Conversational AI

Discover how Conversational AI and EBM can help your organisation to interact with customers and internal users more efficiently, while also offering improved service.

EBM, our enterprise chatbot management platform, is designed and built to integrate with the best-in-class Conversational AI providers:

Microsoft Integration
IBM Watson Integration
RASA Integration

EBM then provides the no code / low code tools and operating model to enable organisations to fully embrace and enhance this technology:

  • Content management. Manage and improve your chatbots easily and intuitively.
  • Collaboration. Empower teams to collaborate more effectively when creating and improving their chatbots.
  • Analytics. Gain insights to learn from and expand user engagement.
  • Integrations. Seamlessly enhance and control the end-to-end customer chatbot journey.
  • Enterprise readiness. Enjoy peace of mind with enterprise-grade security and infrastructure.
So what exactly is Conversational AI and how does this technology help organisations deliver services and support customers and employees…
 
 

What is Conversational AI?

Conversational AI (artificial intelligence) allows enquiries to be handled by technology, in the form of a chatbot or voice assistant. These use artificial intelligence to replicate the kind of interaction that users would expect from a helpful and well-informed human being. Conversational AI can be deployed via either a website or an app and will work across a variety of languages.


How does Conversational AI work?

Conversational AI combines machine learning with natural language processing. This enables it to process and understand what a user is writing or saying, then generate appropriate responses in a natural way. Over time, the system will automatically refine its responses and adapt to changing circumstances. 

Machine Learning (ML) is a form of artificial intelligence that can learn to carry out complicated tasks without explicit instructions like those required by a traditional computer program. Machine Learning algorithms learn by example and can be continuously taught and re-trained over time allowing them to adapt and improve with experience. Starting with a set of algorithms and an initial data set of information, the system will use each new piece of input it processes to get better and better at recognising patterns and making predictions. 

Natural language processing (NLP)is an area of artificial intelligence concerned with the automated analysis and generation of human languages (like English). The NLP systems supported by EBM represent the state-of-the-art in language analysis and include feedback loops that use machine learning to continuously refine the AI algorithms based on user interactions.

The form of natural language processing used in Conversational AI consists of four steps:  

  • Input generation Users provide input in the form of speech or text, delivered through a website or an app. 
  • Input analysis If the input is text, Conversational AI uses natural language understanding (NLU) to interpret what the user is saying. For a spoken input, Conversational AI will use automatic speech recognition (ASR) to turn the sounds into the words, then use NLU to understand their meaning. 
  • Dialogue management An open ended question could elicit millions of possible responses. Dialogue management takes care of understanding how best to respond to the user based on the conversational context and what the Conversational Agent knows about the user’s situation from previous interactions and data from other integrations (e.g. CRM systems).
  • Continuous learning Over time, interactions and feedback are collected from human interactions and machine-learning algorithms refined and tuned to ensure they are as accurate as possible.

Overall, Conversational AI apps have been able to replicate the experience of speaking to a human being well, leading to high rates of customer satisfaction. 58% of users say chatbots have changed their expectations of customer service for good. In the future, deep learning will advance the natural language processing capabilities of Conversational AI even further. 

 

Where to use Conversational AI?

The most popular uses of Conversational AI are online chatbots and voice assistants, but there are many other applications within different types of enterprise. Some examples include:

  • Customer support services Online chatbots can help to free up the time of human agent at many stages, starting with answering frequently asked questions (FAQs) on routine topics like shipping and returns. They can also be deployed to handle complaints and technical questions. While most AI chatbots and apps currently have rudimentary problem-solving skills, they can reduce time and improve cost efficiency by freeing up personnel to focus on more involved customer interactions. 64% of agents who use chatbots are free to spend their time solving more difficult and complex problems.
  • Omni-channel development Messaging bots and virtual assistants on e-commerce sites can provide personalised advice, suggesting the right product for users or cross-selling other items, changing the way we think about customer engagement across websites and social media platforms.  
  • Improving accessibility Conversational AI can reduce barriers to communication – particularly for people who use assistive technologies or speak a second language. Commonly used features of Conversational AI for these groups are text-to-speech dictation and language translation.
  • HR processes Many routine human resources functions (such as training, the onboarding process and updating employee information) can be optimised by using Conversational AI.
  • Healthcare Virtual assistants can make access to healthcare services easier and cheaper for patients. At the same time, they can improve operational efficiency and streamline routine administration (such as claim processing) within the organisation. 
  • Internet of things (IoT) devices Numerous common IoT devices, from mobile phones to smart speakers and smart watches, already interact with users through a form of automated speech recognition such as Amazon Alexa, Apple Siri, Google Assistant or Microsoft Cortana.
  • Software applications Many text-based computer tasks can be simplified by Conversational AI, such as autocompleting search terms, or carrying out grammar and spell checks.

Most Conversational AI applications include extensive analytics built into the backend program. As well as helping to ensure that the conversational experience is as natural as possible, this can be a valuable management tool by helping an organisation to understand the nature and substance of its interactions with users.

 

What are the benefits of Conversational AI?

Conversational AI offers a range of benefits to organisations of all kinds and can be extremely lucrative, helping enterprises and businesses to become more profitable:  

Cost efficiency 

Using Conversational AI to provide customer assistance can reduce business costs, especially for small- or medium-sized enterprises that cannot easily justify staffing a customer service department or helping to drive efficiencies within a customer service department.

Improved customer engagement 

Increased use of mobile devices during more flexible working hours mean that organisations need to be prepared to provide real-time information to their users outside the regular office day. Chatbots and virtual assistants are able to respond instantly, providing 24-hour availability.

Conversations with human agents may provide inconsistent responses to customers. But since most enquiries are information-seeking and repetitive, businesses can use Conversational AI to deliver comprehensive and reliable responses to routine questions. This will free up valuable human resources to handle more complex queries. 

Increased sales  

The ease of access and quick response time offered by a chatbot or virtual assistant means an end to long call-centre wait time. This improves the overall customer experience, with the potential for increased loyalty and additional revenue from referrals.

Chatbots can also be given the ability to provide recommendations, allowing businesses to cross-sell products that customers may not have initially considered. 

Scalability 

Conversational AI is very scalable, because adding the infrastructure to expand it is quicker and cheaper than hiring and training new employees. This is especially helpful when moving into a new territory or new market, or during unexpected high demand/busy periods

Ready to kickstart your chatbot journey?

CTA Footer