Start a conversation: The true potential of conversational AI in the enterprise

You can’t attend Transform 2022? View all summit sessions in our on-demand library now! See here.

From a smart assistant that helps you increase your credit card limit, to an airline chatbot that tells you if you can change your flight, to Alexa, who operates your home appliances you on command, conversational AI everywhere in Daily life. And now it’s making its way into the business.

Best understood as a combination of AI technologies – Natural Language Processing (NLP), Speech Recognition and Deep Learning – conversation AI enables people and computers to carry out spoken or written conversations in everyday languages ​​in real time. And, it is seeing good demand, with one source The market is forecasted to grow 20% annually to $32 billion by 2030.

Wider range of AI

Organizations were quick to accept Conversational AI in front-end applications – for example, to answer common service queries, support live call center agents with helpful alerts and insights, and personalize the experience of cutomer. Now, they are also exploring its potential for implementation in internal enterprise systems and processes.

Common enterprise use cases for conversational AI include IT helpdesk where bots can help employees solve common problems with their laptops or business applications; HR solutions for travel and expense reporting; and recruitment processes where a chatbot guides candidates through a company website or social media channel. It informs them about the documents they have to submit and even preliminary selection of the application.


MetaBeat 2022

MetaBeat will bring together thought leaders to deliver guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

register here

While there’s no denying that conversational AI offers compelling opportunities to innovate and make a difference, it also presents some challenges. Managing the enterprise conversational AI landscape with disparate technologies and solutions that don’t communicate with each other is just one problem. Inadequate automation of iterative processes in the lifecycle of conversational AI and lack of integrated development methods can prolong implementation. Last but not least, AI talent is in short supply.

By adopting some thoughtful practices, businesses can improve their conversational AI results.

Five best practices for successful conversational AI

1. Do it with purpose

Conversational AI should be deployed with a specific purpose in mind, not just as a gimmick. Questions, such as what kind of experience to deliver to customers, employees, and partners, and how to align conversational AI with organizational goals, will help determine the right intent. Additionally, the solution should address activities that involve processing multiple data points – for example, answering loan eligibility questions, which can add significant value to the experience. customer experience – instead of working on tasks that can be done using predefined shortcuts.

2. Pay attention to your language

Taking a conversation-first approach is critical to 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 being used cannot afford it, it will create friction in the interaction.

3. Do it yourself

Low-code/no-code platforms are giving rise to citizen developers, i.e. business or non-technical staff, who write software applications without the involvement of IT staff. In the future, this could help overcome the AI ​​skills shortages that plague most businesses.

4. Personalization, extremely

Among the many features of conversational AI are contextual awareness and intent recognition. Technology can recall and translate huge amounts of information from past conversations in a human-like fashion, and understand what speakers are asking even if they don’t “follow the script.” These capabilities deliver memorized insights that businesses can harness to personalize everything to individual preferences, from products and services to offers and experiences.

5. Looking to the past and future

Conversational AI should take an approach based on historical insights and continuous post-development using telemetry data on user needs, to improve the level of stick and apply. Strategically speaking, organizations must incorporate good governance when automating the conversational AI lifecycle. This means that, regardless of the technology being used, the underlying architecture must support plug-and-play and the organization must be able to benefit from the use of the new technology.

In short, to gain traction in the enterprise, conversational AI must enable smart, convenient, and informed decisions at any point in the user journey. A holistic and technology-agnostic approach, good governance, and internal lifecycle automation with supporting development activities are critical elements of conversational AI implementation success.

Bali (Balakrishna) DR is Senior Vice President, Head of Service Delivery – ECS, AI and Automation at Infosys.


Welcome to the VentureBeat community!

DataDecisionMakers is a place where professionals, including technical people who work with data, can share data-related insights and innovations.

If you want to read about cutting-edge ideas and updates, best practices, and the future of data and data technology, join us at DataDecisionMakers.

You can even consider contribute an article your own!

Read more from DataDecisionMakers

Source link


Kig News: Update the world's latest breaking news online of the day, breaking news, politics, society today, international mainstream news .Updated news 24/7: Entertainment, the World everyday world. Hot news, images, video clips that are updated quickly and reliably

Related Articles

Back to top button