A Comprehensive Guide For Conversational Ai V1

A Comprehensive Guide For Conversational AI

Planning to implement conversational AI in your organization? Read this comprehensive guide to get a full understanding of conversational AI, how it works, and its capabilities and use cases.

What is Conversational AI?

Conversational AI is a set of powerful technologies that empower computers to comprehend, process, and respond to human utterances and text/voice inputs naturally. Used in conjunction with chatbots or voice assistants, it helps organizations deliver meaningful and personalized customer and employee engagement economically on a large scale.

Why Conversational AI?

The conversational AI market size is expected to grow from AUD 6 billion in 2019 to AUD 22.6 billion by 2024, at a CAGR of 30.2%, during 2019-2024.

As per Gartner,

With Conversational AI, an organization can benefit from personalized, context-aware, and differentiated customer and employee experiences. Global organizations leveraging conversational AI technologies like chatbots, virtual assistants and voice bots are significantly reducing support costs, streamlining internal operations, improving agent productivity, and delivering powerful customer service. The scope of conversational AI is vast; the channels are rapidly expanding. You may experience conversational AI through the following means-

  • Social media platforms – Facebook Messenger,, WhatsApp, Twitter
  • Enterprise channels – Microsoft Teams, Zoom, Slack, Web
  • Web and mobile messaging apps
  • Voice devices – Amazon Alexa, Google Home
  • Contact Center – Interactive Voice Response (IVR) system

Additionally, advances in NLP and machine learning, the availability of vast amounts of data, flexible app integrations, and low-code bot-building platforms have made conversational AI a force to reckon with.

How Conversational AI works?

Conversational AI uses a plethora of advanced technology components, including natural language processing (NLP), intent and entity recognition, machine learning, natural language generation, dynamic text to speech capabilities. Below we present a walkthrough of how these technologies make conversational AI a reality –

1. Natural Language Processing (NLP)

Natural Language Processing (NLP) is a component of AI and is the ability of a computer program to understand human or natural language as it is spoken/written. NLP helps a bot/virtual assistant understand the semantics of the language being used, including synonyms, canonical word forms, grammar, slang, and logically respond using natural language, consistent with the user’s query.

Learn More:  Understanding NLP and Its Need in Enterprise Chatbots

2. Natural Language Understanding (NLU)

Natural Language Understanding (NLU) is a technology that deciphers the context and meaning behind the user’s words. With NLU, the AI assistant can easily understand the user’s query, even overlooking grammatical errors, shortcuts, etc., and remember the context throughout the conversation.

Contextual awareness is necessary to recall information over interactions and hold natural, human-like back and forth conversations.

NLU goes above and beyond scripted conversational technology that involves giving a pre-programmed response to a particular phrase or keyword.

Natural Language Understanding extracts intent and entities – precisely what the user is trying to achieve (intent) and elements that define what is required to accomplish the task, such as dates, time, numbers, and objects, also known as entities.

Example – I am trying to find a restaurant that sells blueberry cheesecake.

In the above query, the intent is to “find.” The relevant information (entities) required to fulfill the user’s request are “restaurant” and “blueberry cheesecake.”

3. Training Models

- Machine Learning

Machine Learning is a subset of AI that studies algorithms and statistical models, giving computers the ability to perform a specific task without being explicitly trained to do so. With machine learning, bots rely on patterns, inferences, human-agent conversations, and historical interactions to learn and improve their performance.

- Fundamental Learning

Fundamental Learning ensures input information always produces the same output. It determines intent from user utterance using semantic rules, such as grammar, sentence structure, word match, language context, etc.

- Knowledge Graph

Knowledge Graph groups key domain terms according to similarities and differences. The model then associates them with context-specific questions, synonyms, and ML classes.

- Natural Language Generation

After understanding the user’s intent, the conversational AI assistant uses natural language generation to respond in a textual or voice output that is easily understood by the user.

