Conversational AI Do’s, Don’ts and benefits of it for your business

Since the global pandemic the business landscape for customer and employee experiences have changed significantly.

Customers and employees now expect personalized and immersive end to end brand engagement as interactions become more virtual, mobile and distributed.

Businesses have adopted conversational AI to provide customers and employees exceptional experiences throughout their journeys with the organization.

As per Gartner, the conversational AI market is expected to reach $1.3 billion by 2025, growing at a CAGR of 24%.

Conversational AI is used in chatbot’s, voice bots, and virtual assistants helps conduct simple, natural, human-like conversations with customers and employees. It allows enterprises to design cost-efficient digital experiences, streamline internal operations, and deliver superior customer engagement.

An effective conversational AI design builds the user’s trust and boosts their confidence in the virtual assistant. It keeps them engaged and brings them back over and over.

Here’s everything the benefits for your business:

Benefits for customers

• • Increase revenue through upselling and cross-selling opportunities
• • Build a loyal customer base through personalized and meaningful engagement
• • Reduce customer effort
• • Improve net promoter scores

Benefits for employees/organization

• • Improve employee productivity
• • Reduce voluntary turnover rates
• • Uncover data-driven insights to make intelligent business decisions
• • Reduce cost per contact

The Do’s and Don’ts of Conversational AI

Here are some key chatbot considerations and DOs and DON’Ts that organization must be mindful of:

1. What Purpose Is The Bot Designed To Serve?

Narrowing down the goal of the chatbot will help the business in ensuring that the scripts, content, and features required to make the chatbot effective are not misunderstood.

When it comes to what your chatbot is meant to accomplish, it's critical to create the correct expectations. It is a wise decision to deploy various conversational AI systems for different use cases.

2. Choose The Right Platform And Expertise.

Going into conversational AI without the required knowledge might result in a chaotic and costly approach.

You can choose a custom solution, a specific service/functional offering, or a platform-based strategy based on your needs and anticipated use cases.

A platform-based conversational AI service makes use of no-code or low-code solutions to aid non-technical users in building, improving, and adjusting apps depending on their needs with little or no coding.

3. Security and Privacy Are Must-Haves

Data privacy and cyber security are important factors for conversational AI adoption because chatbot’s can sometimes handle sensitive consumer information, including rigorous security mechanisms is a "must."

• • Staying up to date on the newest security rules in the conversational AI field, including any industry or location-specific needs
• • Perform extensive API security checks before to installing the chatbot.
• • Distribute information in stages based on the users' authorization levels. Implement authorization and privacy at the user, intent level, and channel levels, as well as end-to-end encryption.

4. Keep Working on Making Your Conversational AI Solution Better

As conversational AI technology advances and your organization needs mature ensure that your chatbot is ready to harness user data and provide more meaningful and tailored interaction.

Continue to add additional replies, natural language, and machine learning skills to gradually improve and humanize your bot.

5. Integrate Your Bot With Existing Systems And Digital Channels.

Backend and legacy system integrations, such as ERP, CRM, or databases, are required if you want to give seamless assistance to your customers or staff.

Conversational AI platforms assist in verifying the status of orders, answering enquiries, and addressing difficulties. The Conversational AI platform will be able to deliver significant insights using data from the systems.