Technology Start talking: the true potential of conversational AI in...

Start talking: the true potential of conversational AI in the enterprise


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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 that controls your home appliances on command, conversational AI is everywhere in everyday life. And now it finds its way into the enterprise.

Best understood as a combination of AI technologies — natural language processing (NLP), speech recognition, and deep learning — conversational AI enables people and computers to have spoken or written conversations in everyday language in real time. And it sees a good question, with a source expects the market to grow by 20% annually to $32 billion by 2030.

Wider AI Range

Organizations are quick to implement conversational AI in front-end applications, such as to answer routine service questions, support live call center agents with alerts and actionable insights, and personalize customer experiences. Now they are also discovering the potential for implementation within internal business systems and processes.

Popular business use cases for conversational AI include the IT help desk where a bot can help employees solve common problems with their laptops or business applications; staffing solutions for travel and expense reports; and hiring processes where a chatbot guides candidates through the company’s website or social media channel. It informs them about which documents to submit and even makes a preliminary selection of resumes.


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While there’s no denying that conversational AI offers compelling opportunities to innovate and differentiate, it also poses some challenges. Managing a business AI landscape with disparate technologies and solutions that don’t communicate with each other is just one problem. Inadequate automation of repetitive processes across the entire conversational AI lifecycle and lack of an integrated development approach can increase implementation time. Last but not least, AI talent is scarce.

By adopting some thoughtful practices, companies can improve their conversational AI outcomes.

Five best practices for successful conversational AI

1. Do it with a purpose

Conversational AI should be implemented for a specific purpose, not just as a gimmick. Questions such as what kind of experience to provide for customers, employees and partners, and how to align conversational AI with organizational goals will help identify the right goal. The solution should also address activities that involve processing multiple data points – for example, answering questions about loan eligibility, which can add significant value to the customer experience – rather than working on tasks that can be performed 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 should extend not only to the words used, but also to the meaning. If the NLP solution used is not capable enough, friction will arise in the interaction.

3. Do it yourself

Low-code/no-code platforms give rise to citizen developers, that is, business or non-technical workers who write software applications without the involvement of IT personnel. In the future, this could help address the AI ​​skills shortage that most enterprises face.

4. Personalize, Extreme

Among the many features of conversational AI are contextual awareness and intent recognition. The technology can retrieve and translate huge information from previous conversations in a human way, as well as understand what the speakers are asking even if they “don’t follow the script”. These capabilities provide remembered insights that companies can leverage to personalize everything from products and services to offers and experiences to individual preferences.

5. Eye for the past and future

Conversational AI should adopt an approach based on historical insights and continuous post-production evolution using telemetry data on user requirements to improve stickiness and adoption. Strategically, organizations need to incorporate good governance into automating a conversational AI lifecycle. This means that regardless of the technology 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 intelligent, easy, and informed decisions at every point in the user journey. A holistic and technology agnostic approach, good governance and internal lifecycle automation with supporting development activities are the key success factors in implementing conversational AI.

Bali (Balakrishna) DR is Senior Vice President, Head of Services — ECS, AI and Automation at infosys.

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