioned previously, machine language learning is at the heart of computational linguistics. Within artificial intelligence, cognitive scientists aim to create artificial intelligence that is ‘strong’ – ‘strong’ in that it does not rely on a user for its input. ‘Strong’ in that it can use past inputs and outputs to create new algorithms. Essentially, computational linguists and cognitive scientists want artificially intelligent systems and voice user interfaces that can learn and speak without the help of a human. They want a piece of artificial intelligence that can evolve with time, independent of any individual behind the scenes working on simulating the learning experience of the chatbot’s experiences with the user. We want the artificial intelligence system to be phenomenally conscious, not just simulate it.

Strong artificial intelligence systems such as those mentioned previously are indeed the future. With self-driving cars on the way and mobile GPS apps with voice user interfaces that are almost entirely voice-based, the push for complex voice user interfaces and conversational artificial intelligence will continue to grow. With the success of social media and social communication apps, it only makes sense that cognitive scientists and computational linguists would want to have chatbots and voice user interfaces that mirror human-to-human communication and interaction. Humans are social creatures, and we crave social interaction. Whether or not an artificially intelligent robot can satisfy that craving is up to our ability to make conversational AI and voice user interface systems naturalized.

READ MORE: Machine Learning 101: The Revolutionary Side of Artificial Intelligence

Unfortunately, an area with such high potential results in high expectations. Much like the expectation of strong artificial intelligence to understand us and evolve and learn from their experiences with the user, this would ultimately translate into a piece of conversational artificial intelligence or voice user interface that is more human-like than its predecessor. The study of cognitive science is, after all, to naturalize and formalize the human mind into a machine.

Why Personalization Requires Strong Conversational Artificial Intelligence

With the expectations of users and those creating the robot to be more human-like and therefore more personalized, it becomes apparent that conversational artificial intelligence branches out into a much more complex area of technology and technological development. To make voice user interfaces that are as engaging and realistic as a human at the other end of a telephone would be, we would need it to function as a human. It would need to be personalized to each and every user, not your stereotypical cookie-cutter chatbot. A significant problem with the rise of conversational AI is its lack of personalization and user-friendliness. At this point in time, it is still too robotic, and therefore, unfriendly.

Presently, chatbots and other voice user interfaces are believed by the average user to not handle complex inquiries and tasks. Synthesizing language and natural language processing to allow artificial intelligence systems to process and understand natural language is one thing. Providing personalized implementations of conversational AI to the user that tailors to them and their experiences in a human-like manner is an entirely different story – one that must technologically evolve as the digital era continues.

While hard to imagine, this is crucial to keep in mind whilst thinking about the future of artificial intelligence, chatbots, virtual assistants, mobile apps, and other viable pieces of technology and their future in marketing. Going beyond artificial intelligence that manually simulates learning from a human and actually learns from the user, their prompts and experiences are the next steps in creating artificial intelligence eco-systems that would have consumers be open to the idea that chatbots can, indeed, handle complex tasks in an empathetic manner. Communication automation is the goal, yes, but we must consider that having communication automation be personalized to the individual is the ultimate goal regarding a voice user interface’s strength as an artificially intelligent system.

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This is not to say that we have not already reached artificial intelligence that is strong and, thus, formalized and personalized. We are far from completing such a task with all the natural human languages that exist – especially for voice user interfaces. Unfortunately for machines, human language is not as innate to machines as they are to humans.

The Applications of Conversational AI

Fortunately, natural language processing and conversational artificial intelligence as a scientific discipline of computer science have uses that go beyond user experiences on mobile apps or other hyper-personalized experiences with technology. Because machine intelligence weaves itself into computational linguistics and all its subfields, it is important to note its uses beyond futuristic speech-based interaction with humans. 

Statistical natural language processing is a massive component of this aspect of conversational artificial intelligence. It involves complex computer algorithms combined with machine language learning, deep learning, and neural networks to analyze and categorize voice and/or text data. There are many aspects of technology used in – presumably, types of technology you are familiar with and have encountered. It is an important aspect of voice user interfaces, specifically.

