You have deployed your virtual assistant, now what?

AI is a journey, not a destination. Careful planning and execution is required to make conversational AI commonplace in the enterprise.




Conversational AI has been making waves across industries. From lead generation, customer on-boarding, customer and agent support, the benefits could be generating new revenue or reducing cost. Infosys research, based on a survey of more than 1,000 business and IT leaders, found that 73 percent of respondents agreed that their AI deployments have already transformed the way they do business.


As an enterprise, you have probably established an innovation team or AI lab and may be your initial project has gone live. What do you do next?


AI is a journey, not a destination. Careful planning and execution is required to make conversational AI commonplace in the enterprise. After all, every AI deployment and use-case is different.


From our deployment experience, below are a few ways to move forward after launching a virtual assistant.


1. Set the Team, Process and Optimise

While most conversational AI vendors talk about technology jargon like Machine Learning, NLP and Cognitive, it is important to cut through the noise. Establish a dedicated team to understand the vendor landscape, ask the tough questions, dig deep into the bot’s capabilities and finally, create a process to fine tune and optimise the bot.


A robust bot is one that continuously learns from interactions, improves accuracy and speed of its responses. AI continuously evolves but it's not magic. Gather actionable insights from the performance of your conversational AI and improve your bot.


2. Conversational Experience Beyond Automation

Many organisations use conversational AI to automate simple tasks and answer simple questions. But new advancements in machine learning and AI can now make the bot truly intelligent.


Transition from a simple FAQ chatbot to a more intelligent Conversational AI capable of handling complex decisions such as product recommendations based on user profile and respond to predictive scenarios.


3. Be Proactive

Integrate with existing systems and leverage data to personalise interactions in real-time. Be proactive rather than reactive. Engage with customers on their preferred touchpoint and personalise the interaction. Remember their preferences, analyse their sentiment and proactively transfer to a live agent.


4. Expand to New Channels and Languages

Is your bot deployed across customer touch points? Website need not be the only interface for your bot. Analyse the most frequently used customer touch points and provide an omni-channel experience across touch points such as Text, Facebook Messenger, etc.


Global organisations can cater to customer across borders. Organisations leverage CogniCor’s multi-language understanding pipeline to deploy multi-lingual bots.


5. Scale

Success from one use-case should be experienced in another. The bot should easily be able to extend its capabilities and enable organisations to easily add additional products or services. Using CogniCor, one of the leading insurance providers experienced tremendous success in selling automotive insurance policies. The firm has now extended its capabilities to health insurance.


6. Deploy Across Customer Journeys

Merely having conversations with customers has no impact. Conversational AI can be a boon when used across journeys. Map a journey and leverage flexibility of the bot to cater to customers/users across their journey. Some of the leading financial institutions in Asia and US have deployed CogniCor’s virtual assistant for mortgages, insurance and wealth management related use cases. The bot was built to carry a conversation on either topic and seamlessly engage with customers.

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