When Is the Right Time to Deploy AI Agents?

Matt Jones at EvaluAgent explores how businesses can avoid the pitfalls of rushing into AI by first understanding customer contact drivers, analysing intent, and choosing the right type of AI agent for the job.

Many businesses are rushing to deploy AI – in fact, we’ve seen it already with Klarna and Duolingo missteps. The problem is, they’re often in a race to be the first to make efficiency (and cost) gains, while overlooking fundamental prerequisites.

This approach risks implementing sophisticated technology on top of unresolved operational issues. And perhaps, more importantly, alienating customers you’ve worked hard to win.

So what’s the answer? How do you balance the allure of AI with business aims and customer needs?

Beyond the Bot Race

Reduced wait times, 24/7 availability, and lower operational costs make conversational AI an attractive proposition.

But before deploying conversational AI, there’s one thing you need to absolutely understand: why are customers are contacting you in the first place?

Before investing in AI agents, you should have clear visibility into:

• Call drivers – The specific reasons customers reach out
• Contact volumes – How these drivers distribute across your contact centre
• Root causes – The underlying issues generating these contacts

Without this foundation, you risk automating inefficient processes or addressing symptoms rather than causes.

An AI agent programmed to handle password resets more efficiently might seem valuable, until you discover that poor UX design is causing the high volume of reset requests in the first place.

Wouldn’t it be better to invest in fixing that issue, rather than finding a solution that deals with the fallout only? Your customers will thank you for it.

Understanding AI Agent Varieties

Let’s say you’ve analysed your data and you’ve got a real business case for bringing on a virtual agent. Not all AI agents are created equal. The landscape ranges from simple rule-based chatbots to sophisticated conversational AI systems.

Basic chatbots: We’ve all encountered these. Chatbots follow predetermined scripts and decision trees, and have limited flexibility. They’re effective for simple, predictable interactions but struggle with complexity or unexpected inputs.

Knowledge-based AI: Can access and search information repositories to answer questions. While more capable than basic chatbots, they primarily retrieve rather than reason.

Conversational AI agents: Leverage large language models (LLMs) to understand natural language, maintain context throughout conversations, and generate human-like responses. They can interpret intent, handle multiple topics within one conversation, and adapt their responses contextually.

Autonomous AI agents: The most advanced tier. Capable of reasoning, problem-solving, and taking actions across multiple systems, these virtual agents can understand complex customer needs, access relevant applications, and independently resolve multi-step issues.

The key distinction isn’t just technological sophistication, but whether your AI can go beyond knowledge retrieval to demonstrate reasoning, adaptation, and system-wide action capabilities.

The Critical Role of ‘Reason For Contact’ and Intent Analysis

Before deploying any AI agent, you need systematic analysis of contact reasons and customer intent. That involves…

• Categorizing contacts: Beyond broad classifications to specific granular drivers
• Quantifying volumes: Understanding the distribution of these drivers
• Identifying patterns: Recognizing trends, seasonal factors, and correlations
• Determining complexity: Assessing which contacts are suitable for automation

AI-powered contact analytics can automatically classify interactions and extract intent, providing the data foundation needed for successful agent deployment.

Why is this important? Because this analysis reveals which contact types occur frequently enough to justify automation, and which are standardized enough to be effectively handled by AI.

It all comes back to that crucial question: why are customers contacting you? And what is the best way to resolve their issue?

How AI Agents Can Transform Your Contact Centre

When deployed at the right time and with proper preparation, AI agents can absolutely deliver great results. With them, you can benefit from…

1. Intelligent Triage and Routing

Advanced AI agents can determine customer intent from initial interactions, assess complexity, and either resolve issues directly or route to the most appropriate human agent – ensuring customers reach the right resource the first time around.

2. Predictive and Proactive Service

By analyzing patterns in customer data, AI can anticipate needs before customers articulate them. This might mean proactively addressing an impending service issue or suggesting relevant products based on usage patterns.

3. Continuous Quality Monitoring

AI-powered quality management can analyze 100% of interactions against defined standards, providing consistent evaluation without the sampling limitations of traditional QA processes.

And much more besides!

The question isn’t whether to deploy AI agents, but when and how.

Organizations that develop clear visibility into contact drivers and root causes, then select appropriate AI technologies matched to specific customer intents, will achieve the best results.

Those rushing to deploy without this foundation risk merely automating inefficiency. It’s easy to see which route will benefit your customer, and in turn, your business.

This blog post has been re-published by kind permission of EvaluAgent – View the Original Article

For more information about EvaluAgent - visit the EvaluAgent Website

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: EvaluAgent
Reviewed by: Rachael Trickey