For example, many websites want to check that you are not a robot. What is so ironic about this is that it is a robot making sure you are not a robot. They do this by asking you questions that are typically beyond most standardly automated robot capabilities. To put it more simply, they ask you questions that are more ‘human’ – questions that often require critical thinking, common sense, and a lack of repetition, all of which are core components to the ‘humanity’ of humans. Said subjects are also very complex and have yet to be fully formalized within pieces of artificial intelligence machinery.

Therefore, it is easy to see why conversational artificial intelligence combines with natural language processing and understanding to form a subcategory of artificial intelligence that does not revolve around a user. Instead, it relies upon its own unique machine language learning algorithms to help with spam detection (especially within the financial sector), thorough machine translation, social media rule-analysis and even summarizations of voices or texts.

Just like any digital solution, chatbot solutions are available in varying levels of complexity and customization. Chatbots range from simple decision-tree-based architectures to fully customized conversational AI-powered solutions. And while there are many inexpensive options in the marketplace, you might be wondering why enterprise is investing upfront in a custom chatbot solution.

5 Reasons Enterprises Are Choosing Custom Chatbots

Let’s address the elephant in the room first. Custom chatbots aren’t the cheapest option. To get targeted, use case-specific technology to address a key business need, developers and consultants are going to spend some serious time and effort building you the best solution. But in the long run, even the inexpensive chatbot solutions are going to end up costing money to upgrade, modify or shelf altogether. So getting it right, the first time, is always the best way forward.

The ChatC Group has worked with dozens of enterprise clients globally over the last few years. Each custom AI chatbot brought with it a number of learnings, including providing evidence as to why the custom solution was the only right choice for the customer. Here are five reasons why custom chatbots are best for enterprise.

  1. Front Loaded Design Produced the Best Long Term Solution

A key feature of a custom chatbot solution is that the bulk of the work for all parties involved in the project is at the outset. Gathering the stakeholders, discussing what need the chatbot should meet, and carefully outlining the specific use case are essential to getting things right, the first time. 

In addition to that, a custom solution should always be using KPIs to measure and track its progress against your specific requirements. Custom solutions nail down these KPIs early on and set up dashboards inside the chatbot so that it automatically gathers data, analyzes it and reports what you need to know. 

The upfront burden of work can seem tiresome, but when enterprise clients see the data collected after their efforts, there was no question as to whether it was worth the hard work. The AI-powered chatbot continues to analyze data, which is then used to guide the business, providing accurate insights contributing to the bottom line, long after the chatbot project has concluded. 

  1. Enterprise Needs More than a Pop-up Solution

For some very simple use cases, the decision tree chatbot approach may work. Examples include capturing leads after a webinar, recording sign-ups for an email list or answering very basic FAQs on your website. The benefit of the decision tree chatbot here is ease of use, and keeping users in the same window to collect their information.

For larger businesses though, this solution won’t always work. Enterprise clients need a chatbot that is more involved and solves a larger business issue for them, in a way that more people will benefit. An entrepreneur or small business could use the decision tree chatbot for sign-ups, but SMBs or enterprises need a more polished solution that can do more than reply with pre-canned answers. Understanding language and intent are key.

  1. Recognizing Intent in Conversations Was Crucial

Understanding a person’s intent when they type a message or question into a chatbot window is only possible when it’s run on a conversational AI platform. A simple decision-tree chatbot cannot tell what the person is getting at in their query; they can only identify if a keyword has been entered, and then look-up how to respond to that keyword in their decision tree framework.

When handling customer service or answering more involved questions, in the case of a dedicated internal chatbot, the person’s intent matters. Understanding lingo, undertones of emotion, and getting to the root of what’s being asked when different phrases are used is a tricky thing for a program. But when you want a solution that is more efficient at retrieving information and answering questions correctly, conversational AI is required, and all of our large enterprise clients have agreed and invested in this technology.

