Are chatbots conversational AI, we hear you ask? Well, yes and no, because not all chatbots are conversational AI. This can be unclear, especially if you want to incorporate a chatbot into your project—an app, website, or customer service platform.
With the growing number of different types of chatbots and related technologies, it’s hard to know where to start or which solution is right for you. Understanding what you need from your Chatbot is a good start, but let’s get to the heart of the issue. You don’t want to invest resources into a chatbot and end up with one that doesn’t align with your goals or customers’ expectations.
In this quick guide, we’ll explore the difference between chatbots and conversational AI, demystifying the jargon and giving you the insights you need to make an informed decision.
Traditional chatbots use rules and decision trees. Meanwhile, conversational AI uses natural language processing (NLP) to understand human language and machine learning (ML) to learn from previous interactions. Conversational AI is the technology AI chatbots use to respond in such a human-like way.
What Does Conversational AI Do?
- Uses NLP and Natural Language Understanding (NLU) to understand and interpret human language.
- Generates relevant responses that make sense in context so you don’t end up in an annoying game of digital telephone.
- It uses ML to learn from interactions over time.
- Engages users in conversations that flow naturally, without those awkward “I’m sorry, I don’t understand” moments.
Natural Language Processing
Natural Language Processing (NLP) is like a language tutor, allowing computers to understand the nuances of human conversation.
- Text Analysis: NLP breaks down text into pieces, using tokenization (breaking text into words or phrases) and part-of-speech tagging.
- Semantic Understanding: Here, the system looks into the meaning behind the words. It uses sophisticated algorithms and pre-trained models to grasp context. It’s like teaching a robot to actually know when you’re using sarcasm.
- Generation: NLP uses text generation to provide real-time, conversational responses used in chatbots and virtual assistants.
Machine Learning
Machine learning (ML) is like teaching a computer to learn from previous experience. Rather than sticking to pre-written instructions like traditional chatbots, ML allows computers to discover patterns and make decisions by analysing data.
Here’s a rough breakdown of how it works:
- Gathering Data: Whether it’s images, text, or numbers, having more data supports the learning process.
- Choosing an Algorithm: This is the method for learning. Different problems require different algorithms—mathematical models that sift through data to identify patterns.
- Training: Just as students prepare for exams, the algorithm “trains” on the data. It tweaks its parameters to get better at making predictions or classifications.
- Testing and Validation: After training, we need to assess how well the algorithm has learnt. Testing it with new data ensures the model performs effectively and hasn’t merely memorised the training data.
- Making Predictions: Once trained and validated, the algorithm can use what it’s learned to make predictions or decisions based on new information.
Basically, machine learning helps computers get smarter over time by learning from the data they process.
How It Can Help Your Business
Conversational AI can be a valued tool for any enterprise. Here’s how it can help you out:
- Enhanced Customer Service: Conversational AI can handle customer queries around the clock, offering quick, accurate responses that keep customers happy and reduce frustrating wait times.
- Cost Efficiency: With routine interactions automated, you can save money on operational costs—leaving your human team to tackle the trickier stuff.
- Personalisation: Forget cookie-cutter responses. Conversational AI can tailor interactions based on user data, making customers feel like they’re getting the VIP treatment.
- Scalability: Whether you’re dealing with a handful of queries or a flood, conversational AI can keep up, handling multiple interactions at once without breaking a digital sweat.
- Improved Insights: Conversational AI isn’t just chatting—it’s listening. Analysing conversations can uncover valuable insights into what your customers want (and what they don’t).
What Are the Use Cases?
AI chatbots are just one use of conversational AI. It’s versatile, with applications across various industries:
- E-commerce: Need a personal shopper? Conversational AI can assist with product recommendations and tracking orders.
- Healthcare: Consider it a digital nurse, connecting users to patient support, scheduling appointments, and even helping with symptom checking. To see what we mean please have a look at Pat, the Chatbot we built with Positive East.
- Banking and Finance: From sending alerts and notifications to providing personalised financial advice.
- Human Resources: Streamline onboarding, answer employee questions, and support training.
- Travel and Hospitality: Whether booking or being a personal travel guide.
- Education: 24/7 student support with instant answers to questions, personalised feedback and a virtual teaching assistant.
Choosing the right conversational AI for your business starts with knowing what you want it to achieve. Avoiding common pitfalls is crucial, so if you’re still on the fence, check out our previous blog on Chatbot Fails: Why They Fail and How to Fix Them. It offers valuable tips on steering clear of the usual missteps and getting the most out of your AI-powered tools. With the right approach, you can ensure your Chatbot isn’t just talking—but really helping.
