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The Ultimate Guide to Conversational AI in 2022



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AI has been evolving over the years and is becoming more valuable by the day. As a result, plenty of businesses are now investing in AI.

AI is being used by businesses to improve customer experience as well as the employees working at the company. Not only does AI cut down the amount of time it takes a customer to get help, especially when it is something that can quickly be sorted by an AI, like a booking change or cancellation, but AI can also save your employees valuable time that can be spent on other tasks.

By 2026, the conversational AI market is expected to reach 18.6 billion dollars. Not just is it rapidly growing, but more than half of companies believe that conversational AI is disrupting industries and believe that their competitors will most likely implement such technology.

As you can see, conversational AI is becoming a crucial part of many businesses marketing strategies and customer service.

Getting the hang of conversational AI and implementing it in your business is essential, which is why today we’ll be looking at the ultimate guide to conversational AI in 2022.

What is Conversational AI

Conversational AI is like the upgraded version of a simple chatbot. It’s used to send automated messages and have conversations between computers and humans. It’s still a chatbot but can have a more human-like conversation.

They can communicate like a human by understanding the intent of sentences and then replying in a text that imitates that of a human. The idea is to use these conversational chatbots to engage with customers and make them feel like they’re talking to a real human.

This allows them to feel more important and that their experience is personalized.

A chatbot is also faster and can deal with smaller issues that might take a human longer to respond to and fix.

Chatbots: Who invented them?

ELIZA was the first chatbot recorded in the history of computer science in 1994. It was made by Joseph Weizenbaum at MIT. It was here that the term “Chatterbox” was created.

ELIZA worked by recognizing keywords or phrases from the input and then used those keywords to send a pre-programmed response back. Obviously, this means that ELIZA wasn’t very personalized and would often give the same reaction to different phrases or sentences.

For example, if you mention your family, like, “My father is a fisherman,” ELIZA would reply, “tell me more about your father.”

ELIZA recognizes the word “father” and has an automated response linked to that word. So anytime the word “father” or “dad” is written, it will offer the same answer.

Chatbot operating in live

Tell me the difference between conversational AI and a traditional chatbot.

It’s easy to confuse conversational Ai with an average chatbot, but there are enough differences to separate them from each other.

Conversational AI is at the core of what makes chatbots and virtual assistants tick.

Conversational AI uses machine learning to allow it to analyze and comprehend what humans are writing. From there, it can generate a response that correlates to the user’s writing.

Chatbots can use conversational AI, but there is plenty that doesn’t. For example, basic chatbots usually use pre-determined responses or are programmed with rules instead of an AI deciding what to answer.

Conversational AI isn’t rule-based and chooses how to respond according to the context and intent of the user’s response.

A recent study suggests that by 2030 the conversational AI market will reach 32 billion dollars. It’s currently being invested in by plenty of companies with no end.

How does conversational AI work?

Conversational AI uses a platform of structures that can send individual outputs depending on the input.

Using machine learning, conversational AI can keep learning and widen its range of queries to which it can successfully answer or respond. This is because each time a user talks to the AI, it can examine the context and intent of the user’s response, thus learning new questions that might need the same answer.

It might seem simple initially, but machine learning is much more complicated than questions and responses. Therefore, having the right AI structure is crucial.

Here are some of the primary components that make up the natural language processing of conversational AI.

  • Machine Learning (ML). Machine learning is part of AI built around algorithms and data sets constantly developing and improving. These algorithms learn from previous messages with humans, learning what a human’s response is to specific questions and answers and what the correct response is to the human response.
  • Natural Language Processing (NLP). This is a method of language learning that works together with machine learning. It’s currently being used, but with deep learning around the corner, most conversational AI will switch to deep learning to help AI understand the language better.
  • Analyzing Received Input. This is the part where AI analyzes the text sent in by the user and scans to figure out the context and intent of the message.
  • Dialogue Management: After NLP is done and the input has been analyzed, the AI needs to reply with an appropriate response. Dialogue management is where the AI decides which answer is best suited to send to the user, using the previous processes to choose the response.
  • Reinforcement Learning: Lastly, the user’s and the AI’s response is stored. Then, machine learning analyzes the input and output and whether they match correctly. From there, machine learning can check whether the user’s intent and the AI’s answer matched and better learn to answer the following similar input.