  1. Improvement over Time Made the Best Business Case

One additional benefit of custom chatbot solutions: they actually improve over time. As their database of conversations grows with each passing day in operation, they learn to deliver better service, all on their own. 

With other forms of software and technology, improvements are often at the cost of a new round of development or diverting current IT professionals to instead focus on bringing an older technology back to today’s standards. Enterprise clients are thrilled to hear that once they’ve invested in the conversational AI and spent the time training the code on their own data, it will continue to be relevant and useful without any other significant expenditures.

  1. Custom Chatbot Solutions Meet Individualized Use Case Requirements Best

Custom solutions to meet very specific use cases may seem like an obvious answer, but the complexities associated with a modern-day enterprise and their tech stacks are not trivial. Big businesses have already invested heavily in their infrastructure and myriad technical solutions and software.

A custom AI chatbot can be developed on a platform that integrates seamlessly with a business’s current tech stack. Building a custom chatbot means it will fit into your existing architecture and can be deployed onto the server of your choice (from Azure and AWS to Google Cloud and more). All of this work upfront is again saving you time and frustration in dealing with a new system. Chatbots and consultants that cater to your needs make your life easier, not yet another thing to learn and add to your to-do list. 

Chatbot Consultants Deliver Personalized Chatbot Solutions

While a custom chatbot solution is the most versatile offering for enterprise, the only way to achieve that end is by working with a chatbot consultant. These are experts versed in all things conversational AI, who don’t just have the tech background, but also the ability to work well with all parties, managing a project for big business, and ensuring seamless execution.
If you’re curious about our process at The ChatC Group, take a look at our webpage, or reach out to one of our experts to discuss what we do. Talking tech and helping your business reach its goals are two things we can’t get enough of. Book a call; it’s easy to chat with us.

Google has been supporting enterprises with a whole host of productivity-enhancing software for years now. From Gmail to Google Docs to Hangouts and your Google Drive, it’d be difficult for many of us to live without their tools. But did you know that Google is also heavily invested in technology for niche markets? 

And, did you know that Google has its own conversational AI platform that is specifically designed to support call centres? Simply named Contact Center AI, it’s built to drive efficiency and next-level customer experience. 

Before you write this off as something too advanced for your operation, take yourself back 16 years ago to 2004 when Gmail was first released. Not everyone jumped on the Gmail train then, but look at how ubiquitous the email service is now. 

The same is true when it comes to AI for contact centres. Take a look below at how this cutting-edge technology can be put to use today in order to set your business apart from its competition. 

Case Studies: How to Use AI for Contact Centres

While Google’s application of AI for contact centres is one example, there are a few others in the marketplace that are worth showcasing. Conversational AI is the baseline technology that is deployed differently by each engine, like Google’s Dialogflow or IBM’s Watson. The conversational AI platform enables the chatbot uses to first train on a data set and then to converse with the end-user. In the case of contact centres, the conversational AI could be trained on past call transcriptions, and as it’s in use for longer periods of time, it automatically gets better, maximizing your upfront investment in the technology. Here are two case studies specifically on how to use AI for contact centres.

1. Streamline Call Quality Assurance with Conversational AI

National Debt Relief (NDR) decided to invest in Observe.AI’s conversational AI platform to streamline its call centre quality assurance (Audiotex Update, 2021). 

The AI Solution:

Observe.AI used conversational AI combined with automatic speech recognition and Natural Language Processing (NLP) to analyze 100% of their contact centre calls. Traditionally, only a fraction of calls could be analyzed from recordings by people, because the amount of data was simply too large. But now, there is full transparency into the contact centre’s calls.

The Result:

By streamlining their processes and having the conversational AI software record, transcribe and decipher the calls, NDR has been able to improve its frontline coaching for agents. The massive amount of data collected ensures that training is focused where it’s most needed, and agents are now armed with the training they need to provide excellent customer service. 

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2. Bring Automation to Customer Service

One start-up has developed a conversational AI platform specifically for contact centres in order to use automation to empower the agents already in place. PolyAI was created in the UK with a small group of engineers from Cambridge’s Dialog Systems Group (NewsRX LLC, 2019). 

