3 Maturity Models for Applying AI in Customer Service

In the race to implement artificial intelligence in customer service, many companies forget a key question:

Who is in control of the virtual agents?

Just like human agents need training, supervision, and clear guidelines, virtual agents do too. Technology is not the biggest risk. The biggest risk is losing control of the service experience. That’s why today we explain three maturity models for applying AI in CX: Human in the Loop, Human on the Loop, and Human out of the Loop.

The discussion is no longer whether you should use AI in customer service, but how to do it correctly.

These three AI models represent maturity levels that any company can follow, regardless of industry. They are a roadmap.

The 3 Maturity Models

1. Human in the Loop

AI does not make decisions on its own. A human reviews and validates.

Ideal for: critical processes, healthcare, payments, claims
Benefits: documentation, bot learning, full control

“If AI fails, the customer doesn’t forgive. A human agent can catch it.”

2. Human on the Loop

AI already responds and acts, but there is human monitoring.

Ideal for: FAQs, repetitive support, standardized processes
Requires: rules, limits, alerts, and escalation mechanisms

“Many companies fail here: they leave the bot alone, without supervision.”

3. Human out of the Loop

AI operates without direct human intervention, only with post-audits.

Ideal for: hyper-controlled, low-risk processes
This is not the starting point. It is the destination.

“This model is not for everyone—and definitely not to start with.”

Maturity Journey

It’s not about choosing just one model. It’s about scaling progressively:

  1. Start with human assistance: your team validates and trains the AI.

  2. Scale with supervision: automate repetitive tasks, but with clear KPIs.

  3. Automate with confidence: only when you have enough data, rules, and control.

Companies that implement AI successfully do it with strategy, iteration, and continuous improvement.

Risks of Misusing AI

One of the most common mistakes when implementing AI in customer service is assuming the technology works autonomously and perfectly. Without proper supervision, invented responses can appear that sound completely credible, leading to hallucinations. In addition, there is often no clear record of why or how a response was generated, creating a serious lack of traceability.

Another critical issue is that customers can get trapped in conversational loops with no option to reach a human, severely damaging the experience. If AI is not aligned with your brand tone and knowledge, it may produce responses that contradict your values or policies, causing brand misalignment. All of this results in a loss of trust—both internally and externally—and the cost is not technical, but reputational and commercial.

The consequences are not technical. They are reputational and commercial.

Requirements Before Scaling AI

Clear Policies

  • Define AI action limits

  • Define cases where it must escalate to a human

Defined Processes

  • Map every service flow

  • Include exceptions and business rules

Organized Data

  • Updated knowledge base

  • Aligned channels and users

  • Structured data for training and validation

What Is RAG and Why It Matters

Retrieval-Augmented Generation (RAG) is the technique that allows AI to consult a reliable source before generating a response.

  • Reduces hallucinations

  • Ensures brand-aligned answers

  • Uses curated company information

Companies that use RAG achieve better results and fewer errors.

How to Avoid Hallucinations

  • Limit the domain: Ensure AI only answers questions within previously trained and validated topics. The narrower the scope, the lower the risk of errors.

  • Force evidence: Every response must be backed by a verifiable company source. This is done by integrating AI with structured and updated knowledge bases.

  • Immediate escalation: When the bot detects ambiguity or uncertainty, it should automatically escalate to a human agent.

  • Constant monitoring: Just like human agents, AI needs real-time supervision to identify failures and adjust behavior.

  • Post-audit: Periodically reviewing bot interactions helps improve training, document exceptions, and update knowledge to reduce future hallucinations.

Supervising AI is not optional. It’s part of operational success.

Are You Ready to Use AI Without Losing Control?

It’s not about whether to use AI—it’s about whether your company is ready to use it well.

Sagicc is an omnichannel platform that allows you to move through this journey step by step, integrating humans, AI, and data in a safe and measurable way.

With Sagicc you can define flows and escalation, supervise bots and human agents, implement AI with RAG and a knowledge base, and control multiple channels from a single interface.

Discover Your Maturity Level

Before taking the next step, understand your starting point:

👉 Take the Sagicc AI Technology Maturity Test

Because with AI, order matters.