Key Considerations When Building Your AI Virtual Agent

Key Considerations When Building Your AI Virtual Agent

Amongst the various types of AI, conversational AI has led to the widespread adoption of AI Virtual Agents to drive customer experience, cost efficiencies, and more. An AI virtual agent serves as a powerful tool for delivering on a variety of use cases including instant support, streamlining interactions, performing actions, and enhancing user experiences. However, building an AI Virtual Agent that is effective and a reflection of your brand requires various factors that need to be considered. In this article, we explore these factors and provide insight into what areas need to be addressed when embarking on the journey of building your AI Virtual Agent.

Defining Goals and Objectives

Before the technical build of your AI Virtual Agent, it is key to define exactly what the goals and objectives are that you want your agent to achieve. Consider the specific tasks it will perform, the actions it can undertake, and the information it can leverage, so that it brings the most value possible to its users. A good approach to doing this is looking at different business areas, support and sales, and listing out tasks within each. Whether providing instance support through retrieving information from FAQs or facilitating transactions, a well-defined process will help shape the agent's capabilities and development roadmap.

Understand Your Target Audience

To deliver a great user experience and to ensure users of your AI Virtual Agent are satisfied with the outcome of their conversation, a deep understanding of your target audience is required. Consider the demographics, preferences, and behaviors of your audience. Ensure the language and tone of the virtual agent are aligned with the expectations of your audience. Conduct user surveys, analyze customer feedback, and incorporate user personas into the design process to ensure that the virtual agent resonates with its intended users.

Choosing A Technology

Which platform you use to create your AI Virtual Agent is an important decision in ensuring the performance and scalability of your agent. The most popular platforms are Google Dialogflow, Microsoft Azure Bot Service, and IBM Watson Assistant. With each of these offering unique features, assessing your specific requirements, and evaluating them against these different platforms, will ensure that the platform you choose to build your AI Virtual Agent on will align with your goals and objectives.

Natural Language Understanding and Intent Recognition

Central to your virtual agent is its ability to understand and interpret user input, a concept known as Natural Language Processing (NLP). Investing time in training your agent to accurately recognize the intent of the user, and responding appropriately with the relevant action, will allow your AI Virtual Agent to adapt to user behavior over time. Of course, when your agent is not able to deal with or understand a query, some platforms ( such as Quickdialog), can automatically hand the conversations over to a human.

Designing Intuitive Conversational Flows

The design of conversational flows is a crucial aspect of creating an effective virtual agent. Craft intuitive dialogue structures that guide users through interactions logically. Consider user context and maintain continuity in conversations. Implement fallback mechanisms to gracefully handle situations where the virtual agent encounters queries it cannot comprehend. A well-designed conversation flow ensures a positive user experience and minimizes frustration.

Example AI Virtual Agent Welcome Intent

Integration with External Systems

For your AI Virtual Agent to maximize what it can offer and the actions it can take on behalf of its users, integrate it seamlessly with external systems and databases within your ecosystem. Potential use cases could be retrieving real-time information, processing transactions, or accessing customer data and historical purchases, ultimately robust integrations enhance the agent's functionality. Utilize webhooks, APIs, or custom connectors to establish connections with backend systems, ensuring a seamless flow of information between the virtual agent and external services.

Prioritize User Privacy and Data Security

As virtual agents handle sensitive user information, prioritizing privacy and data security is non-negotiable. Implement robust encryption protocols, adhere to industry standards, and ensure compliance with data protection regulations such as GDPR or HIPAA, depending on the nature of the data being handled. Communicate privacy policies to users, assuring them that their data is handled with the utmost care and transparency.

Multi-Channel Deployment

In today's digital age, consumers operate across multiple channels and expect the same level of experience across each. By deploying your AI Virtual Agent across multiple channels, such as websites, mobile apps, messaging platforms, and voice interfaces, you not only create a consistent experience but you increase the accessibility of your agent. This allows users to engage with it on their preferred platform.

Insights and Optimisation

Due to the nature of AI, and its ability to learn, means that the building of your AI Virtual Agent doesn't end with deployment - it is an ongoing process. Constant optimizations and learnings need to be considered by analyzing user interactions, bottlenecks within the conversational flow, and more. Therefore, you should regularly update and refine the agent based on these insights.

The benefits of building an AI Virtual Agent are endless. From reduced costs to higher customer satisfaction, and greater efficiency, virtual agents create an extra pair of hands to drive your company into the digital world. From defining its purpose and understanding the target audience to choosing the right technology stack and prioritizing data security, various considerations need to be addressed to ensure the success of the virtual agent. By adhering to these key considerations, you can create virtual agents that not only meet user expectations but also contribute positively to the overall user experience.