The AI Solution:

PolyAI deploys enterprise-ready voice assistants, based on proprietary machine learning and Natural Language Process (NLP) technology. The conversational chatbots can scale seamlessly and can even detect and converse in many world languages. Similar to the use case above, PolyAI listens to the calls received and learns to provide improved responses. When there are too many calls for the human agents to tackle, the automated AI chatbots can handle calls themselves.

The Result:

PolyAI recognizes that excellent customer service in a call centre, and the resulting CSAT scores, can only be achieved with human customer service agents. So the AI chatbots are there to assist, solving routine issues when customers call in and leaving the more complex issues to the experienced agents. The Co-founder and CEO of PolyAI, Nikola Mrksic emphasizes that “AI agents are not a replacement for the human touch, which is essential for great customer experience. However, automation is key for changing the economics of a contact centre” (NewsRX LLC, 2019).  

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Planning for the Future using AI in Your Contact Centre

The case studies show just how useful a chatbot running on a conversational AI engine can be for increasing your CSAT scores and improving your overall operational efficiency. That’s reason alone to look at how a chatbot could help you put in place money-saving systems now. 

As some businesses take a more cautious approach to a post-COVID world, spending on certain customer service and marketing tools will be limited. But if the technology helps to reduce operational costs, offsetting the investment, and creating for a long-term financial benefit, then companies have a solid business case supporting AI for contact centres. 

On top of that, a conversational AI chatbot designed exactly for your particular business type and contact centre use case will deliver the largest ROI for your investment. Spending on technology can be seen as risky in some industries and during certain unstable time periods, but a chatbot is actually a low-risk way for you to start figuring out how to put this technology to use so you stay ahead of the curve.

You are in control of the chatbot’s roll-out, and using an agile approach, The ChatC Group advises that you design and release the software in stages. This also saves you time and money, because you focus on the minimum viable product, validate the use case you’re working with on a small group of users, and then commit to developing the chatbot for a wider release to your contact centre customers and agents. 

When you work with a chatbot consultant, rather than contracting out the project to one of the players described in this article, you also ensure you have someone negotiating on your behalf. Our team of seasoned experts knows which conversational AI platform you need and how to get the best results for your budget. If you’re curious to learn more about us, book a call! We love to chat about how to put AI to work for your contact centre.

Why do you need to heighten chatbot security, isn’t this already thought of in development? 

The answer is just as complicated as the security requirements you may need in place. Chatbots are one of the most popular digital trends especially with evolving business needs, not to mention, advancements in different messaging platforms, which are resulting in rapid chatbot development. Rapid development can mean missing pieces and this is why we need to focus on our security levels.

When To Heighten Chatbot Security

When you first get started with your chatbot design it can be easy to get excited and lost in the advancements in AI technology.  Advanced bots can now break down barriers between apps, content creators, employees, and most importantly, consumers. Allowing you to automate a wide range of tasks and information. However, as with all new and powerful technology, it also carries certain security sensitivities that are not yet fully explored by every chatbot design company.

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The financial industry, for example, hosts a vast amount of information and requires the highest levels of security. Vital personal information, such as credit cards, bank accounts, social security numbers, and more play a massive role in the digital transactions that are happening in each of these spaces. The Majority of this information can be encrypted and monitored for data loss or malicious intrusions.

Data protection involves information when it is both at rest and in transit. 

While there are many benefits to having a chatbot as part of your business plan, you must also consider the new cybersecurity challenges that come with new technologies. There are two main security concerns that every organization must keep into consideration:


Threats can be known as isolated events or Malware. A cyberattack of a global nature that targets specific industries has the potential to result in a long term system lockdown and loss of access. Moreso, attackers may also threaten to release confidential information of a critical nature or demand a ransom. In 2020, Microsoft announced their plans for a new $510 million cybersecurity centre in Vancouver, Canada. 

