Ada: Charbots vs Conversational AI Agents

Today, nearly every brand’s website features a chatbot to support customer interactions. But just because chatbots are widespread doesn’t mean they provide the best possible customer experience (CX). Many scripted chatbots are clunky, unhelpful, difficult to navigate, or all of the above.

Nearly three-quarters of consumers say chatbots aren’t able to handle complex questions and often provide inaccurate answers, while half say they often feel frustrated in their interactions with chatbots. 

Forrester Chatbot Survey

Rather than enabling greater convenience and connection, basic chatbots can worsen experiences for consumers. Even a single negative chatbot interaction can prove detrimental for brands:

30% of consumers saying a negative chatbot experience makes them more likely to abandon their purchase, buy from a different brand, or tell family and friends about their poor experience. 

Forrester Chatbot Survey

Why are so many chatbots falling short of expectations — and what can brands do to get more out of these technologies? The answer isn’t scrapping automated CX tools entirely; it’s replacing them with more advanced AI tools. 

In particular, more brands are leveraging AI agents to refresh their customer service organizations. These conversational CX tools use LLMs, NLP, and NLU to intelligently reason through customer queries and generate unique, personalized responses. As CX enters the AI era, brands need more human-like tools than chatbots. 

What Distinguishes AI Agents from Chatbot Predecessors? 

The first generation of chatbots relied on pre-written scripts designed to handle basic, direct queries. When the second generation of chatbots arrived, they incorporated minor personalizations and performed limited actions by accessing various business systems. However, these later models were still too confined by predetermined scripts without any real problem-solving capabilities. 

Even today, chatbots remain static. They require frequent updates to avoid delivering outdated or incorrect information. And they still struggle to adapt to customer needs and fail to provide contextually complex interactions outside of pre-programmed scenarios. The result is frustrating or ineffective experiences for consumers:

60% of consumers would still rather wait in a queue for a real agent than receive an instant response from a chatbot. 


The evolution of AI agents breaks free from the constraints of scripted responses. Unlike script-based chatbots, AI agents continuously learn and reason to respond to complex customer interactions based on an ever-growing knowledge base that includes internal documents, help articles, websites, and much more.

These capabilities enable AI agents to generate dynamic responses tailored to the specific context of each inquiry and the customer’s preferences. Interactions with AI agents can be so personalized, fluid, and natural that consumers often feel they are conversing with a human. In fact, these conversational tools are so sophisticated that major brands like Chipotle, Taco Bell, McDonald’s, and Domino’s have deployed them to take orders at drive-thrus and over the phone.

In marketing, AI agents can play a valuable role in expressing a brand’s unique voice and ethos. That’s because they’re able to mimic the way human agents modify their communication style and tone based on the context of the conversation. For instance, some fast food restaurants assign unique personalities to AI conversational tools, and AI agents can adjust their tone and voice to align with holidays, promotions, and other special occasions. 

AI agents empower companies to build customer loyalty and enhance brand identity through more sophisticated, meaningful customer interactions. With AI, every customer service experience turns into a brand-building opportunity. 

Four Components Of Effective AI Agents

The transition to AI agents requires more than just swapping out chatbots for AI tools — it requires your organization to develop robust strategy, structure, and skill sets to support them. 

With that in mind, the following four components are essential to leveraging AI-powered conversation tools.  

1. Cohesive, Comprehensive Content

You may have heard the adage, Garbage in, garbage out. AI agents learn and adapt from the information available to them, which means you need to maintain a robust knowledge source to ensure that the AI agent provides accurate and relevant information to end users.

Content and information that’s well-structured and all-encompassing enables AI agents to consider context and provide relevant, personalized responses. For example, an online retailer would need to provide its AI agent with access to detailed product descriptions and updated promotional details to field questions about current sales or promotions in real-time.

2. Connected Data Infrastructure

AI agents need seamless access to your organization’s business systems to provide accurate, up-to-date information to customers. Most often, this is facilitated through an API strategy that integrates various data sources without compromising personally identifiable information (PII), enabling AI agents to pull real-time data from various segments of your organization. 

An AI agent can access up-to-date inventory levels or customer purchase history to provide real-time information and updates for customers. Effective API integration ensures that AI agents are not just functional but are a powerful extension of your business’ operational capabilities.

3. Continuous Learning and Coaching 

Unlike traditional software, AI agents require ongoing training and refinement, akin to onboarding and managing a new employee. Continuous learning and coaching are essential for your systems to evolve and adapt to new customer service challenges, while regularly updating the AI’s knowledge sources and adjusting its parameters will continue to improve performance. 

AI managers play a crucial role here. If you were to stop coaching a human employee after the first 90 days, they would plateau. The same is true for AI agents, which require you to continuously measure performance, coach and improve outcomes, and look for new opportunities to expand and refine the AI agent’s capabilities. This hands-on approach ensures the AI agent continues to grow more sophisticated — taking on a higher conversation volume, more sophisticated use cases, and a greater number of channels over time. 

4. Changing Skill Sets 

The role of traditional bot managers has evolved alongside chatbot capabilities themselves. Now, these practitioners have the opportunity to up-level their skills to become AI managers who focus less on scripting and more on AI coaching, as well as the strategic integration and optimization of business systems. 

AI managers are tasked not just with overseeing AI agents but also with managing and coaching them to align with business goals and drive financial impact. This role is dynamic and requires a deep understanding of the technology, expertise in the customer service landscape, and business acumen.

AI Agents Are A Brand Differentiator 

AI represents a fundamental shift in how you interact with your customers — and it needs to be at the core of your customer service strategy. You can’t simply layer AI capabilities onto existing platforms. Like onboarding and managing someone on your team, you need to integrate AI agents and other AI-native technologies to deliver a seamless, personalized, and consistent experience. 

Digital-savvy consumers expect immediate, accurate, and personalized service when interacting with brands, which makes CX a major brand differentiator. By embracing AI agents to fuel your CX, you can leverage every interaction as an opportunity to reinforce your market position, meet customers’ evolving needs and expectations, and build lasting relationships. 

 The chatbot era is coming to a close. Are you ready for what comes next?

©2024 DK New Media, LLC, All rights reserved.

Originally Published on Martech Zone: Why Brands Are Firing Basic Chatbots And Onboarding More Impactful Conversational Tools