The AI Customer Service Use Cases Worth Starting With
Five, in the order the evidence usually supports rather than the order they get demonstrated.
Routing and Triage, the Least Glamorous and Most Reliable Win
Every incoming contact has to reach the right place. In most companies that happens through a drop-down the customer picks from, and customers pick wrongly, because the categories were written by the people who handle the work rather than by the people asking.
Intent recognition fixes this without anyone noticing. The system reads the message, works out what it is about, and routes it. A mistake means the message is rerouted, which is what already happens today.
The payoff is larger than it sounds. Misrouted contacts are handled twice, and each transfer means the customer repeats themselves. Fixing routing shortens resolution times without changing anything a customer can see.
It is also the safest place to learn. Routing accuracy is easy to measure, easy to correct and impossible to embarrass yourself with.
AI Chatbots, and the Difference Between Deflection and Resolution
A chatbot can do two different things and they are often reported as one number.
Deflection means the customer did not reach a human. Resolution means the customer got what they needed. Every conversation that resolves also deflects. Many conversations that deflect do not resolve; the customer gave up, or went to a different channel, or has been quietly annoyed with you since Tuesday.
When a chatbot is reported on deflection alone, the incentive points the wrong way. The easiest way to raise deflection is to make it harder to reach a person, which raises the number and damages the experience.
Start narrow. One or two contact types that are high in volume, low in stakes, and answerable from a document that already exists. Order status. Password reset. Opening hours. Build an obvious route to a person into the first reply, not the fifth, and watch what proportion of conversations use it.
Personalization That Is Not Just a First Name in an Email
Personalization has a bad name because most of it is a merge field. Useful personalisation changes what is offered, not how it is addressed.
In a customer experience setting it looks like: a help page that leads with the issue affecting your plan, a support reply that already knows what you bought and when, a recommendation engine that suggests the thing that fits what you own rather than the thing with the best margin, and a renewal message that acknowledges you have contacted support twice this month.
All of that depends on the joined customer record from the previous section. This is the use case most damaged by skipping the data work, because the personalisation is either absent or, worse, wrong in a way that shows the customer you were not paying attention.
Predictive Analytics: Churn Signals and Next Best Action
Predictive analytics estimates something that has not happened. In customer experience the two common versions are churn prediction and next best action.
Churn prediction assigns an account a likelihood of leaving, based on what accounts that left looked like beforehand. It needs history, including the accounts that left, which many companies keep badly. It also needs somebody to do something with the score, because a list of at-risk accounts that nobody contacts is a report, not a system.
Next best action suggests what to offer or say to a particular customer at a particular moment. It is genuinely useful in a support conversation and genuinely irritating when it ignores the reason the customer made contact.
Both are further up the risk ladder than they look, not because the model is complex, but because acting on a prediction means treating customers differently on the basis of a score nobody can see. Decide in advance what the system is allowed to do with a high score, and what it is not.
Self-Service and the Knowledge Base Nobody Maintains
The most common finding when a team labels a month of contacts is that a large share of them are answered somewhere on the website already.
That is a retrieval problem, and retrieval is the cheapest of the four verbs. A search that understands what somebody meant rather than which words they used will resolve a meaningful share of contacts before they become contacts.
The constraint is the knowledge base. Retrieval over out-of-date documents returns out-of-date answers with new confidence. Before building anything here, check when each help article was last reviewed. If the answer is nobody knows, fixing that is the project, and it is not an AI project.
This is also where the role of AI in mobile app development becomes relevant if your customers live in an app: in-app self-service that knows the account state resolves things a generic help centre cannot.
