How AI is Charting the Use of Chatbots in IT
Artificial Intelligence technology helps to improve enterprise workflows and IT practices. Today, chabot and linguistic engineers have created algorithms that offer a unique solution for enterprises trying to address conversational system challenges: a patented hybrid approach. Conversational systems that combine the best of linguistic and machine learning tools allow enterprises to quickly build AI applications, collect data, and then use real-life inputs to optimize the application from day one.
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 be analyzed to deliver actionable business data. Alongside data ownership, IT can look at the data analytics 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.
Virtual Support Line
AI implemented in enterprises will improve training and distribution by being a secondary virtual assistant. Enabling workers to turn to the assistant with training related queries or for intranet use to ensure a consistent and accurate message to customers. Proven to reduce repeat calls and waiting times by up to 65%, they are invaluable in sectors that have a high turnover of staff but complex products to give advice on.
IT developers want to create bots that accept natural language input and can decipher what a user means—regardless of how they say it. A traditional platform requires upwards of two days of coding to be able to understand conversation and context. With new platforms this can be done in minutes.
Few conversational AI development platforms were built with the future of IT in mind. Consequently, features you might expect to be standard such as adding more bots or connecting multilingual platforms are missing.
In addition, IT needs features that will aid speed of development including automated coding, or web-hooks to allow flexible integration with external systems, and ease of portability to new services, devices, and languages.
Security of data is a key consideration for any enterprise, particularly in IT 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, it is important to ensure that there are security policies that meet the appropriate standards because the “cloud” is not always an option.
As enterprise and IT departments become more comfortable in implementing AI and chatbots, there is much to be garnered from these brilliant bots and personalization. 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 previous interactions that really harnesses the power of conversational AI for chatbots.
Speech Technology Magazine interviewed Greg Sparrow, senior vice president and general manager at CompliancePoint, about some of the security issues that surround these devices, and how people can protect themselves.
The customer journey has changed considerably in recent years. Now, whether it's from the web to phone or vice versa, customers are able to interact with your brand across various channels. However, this has presented a challenge for data-driven marketers: how exactly can you bridge the online-offline gap to profitably acquire customers if you can't piece their sales journey together? By integrating call intelligence and CRM systems.