The Essential Guide to Conversational AI & Automation

Learn Everything About Conversational AI & Automation

When Alan Turing devised the Turing Test in 1950, a framework to determine whether a computer can be characterized as “intelligent”, little did he know that it would take almost 64 years for a machine to actually beat it!

And since this feat, where a virtual 13-year old boy named Eugene tricked experts into believing that it is human, the fields of conversational AI and automation have never been the same. Today, the use of virtual assistants, chatbots, and voice assistants are giving rise to a plethora of use cases that automate customer communication and personalize experiences to the point of pan organization transformation.

But there is a catch (there always is)!

To successfully reap the benefits of this shift, it is crucial that you are not left far behind in the curve since technologies often have an uncanny ability to spiral out of reach! Maybe that’s the primary reason you are here, after all. And perhaps the best way forward is to first educate yourself on the tricks of the trade before making any strategic commitment.

If this is even remotely true, you’ve come to the right place!

About This Guide

This guide is for any organization or leader that has been planning to dive deep into the world of conversational AI and automation. By the end of it, you will be in a comfortable position to answer tricky questions such as:

“The convergence of Conversational AI and intelligent, smart automation creates an extended omnichannel experience meant to facilitate faster, more personalized, and responsive customer service.”

Cognigy - Conversational Automation & Voice AI

What is Conversational AI?


As the name suggests, Conversational AI is all about executing automated conversations between computers and humans. Being communication-centric, the technology finds natural use-cases in marketing, sales, or customer service frontends in the form of messaging apps, chatbots, and voice-based assistants. By mimicking the nuances of human conversations, customers get the impression that they are communicating with a human agent instead of a program. The end result? Hyper personalized customer experiences!

To begin using Conversational AI, a great starting point is to integrate your systems with Natural Language Processing (NLP) and Machine Learning (ML). This empowers the tech to accurately interpret customer conversations and automatically respond with relevant answers. Not only does this improve business processes, but it also improves communication experiences at customer-ends.

But what turned conversational AI from a mere trend to a technological pillar? Ease of use and better accessibility top the list. Influenced by today’s on-demand digital economy, customers have a natural tendency to expect quick query resolutions and seamless conversations while interacting with your organization. Add to this the constant shift from phone calls to text-heavy social media channels, such as Facebook and Whatsapp, and the picture becomes clearer!



How Leading Businesses React to These Changes

A leading business today will move ahead of such behavioral shifts and make use of Conversational AI to enhance communication between customers and employees. The benefits of using these tools include:

  • Increased employee satisfaction and productivity
  • Improved customer satisfaction
  • Growth in revenue and sales

Value of Conversational AI: Evaluating the Intricate Cogs

While it’s a no-brainer that conversational AI thrives on customer empowerment, here’s a look at everything that it brings to the table:

1. Round-the-Clock Query Resolution

Since it does not require any human intervention, conversational AI can work 24/7. In fact, it does so at a near-instantaneous level, boosting lead conversion rates to as high as 700%. And that’s just the tip of the iceberg. Quick response times naturally lead to better customer satisfaction, minimizing negative experience associated with long response times. Therefore, there is a positive impact on brand perception and reputation that comes with it.

2. Meet Changing Customer Preferences

What is the one thing that we all hate as customers? Being put on hold. With a poorly managed customer communication system, even simple queries can take hours to resolve. This is the main reason why most customers expect live chat features on websites today, with 30% of them preferring it over phone calls.

3. Engaging
and Conversational

Let’s face it - no one likes engaging with or being pitched to by bots. By mimicking human-to-human encounters, Conversational AI combats this with a more engagement friendly framework, leading to improved customer retention and business growth!

4. Infinite Scalability

Human-powered call-centers are forever plagued by the hassle of scalability. Deployment of additional agents is a resource-intensive ordeal that takes concentrated effort and time to overcome. Conversational AI brushes aside such hiccups, handling large amounts of customer interactions without increasing HR costs. What’s more? They also free up valuable human agents, empowering them instead to turn their attention to higher-priority tasks!

5. Omni-Channel Customer

Imagine being a customer support agent that handles communication across multiple platforms. What happens when the same customer drops a conversation on one platform, only to follow up on it on another? You would have no other choice but to keep playing catch up, being lost while trying to tie critical customer information together. Additionally, there would also be the risk of customers ending up with other agents, having to explain their issue multiple times before remediation. Intelligent Conversational AI chatbots know what to do in such situations since they can associate conversations with the relevant customer details. This means that under the right conditions, they can easily differentiate recurring customers from new ones!

