All That Glitters Isn’t Gold: Why Conversational AI Needs More Than Just a Shiny UI

Written by Aneta Ranstoller on Mar 24, 2023

Customer service (CS) platforms have been adopting conversational AI at incredible rates as consumers  expect a higher quality customer experience. Many of these platforms are rushing to break into the market and boost sales but are making some basic mistakes in the process. 

The focus for newly emerging technologies has been predominantly on user interface (UI) and aesthetics rather than functionality and performance. And while we all like an attractive UI with the latest bells and whistles, it’s not enough to keep customers satisfied long-term, especially when cracks begin to appear beneath the surface. 

We notice that early-generation chatbots have limited functionality that keeps causing significant issues for its customers. For example, their AI solution is relatively weak as it relies solely on keyword matching, resulting in limited and inaccurate responses to customer inquiries like the ones shown below. 

They also use a linear workflow builder that doesn’t allow visualization of the work. And it doesn’t support email automation, one of the toughest channels for AI to crack. These issues may not be noticeable right away, but soon enough, the fissures become more apparent as customer satisfaction drops and sales begin to stagnate.

There is much more that goes into building a robust conversational AI platform than UI. In this article, we’ll go beyond the surface – under the hood, so to speak – of conversational AI platforms to identify the components that add quality mileage. You’ll know exactly what to look for in a solution so that you’re not stuck with a lemon down the road. 

Start With Core Needs

For CS teams to succeed long-term, it’s essential to identify customer needs and incorporate those relevant functions into your platform’s core before building around them. Top-performing conversational AI platforms have a core comprised of three main components:

  1. Quality Customer Experience

A quality customer experience means removing friction from the customer journey so that quick resolutions, short wait times, and customer self-service across all channels are achieved. With an automatic, self-learning conversation operating system, Netomi’s platform achieves this through intent narrowing, episodic memory and contextual reasoning. The result is a more accurate natural language understanding than that of IBM and Microsoft, with an intent confidence of over 90%.

  1. Contact Center Efficiency

Contact center efficiency is the technical implementation of how you support your customers. Efficiency is related to how easily customers interact with your support center and get their issues resolved. Netomi’s AI is built with discovery and trending models that allow it to automate responses to common topics. This gives agents more time to focus on highly complex issues and provide higher-quality care. In co-pilot mode, Netomi’s AI drastically reduces the amount of work needed from support agents, allowing them to service more customers with less effort. The models are also self-learning, allowing for script automation and a lower workload for the contact center.

  1. Growth

Being able to scale your customer service function while maintaining high-quality support is key as your organization grows. Taking care of your contact agents’ workload and improving the quality of customer experience is critical. The key is using AI in every step of the process, from discovery to optimization, to help increase conversion rates, offer upsell and cross-sell opportunities, and reduce abandonment rates. Netomi’s platform allows enterprises to easily scale their operations without the need for additional resources. The net positive ROI achieved by all of Netomi customers is proof of the platform’s long-term robustness.

The Top Five Must-Have Features for a Conversational AI Platform

Standing out from your competition requires a platform supplemented with features that actually address customer needs. Earlier generation chatbots that didn’t have the right tools running in the background eventually got the wind knocked out of their sails. To ensure your success, here are the top five features to look for when investing in a conversational AI platform:

  1. Tools that understand the way we speak: Natural Language Understanding (NLU), Reinforcement Learning, and Sentiment Analysis tools are essential so that the AI can learn how to interpret the way we use language. It needs to be able to understand the context and generate responses that mimic human semantics.
  2. Back-end systems integrations: These include supporting systems that promote business goals, such as CRM and OMS. Agent desk integrations allow for timely escalation to agents, while knowledge-based integrations allow for faster responses to product or service-related questions.
  3. Omnichannel support capability: The top chatbots allow customers to reach out and communicate through their preferred channel and provide consistent support and information regardless of the channel used.
  4. Analytics and real-time reporting: An essential feature of any top-performing chatbot, which improves decision-making by identifying trends, among many other benefits
  5. Multilingual capabilities: With so many languages being spoken by an organization’s customer base, being able to support them in their native language provides a major competitive advantage by facilitating easier communication. 

Evolve From Chatbot to Conversational AI

It’s understandable that chatbots often get confused with conversational AI. In reality, the latter is an evolved form of the former. 

Early-generation platforms use a rules-based algorithm which acts on manually-crafted rules, making it difficult to train and scale; this is what we commonly refer to as a chatbot.

In contrast, Netomi’s conversational platform is powered by deep-learning AI, which learns from real interactions and matures linguistically, almost like a growing child. Incredibly, this allows for progressively easier training and scalability, making it more robust with each new inquiry.