Capabilities of Conversational AI

Through conversational AI, conversations truly feel human-like. Just as humans remember context throughout the conversation, a conversational AI chatbot retains context from one response to the next. Because of its conversational ability, interactions don’t feel scripted. You can hold conversations about anything – as long as the bot has the data to build the conversation.

Below we take you through the various capabilities of conversational AI –

  • Context Management – The most significant capability of conversational AI lies in the fact that interactions will always have the human touch. Conversational AI allows bots to remember critical details from past dialogue, user preferences, and client information, making it easy to deliver personalized user interactions. 
  • Dialogue Management – Human conversations are seldom straightforward and are layered with twists, turns, and context-switching. Examples include but are not limited to pausing tasks, changing entities at any point in time, and processing multiple entities in a single message. Conversational AI allows bots to hold both simple and complex conversations by handling dialogue turns effectively. 
  • Omni-channel experiences – Conversational AI’s distinguishing feature lies in its ability to allow users to start a conversation on one channel (for example, website) and complete it in another (maybe, Facebook Messenger) – without losing context or continuity, so customers receive consistent support. 
  • Real-time personalization – Depending on the user context, needs, and profile, conversational AI enables AI assistants to provide personalized information, products, offers, and other services. 
  • Multilingual support – Conversational AI supports multiple languages, including Italian, Dutch French, German, Spanish, etc. – allowing you to expand to global markets and serve more customers and employees from different geographical locations.

Learn More: Chatbot In Different Languages: A Guide To Multilingual Chatbots

  • Sentiment Analysis – Tone and emotion can significantly alter what you want to convey. Conversational AI identifies, extracts, and signals the type and intensity of a user’s sentiment, allowing bots to steer conversations, change the tone, or bring in human agents for support.

Learn More: Understanding The Role Of Sentiment Analysis In Chatbots

Approaches to Conversational AI

Conversational AI can take one or both of the following approaches –

  1. Reactive Engagement – It can respond to a customer’s query by providing an easy path to find information and answers without reaching out to a human agent. 
  2. Proactive Engagement – It may anticipate the user’s demand in advance and push personalized and contextual information, thus creating opportunities to establish new relationships, intervene at critical moments (for example, when a customer toggles between two product options), and support users 24×7.

Advantages of Conversational AI

By incorporating conversational AI into everyday organizational functioning, you can reap the following benefits –

  • Reduce cost per contact – You can reduce the cost to contact depending on the human-agent calls deflected to conversational AI powered channels, thus improving service resolution and human agent utilization and productivity. 
  • Increase revenue via cross-sell and up-sell – Through proactively reaching out and engaging with customers, conversational AI enables you to create personalized customer experiences, recommend the right products, notify about promotions, up sell, cross sell and build a loyal client base.
  • Uncover data-driven insights – Conversational AI gives your business a competitive edge by providing valuable customer data that can be used for product innovation, improving customer service quality, and personalizing marketing campaigns. 
  • Reduce churn – By providing instant, accurate and 24/7 support for resolving customer issues, conversational AI helps reduce customer churn.
  • Improve employee productivity – Conversational AI improves employee satisfaction and motivation in more ways than one, some of which include –
    1. The virtual agents can complete mundane, routine tasks, allowing agents to focus on critical and high-value work
    2. The AI bot can draw-up information from the enterprise systems like CRM that helps human agents serve customers quickly
    3. Employees can easily access necessary data insights from analytics systems, thus empowering them to take more data-driven decisions.
  • Scale efficiently – With conversational AI operating 24×7, you can serve more customers and employees across business units and geographies, even outside your organization’s working hours.

Learn More: 10 Powerful Benefits Of Chatbots In Customer Service

Conversational AI: Customer and Employee Use Cases

1. Employee Processes

- IT and Security Management

The IT helpdesk is often inundated with routine questions.  As soon as a request is raised, IT chatbots help the user do basic troubleshooting and in most cases fix the issue and thereby reduce the employee downtime.