Conversational AI – Beyond FAQ Bots

Conversational AI bots lie poles apart from simple FAQ bots. Here are the leading factors that set them apart:

  1. Uses natural language processing to understand customer needs
  2. Understands the context and nuances of every conversation
  3. Solves problems instead of doubling as a search engine
  4. Engages and connects with users through personalized conversations
  5. Makes use of automation capabilities to bridge process gaps


Conversational AI – Beyond FAQ Bots

How Conversational AI Works

Conversational AI is all about simplifying the human to machine interaction by understanding user queries and coming up with an appropriate response. But how does it function? The following diagram sheds some light:


How Conversational AI Works


Our process kickstarts with the customer using the virtual chat option (represented on the left) to engage with an agent (or at least that’s what he/she thinks to be). Meanwhile, the AI smartly works at the backend to decode the conversation and churn out relevant replies. This is done by leveraging natural language and machine learning models to understand sentence structure, user intents, and important terms such as dates and times.

For instance, imagine a user coming to your website and using the AI-powered chat option. She inputs the phrase “I want to order a pizza with ham.” How would the AI process this? It would first decode the conversation intent (order food) and would then capture details such as food type (pizza) and choice of topping (ham).
Similarly, had the phrase been “I want to order a pizza with ham but not cheese for tomorrow morning at 8:00”, additional details would have included a negative marker for ‘cheese’ as a topping and the delivery slot would have been set to ‘tomorrow morning at 8’.

If you have been able to follow up until now, it is easy to note that this is possible only when the AI can grasp the context of conversations. The Dialogue Automation Layer is the arm that takes care of this, making the results from the AI engine to sift through a conversation logic. And for a 360-degree overview of the conversation, this layer keeps close tabs on customer profiles by interfacing with back-end systems such as SAP or Salesforce.

Let’s see this in action with another example where a user says “I want to see your specials.” The AI engine quickly jumps in to identify the intent as a “specials request” and passes the information through the dialogue automation layer. The layer quietly engages with the e-commerce system at the backend to retrieve the specials for the day or week. It is important to note here that the communication medium need not always be textual conversations. In fact, besides voice (SSML), images, and videos, it can even be in the form of commands through a unique channel, such as a car.

Realizing Win-Win Benefits: Better Customer Experiences at Lower Costs


Realizing Win-Win Benefits Better Customer Experiences at Lower Costs


Conversational AI and Automation are no longer buzzwords. They have transformed into full-fledged and proven business strategies, one that can be used for multiple touchpoints in customer journeys. And once you scrutinize closely, the benefits are evident - high-quality, consistent, and personalized customer service experiences along with increased revenue and support efficiency.

But are we jumping the gun here or is all this actually possible?

The logic is quite binding. Conversational AI can quickly streamline support infrastructures with the help of self-service portals for customers. This naturally leads to quicker query resolutions, saving countless hours of support agents (and reducing the associated costs)!

To understand this better, imagine a scenario where a customer is unable to understand how a particular feature of your platform or tool works. Instead of engaging a live agent for such a small query, she can now access your knowledge base to find the relevant answer. Although there are some prerequisites - that the customer knows exactly what he is looking for and your knowledge base is powered by an impeccable navigation system - the benefits are too significant to ignore at scale. Also, Conversational AI takes this to the next level by allowing customers to connect not just through the website but also on everyday social media platforms such as Facebook Messenger, SMS, webchat, or telephone.

All this and more leads to natural (and welcome) side effects, answering long-standing industry concerns such as:

  • Consistency: As the number of agents grows, how do we ensure our agents follow best practices and maintain the brand’s integrity?
  • Scale: During seasonal spikes, how do we temporarily grow our team while maintaining quality?
  • Availability: How do we provide 24/7 support while maintaining consistency and quality?

Virtual Agents For Contact Center Automation

Build fast and high-quality virtual agents at scale - without ever writing a single line of code!

Enterprise Platform Selection

Here is a look at the core features your Conversational AI platform should have:

Voice & Text-Based Interactions

You should be able to automate customer interactions on leading chat and voice features, making it easy to manage several conversations in a user-friendly interface. These features include:

  • Low-Code Conversation Editory: Manage all conversations with a flexible and easy-to-use graphical interface.
  • AI-Based Language Understanding: Offers wide language support via NLU engines like Watson and RASA, but you can also bring your own NLU functionality.
  • Omni-Channel Integration: Deploy your bots immediately on 50+ channels.
  • Agent Live Chat: Integrate with your contact center solution.

Open Source Integration Framework

Virtual agents need access to an Enterprise System (CRM or ERP preferred) that includes:

  • Customizations Without Constraint: Easily customize your Conversational AI stack via Javascript, Python, or another programming language.
  • Third-Party Services:Wide language support via NLU engines like Watson and RASA with the added ability to bring your own NLU functionality.

Enterprise-Level Operations

Available as SaaS or on your own infrastructure and made for deep integration into your IT infrastructure.

