How to Create AI Assistants Using a Conversational AI Platform
According to research, the conversational AI market is expected to grow from $3.79 billion in 2018 to $19.63 billion in 2025. In fact, according to Gartner (PDF), by 2020, customers will manage 85 percent of their relationships with the enterprise without human involvement.
From Apple to Google, Amazon to Facebook, fast food drive-throughs to banks, contact centers to healthcare, companies the world over are investing in AI assistants to help with sales, service, document review and more. For companies looking to add conversational AI, there’s never been a better time.
Unfortunately, many companies looking to deploy conversational AI don’t know the best way to do it. There’s a myriad of companies offering pre-packaged AI assistants or chatbots available for purchase or license. Still, other companies choose to go their way, developing an AI assistant from the ground up.
If your company is looking to deploy conversational AI, which route should you choose?
One popular way that companies choose to roll out conversational AI is by purchasing a pre-packaged AI. There is a myriad of companies that offer different chatbots and AI assistants for sale; some of them are rudimentary while others are advanced.
For many companies, the advantage of buying a prepackaged bot or assistant is the speed of deployment. Within hours, you can have an AI up-and-running, answering questions, supporting customers, assisting with sales, and more.
Another advantage of a pre-packaged AI is the ability to deploy it without a development team. Because a pre-packaged option is already made, with all the necessary hooks to use it in its target environment, it requires minimal investment in development. You’re essentially buying a turnkey solution that’s ready to go.
The downside to this model is that pre-packaged AIs don’t have the flexibility of a custom solution. All too often, a pre-packaged AI is designed to run within the confines of its design, whether it be a chatbot, messaging bot, document review bot or the like.
The second option some companies opt for is to develop an AI from the ground up on their own. The advantage of this method is that it gives a company full control over the AI they develop, its focus, capabilities, and environment.
Unfortunately, this is by far the most labor-intensive method of deploying an AI assistant. To succeed, a company must have an experienced development team that understands the challenges involved. One of the biggest of these is deciding what kind of AI to develop: rule-based or one based on machine learning.
Rules-based AI is the easiest to create, making it the most common form of conversational AI on the market. Consequently it is also the most brittle and ineffective form of chat technology. Essentially, a rule-based AI uses complex if-then-else statements to establish criteria that serve as a basis for decision-making.
The obvious downside to this kind of AI—if it’s fair to call it a true AI—is that it’s limited by the pre-programmed rules. If a question, query, or conversation falls outside of its established rules, a rule-based AI falls flat. Unfortunately, even if a development team tries to add new rules and if-then-else statements to help such an AI improve after-the-fact, often rules added at a later date run the risk of conflicting with existing rules. The more complicated the AI becomes, the greater the risk of conflicts.
The second, more capable AI is one based on machine learning and natural language understanding (NLU). The ability to learn, adapt, and grow is one of the hallmarks of a true AI.
Thanks to machine learning, this type of AI doesn’t have to be constantly updated with new rules, criteria, and if-then-else statements to provide for new functionality. Instead, the AI continues to learn based on the very interaction it’s designed for. With each query, each question and each conversation, the AI continues to grow and improve. Even when the AI gets something wrong or misinterprets a query, machine learning allows it to learn from those mistakes and do a better job the next time.
This type of AI is infinitely harder to develop in-house. It requires developers who understand how to implement machine learning, how to program an AI to understand natural language, and how to respond in a way that’s nearly indistinguishable from a human being.
Conversational AI Platform
The third choice is to invest in a conversational AI platform. This is different from a simple AI product—such as a chatbot or virtual assistant—in that it gives you everything you need to deploy AI assistants across the entire spectrum of applications.
By licensing an established AI platform, it saves your company time and money over developing one from the ground up. Licensing a platform means you don’t have to invest in an army of developers to create an AI with machine learning, natural language understanding, and the neural engines necessary to create a true AI. Best of all, a well-designed, competent AI platform will have all the APIs and hooks necessary to implement it with minimal effort and cost.
The Clinc Advantage
Our AI platform is a patented technology stack we designed from the ground up with state-of-the-art natural language understanding and machine learning. This means there’s no scripts, no keywords, and no rule-based conflicts and inadequacies.
Just as important, our dialogue management and response logic are decoupled, which allows an infinite amount of conversational back-and-forth. The platform’s experiential learning ensures the AI improves and grows over time and use. Our platform even provides multiple personalities designed to help you tailor your AI to different audiences and demographics.
All of this together results in an AI that can be deployed in days or weeks, rather than months or years, with a 95% containment rate and massive cost savings. Contact us today to see how our AI platform can help your company.
Photo by Hitesh Choudhary on Unsplash
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