Can AI Really Handle Customer Complaints?

One of the biggest challenges in AI-driven customer service is ensuring complaints are resolved effectively without frustrating customers.

So, what does good look like? Is it really possible for AI to handle complaints?

We asked our panel of experts to find out…

How to Make Sure AI Handles Complaints Well

Keep the AI’s Dialogue Focused on Practical Information

The challenge of AI complaint handling is that you’re using an emotionless system to handle frustrated customers. This means you have to configure the AI to be an absolutely flawless communicator.

There are two key aspects here.

1. What the AI Says

It needs to ensure that the customer always has complete clarity about:

• Their options – Can the AI assist them by (for example) retrieving information or resending missing communications?
• Next steps – What can they expect after this interaction? Will there be a follow-up, and if so when?

2. How the AI Says It

Statements like ‘your call is important to us’ from an automated voice are eyebrow-raising at the best of times. But in moments of stress, they’re just going to make your customer more frustrated.

Keep the AI’s dialogue focused on practical information designed to reassure the customer that their query will be dealt with.

Contributed by: Pierce Buckley, CEO & Co-Founder, babelforce

Give Your Virtual Agents the Same Tools as Live Agents

Too often, virtual agents aren’t empowered to actually solve problems. If they can’t resolve the issue, all the empathy and information gathering won’t matter. In fact, it’ll leave customers more frustrated by the time they reach a live agent.

To be effective, virtual agents need the same tools as live agents: the ability to refund, cancel, process returns, adjust pricing, and more. While this often requires back-end integrations, it doesn’t have to be overly complex. Focus on the most critical integrations first and use AI to help streamline the process.

Contributed by: Shaun McCurdy, Enghouse Virtual Agent Product Manager, Enghouse Interactive

Make Sure the AI Can Differentiate Between Urgent and Non-Urgent Issues

Unlike traditional IVRs that rely on static menus, AI-powered voice agents use natural language processing (NLP) and machine learning to engage in natural conversations, understanding both the context and urgency of customer issues.

For example, if a customer calls about a delayed order, an AI voice agent can verify the order status, provide real-time updates, and issue a refund or compensation if necessary – all without human involvement.

These AI agents can also recognize when a customer needs additional assistance and either refine their approach or escalate the issue appropriately.

Contributed by: Tatiana Polyakova, COO, MiaRec

Monitor and Tune Your AI Agents (Much Like You Coach Human Agents)

By continuously monitoring and tuning AI-agents – much like we coach human agents – contact centres can align AI performance with business objectives.

Bot analytics provide a comprehensive view of conversations from bot to live agent, allowing teams to identify and address gaps that impact customer satisfaction.

Equipped with deeper insights into sentiment, tone, and context, AI becomes better prepared to offer resolutions that feel genuinely personal.

Contributed by: Magnus Geverts, VP Product Marketing, Calabrio

Push Customers Towards Digitized Complaint Channels

Digitizing complaint channels (including manual methods like paper submissions) using AI-driven intelligent document processing reduces frustration, eliminates bottlenecks and ensures faster responses.

This approach opens continuous improvement mechanisms for broader process improvement, reducing complaint volumes over time and boosting CX.

Underpin Your AI Tools With Access to Good Quality Data

AI can significantly improve the handling of customer complaints by resolving many issues without needing to escalate to a human. To achieve this, however, AI tools must be underpinned by access to good quality data and robust business processes.

Integrating AI with low-code case management tools and contact centre solutions ensures evidence can be retrieved/surfaced from past customer engagements to enable more personalized and appropriate responses to be curated.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Analyse Word Choice and Conversation Patterns to Detect Dissatisfaction

Traditional AI models, such as rule-based chatbots, often fail to understand customer frustration, leading to unnecessary escalations.

However, with advancements in Generative AI and sentiment analysis, AI can now analyse context, word choice, and conversation patterns to detect dissatisfaction and adjust its responses accordingly.

For instance, if a customer repeatedly asks for a resolution or uses language indicating frustration, AI can proactively modify its approach – offering alternative solutions, escalating to a human at the right moment, or providing more detailed reassurance.

Contributed by: Tatiana Polyakova, COO, MiaRec

Identify Trends to Implement Proactive Improvements

On an operational level, AI holds value beyond individual interactions by identifying trends and systemic issues, empowering organizations to implement proactive improvements.

These capabilities reduce escalations while enhancing customer satisfaction, preventing churn, and strengthening customer loyalty – all while driving significant cost efficiencies.

Features like sentiment analysis further enhance service quality by prioritizing urgent interactions and mitigating frustration before it escalates.

Contributed by: Richard Bassett, VP Digital and Analytics, NICE International

There Will Always Be a Few Escalations (and They Must Be Managed Smoothly!)

A Clear Summary Prevents Customers Repeating Themselves

Sentiment and emotion detection are critical – AI must recognize frustration or urgency in customer interactions to determine when escalation is necessary.

Regular performance evaluations, including monitoring success rates and error trends, ensure the system evolves and improves over time.

When escalation is required, the AI should provide a clear, concise summary of the interaction to avoid customers having to repeat themselves.

This balance of smart thresholds, emotional intelligence, and seamless handovers not only enhances the complaint resolution process but also builds trust, encouraging customers to self-serve with confidence in the future.

Contributed by: Nicolas Marcoin, Product Marketing Manager, Odigo

AI Shouldn’t Be Expected to Handle Everything

AI shouldn’t be expected to handle everything as there can be incredibly complex cases and situations that require a human touch and empathy, but AI can handle many complaints – as long as it is trained well.

The training should start with teaching the AI how to deal with the most common, then the five most common and then the ten most common items. Always start slow and ramp up!

When a complaint fits within predefined scenarios, an AI delivers immediate, accurate resolutions or suggests relevant self-service options if available.

Contributed by: Jonathan Mckenzie, Sr. Contact Centre Product Manager, 8×8

AI Can Revolutionize Complaints Handling

AI has the potential to revolutionize the way customer complaints are handled, offering faster, more efficient resolutions while reducing the burden on human agents.

However, for AI to truly succeed in complaint management, it must be implemented thoughtfully, with a focus on transparency, personalization, and customer reassurance. Ultimately, AI is not a replacement for human agents but a powerful complement to them.

Are You Trusting AI to Handle Customer Complaints in Your Contact Centre?

Join our LinkedIn community and let us know.

For more great insights and advice from our panel of experts, read these articles next:

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• 10 Questions to Ask When Buying Your Next CCaaS Solution
• Why Workforce Management Deserves a Seat at the Strategy Table
• Top Tips for Capacity Planning to Meet Customer Demand

Author: Megan Jones
Reviewed by: Xander Freeman