Methodology for adopting AI in customer service

Last year, we were talking about conversational artificial intelligence. Before we could blink, generative AI had taken over the conversation. And in just a few months, we’re now hearing about AI agents, copilots, and agentic AI.

This technology is evolving at lightning speed. And although everyone’s talking about it, many in the industry are still in testing mode. Understandably so — a tool this powerful, with nearly endless applications, isn’t easy to implement.

That’s why we’ve put together this article with practical advice to help you define a clear methodology for implementing AI solutions in your contact centre effectively — maximising value without compromising customer or agent experience.

GUIDELINES FOR ADOPTING AI IN YOUR CONTACT CENTRE

Even if we’re fully aware of AI’s immense potential, market volatility, regulatory uncertainty, and general scepticism can all become major barriers when it comes to adoption in customer service teams.

However, there is a structured methodology that ensures a step-by-step approach — minimising risks, building trust, and above all, improving operations, agent workflows, and customer experience.

1. TRAINING

Start by introducing your team to the technology. Develop a plan for regular training sessions, encourage participation in workshops, and attend relevant industry events. This will help staff understand AI’s capabilities and limitations — and shape a realistic perspective of what it can do.

You can start small with weekly workshops on what AI is, how it’s being used, and by sharing use cases from other companies in your sector.

2. DEFINE THE PURPOSE

Set a clear objective for applying AI in your operations. For example:

• Reducing friction in customer interactions,
• Accelerating agent onboarding, or
• Breaking down information silos to create a more autonomous and motivated team.

Ensure the process you aim to automate is clearly structured. Automating a flawed process won’t solve the problem; AI only replicates what it’s taught — it doesn’t fix broken workflows on its own.

3. RESOURCE RESTRUCTURING

Once the purpose is set, identify which tasks AI agents will handle and what role your human agents will play.

Bear in mind that this technology requires ongoing supervision and optimisation: someone to refine responses, update flows, and align outputs with evolving customer needs.

4. KPI DEFINITION

Before implementation, define clear KPIs to measure success. Some useful examples include:

• Ratio of calls handled by AI vs. human agents,
• Number of chats answered,
• NPS (Net Promoter Score),
• Post-interaction satisfaction level,
• Average handling time.

5. DEVELOPMENT AND INTEGRATION

A key advantage of AI in contact centres is its ability to integrate with CRMs and other systems to access, cross-reference, and analyse data.

For example:

AI can detect customer frustration and, by reviewing their history, identify billing errors in the last two invoices. Based on that, it can recommend a personalised action to the agent, such as offering a goodwill credit.

6. DEPLOYMENT

Once flows are tested and resources aligned, it’s time to deploy your AI solution. Expect some fine-tuning — spending enough time on preparation will help you avoid surprises later on.

7. AUDITING AND CONTINUOUS IMPROVEMENT

AI must be audited regularly and evolve with your organisation. Establish a review process to tweak your logic, assess performance, and realign goals as needed.

It’s also important to get familiar with the terminology around AI to make well-informed strategic decisions.

FINAL THOUGHTS

A solid methodology, combined with robust, integrated technology and support from a trusted implementation partner, is the key to safely testing and deploying AI in customer service.

WHAT PROCESSES COULD YOU AUTOMATE?

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