If the issue isn’t resolved or the user isn’t satisfied with the outcome, bots provide the option to connect with a support agent – thereby leaving the more complex queries to human agents. This leads to faster resolution times, improved incident management, improved security, better handling of outages and ensuring that employees are kept informed with steady and timely alerts.

Some of the use cases include:

  • Check the status of tickets
  • Answer common troubleshooting questions like VPN or password not working
  • Ask instructions for common IT issues
  • Reset passwords for devices and network
  • Talk to a live agent (human-handoff)
  • Raise tickets
  • Fill form fields via conversation
  • Access the knowledge base
  • Check on the pending case reports
  • Look-up case-related information
  • Receive information on – Incident notifications, New change request notifications, Task notifications, Access request notifications, Asset request notifications and Outage alerts

- Sales

AI-enabled sales virtual assistants can integrate with data warehouses, CRM, BI and LOB Systems to perform tasks such as creating new leads, updating lead status, getting visual reports in multimedia formats, updating CRM records etc.

Use cases include:

  • Check the lead status
  • Ask pinpointed prospect-related queries
  • Check on the sales KPIs
  • Get pin-pointed answers of any information available in the CRM or BI systems.
  • Fill lead details
  • Send email of the desired dashboard
  • Set and get alerts about dip or rise in any sales KPI.
  • Receive notifications about change in lead’s status
  • Access reports available in the CRM, BI or LOB or DWH systems.
  • Get links to the desired dashboards

- Marketing

Conversational AI marketing assistants can gather data about potential customers that equips marketers with essential information to design their products and advertising strategies. They can be integrated with various social media channels and used to reach out to customers of various demographics.

Following are the use cases:

  • Lead generation
  • Lead qualification
  • Book sales meetings
  • Schedule demos/consultations
  • Suggest relevant content based on user’s website activity
  • Capture email addresses and other visitor details in a simplified manner

- Intranet Assistant

Employees can use the company’s intranet chatbot to perform simple actions such as checking on internal company updates, accessing documents, applying for leaves etc.

Use cases include:

  • Proactively take the announcements and news in the intranet to the employee
  • Get intranet information via natural language questions
  • Get links to desired intranet documents
  • Content authors can update content with a chat interface
  • Get personalized alerts and timely updates
  • Perform tasks like leave requests, travel settlement requests, IT requests etc.

- Human Resource

AI-enabled virtual assistants can be used at different stages of an employee’s life cycle – right from recruitment and onboarding to engaging the employee and fostering retention, in order to optimize the whole process.

Use cases include:

  • Delivering updates about the status of an application
  • Responding to FAQs, thereby saving the recruiter’s time and efforts for other tasks
  • Conduct and record feedback surveys from the candidate about their recruitment process and gain insights on any areas of improvement
  • Reduce recruitment time by qualifying and disqualifying candidates swiftly at scale
  • Automate the manual and administrative recruitment work
  • Streamline and speed up the process of collecting and recording KYC documents, tax forms, etc. by tracking the same for employees and reminding them to submit the required documents on time
  • Share standard operating procedures and company policies, and severely reduce the HR workload by handling the process and queries online

2. Consumer Processes

- Banking and Financial Services

Conversational AI in banking aims at delivering personalized customer services to improve customer satisfaction and engagement. Some of the use cases include:

  • Checking the account balance, transaction history, credit limit etc.
  • Help in upsell
  • Finding the nearest ATM or branch
  • Inquiring about different offerings and products
  • Generating a mini statement for the desired time period and the interest rate report
  • Updating contact information
  • Connecting to a live agent (human hand-off)
  • Transferring money from one account to another
  • Suggest money saving ideas
  • Generating bill payment alerts and personalized financial advice
  • Resetting the card PIN

- Retail

CPG and retail companies are increasingly using conversational AI to transform customer experience.  Following are the use cases in the retail sector –

  • Product exploration and discovery
  • Product recommendations
  • Check the shipment status
  • Add items to cart
  • Place orders
  • Book appointments
  • Connect to customer support agents
  • Provide product related information, and alerts on a new product launch, and suggestions on discounts or coupons or any other sales offers

- Insurance

Insurance companies may use conversational AI to handle routine customer questions, address minor insurance related challenges, provide quotes, automate the claim process, and reduce call center costs.

