You Chose Conversational AI. What's Next?

Many companies are jumping on the bandwagon of utilizing conversational AI-powered bots and using them within their daily business processes. The usage of these intelligent agents soared astronomically during the height of the 2020 Covid-19 pandemic. In a 2020 study by Mordor Intelligence, this market was valued at $17.17 billion, and it is expected to reach $102.29 billion by 2026. There was also a 67% increase in conversational AI usage between 2018 and 2020. The widespread use of smart bots gained traction through modern and custom capabilities. Conversational AI helps to prioritize customers, products, and interaction data to enhance experiences in real-time, thus minimizing wait times, service expenses, and customer churn.

Though many companies invest in conversational AI agents, there seems to be some confusion over what happens after the investment. When your business chooses to utilize conversational AI, opportunities arise to express the brand and create stronger relationships with customers. In this article, we will further be discussing the path to reaping all of the benefits of these intelligent tools.

Ensuring Adoption

Gartner predicts that in 2022, 70% of customer interactions will involve emerging technologies such as AI-powered bots — an increase of 15% from 2018. This statistic is not surprising since most customers prefer interacting with a bot over other virtual agents for reasons including time management, simplicity, and no wait times. 

Your business can take specific steps to prepare for the accelerated adoption of conversational AI adequately.

1. Plan, plan, plan: Designing a strategy that will help foster success and emphasize the benefits of a bot throughout the organization is essential. Designing a process that takes time to train and familiarize your staff and customers with conversational AI will help all involved parties. Be sure to address both benefits and challenges, give a timeline and deployment plan, explain how data will be collected and used, and security. 
   
   
2. Build multiple bots with specific jobs: Interacting with customers and staff can be difficult, but Natural Language Processing (NLP) technology has vastly improved and is constantly advancing. Your intelligent bot must keep up with the conversation, be present, and stay within the context of the conversation. By asking too much of your AI virtual agent, you may feel that it is not doing its job correctly; that is why you should start small and build bots to handle fewer tasks yet can run them perfectly. With time, your bot can take on more skills. Just like your team members, AI-powered bots can only handle so many tasks at once (especially during the training or initiation phase). By building bots for specific tasks, you can employ an entire team of bots that cater to your diverse population of customers and team members.
   
   
3. Virtual assistants can orchestrate your bots: Creating a strategy based on one AI-powered bot is not the best plan of action if you use multiple bots for different purposes. Having a centralized Virtual Assistant (VA) can be helpful. This VA can manage a conversation with a customer and direct it to the conversational AI agent best suited for a solution. With VA orchestration, an organization can centralize language detection, sentiment analysis, de-escalation mechanisms, small talk, and more within the virtual assistant. This VA should be accessible to all departments and create a centralized and consistent user experience. This enables faster adoption of bots and makes scaling much more manageable. 
   
   
4. Strengthen the context with live data: Many early versions of smart bots could not quickly assist human agents. They could not provide the most up-to-date customer information. This was often due to a lack of NLP and detection of the conversation's intent and context. Each business has its preferences for systems, processes, and even languages. Customers and team members who often interact with a bot present an opportunity to access information from multiple sources and customize the experience based on this information. Integrating different systems with your smart bot can provide this context and help the technology fill in any gaps in information in real-time (by requesting it from the user, for example).
   
   
5. Prioritize security: Security is a significant element of conversational AI implementation. Consider where conversations will be stored, what aspects of conversation will be redacted, how to handle disaster recovery and user authentication, and more. These aspects are incredibly important and must be protected adequately against potential security threats.

Collecting Data

Utilizing conversational AI is becoming a common occurrence for businesses. These intelligent, virtual agents act as conversation catchers. They help address a customer's problem through conversational automation, workflows, and integrations with business systems, with the added value of routing to people when the problem needs a human touch. With this, the bot records the interactions and keeps a log of this data to help build stronger versions of the itself. For instance, by analyzing unanswered questions and common words used by customers, business users can build new triggers and connect existing answers to enhance the technology's capabilities.

When teamed up with NLP, collecting data can help us learn about how clients are communicating. You can assess the words chosen and demographics and create personalized interactions.

You can also use data collected with conversational AI to see how often issues occur, develop more ideas for products, and better explain or market products or services. Before AI-powered bots, the primary data collection sources were emails, social media, activity, and qualitative surveys. 

Analyze The Data Gathered

Gathering and organizing data is the first step in benefiting from conversational AI's data collection processes. The value comes from analyzing the data collected. These conversational AI smart bots use advanced NLP and AI technology to understand and organize data, whereas basic bots do not understand the words we are saying; they just recognize patterns in the terms used.

More complex conversational bots can recognize words within the context and assign a specific feeling by analyzing how a customer speaks. This is called sentiment analysis and helps organizations retain customers and employees (if a bot is used for internal reasons) by catching frustration early on and resolving underlying issues before they erupt.

Creating Useful Insights with Your Data

It is not enough to only collect data and analyze it. Every piece of information should be utilized in a way that will benefit the organization in some way. One way is by building unique recommendation engines. Using predictive analytics and customer-specific data, a bot can create personalized content. This helps build strong relationships with customers by making them feel valued!

Conversational AI-enabled bots are available 24/7 and are focused on the customer's needs. By looking at their data log, they can imitate a human agent and offer personalized solutions. A single bot can replace a whole customer service department and yield a competitive advantage for smaller organizations.

Choosing to invest in conversational AI for your business is a step into a world of possibilities and opportunities to engage with customers and enhance their experience. These intelligent bots can be quickly adopted by both staff and customers when you create a great plan of action. Planning and training are essential to a productive relationship with conversational AI.