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Read the blog to find out how we bust six myths pertaining to conversational AI and put an end to them once and for all.
With technological innovation happening at an alarming pace, it is very easy for people to start circulating their understanding or interpretation of the new tech. But this isn’t true in any sense. These myths are simply interpretations of the technology by people who haven’t been able to fathom its extent. And conversational AI, thanks to its learning capabilities and interaction abilities, falls exactly into this category.
The more you talk to a predictive AI, the more difficult it would be for you to contemplate how it does what it does. The technological background behind a voice AI runs very deep indeed. That is exactly why there is a major sense of urgency surrounding its implementation in the business sector.
Thanks to its machine learning capabilities, the AI is completely capable of learning from every conversation it has and is an improved iteration of itself the next time you engage in a conversation with it. But how does all this happen? What is the science behind conversational AI? Read on to find out how we bust six myths pertaining to predictive AI and put an end to them once and for all.
This assumption about predictive AI is false in all senses. Conversational AI is far from simple machines that can give you basic automated responses. They don’t just perform a keyword search and provide you with a standard google webpage response.
Voice AI is a lot more complicated than that. The technology responsible for a chunk of a conversational AI’s capabilities is Natural Language Processing (NLP). NLP further branches out into NLU (Natural Language Understanding) and NLG (Natural Language Generation).
Human language is highly unharmonized. So, to make complete use of this, the AI has t be capable of deciphering what the customer is trying to say. This is where NLP makes a stellar entrance. NLP can go through conversations, understand what the customer is trying to say, and decipher customer intent.
It contextualizes what the customer is trying to say and figures out the best possible response from a plethora of automated responses. In fact, a study conducted by Comcast says that there are 1,700 alternate responses to the phrase, ”I’d like to pay my bill.” This is how deep NLP runs. And the funny thing is, predictive AI is not simply NLP-based.
NLP is a part of seven other pieces of technology that power conversational AI. They are voice-optimized messages, machine learning, contextual awareness, entity recognition, intent recognition, fulfillment, and dynamic text-to-speech. So making use of all these technologies, voice AI can easily understand what the customer wants. Not just that, but the more responses the AI gathers, the more it learns and the better it performs.
Pre-built conversational AI templates are the worst way to have your business make first contact with automation. But why so? First off, the AI template will be pre-built. Since that is the case, personalization goes out of the window. Forget personalization for your company; the AI won’t be personalized to your business either.
Secondly, it lacks customization. Since the AI does not have flexible learning capabilities, you will have to spend a lot of time feeding conversations into the model and training it. Or, instead, just use the pre-built conversational model, which would be a very frustrating ordeal for the customers. You will never be able to achieve full-scale integration of predictive AI with your business in the case of a template.
Thirdly, security is a concern when it comes to a pre-built conversational AI template. Since the template has been created with zero code/low code, they are not really designed to approach more than one scripted problem at a time. So if complex problems do appear in the future, the AI template will be incapable of coming up with a unique and out-of-the-box solution to the problem.
Machine learning is not magic. Although it opens up a plethora of new possibilities, it cannot go out there and learn independently. In a way, it does, but that is only possible because of the data it is being fed. It can only learn from the constant data feed being supplied to it.
Data scientists have to prepare the data, determine appropriate datasets, and update the software to train the ML model to its full capabilities. The ideal solution for the seamless functioning of your ML model lies in appointing a team to supervise your AI training.
This team must consist of non-technical members, as well as developers. After using rules-based tools and rigorous neural net training, your predictive AI is good to go.
Conversational AI is highly complex and needs a person familiar with the technology behind it to set up. The business minds of your company are not entirely familiar with the technology behind the predictive AI, and therefore is never a good option to have them make the AI related-decisions.
Now, the ideal person to set up your AI shouldn’t only be someone who knows the tech inside out but also has strong relations with your customers. This person completely understands all aspects of the business and is in the right frame of mind to set up to its optimum capacity.
Suppose you are planning to set up a voice AI and have it hyperautomate. In that case, it is always a good idea to create an ecosystem where your hyper-automation can thrive. Once you have created a personalized ecosystem according to your business’s needs, you will have valuable insights and solutions in no time.
This is a widespread misconception pertaining to voice AI. The one thing you have to understand about conversational AI is that they are specialists, not generalists. Therefore, they will face certain difficulty making general everyday conversations with you. This is because they aren’t made for this. Not even the big names like Siri and Alexa.
The workaround for this problem is that if your brand is dear to your heart, a conversational AI should not be allowed to freestyle language on your behalf. Always remember that it is a part of your customer support team, not its replacement. They will learn as much as they can from existing data fed to them and conversations with customers.
Once their model is trained, they simply assist customer support teams and make their work a lot easier.
Setting up a predictive AI to its optimum extent is still relatively new to the business sector. Since it is such a new piece of technology, companies are wary of setting it up themselves in fear of making errors. Very few companies worldwide have successfully implemented a voice AI into their system and have it function at maximum efficiency.
Also, you have to remember that there are no third-party experts in conversational AI who will bring you up to speed with the push of a button. That would be a great idea, but sadly, it isn’t in the realm of possibilities. There are still consultancies who will help you out, but be wary of where you invest.
Train your team to successfully implement the system instead of wasting your resources on training members outside your company.
Hopefully, by now, you have a proper understanding of how complex it can be to integrate a conversational AI into your system seamlessly. Hence, it is about time to let go of all the myths pertaining to a voice AI and take the necessary steps to integrate it seamlessly with your system.28
AI is capable of learning, but it does need a push. If you are planning to incorporate one, you must appoint a whole team to take care of the day-to-day operations of the predictive AI. Open a 1:1 communication channel, and keep iterating and improving your AI every single day for a personalized conversational AI.
Rakhin has over 10 years of experience driving business development and client services. In his prior roles, he stayed close to customers to understand their requirements and help them achieve their business goals. He is passionate about customer success.
Published 3 Dec 2021, Updated 3 Dec 2021
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