  • Insightful Analytics: Use granular conversational AI analytics to improve customer experiences and operational efficiency based on actionable insights.
  • Contact Management: Create user-profiles and track information from conversations.
  • Automated Testing: Perform automated regression testing of conversations to ensure business objectives are met.
  • Unlimited Scalability: Use a modern platform stack to provide unlimited scaling to handle peak traffic.
  • Built-In Logging: Access application and system logs to help you stay compliant and quickly monitor your AI stack.
  • Compliance & Data Management: Use GDPR-compliant local data storage, versioned AI models, configurable user profiles, personalization, and logging features.



Strong Technology Partnerships & Partner Ecosystem

Your vendor should have strong technical integrations and be able to provide industry-leading support. This is best achieved by having strong partnerships with other vendors who specialize in CPaaS, Contact Center Technology, Robotic Process Automation (RPA), Intelligent Automation, Agent Assist solutions, and Omni Channel Engagement. Vendors that work with a (global) implementation partner ecosystem are desirable because they have the resources and expertise to provide hands-on implementation support and services such as strategy consulting, integration support, operation, and testing.

Access to industry-leading subject matter experts in Conversational AI can help accelerate your time-to-market, educate or supplement your internal teams, and drive strategy to roll out Conversational AI across your enterprise.

Virtual Agents For Contact Center Automation

Build fast and high-quality virtual agents at scale - without ever writing a single line of code!

Virtual Agents for Enterprises

Automation has been constantly pushing boundaries lately, finding itself as a key ingredient in most business strategies. Any conversation that can be automated in theory is finding itself exploring that arena in one form or another.

And statistics back this up!


Virtual Agents for Enterprises




Given the many benefits of the technology, such widespread usage is no longer surprising. And that’s not all. Even marketers are catching up with the shift by using Conversational AI in email and content marketing.

As with any new technology, most engagements have modest beginnings as experiments or project-based roles. This has led to “Conversational Sprawl”, or the rapid and unexpected growth of Conversational AI which may pose risks to an enterprise.

Unlike previous technologies, Conversational AI poses a security/privacy risk because it helps enterprises collect personal information from users such as PII, passwords, health, and financial information. However, this risk can be mitigated by using marketing, support, legal, and internal business processes to ensure appropriate data management procedures are followed. IT can also be leveraged to establish standards for data integration, governance, and deployment.

The benefits of Conversational AI almost always outweigh the risks. Two key benefits of using a Conversational AI platform are that they are governable and provide multiple user interfaces. Since the platforms are governable, it is possible to make them more intelligent. By integrating with internal systems like CRM, Ticketing, HRIS, or Inventory Management, users can complete end-to-end processes through a more conversational interface. User interfaces may be either code-based or UI-based, which empowers users in and outside of IT and helps avoid bottlenecks.

Conversational AI is particularly valuable for Contact Centers. Service requests can be processed more efficiently and agents can be onboarded more quickly with the help of Conversational AI. This can help lower costs, boost agent availability, and improve customer satisfaction. Conversational AI also makes it much easier to deploy automated virtual agents.


Learn More About Contact Center AI and Automation here.

Center of Excellence

Automation has been constantly pushing boundaries lately, finding itself as a key ingredient in most business strategies. Any conversation that can be automated in theory is finding itself exploring that arena in one form or another.

And statistics back this up!


Building your center of excellence (CoE)

This can be a tricky task since you need to have a complete overview of what works and what does not. Consider the following checklist to ensure that your CoE is competitive enough to add every possible value to your organization.

Conversational Center of Excellence

Conversational Center of Excellence


  • Leverage AI across your organization
  • Be prepared for cultural change
  • Think in customer centricity
  • Demographic change using a smaller workforce


  • Predictable AI
  • Governed AI
  • AI regulations & guidelines
  • Demographic change using a smaller workforce


  • Scalable across the organization
  • Leverage network effects across departments, business units, and line of businesses
  • Centralized AI provider within the organization


  • Start with simple FAQ and move to transactional conversational automation
  • Leverage and learn as you go
  • Build a repeatable process for launching AI projects within your organization

People & Culture

  • Upskill your workforce
  • Deploy virtual agents where repetitive tasks can be automated
  • Let your workforce serve high-priority customers
  • Let your workforce solve complex customer requests
  • Do not block your workforce with repetitive unproductive tasks


  • Consulting, implementation, and technology partner network
  • Support infrastructure
  • Global-scale delivery

Intelligent Automation

The merging of Conversational AI and Intelligent Automation creates unparalleled customer service capabilities. Similarly, the convergence of Conversational AI and Intelligent, smart automation creates an extended omnichannel experience that is purely meant to facilitate faster, more personalized and responsive customer service.