What is conversational AI used for?

Most people have encountered some form of conversational AI before and might not even have known they were talking to AI instead of an actual human. Some chatbots are easy to spot, but some aren’t.

Customer service

There are plenty of uses for conversational AI. For example, if you’ve ever spoken to customer service using a messenger on their website, there is a high chance that it was a chatbot. It’s regularly used for customer service at this point since FAQs are easily programmed as responses for the chatbot, as well as managing bookings, schedules, and cancellations.

IT desk service

Conversational AI can also be used for IT desk service, helping with basic IT queries and fixes. Instead of keeping IT personnel busy all day with simple fixes, chatbots can help people who might have simple fixes and solutions. Chatbots can still send users through to a real person if the problem cannot be fixed.


Conversational AI can also be used to advertise and sell products. These bots can be set up to offer promotions or just sales and send them to a target audience. If you have a well-set-up chatbot, it should be able to address the person by their name and possibly know some basic information about them.

These bots can get users to sign up for subscriptions or down the funnel toward your product page.

Data Collection

Many businesses forget that conversational AI can be used to collect data.

With countless interactions a day, your conversational AI program should be able to store all the information gathered throughout the day and offered specific analytics about the day’s activities and messages.

  • Record all messages and customer calls.
  • Make all conversations searchable, so you can identify issues customers might be having.
  • Track specific keywords related to issues on all calls and messages and look for customer replies.
  • Collect essential data like call times, how many daily responses, and the outcomes of the reactions for the day.

Conversational AI examples across industries

Conversational AI is used across plenty of different industries for different uses. Here are three examples of conversational AI used in various industries.


SmarAction is scheduling automation software with built-in conversational AI that can understand queries about bookings, which we all know can be more complicated than just giving a date and time and booking it.

This AI excels at understanding natural language and can handle any scheduling issues or requests a user might have.

Watson Assistant

IBM created Watson Assistant, and who better create a conversational AI that can take care of customers’ transactions?

This AI assistant can work in many industries, including fashion and healthcare.

It’s able to answer simple questions, execute transactions, and contact agents when the need is there.

A study showed that companies using Watson Assistant could reduce handle time by 10%, improving customer satisfaction.


Cognigy is an excellent conversational AI tool that allows for efficient customer service 24 hours a day.

Cognigy is best used for customer service, optimizing the time it takes for a customer with queries to get the necessary answers.

Plenty of airlines use this software. This became especially the case after Covid when airlines had to deal with numerous customer service-related issues due to cancellations and refunds. Here, AI like Cognigy can be utilized to reschedule or refund customers that qualify without contacting a customer service representative.

If you’re looking for more conversational AI tools, look at this list of the best conversational AI tools.


With plenty of uses for conversational AI, it’s no wonder it’s been slowly taking over specific business sectors. Of course, it’s not to say that you’d never need to speak to a real person, but with simple tasks, conversational AI can speed things up when real humans are too busy with other, more important things.

Featured Image Credit: Photo by Andrea Piacquadio; Pexels; Thank you!

Shane Barker

Shane Barker

Shane Barker is a digital marketing consultant for 15 years with an emphasis on Influencer Marketing in the last 5 years. He is specialized in sales funnels, targeted traffic and website conversions. He has consulted with Fortune 500 companies, Influencers with digital products, and a number of A-List celebrities.


Fintech Kennek raises $12.5M seed round to digitize lending



Google eyed for $2 billion Anthropic deal after major Amazon play

London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

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Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs



Deanna Ritchie

As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations



Deanna Ritchie

As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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