Overview
Virtual assistants (VAs) help users or enterprises with a set of tasks previously only made possible by humans. VAs use semantic and deep learning (such as deep neural networks [DNNs], natural language processing, prediction models, recommendations and personalization) to assist people or automate tasks. VAs listen to and observe behaviors, build and maintain data models, and predict and recommend actions. VAs can be deployed in several use cases, including virtual personal assistants, virtual customer assistants and virtual employee assistants.
Our speakers will discuss
- Know about the journey of Virtual Agents and how they have embedded themselves in our world:
With artificial intelligence making a presence in a number of areas of our life, just an app is no longer enough. AI-powered virtual assistants are a necessary addition for efficiency and scalability as we move further into the digital realm. What it comes down to is personal preference, which is unlikely to be exactly the same across various consumer and patient populations – especially because consumers have shown that they want access to both types of engagement.
- Virtual Agents with low-code/Utilize readily available functionalities to improve your conversations:
A no-code/low-code approach has many benefits. It decreases the dependency on developers which means less effort spent on customizations The departments who need these programs can build the system as they envisioned it, in low turnaround times. They can also quickly change and alter the product along the way without requiring extra costs. This essentially means users can design applications without any coding knowledge, by simply checking/unchecking the features or using only a drag and drop interface to program the logic. These features act as a means to engage more organisations to adopt the platform rather than as prohibitive mechanisms. - Case Study on Virtual Agents in Customer and Employee Experience for a Large Software Antivirus Company