Now the question is - how to best bridge the gap between Conversational AI and Intelligent Automation? The answer lies in the efficient handling of the following key factors:

1. Organization

Make sure that you streamline both internal and customer-facing processes and incorporate automation when possible. Tying up your backend operations with customer-facing functions would result in more value-creation.

2. Seamless End-to-End Automation

Most companies already run an Enterprise Service Bus (ESB), Robotic Process Automation (RPA), or Business Process Management (BPM) practice. Through conversational automation, you can create next-level user interactions with native process automation integration.

3. Technology

Using an open integration framework, you can leverage existing integrations for Kofax, Automation Anywhere, UiPath, BluePrism, SAP, and many more. By unlocking more insights, you can go further by creating your own integrations using an open-source framework to connect your system of choice.

4. Processes

If you are new to Conversational AI, a smart strategy would be to use it to create a simple FAQ bot (or virtual assistant) and then gradually move toward transactional or conversational automation. Follow a “learn as you go” model and work on building a repeatable process for new AI projects in your organization. You can then scale your processes as business grows!

5. Data & Analytics

Seek to make data-driven decisions that are based on user inputs. It pays (both in short and long terms) to learn what users are struggling with and address their most profound concerns via AI analytics.

6. Intelligent Hyper Automation

Bridge the gap between your Intelligent Automation stack - Enterprise Service Bus (ESB) - Robotic Process Automation (RPA) - Business Process Management (BPM) – OCR. This enables you to create a seamless automation experience with customers, which can help extend your omnichannel experience and boost speed and personalization of your customer experience.

Conversational AI Across the Enterprise

It usually starts like this.

An enterprise’s adoption of Conversational AI often starts with a few developers who work with Google Dialogflow or IBM Watson or play around with an open-source natural language processing technology stack such as RASA NLU. It does not take much time for developers to figure out that you actually need more than natural language processing and intent mapping to use virtual assistants.

In fact, you need dialog control, interfacing with corporate systems, and testing of new implementations. This, in turn, drives developers to start writing APIs and additional components to connect the NLU services to the rest of the organization.

For a Conversational AI project to go live, legal approval is needed. In practice, this means addressing specific queries of legal teams that may include the likes of:

  • Have you thought about GDPR and data privacy?
  • How is personalized data processed?
  • What information are we requesting from users?
  • How long are we storing user data?

In order to succeed, It is critical that both developers and the organization consider these questions in addition to how the Conversational AI project will function across the organization. It is important to avoid the trap of building a customized solution that only a few developers know how to use.

Whatever the case, every bold digital transformation initiative requires efficient collaboration to deliver Conversational AI across multiple projects, departments, regions, and languages.

Conversational AI for Customers

Conversational AI is a valuable tool for optimizing sales and customer support. A good website should not only deliver relevant information to customers, but it should also take them through a seamless purchase funnel that is geared towards conversions.

And this is exactly where virtual assistants come into the picture: integrated with the website and helping customers find information, compare options, and easily buy items. Instead of navigating through search tools, customers can simply speak or type what they need, and the Conversational AI arm takes care of the rest.

It even empowers existing customers to have consistent access to information and assistance whenever they need it. For example, customers can easily access their information or request status updates when they log into your app, visit your Facebook page, or sign into your website.

Conversational AI for Suppliers

Conversational AI can also streamline interactions such as vendor setup, quotes, purchase orders, invoicing, and payments. Throughout these interactions, there are multiple questions that are very likely to come up:

  • Has the vendor provided the required insurance information?
  • Has the quote been agreed to?
  • Is there a valid purchase order?
  • Whom do we send the invoice to?
  • Has the payment been received?
  • Was the item or service delivered?

While it is common for such questions to be handled via human interaction or vendor portals, these can often be difficult to use due to a lack of features. Conversational AI becomes a valid alternative that delivers a simple interface to answer these questions and complete orders quickly. And all this while limiting human intervention to only the most complex situations.

Conversational AI for Employees

Conversational AI unlocks great value even at employee levels, doubling up as a go-to IT helpdesk or HR inquiry box. It can answer common IT queries on redundant topics such as laptop support, system access, and password management. Instead of using a laidback and manual ticketing system, a chatbot can engage employees and walk them through many recurring day-to-day troubleshooting processes.

As a direct result, technicians can turn their valuable attention to resolving complex issues that require one-on-one engagement. Similarly, HR applications can help employees complete mundane tasks such as changing addresses, updating contact information, and checking PTO balances - all without any human assistance!

Conversational AI for Any User

At a conceptual level, Automation is possible in any situation that involves a user asking a question that a knowledge base can answer. Conversational AI can therefore span across varying business interactions, deliver consistent issues to problems, and is not necessarily limited to front-end conversations such as those with customers, suppliers, and employees.