Top 5 things to do before adding AI to customer support

This article is a submission by Peak Support, a BPO company delivering award-winning, AI-driven outsourcing solutions to growing businesses across the U.S. and beyond. Peak Support specializes in customer service, technical support, trust & safety, sales and marketing, back-office support, and accounting services.

Article Overview: Adding AI to customer support is not just a technology decision. It requires a strong operational foundation. This article breaks down the five essential steps companies need to take before implementing AI, from cleaning up knowledge bases to defining workflows and training systems on real customer interactions. It also covers three often-overlooked factors that can make or break AI performance.

AI has quickly become a priority for customer experience leaders. The promise is compelling: faster responses, lower costs, and the ability to scale support without scaling headcount at the same pace.

However, adding AI is not simply a matter of plugging in a tool and expecting immediate results. AI isn’t usually the problem. The real issue is that the support operation behind it isn’t ready.

That gap shows up in the data. A Gartner survey revealed only 54% of AI projects make it from pilot to production. Forbes.com also reported that up to 85% fail due to poor data quality or lack of readiness.

The pattern is consistent. Teams move too quickly to implementation and skip the operational work that makes AI effective.

In a recent conversation with Greg Sanchez, Manager for CX Solutions and Architecture at Peak Support, one idea stood out: “AI only performs as well as the foundation it is built on.”

Before introducing AI into your support operation, there are five critical areas to address.

1. Get your knowledge base in order

Your knowledge base is the foundation of any AI system.

If it is outdated, inconsistent, or written in internal language, your AI will reflect those same issues. It will not correct them.

The most common gap is language. Customers do not speak the way companies write. They use shortcuts, different phrasing, and multiple variations of the same question.

If your content does not reflect that, your AI will struggle to match intent.

There is also a structure issue. Many knowledge base articles try to answer too many things at once. That creates ambiguity, which AI handles poorly.

Before introducing AI, your knowledge base should be:

• Up to date
• Written in customer language
• Structured so each article answers one clear question

When this is done well, AI can retrieve and deliver accurate answers consistently.

2. Clean up and standardize your macros

Macros are often overlooked, but they play a critical role in how AI responds.

If your macros are inconsistent in tone, overly long, or disconnected from how customers communicate, they introduce noise into the system. AI will pull from these responses just as much as it does from your knowledge base.

The goal is consistency.

Macros should:

• Mirror customer language
• Be concise and direct
• Align with how issues are actually resolved

This creates a cleaner dataset for AI to learn from and reduces the risk of conflicting or confusing responses.

3. Assign ownership for data and content

AI is not a set-it-and-forget-it solution.

One of the most common mistakes is assuming that once AI is live, it will continue improving on its own. In reality, it requires continuous maintenance.

This includes:

• Updating knowledge base articles
• Refining macros
• Adding new scenarios as they emerge

Without clear ownership, content becomes outdated quickly, and AI performance declines over time.

A strong implementation includes a dedicated owner responsible for maintaining the resources AI depends on. This ensures the system stays accurate and aligned with current operations.

4. Define use cases and test before launch

AI should not be deployed broadly from day one.

The most effective approach is to start with clearly defined use cases, typically high-volume, low-complexity inquiries. These are the areas where AI can deliver immediate value.

Before launch, teams should:

• Identify the specific cases AI will handle
• Use historical tickets to understand how customers ask those questions
• Run batch testing to validate responses

This step is critical. Without testing, AI is more likely to return incomplete or incorrect answers.

That risk is real. A Forrester study shows that 30% of customers abandon chatbots after a bad interaction. Poor early experiences can reduce trust and limit adoption.

Testing allows teams to refine responses before customers ever see them.

5. Train AI using high-volume, real customer interactions

AI performs best when it is trained on real data.

Historical tickets are one of the most valuable resources available. They show how customers actually ask questions, including variations, tone, and context.

The focus should be on high-volume issues. These provide enough examples for AI to learn patterns and respond accurately.

As Greg emphasized, “The most important aspect of a strong AI are your foundational elements such as historical tickets, knowledge base articles, and macros. Make sure that your knowledge base articles and macros read as if it’s a customer responding to a customer so that your AI can really pull from what the customer is saying and then get the information back to them without having to try to guess at an article that may not be phrased correctly or worded correctly.”

Training AI on limited or overly curated data leads to gaps in understanding. Training it on real, high-frequency interactions creates a stronger and more reliable system.

Bonus: 4 things most teams miss

Even when the basics are in place, there are four areas that often get overlooked. These can make the difference between a functional AI setup and a high-performing one.

1. Structure content for AI readability

It is not enough for content to be clear to humans. It also needs to be structured in a way that AI can easily process.

This means:

• Clear titles that match customer intent
• Logical organization of information
• Avoid vague or unclear phrasing

When content is structured well, AI retrieves answers more accurately and consistently.

2. Define clear guidelines for AI behavior

AI needs guardrails.

This includes:

• Tone of voice
• When to escalate to a human
• What information must be captured before escalation

Without this guidance, AI responses can feel inconsistent or incomplete. With it, the experience becomes more predictable and aligned with your brand.

3. Build AI procedures for different request types

Think of this as creating standard operating procedures for AI.

Different types of requests require different handling. Returns, order issues, billing concerns, and technical problems all follow different paths.

This is also the right moment to confirm that those processes are actually working. If a workflow is unclear, inconsistent, or inefficient today, adding AI will only make those issues more visible at scale.

Defining these procedures helps AI:

• Route requests correctly
• Provide the right information
• Escalate when needed

This adds structure to the system and reduces the likelihood of incorrect or irrelevant responses.

4. Prepare your frontline team to work alongside AI

AI implementation is not just a systems change. It is also a team change.

Frontline agents play a critical role in making AI successful. 

During the build and testing phase, they act as a source of truth, similar to your knowledge base. They help validate responses, identify gaps, and provide feedback on how AI performs in real scenarios.

As Greg noted, preparing the team early is important. In many implementations, agents are involved in testing, training, and refining the system before it goes live. 

This helps ensure that AI is grounded in real customer interactions, not just assumptions.

It also addresses a common challenge. Teams may be hesitant about AI at first, especially if it is positioned as a replacement. Involving them early shifts that perspective. 

AI becomes a tool they help shape, not something imposed on them.

When frontline teams are part of the process, the result is a more accurate system and a smoother rollout.

What this looks like when it’s done right

AI can improve customer support in meaningful ways. It can reduce costs, improve response times, and free up agents to focus on more complex work.

But none of that happens without preparation.

According to Oxford Global Resources, 57% of consumers will switch to a competitor that provides them with a better experience. That includes experiences driven by poorly implemented AI.

The risk is not just inefficiency. It is churn.

The teams that succeed with AI are not the ones that move fastest. They are the ones that invest in the foundation first.

They clean up their knowledge base. They standardize their workflows. They align with how customers actually communicate.

Then they introduce AI.

And when they do, it works.