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7 lessons about AI in customer service

We have spent years running AI in customer service across markets.
Based on insights from 700,000+ conversations, we know what works - and what most teams get wrong before they even start.

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What you will learn

AI in customer service - beyond the automation rate

Agentic commerce and AI-driven customer service are high on every agenda right now. But between the hype and the reality, there's a few details many teams don't think about.

At MakesYouLocal, AI is the foundation we build on: from customer conversations to the automations and insights that run underneath. Based on insights from 700,000+ customer conversations, we gathered what we know into a practical playbook.

01
Why a high automation rate doesn't always mean a better customer experience
02
Why most automation projects struggle
03
Move from reactive to proactive customer service
04
AI needs a foundation before it can perform
05
Every customer contact is a symptom - find the friction first.

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Do you have questions?

Frequently asked questions

Where should I start with AI in customer service? Start by analysing your existing conversations. The patterns are usually clear: a small number of question types account for the majority of contacts. Those are your first automation candidates.
How much of customer service can be automated? Between 50% to 70% is realistic - but only with the right data, processes and starting point. We always recommend that companies begin with a handful of high-volume question types where the answer is predictable and the data is accessible. The rest are questions that needs human involvement - but with the right automations your customer service will have more time to handle the high value conversations.
What do I need in place before implementing AI? Three things: structured knowledge your AI can draw on, integrations with order and delivery systems, and clear rules for when a human agent should take over.
What does proactive customer service mean? It means reaching customers before they contact you. With automation, this becomes easier to handle. If your logistics data shows a delay in an order , the system can send an update automatically - before the customer even notices. The customer feels looked after, while your team handles fewer contacts - because that customer might have contacted you to ask about the delayed package. That is the kind of experience that is hard to deliver manually, but simple to scale with the right setup.
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