Customer service departments have long been at the forefront of the AI revolution. For years, they’ve been deploying chat bots to accelerate time to resolution for customer challenges, or using AI tools to analyse vast sets of data on customer interactions.

Yet with today’s advanced generative AI models, how customer support teams can use AI is evolving further. There’s a new opportunity to use these tools to deliver an even better service - one that can keep up with growing customer expectations across different types of businesses.

Take banking as just one example - McKinsey estimates there are more than 25 different use cases where AI can be deployed in customer service to boost revenues. And for each, it does this by providing a more personalised experience to customers and employees.

By using AI to help deliver personal interactions at scale (rather than simply attempting to replace teams with it), businesses can make their services shine. The result will be more returning clients, more productive teams and rising satisfaction levels. However, to unlock these prizes, organisations need to start by supporting their support teams. That means arming them with the right approaches and platforms to untap AI’s potential.

Identifying where AI fits in

Simply deploying AI isn’t an instant win for customer service teams, and not all AI-powered features are created equal.

Let’s imagine two different interactions with a service team. A customer, Rob, ordered a beach ball, but, unfortunately, he received an inflatable ring by mistake. In the first scenario,  Rob reaches out via an online chatbot, but the AI that’s powering it can only offer a generic response asking him to return the beach ball. There’s no way to explain the mix-up. Worse, he’s not offered a way to connect with a human member of the team to resolve the issue. Frustrated, Rob returns it for a refund and goes to another business.

In the second scenario, the AI is plugged into the organisation’s workplace, and has been learning from previous responses given by its human reps. It understands the nuances of the problem, and can personalise its chat with Rob - who it recognises as a regular customer.

It quickly escalates the challenge to a real team member who can see that this issue has taken place before, and can then get in touch with a personal note and resolve it instantly. Rob’s summer holiday is saved, and he’ll return again next year.

In the first scenario, AI has been used to cut corners - and it shows. In the second, it’s learning from workplace data and being used in partnership with the human teams who can deliver a memorable, personal experience.

This is where AI can make the difference: connecting different departments faster, surfacing past insights so that the problem can be solved quickly and making for seamless customer interactions that boost both success and the support team’s productivity.

Building a foundation for AI in customer support

For AI to make a real difference for support teams and customers like Rob, organisations need to give it a solid foundation to learn from. And the best data for AI tools to build upon are the rich insights that leading customer support teams are already gathering: the solved cases, the conversations around customer challenges, the previous customer touch points and more.

By keeping all of those insights united in a productivity platform, integrated with best of breed CRM tools, teams can easily start deploying AI while providing it with the data it needs to offer meaningful help. In this way, they can accelerate resolutions and boost customer satisfaction with real-time personalisation. And, they can do all this with fewer resources, while scaling faster.

Meanwhile, having that knowledge easily accessible across the entire organisation doesn’t only help the AI learn - it helps teams share information, stay connected and engage with one another.

With tight integration between their service tools and their productivity platform, they can find the answers they need in a single space without swapping tools - meaning when a customer challenge arises, they’re ready to help without wasting any time.

For example, as soon as a customer gets in touch, AI-powered tools can pull together a summary of the case based on recent touchpoints andoffer smoother conversations with advanced chatbots. This sets teams up with a clear view of the customer so, once the interaction comes to them, they can instantly provide a personalised, meaningful response that gets to the crux of their challenges.

The team over at fintech company Revolut has embraced this way of integrating tools to maximise the value of the information they have available. In doing so, they’ve ensured they spend less time on process work, and more time engaging customers.

By using a productivity platform like Slack that’s integrated with their Salesforce CRM, they’ve been able to cut time spent on routine tasks and reduce the number of calls the team has to join. As a result, the team has built a more efficient business and closer connections - both between coworkers and their customer contacts.

AI joins the support team

Customer service departments around the globe face a choice as the new AI era begins in earnest. They can try to offload important interactions to AI, and lose the spark of personalised engagements that make a real difference to customers. Or, they can deploy AI to surface insights, accelerate and automate work, learn from existing knowledge, and help service reps deliver incredible moments.

As AI investment in the customer service sector continues, it will quickly become clear to customers which path businesses have chosen. What’s more, it will be clear to businesses which approach leaves those customers delighted and ready to return for more.

Chris Mills is Head of Customer Success, EMEA, at Slack.