More than 2 billion people are using chat platforms. More than 100 million homes contain a smart speaker. And, nearly everything holds a conversational AI assistant, from TVs to doorbells and locks to light bulbs.
Consumers expect to be able to talk intelligently to anything.
The massive confluence of mobile devices, AI assistants, and the Internet of Things (IoT) surrounding consumers is rapidly changing their preferences and expectations of customer service. According to a recent Gallup poll, 85% of Americans are already using at least one AI-enabled product.
These trends are only accelerating.
More than 60% of consumers own some form of AI technology, and the other 40% are probably unknowingly users of AI assistants in some way.
Recent surveys also reveal that the majority of the technologies that consumers deem to be the most important are components in the growing AI ecosystem.
All of this data should be inspiring your company to launch one or more enterprise conversational AI projects this year. Conversational AI – enabling the voice user interface – is swiftly becoming the table stakes for consumer engagement.
If you and your IT group aren’t already dabbling or at least monitoring emerging AI technologies, this need to leap into the world of AI can seem daunting.
Fortunately, companies like Clinc have been aggressively democratizing AI. Their conversational AI management platforms make AI accessible to technical and non-technical users.
But, if you’re considering investing in conversational AI, there are several essential points of evaluation that you need to consider before purchasing conversational AI technology.
Keys to Buying an Enterprise Conversational AI Platform
One of the fastest ways to get your voice user interfaces to market is to leverage a conversational AI platform. These AI management platforms combine several of the critical components for enabling voice assistants and help you to get to market quicker.
These are a few of these critical features that you should expect your platform to offer:
1. Methods to curate, organize and optimize conversations for Natural Language Processing (NLP)
2. Tools to develop conversational structures to assist with Natural Language Understanding (NLU) and NLP
3. Machine learning tools to help with the training and optimization of your chatbot
4. AI assistant platform support – the ability to communicate with your customer through popular platforms like Alexa, Google Assistant, and Bixby
5. Capabilities to translate or learn different languages
Most conversational AI platforms offer these crucial components. However, how they accomplish and makes accessible these features can make all the difference to your business.
Because conversational AI is an emerging technology, the quality of execution can fall along a broad spectrum from one platform to the next.
Let’s go a little bit deeper and review some of the unique innovations that are available from the best enterprise conversational AI platform solutions.
No one wants to talk to a robot or guess what keywords will give them a relevant response. Here at Clinc we continually talk about delivering a “human in the room” experience. This “human in the room” concept is an essential aspiration for your conversational AI deployment because we often only get one chance to delight a customer.
In the world of voice user interfaces – AI assistants and smart speakers – a delightful experience requires the understanding of “messy language.” Your conversational AI should be able to understand and appropriately respond to complex conversational flows as well as support natural correcting and backtracking to “heal” broken conversations.
Time to Value
How fast can you get into the market and deliver a valuable experience to customers? This question is often the first and probably the most important with emerging technology. You expect the need for testing and optimizing, but experience matters.
You want to engage customers with something that makes them say, “wow!”
When selecting a conversational AI platform or team, it’s critical to work with someone that has experience in your industry and deploying similar use cases.
Ideally, your conversational AI platform will have pre-built, proven conversational structures that will reduce your time and labor to deploy and test your first conversational AI experiences.
Some of the most significant opportunities for new voice user interfaces are in the areas of e-commerce channels, employee and customer service centers, and sales and marketing automation.
All of these areas are mission and revenue critical, which makes the potential impact of your AI project exciting. But, it also makes it crucial that you are working with an enterprise AI solution – one that is capable of delivering enterprise-level transaction handling, security, and data privacy.
The current state of the voice user interface market requires your customers to interact with one or more popular AI assistant platforms – Alexa, Google Assistant, Bixby, etc. Consequently, your conversational AI platform must interface with one or more of these platforms to reach your customers.
To assist you in determining the right AI assistants to target, Google Assistant and Amazon Alexa are dominant market share leaders in ai assistant platforms, with projected 43% and 34% market share by 2020.
Flexible and Secure Data
As you’re probably aware, data is an essential part of any AI system. However, in the context of conversational AI, this data is often more sensitive than typical AI data sets. Conversational datasets often contain voice interactions that most customers would expect to remain private.
This expectation of privacy makes it vital to understand and consider your data storage and management options with your conversational AI platform.
Any enterprise-level conversational AI platform must have the ability to support both private cloud and on-premise deployments. Protecting your customer’s data and complying with privacy best practices and regulations is a minimum technical requirement for any new conversational AI deployment.
The world is becoming a much smaller place. Devices connect us and blur spatial, cultural, and national boundaries. This fluid environment makes supporting your customers’ native cultural and language experiences essential.
In many ways, this is becoming better and easier with the best conversational AI platforms. The current approach to supporting multiple languages – in traditional graphical user interfaces – is to either translate a small subset of web pages or rely on a service like Google Translate to “do it’s best.”
Both are not optimal experiences for non-English speaking customers.
Conversational AI can offer a new and more natural approach. Conversational AI platforms are increasingly capable of allowing your voice interface to “learn” your customer’s native language.
Leveraging the same deep learning approaches that support improved understanding, processing, and responses from curated conversations, we are starting to use AI to learn new languages.
Case Studies: Enterprise Conversational AI
Let’s take a look at a few real-life case studies and how some of these important features come into play.
USAA Case Study
The financial services industry is teeming with potential use cases for conversational AI. Consumers need to accomplish simple and routine tasks, but these tasks are often made unnecessarily complicated by financial jargon, regulation, and confusing or clumsy web interfaces.
In the case of USAA, they were looking to make bill pay more intuitive, while still offering consumers a full-featured, complex, multiple options, transactional solution.
USAA was able to leverage Clinc’s deep financial services experience to deliver rapid time-to-valuebuilding a robust conversational AI solution within two months.
Ford Motor Company Case Study
Vehicles are changing. They’re running on different fuel, beginning to drive more autonomously, and now we’re preparing them to respond to our voices.
This voice user interface experience was the objective of Ford’s virtual assistant project to enhance their in-car, connected vehicle experience. By using Clinc’s experience, time-to-value and enterprise-level solution Ford was able to bring a messy-language, conversational, and commercially-capable experience to the in-car experience in a matter of weeks.
Isbank Bank Case Study
Developing a voice-enabled, mobile personal finance management experience is one thing, but delivering it in the Turkish language is a whole different level of complexity.
Combining Clinc’s extensive experience in financial services and its unique ability to support multiple languages – using deep learning to acquire and support multiple languages quickly – Isbank was able to begin training their new solution within days and deploy internally within a few months.
Begin Your Enterprise Conversational AI Project
With your critical features list in hand, the next step is to select a conversational AI platform partner that can move you quickly to a first iteration deployment.
Clinc can help expedite that first launch. Using a conversational AI platform, like Clinc, you can build real-world AI solutions in a few weeks like Ford did for their new in-car experience or even develop intricate financial AI assistants like USAA, Isbank and Barclays did in a matter of months.
Clinc, the Fastest Way to Wow! Market-ready Conversational AI Experiences
Are you thinking about a conversational AI project for your business? Let us help you through the process of creating an applied AI experience within your company.
Schedule a discovery session with one of our AI business analysts.