There’s no denying that Generative AI is all the rage right now. Everyone has an opinion on its influence and capabilities, companies are exploring how Gen AI is being used in their solutions, and some early adopters are really pushing the limits of what can be created using the technology, that to date, lacks any real governance.
That said, there’s a lot of noise in the market right now, and for good reason. If there’s one key area of the tech sector primed to shout about use cases for ML, AI and Gen AI, it’s within contact centres. So let’s take a step back and delve into the meanings and use cases for Machine Learning (ML), Artificial Intelligence (AI), and Generative AI within Contact Centre software and applications.
Machine Learning, AI, and Generative AI are all related technologies, but they serve different purposes and have distinct characteristics in the context of contact centre software and applications. We take a look at each of the technologies below.
Machine Learning (ML)
Machine Learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
In the context of contact centre software and applications, ML can be applied in various ways, such as:
Predictive Analytics: ML models can analyse historical data to make predictions about customer behaviour, such as predicting which customers are likely to make a purchase.
Routing and Queuing: ML algorithms can optimise call routing by considering factors like customer profiles, agent availability, and historical data to connect customers with the most suitable agents.
Sentiment Analysis: ML can analyse customer interactions (text or voice) to determine customer sentiment and help agents respond appropriately.
Quality Assurance: ML can be used to monitor and evaluate agent performance by analysing customer interactions and providing feedback.
Artificial Intelligence (AI)
A general definition of AI states that it is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Where ML supports organisations in predicting behaviour, optimising processes, analysing interactions and monitoring performance based on the data available, AI has the ability to apply the data using human characteristics and manners to also predict behaviour, triage situations, and simplify actions prior to human intervention. We see this occurring in the following applications:
Natural Language Processing (NLP): AI can analyse and understand human language, enabling chatbots and virtual agents to interact with customers in a more human-like manner.
Predictive Analytics: AI can analyse historical data to make predictions about customer behaviour, such as predicting which customers are likely to churn.
Speech Recognition: AI can convert spoken language into text, making it useful for transcription services and voice-based applications in contact centres.
Image and Video Analysis: AI can analyse images and videos to extract relevant information, such as identifying objects or sentiment analysis in customer videos.
Generative AI is also a subset of AI that focuses on generating new content, typically in the form of text, images, or videos. This technology uses neural networks and deep learning techniques to create content that is often indistinguishable from human-generated content – writing a story, creating artworks, even rendering fake imagery. However, in relation to its use in contact centre software, Generative AI can be used for:
Chatbot Responses: Generating natural-sounding responses in real-time for chatbots, making customer interactions more engaging and efficient.
Content Creation: Generating marketing copy, email responses, or other textual content for communication with customers.
Voice Synthesis: Creating synthetic voices for interactive voice response (IVR) systems or virtual agents to provide more personalised and dynamic interactions.
When used in a controlled or restricted environment, the data set available to support Generative AI capabilities ensures the responses and interactions are specific to the organisation leveraging this technology with their customers.
In summary, AI encompasses a wide range of technologies and approaches, including Generative AI and Machine Learning. Generative AI is focused on content generation, while Machine Learning is more about pattern recognition and prediction. In the context of contact centre software and applications, these technologies can be used individually or in combination to enhance customer service, automate tasks, and improve overall efficiency.
Genesys Cloud CX provides native AI and ML technologies to help enhance customer experience within the contact centre. Through the use of predictive engagement, voicebots and chatbots, predictive routing, agent assist, speech and text analytics, and workforce engagement tools, you have a suite of capabilities baked in to support revenue growth and to boost performance.
To explore this journey further, please reach out and a team member will be in touch.