The ultimate guide
to chatbots for
enterprise

The ultimate guide to chatbots for enterprise

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:

  • What is a chatbot, anyway?
  • The history of chatbots
  • Conversational AI vs. chatbots - what’s the difference?
  • Chatbot technology and how conversational AI works
  • Enterprise chatbot features and benefits
  • How AI chatbots drive business value
  • Key chatbot use cases and automated customer service success stories
  • How to get started: chatbot best practices for enterprise

Chatbots 101 - learn the basics

What is a chatbot, anyway?

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.

what is a chatbot

Chatbots 101 - learn the basics

A brief history of chatbots

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.

history of chatbots
1950

The Turing Test

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’.

1972

PARRY

Described as “ELIZA with an attitude”, this Standford-developed chatbot attempted to simulate a person with paranoid schizophrenia and successfully fooled many experienced psychiatrists.

1995

A.L.I.C.E

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.

2010

Siri

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.

1966

ELIZA

The world’s first chatbot was proof positive that humans were eager to communicate with machines. ELIZA could carry on (relatively) convincing conversations by mimicking responses.

1988

Jabberywacky

Jabberwacky was an early attempt at creating an artificial intelligence through human interaction and was designed to simulate natural chat in a humorous way. It was eventually released online in 1997.

2001

SmarterChild

Available on AOL Instant Messenger and MSN Messenger, SmarterChild was the first chatbot to achieve mainstream adoption by millions of users in the early 2000s.

2016

Facebook Messenger Bots

The wide-spread adoption of chatbots exploded when Facebook announced that it would begin allowing bots onto its popular messaging platform. By 2018, there were more than 300,000 active chatbots on Facebook Messenger.

Chatbots 101 - learn the basics

How AI chatbots drive business value

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.

70%
of white-collar workers will interact with a chatbot daily by 2022
Gartner
up to 30%
of operational costs can
be cut down by
implementing a chatbot
Chatbots Life
$112 billion
expected in retail revenue by 2023, driven by
conversational AI
Juniper Research
92%
increase in usage of
chatbots since 2019
Drift

Here’s a list of key areas where virtual agents can drive value to both businesses and consumers:

  • Respond to customers instantly eliminating the need for them to wait on hold
  • Increase revenue by guiding customers to products and services they may be interested in
  • Keep costs down by doing the work of multiple employees
  • Increase employee efficiency, freeing them up to focus on high-value customer interactions
  • Open up new channels for sales, service and support without tying up additional resources
  • Bolster brand loyalty through dynamic and memorable self-service experiences
  • Make your business available 24/7 so that customers can reach you at their convenience
chatbot best practices
Download the full guide to learn more about how AI chatbots can improve customer experience!
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Chatbot technology - what you need to know

Chatbots vs. conversational AI - what’s the difference?

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.

Online 24/7
Natural language understanding
Dynamic, context-based navigation
Multi-level intent hierarchy
Unlimited scalability
Broad scope
3rd-party integration support
Self-improving over time
Consistenly high-resolution rates
Omni-channel
Entity extraction
User authentication
Voice and conversational IVR
Multilingual
Privacy & security compliant
Basic chatbot
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Keyword-based tech
Button-focused navigation
If/Then statements
Limited improvement capacity
Narrow scope
Limited understanding model
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no sign
no sign
no sign
no sign
no sign
no sign
Chatbots with
conversational AI
check
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So, what is conversational AI? Essentially, it’s the synthetic brainpower that makes chatbots capable of understanding, processing and responding to human language.
what is a chatbot

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:

  • Blocking a credit card
  • Filing insurance claims
  • Upgrading mobile data packages
  • Upselling products and services
  • Automating mortgage deferment requests
  • Generating and issuing invoices
  • And much, much more!
types of chatbots

Chatbot technology - what you need to know

The 3 common types of chatbot technology

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.

Rule-based programming

Pros: Easy to implement
Cons: Does not
scale

Computational
linguistics

Pros: Easily adapted to new languages
Cons: Requires regular maintenance due to high variation

Machine learning (conversational AI)

Pros: Scalable with high accuracy

Cons: Resource-intensive deployment

Chatbot technology - what you need to know

How conversational AI works

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:

Natural language processing (NLP)

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.

Natural language understanding (NLU)

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.

Response generation

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.

chatbot technology
Download the full guide to find out why advanced NLU is key to creating natural chatbot conversations!
Download
chatbot guide

Enterprise benefits and best practices

Chatbot features and benefits

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:

High accuracy and resolution rates

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:

  • Interact with customers in a conversational manner
  • Understand and act on customer requests, regardless of how complex the request is
  • Identify when a request has multiple topics and provide actionable responses for each
  • Understand context to keep interactions from veering off course
  • Ask follow-up questions to clarify information and gather additional data

Scalable intent hierarchy

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.

Broad scope vs. narrow scope

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:

  • Total cost of ownership
  • Pre-built, vertical-specific content
  • Conversation analytics
  • Seamless hand-off to human agent
  • Self-learning AI
  • Voice and conversational IVR
  • 3rd-party integrations
  • Virtual agent networks
  • And much more!

Enterprise benefits and best practices

Chatbot best practices
- how to get started

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.

Decide on the correct use-case(s)

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.

Set and agree on a clear scope

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.

Assemble the right team

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.

Choose the correct KPIs

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.

Download your copy and discover even more best practices for chatbot success!
  • Integrating your chatbot with your support team
  • Why chatbot visibility matters
  • Considering a ‘chat-first’ strategy
  • Anticipating and mitigating risks
  • And much more!