What does the next-generation conversational AI look like? Most likely conversational AI tools like ChatGPT, Bard, Klaus, and the many others built on Large Language Models have opened your eyes to the possibilities. Generative AI makes it seem like, more than ever, the chatbot resembles a human.
This is great, and already in customer service, retailers are plugging in product information, FAQs and other domain information to train the AI. This means the AI can have up to the minute information. But what's next?
Imagine having a virtual assistant that not only understands your every word but also picks up on your tone, emotions, and intent. It's like having a super-smart, always-available buddy who's there to help you out, 24/7. And the best part? It's happening right now, and it's changing the game for businesses and customers alike.
From Basic Bots to Advanced Virtual Assistants
Remember the early chatbots? They were pretty rudimentary, often just providing scripted responses to specific keywords. But over time, thanks to advancements in machine learning and natural language processing, these bots have evolved into advanced virtual assistants that can understand and respond to human language in much more natural ways.
Today's AI platforms enable the creation of virtual assistants that can engage in contextual conversations, remember previous interactions, and even adapt their language and tone to better connect with users. It's a far cry from the early days of simple pattern matching and canned responses.
Generative AI and Its Impact on Conversational Systems
One of the most exciting developments in conversational AI has been the rise of generative AI. This technology has revolutionized the way machines can mimic human conversation by enabling them to generate new responses based on patterns learned from vast amounts of conversational data. With generative AI, chatbots and virtual assistants can engage in much more natural and dynamic conversations.
They can understand context, handle ambiguity, and even inject personality and emotion into their responses. This has huge implications for customer interactions, as it enables businesses to provide much more human-like and engaging experiences through conversational interfaces. At the heart of these advancements in conversational AI are large language models (LLMs).
These AI models, trained on massive amounts of text data, have become the cornerstone for developing AI solutions that can engage in complex conversations. LLMs have unparalleled capabilities across multiple modalities, including text, image, and video. They can understand context, learn from mistakes, and even use tools like code or video transcriptions to enhance their conversational abilities. It's thanks to LLMs that we're seeing conversational AI systems that can handle much more nuanced and contextual interactions. They can maintain coherence over long conversations, draw insights from various sources, and even generate creative or analytical content within the conversation.
The next stage: Deep integrations
This is the foundation on which great virtual assistants are built. But the truth is that most customer service professionals have barely scratched the surface of what is capable, and that's because of a lack of deep integrations.
If you think about the questions you get asked most frequently as a customer service professional, how many of them are about product features, and return policies? These are both things that are most likely somewhere on your website, so it's just about surfacing the information. Generative AI is great at that, but it probably accounts for maybe 10-30% of your queries, unless you have a particularly complex product.
For everyone else, the most common queries are to do with an order – where is it, can I change it, can I cancel it, etc. – or returns and refunds. Those queries and those like it make up most of what our customers see, for example.
To enable Conversational AI to actually solve these problems for customers means creating deep integrations with a range of other platforms. These range from order management systems, ecommerce platforms, payment systems, carriers and logistics platforms, and many more.
An example: returns
Take returns for instance. A "simple" conversational AI could tell a customer what the return policy is, summarise what the process is, and perhaps even offer some helpful tips.
With deep integrations, the conversational AI can connect to an order management system or CRM and find the exact order. It can then pull out all the items from that order and ask which one(s) the customer wants to return.
With that answer, the AI assistant can then enter the relevant details to generate a return label. It can also alert the receiving warehouse. If the customer wants to return because of a bad fit and wants an exchange, then the replacement order can be triggered when the return label has been scanned. This is only possible if the AI assistant is able to integrate deeply to fetch information and also write data into the system.
These kinds of deep integrations are the ones that enable On to generate return labels automatically and sort out refunds and returns here. This has allowed the team to save over 800 hours each month.
Conclusion
Next-generation conversational AI is here, and it's not just a fancy buzzword. Think of it as flipping the script on our usual tech interactions and business dealings. Imagine operations running smoother than ever, alongside experiences crafted to fit you perfectly - the sky's truly the limit.
So, whether you're a customer looking for a more engaging and efficient way to get things done, or a business aiming to stay ahead of the curve, embracing next-generation conversational AI is the way to go. Trust me, once you experience the magic of these AI-powered conversations, you'll never want to go back to the old ways.
The future is now, and it's all about making technology work for us, not the other way around. Are you ready to join the conversation? Talk to our team today.