Therefore, some of the use cases in insurance are –

  • Help in filing a claim
  • Answer scheme and plan related questions
  • Provide recommendations to prevent loss
  • Provide guidance for choosing the right plan
  • Send Insurance documents of the customer
  • Send personalized quotes to users

- Healthcare

AI-powered virtual health assistants and health bots offer users personalized access to health-related information and natural conversational experiences.

Providers, pharmaceutical companies, and insurance companies can use conversational AI for certain healthcare-specific use cases, including –

  • Inquire about the status of an insurance claim
  • Ask queries about health plan pricing, benefits, and services
  • Triage patient issues with a symptom checker
  • Find general information about conditions, symptoms, causes, etc.
  • Schedule doctor appointments
  • Patient registration, delivery of post-op instructions, etc.

- Education

Chatbots are changing the face of education right from personalizing education, helping people learn new languages, spaced interval learning, student feedback, professor assessment, essay scoring, acquaint students with school culture and for administrative formalities.

Uses cases include –

  • Handling queries related to the university and courses during registration, assessment related questions, tuition fees, time tables, scholarships, grades etc.
  • Get university policy documents, enrollment certificates, academic information, disability and other personal information
  • Provide course and administration related information
  • Access course documents
  • Handle university registration
  • Send feedback about professors, courses etc.
  • Update contact information
  • Register for courses
  • Fill applications
  • Apply for permissions

Getting Started with Conversational AI

The technologies that support conversational AI like NLP, machine learning, conversational interfaces and advancing at a rapid pace. As virtual assistants and chatbots become more popular across the globe, your customers expect powerful self-service conversational experiences from your brand as well.

In order to stay competitive, enterprise can no longer ignore the business value delivered by these technologies. Laggards in this space will fail to keep pace with the evolving customer and employee needs and get left behind.

Here are some tips for organizations getting started on their conversational AI journey:

  • Defining the strategy – Understand how conversational AI can be integrated into your organization. Capture the right use cases by collaborating with different business units.
  • Build a powerful business case – An enterprise-wide conversational AI journey involves cross-functional stakeholders. Spread awareness internally on the benefits of conversational AI and clearly demonstrate the ROI.
  • Evaluating solutions – Choose the right enterprise conversational AI platform that helps you build, deploy, manage and train virtual assistants. You can read market reports from industry analysts like Gartner to compare different vendors.
  • Assess your readiness – Determine your existing human and technical resources and identify the gaps to be filled.
  • Define Metrics: Clearly quantify the business value the project delivers. Establish realistic metrics like increased CSAT, reduction in cost per contact, reduction in support costs etc.
  • Launch pilot projects: Pick a high-value use case and launch a pilot project. Monitor the virtual assistant performance over time and document the lessons learned.
  • Scaling and optimizing – Continuously scale, improve, and optimize conversational AI technologies and look for opportunities to streamline workflows.

Free bot workshops like Acuvate’s Build-A-Bot program helps you get a subject matter expert opinion to plan your bot journey, identify specific use cases within your enterprise and evaluate different technologies. Acuvate provides a 1 day bot strategy workshop within your company premises for both business and IT leaders.

Introducing BotCore

Over the past several years, we have helped several enterprises build & launch chatbots to meet the needs of their employees, customers and vendors. We leverage our enterprise chatbot builder platform – BotCore to build, deploy and manage custom chatbots for companies across industries.  BotCore is fully deployable on both on-premise and cloud environments.

If you’d like to learn more about this topic, please feel free to get in touch with one of our enterprise chatbot consultants for a personalized consultation.


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