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“The Vancouver centre will help us meet the growing demand for technology solutions to reduce the cost of cyber-attacks, enable today’s connected devices to become tomorrow’s secure payment devices, and address the growing vulnerabilities associated with the Internet of Things,” Banga stated in a news release.

As a consumer, what are your thoughts on cybersecurity? 

As a business owner, do you have the same thoughts?

We’d love to hear from you – let us know those thoughts in the comments.


When state-of-the-art technology is not being used for protection, vulnerabilities become more apparent and accessible This can be attributed to weak coding and poor protection standards. Making it important to have a dedicated cyber-security team who knows where your weak spots are and how to strengthen them.

Tips To Heighten Chatbot Security

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No matter if you are a business, developer, or currently the user of a bot, these following tips should be considered to stay ahead when it comes to chatbot security.

1. Crossing Channels Isn’t Always Seamless

A key advantage of chatbot technology is the ability to cross channels when gathering information needed to execute a task. But with all this crisscrossing and the exchange of private information, the chances of it being leaked increases dramatically. Everyone in the chatbot ecosystem needs to consider each of the channels that could be accessed. 

While a bot can be created to secure information via a private channel, data that is shared in a public channel, such as Facebook, is subject to the security sensitivities of that channel. Private channels are good. Public channels aren’t secure.

2. Fall In Love With Encryption

We know that bots offer users, developers, and companies greater speed, flexibility, and convenience, this doesn’t come without strings attached. If you don’t have the proper safeguards in place, such as bots encrypting stored user information, a malicious hack is possible. 

Businesses understand this issue and employ encryption across channels to protect data when at rest and in motion. Your emails and SMS messages are encrypted, as well as any other service that exchanges sensitive information. 

When it comes to bots that can access personal information, developers who are interested in protecting data, and communications will grant bots access to only encrypted channels. However, it is a different story on public bots, you don’t have control over these platforms, what they encrypt and don’t encrypt. If you are not careful, it has the potential to be a huge cybersecurity storm. 

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So how do you stop that? End-to-End Encryption. 

This will stop anyone other than the sender and the recipient from seeing the information. A great example of this is WhatsApp, their end-to-end encryption is used when you send a message to another user, only you and that individual can read, view, or listen to the content you sent, not even WhatsApp has access to it.

Speaking of WhatsApp, recently they made some changes to their Privacy Policy, however, they still claim your messages are encrypted and safe from their staff or third parties from having access. 

We offer end-to-end encryption for our Services. End-to-end encryption means that your messages are encrypted to protect against us and third parties from reading them. Learn more about end-to-end encryption and how businesses communicate with you on WhatsApp.

Whatsapp February 2021 Privacy Policy 

3. Authentication Limits

If you have ever had to use a bank key before you know how time-based restrictions can result in higher levels of security. Access to the authenticated tokens is only valid for a specific period of time. After the expiry of the token, the chatbot automatically revokes access. This is extremely helpful when trying to prevent a hackers repeat attempts of trying and guessing their way into a secure account.

It makes a lot of sense that security-related issues are viewed by IT professionals as the main obstacle to the acceptance and use of intelligent systems. Some of their foremost concerns include:

  • 47% believe automated/unexpected API access will become a risk
  • 48% indicate automated/unexpected file access is poised to become a problem
  • 42% report that cyber threats will become more difficult to detect

But remember, when you heighten chatbot security, you also reduce your vulnerability.

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One Last Note

There is a lot of due euphoria surrounding bots and their potential, organizations need to pause and assess the bot, its security capabilities, and management controls before jumping into the “bot pool.”  We have forgotten that data, in the new world of chatbots, resides in places it has never resided before.  As chatbots become increasingly popular, expect developers to start to restrict bot access to secure channels only.

Talking to an expert is the first step, creating a secure bot that handles all your needs without causing any worry. If you feel like you and your business could use some advice, book a 30 min discovery call with us today.

Conversational Artificial Intelligence (AI) is the technology behind the automated messaging of speech-enabled applications that offer human-like interactions. In other words, conversational AI can communicate like a human being. The tech recognizes speech and text, is able to identify multiple languages and responds in a way that mimics human conversation.

