Intent-driven Data Critical for Sales Growth

Many of us get the understanding of artificial intelligence from the film industry. It creates an image of smart, humanized machines that are helpful, efficient and omnipresent. It is true that AI has seen rapid advances in the past several years, to the point that it became an integral part of our everyday life.  In real life, however, AI is far away from the level portrayed in sci-fi movies. And yet there are affordable AI tools and solutions that can make a significant impact on your business.

Here are three main reasons why a company, especially if it is a B2C company, should consider integrating AI into their business process.

AI makes your sales process scalable

AI solution dealing with your prospects and customers works 24/7 without sick days, holidays and breaks. It can handle any level of traffic, incoming inquiries and conversations. It does not need to be trained. It does not have personal issues or bad days. It is always polite and uses professional jargon. It is fast.

AI creates better user experience

Some might find it surprising but this is only because they have experienced low quality AI solutions. A professional AI solution makes customer experience better primarily because it delivers the results with a minimum of fuss and maximum efficiency. A good AI eliminates bureaucracy, makes customer experience speedy and seamless, and that’s what consumers are looking for today.   

AI offers sustainability

Adding AI to your business model creates long-term sustainability for the business. It allows your business to grow while controlling, or even minimising the costs. More importantly, it ensures that the business remains competitive in providing the level of customer service consumers became accustomed to. Lastly, it creates platform for future technical improvements and integrations which, without a doubt, will be based on Artificial Intelligence components.

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically)  that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.