Download our comprehensive guide to learn everything you need to know about launching an AI chatbot for your business
Whether you’re just curious about automation or getting ready to deploy your 8th chatbot project (go, you!), you’ll find everything you need in ‘The enterprise chatbot guidebook’. You’ll learn:
In its purest form, a chatbot is a computer program that allows interaction between humans and technology. As the name suggests, chatbots were originally limited to text-based communication; however, as the technology has improved this interaction paradigm now extends to other input methods including touch and voice.
Chatbots are often used as a primary channel for customer service because they allow consumers 24/7 access to the brands they care about in a way that’s instant, familiar and conversational. Thanks to artificial intelligence, the 21st-century chatbot has evolved beyond simple question-and-answer logic into a swiss-army knife of automation and self-service that can enhance not just customer support and service, but an organization’s operational efficiency, too.
The first chatbot was developed in 1964 at MIT. Named ELIZA, it was the brainchild of computer scientist Joseph Weizenbaum and was an early example of a natural language processing computer program designed to simulate human conversation.
ELIZA was pretty basic compared to today’s AI-powered chatbots; it used a basic pattern-matching algorithm to give the illusion of understanding but couldn’t actually contextualize events. What it did do is lay the groundwork for the future of human-machine communication, setting the standard for how we interact with customer service chatbots, virtual agents and virtual assistants today.
Even before ELIZA was a glimmer in its creators’ eye, Alan Turing posed the question of whether a machine could think in his seminal paper ‘Computing Machinery and Intelligence’.
Described as “ELIZA with an attitude”, this Standford-developed chatbot attempted to simulate a person with paranoid schizophrenia and successfully fooled many experienced psychiatrists.
Inspired by ELIZA, the Artificial Linguistic Internet Computer Entity was the natural language processing chatbot that itself served as the inspiration for the 2013 film Her.
Launched initially as a standalone iPhone app, Siri was integrated into iOS with the launch of the iPhone 4S in 2011. This ushered in the era of voice-enabled virtual assistants that included Google Assistant and Amazon’s Alexa.
Chatbots offer a variety of benefits over legacy customer service channels such as phone, email and live chat. With the help of artificial intelligence, enterprises can utilize the technology to not only empower customer self-service but also boost employee productivity and reduce operational costs.
Here’s a list of key areas where virtual agents can drive value to both businesses and consumers:
Chatbots vs. conversational AI - what’s the difference?
Basic chatbots are useful for handling a very limited number of tasks. They use rule-based programming to match user queries with potential answers, typically for basic FAQs. Where basic chatbots show their limitations is if they receive a request that has not been previously defined; they will be unable to assist, and spit back a “Sorry, I don’t understand.” response.
In order to meet the requirements of larger organizations like banks, insurance companies and telcos, chatbots need artificial intelligence to enhance their ability to understand human language and perform more complex tasks and transactions. In relation to chatbots, this branch of artificial intelligence is called conversational AI.
Conversational AI can be used to power chatbots to become smarter and more capable. However, not all chatbots are powered by conversational AI - which is an important distinction!
Using sophisticated deep learning and natural language understanding algorithms, conversational AI unlocks the true potential of what customer service and support automation can achieve, going beyond translating website content into simple chat responses and empowering customer self-service through automating actions like:
In the 2019 report, ‘Competitive Landscape: Virtual Assistant Platforms, Worldwide’, Gartner outlined three common architectures that most conversational agents you will come across on the internet are built on.
Understanding the difference between these three technologies will help you make the right decision on which one is right for your business.
Pros: Easy to implement
Cons: Does not
Pros: Easily adapted to new languages Cons: Requires regular maintenance due to high variation
Pros: Scalable with high accuracy
Cons: Resource-intensive deployment
In order to successfully automate customer interactions at scale, large enterprises need conversational AI to help translate human language into information that virtual agents can understand and action on.
This is achieved using a collection of advanced algorithms that make up the conversational AI technology stack. They can be split into three distinct groups:
NLP algorithms clean up incoming requests by correcting spelling, identifying synonyms and interpreting grammar. They essentially break down a customer request into words and sentences that are more easily understood by a chatbot.
NLU is actually a subfield of NLP and is composed of a variety of deep learning and machine learning models responsible for identifying the correct intent of a customer’s request and extracting important information. This important information, along with proprietary algorithms like boost.ai’s own Automatic Semantic Understanding, can be used to trigger additional actions and helps a chatbot to understand more complex requests.
Sometimes called Natural Language Generation (NLG), this is how a correct response is formulated and where conversational AI outshines basic rule-based solutions. By combining the information gathered by the NLU (customer intent, context, entity extraction, etc.) with a structured hierarchy of conversational flows, conversational AI can generate the right response, whether it's answering a simple question or carrying out a complex transaction on behalf of a customer.
When deciding on the right AI chatbot platform to deploy for your business, it’s important to have a clear understanding of the key features and benefits that conversational AI can deliver.
Here are some enterprise-grade features you should look for when choosing a solution:
A chatbot can’t help if it doesn’t understand. That’s why conversational AI built on a robust natural language understanding foundation is so important in order for a virtual agent to be effective at automating customer requests.
If you want your AI chatbot to achieve consistently high accuracy and resolution rates, and avoid running the risk of frustrating your customers, you need to ensure it can do the following:
Many basic chatbot solutions are only able to automate actions and answer questions on 100-200 topics at most. This is insufficient for larger organizations that often have complex product and service offerings that may require a more scalable solution.
Conversational AI can enable a chatbot to answer questions on topics orders of magnitude more complex than rule-based chatbots. This is achieved by placing intents (or topics) into a hierarchical structure, categorizing them by subject matter (i.e. insurance types, banking services, etc.) so that the AI can easily sort and search for the answers it needs, negating the need for it to scroll through a long list. This allows a chatbot powered by conversational AI to instantly answer questions on thousands of topics, rather than just a few hundred, without any reduction in quality.
Trying to solve a narrow set of problems with a virtual assistant may seem like a quick fix but, ultimately, it may not lead to sustainable returns on your investment. With conversational AI, it’s possible to cast a wider net of customer service automation by deploying chatbots that have a broad scope.
This approach is particularly applicable to enterprises that have large volumes of daily customer service traffic and goes hand-in-hand with a virtual agent’s capacity to scale and maintain consistently high resolution rates. A conversational AI chatbot with a broad scope can help an organization achieve long-term strategic goals, instead of just short-term wins.
Download our guide and learn about these other critical features and benefits for enterprise chatbots:
Want to build and implement an AI-powered virtual agent but don’t know where to start? Following these recommendations will help you deliver your next conversational AI project quickly and without compromising on quality.
Identify which parts of your organization would benefit most from automation and then decided on relevant use-cases. Use-cases are typically split across three types: ‘service and support’, ‘internal knowledge’ and ‘sales optimization’.
In this guide, we explore many use cases that fall under these categories split across common business verticals including banking, insurance, healthcare, e-commerce, telecommunications and more.
Establish the scope and KPIs for your chatbot and ensure all stakeholders are aligned. This is where you can set goals for your project and assess what resources may be needed.
Buy-in across all levels of your organization can be crucial to your project’s success. You will need a combination of internal (your company) and external (the vendor) stakeholders ranging from an executive sponsor to project managers and content designers/AI trainers.
When setting KPIs, you need to be mindful of the use-case and scope you have selected for your chatbot. Common performance metrics that may be relevant to measure include resolution rate, deflection rate, average handling time, and more.