Communication is important in any business, whether it’s between employees or from the business to a customer. Within each of these touchpoints is an opportunity for increased efficiency and a better understanding of what’s working and what isn’t in your business. You see, we’ve reached a place machines are able to understand human intent behind chat messages and produce human language to respond. 

Conversational AI is drastically changing how customers interact. By 2021, 15% of all customer service interactions will be completely handled by conversational AI, which is an increase of 400% from 2017. If thoughtfully deployed, your conversational AI chatbot can be configured to collect data on the backend that helps you steer the business to what people actually want, not your best estimate.

The 5 Components of Conversational AI Chatbots

How does a conversational AI chatbot actually work though? Glad you asked! It’s a collection of technologies that work to create conversations that improve over time. Conversational AI chatbots actually need to be “trained” on datasets in order to have a baseline of understanding before they are released to the public. In its simplest form though, there are five key components to this technology:

  1. The application receives either written text or spoken sentences from human input. For spoken words, ASR, which is the technical term for voice recognition, makes sense of the words and converts them into text. 
  2. The application then must try to decide what the text means. It uses Natural Language Understanding (NLU) to determine the meaning.
  3. The application then forms the response. Using Dialog Management, it will build a response based on its understanding of the intent. The response is converted into a conversational format using Natural Language Generation (NLG).
  4. The response will then be given either in text or speech. 
  5. The application also has the ability to learn and improve over time (not just at the outset with the training dataset), in order to deliver more concise and correct answers. For you, this also means that the investment in a conversational AI chatbot continues to pay back itself in higher quality and more data.

Conversational AI Chatbots Deliver More Value

Conversational AI Chatbots

Conversational AI chatbots deliver customized, highly-intelligent solutions that are designed from the get-go to provide next-level customer or employee experiences. They require a detailed specific process and the aid of specialized consultants to ensure the tech is put to use correctly. 

The benefit of these chatbots is that they continue to learn, improve and deliver value without needing to recode or improve the technology. Translation: the investment upfront will continue to pay you back over the years that it is in use.

However, there is also a group of chatbots that do not use the advanced and ever-learning techniques of conversational AI. Many DIY chatbot platforms use a much simpler approach and have a database of expected inputs, each with a predetermined output response. 

There is no detailed “learning” and “improving” process as the chatbot goes into use. DIY chatbot platforms are great for simple FAQ or customer query routing applications because the answer to the question remains the same. These chatbots are often referred to as decision-tree-based chatbots, because that’s the extent of the backend technology. 

DIY platforms are inexpensive and quick to implement, but you may need to spend money down the line to improve your chatbot as required. Because the bots are so simple at the outset, the use cases per bot are limited, and the quality of the deployment is not as high, so you may find that your end users aren’t happy with the solution, meaning more time and money to adjust and re-deploy the bot.

Translating Conversational AI Chatbot Data into Business Insights

Executives are increasingly looking for creative and effective ways of obtaining data to drive business decisions. The conversational AI chatbot is a relatively new avenue to explore. The technology can be used to collect valuable data about customers and/or employees. 

On top of that, conversational AI chatbots can even deliver analytics and custom data dashboards when set up with the help of experienced consultants. You’ll save time and money on data collection and analysis, while also improving efficiencies and customer experience. 

For example, when customers have questions about an order, product or process, commonly asked questions or pitfalls in your systems can be identified and rectified. Not only will the customers be happier having their queries responded to by a chatbot 24/7, but you can direct resources to solve that problem faster using the data from the chatbot.

You can use all the data you can get your hands on to get a leg up on the competition. With a strategically implemented conversational AI chatbot, you can gather data and put it to use quickly. As an added efficiency bonus, it’s an effective way to make personalized conversational experiences scalable. 

The fastest route to reaping the benefits of this technology? A team of trusted consultants who can identify the use case tailored to your greatest need. 

Are you interested in learning more about the features of conversational AI chatbots? Get in touch with a Chatbot Consultant today to have your questions answered!