Were you unable to attend Transform 2022? Check out all Summit sessions in our on-demand library now! Look here.
From a smart assistant that helps you increase your credit card limit, to an airline chatbot that lets you know if you can change your flight, to Alexa operating your home appliances on command, conversational AI is ubiquitous in everyday life Life. And now it’s making its way into the company.
Best understood as a combination of AI technologies—natural language processing (NLP), speech recognition, and deep learning—conversational AI enables humans and computers to have real-time spoken or written conversations in everyday language. And it’s seeing good demand, with one source predicting the market will grow 20% year over year to $32 billion by 2030.
Wider AI range
Businesses have been quick to adopt conversational AI into front-end applications — for example, to answer routine service requests, empower live call center agents with alerts and actionable insights, and personalize customer experiences. Now they are also discovering its potential for use in internal company systems and processes.
Popular business use cases for conversational AI include the IT helpdesk, where a bot can help employees solve common problems with their laptops or business applications; HR solutions for travel and expense accounting; and recruitment processes where a chatbot guides candidates through the company’s website or social media channel. It informs them of what documents they need to submit and even pre-selects CVs.
MetaBeat will bring together thought leaders on October 4th in San Francisco, California to provide guidance on how Metaverse technology will transform the way all industries communicate and do business.
While there’s no denying that conversational AI offers attractive opportunities for innovation and differentiation, it also comes with some challenges. Managing an enterprise-wide conversational AI landscape with disparate technologies and solutions that don’t communicate with each other is just one problem. Inadequate automation of repetitive processes throughout the conversational AI lifecycle and lack of an integrated development approach can increase implementation time. Last but not least, AI talent is in short supply.
By applying some thoughtful practices, companies can improve the results of their conversational AI.
Five best practices for successful conversational AI
1. Do it on purpose
Conversational AI should be implemented with a purpose and not just as a gimmick. questions such as B. what kind of experience to offer customers, employees and partners and how to align conversational AI with business goals help to identify the right purpose. Also, the solution should handle activities that involve processing multiple data points — for example, answering creditworthiness questions that can significantly improve the customer experience — rather than working on tasks that can be accomplished with predefined shortcuts.
2. Watch your language
A conversational approach is important for scaling technology across the enterprise. But since different people naturally speak in different ways, understanding must extend not only to the words used, but also to the intent. If the NLP solution used is not powerful enough, it will create friction in the interaction.
3. Do it yourself
Low-code/no-code platforms result in citizen developers, ie business or non-technical workers who write software applications without the involvement of IT staff. In the future, this could help overcome the lack of AI capabilities that plagues most businesses.
4. Personalize, extreme
Conversational AI’s many characteristics include context awareness and intent recognition. The technology can retrieve and translate massive information from past conversations in a human-like manner, and also understand what the speakers are asking, even if they’re not “following the script”. These features provide stored insights that businesses can use to tailor everything from products and services to offerings and experiences to individual preferences.
5. Consider the past and the future
Conversational AI should adopt an approach that draws on historical insights and continuous post-production development, using telemetry on user requests to improve stickiness and adoption. Strategically, when automating a conversational AI lifecycle, companies need to incorporate good governance. This means that whatever technology is used, the underlying architecture must support plug and play and the organization should be able to take advantage of the new technology.
In short, to gain traction in the enterprise, conversational AI should enable smart, convenient, and informed decisions at every point of the user journey. A holistic and technology-agnostic approach, good governance, and internal lifecycle automation with supporting development operations are the key factors for success in implementing Conversational AI.
Bali (Balakrishna) DR is Senior Vice President, Service Offering Head — ECS, AI and Automation at Infosys.
data decision maker
Welcome to the VentureBeat community!
DataDecisionMakers is the place where experts, including technical staff, working with data can share data-related insights and innovations.
If you want to read about innovative ideas and up-to-date information, best practices and the future of data and data technology, visit us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read more from DataDecisionMakers