10 features to look for in a conversational AI platform
As if starting your AI journey isn’t daunting enough, choosing the right conversational AI platform can leave your head reeling. To help point you in the right direction we’ve put together the top ten features you need to consider regardless of application.
It may seem obvious but there’s a world of difference between answering a question and holding an intelligent conversation. An engaging exchange will not only improve the customer experience, but will deliver the data to help you increase your bottom line. But in order to achieve this, the user interface needs to be as humanlike as possible.
It must understand the user’s intent, no matter how complex the sentence. Be able to ask questions in return to remove ambiguity or simply to discover more about the user. It needs a memory in order to reuse key pieces of information throughout the conversation for context or personalization purposes, and be able to bring the conversation back on track, when the user asks off topic questions.
If you’re a multi-national company, you’ll need the conversational AI development platform you choose to do all this, and in your customer’s native language too.
It’s very difficult to anticipate how people might use, or abuse, an AI application. Certainly Microsoft didn’t envisage that “helpful” members of the public would teach Tay to start Tweeting inappropriate messages. The problem with some AI development tools is they lack the control required to supervise Machine Learning behavior.
By ensuring a level of control within the application enterprises can not only avoid awkward mistakes, but provide a safety net for managing exceptions during a conversation ensuring a smooth customer experience at all times.
Few conversational AI development platforms were built with the enterprise in mind. Consequently features you might expect as standard such as version control, roll back capabilities or user roles to manage collaboration over disparate teams are missing.
In addition, look for features that will aid speed of development including automated coding, web-hooks to allow flexible integration with external systems, and ease of portability to new services, devices and languages.
The majority of AI development tools today are either linguistic or machine learning models. Both have their drawbacks. Purely machine learning systems function, as far as the developer is concerned, as a black-box that cannot work without large amounts of curated training data; something few enterprises have initially. While linguistic-based conversational systems, which require humans to craft the rules and responses, cannot respond to what it doesn’t know, using statistical data in the same way as a machine learning system can.
A hybrid approach that combines the best of linguistic and machine learning models, allows enterprises to quickly build AI applications whatever their starting point – with or without data – and then use real-life inputs to optimize the application from day one. In addition, it ensures that the system maintains a consistent and correct personality and behavior aligned with business aims.
Personalizing an automated conversation, whether it’s simply accessing account information to answer a billing query or taking into consideration a customer’s love of Italian cuisine when recommending a restaurant, not only delivers a more accurate response, it increases engagement too.
While some information can be learned explicitly such as the customer choosing a preference from a list of features, it’s the automated learning through implicit methods including previous interactions that really harnesses the power of conversational AI. This can then be combined with other information such as geo-location to personalize the conversation even further.
One of the key considerations in choosing a conversational AI platform is data. People reveal vast amounts of information in conversations. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. This information can then be used to feed-back into the conversation to increase engagement, train and maintain your conversational AI interface and analyzed to deliver actionable business data.
That’s why it is so important that enterprises maintain ownership of their data. It’s surprising how many development tools allow businesses to create chatbots etc, but don’t actually provide any of the details of the conversation, just the outcome, such as a pizza delivery order.
Alongside data ownership, make sure you look at the data analytics package provided as part of the platform, including the flexibility in drilling down through the information and understanding the context of conversations, as well as the level of detail provided.
Conversational applications are gradually infiltrating all aspects of everyday life, so it makes sense to ensure that you can easily port your conversational applications to existing and future devices. While it’s easy to state that applications can be built to run on a variety of platforms or services, all too frequently each one requires a complete new build. Investigating now how much of the original build can be reused, may save significant resources in the long term.
In addition, to cross platform capability, it’s also worth looking at how the application will support your users as they swap from device to device during the day. Seamless persistence of conversations increases engagement and customer satisfaction.
Data security is a key consideration for any enterprise, particularly when dealing with regulatory frameworks and your customers’ personal information, and flexibility is essential in a conversational AI platform to meet today’s exacting security conditions, across multiple geographies and legal requirements.
While most enterprises have no issue with a standard cloud deployment, when complying with industry regulations, or ensuring security policies are met that the cloud isn’t always an option. If this applies to your business you need to ensure that an on-premises option is available.
By adding an intelligent conversational UI into mobile apps, organizations can truly differentiate themselves from their competitors while increasing efficiency. Customization enables you to add your brand not just visually, but in the persona of your conversational UI from the language they use to the actions they take.
And finally before you make your final decision ensure you look beyond the marketing blurb. Check out real-life applications and talk to existing customers. Find out from them how easy it was to develop; have they tried porting to new languages or services; what benefits they’ve seen; and how they believe their Conversational AI platform will enable their digital strategy in the future.
Avoiding the “False Choice” Between Linguistic Models and Machine Learning. The backbone of Artificial Solutions’ Teneo platform is a linguistic-based algorithm with the key ability to embed and operate in tandem with machine learning algorithms.