When AI Takes the Simple Work, Your People Need Better Service Skills

The straightforward requests are increasingly being handled without a person. Customers can reset passwords, check an order, update details, find information or complete simple actions themselves. AI can support employees with knowledge, summarise conversations, classify tickets and help answer common questions much more quickly.

That can be a real improvement. It can reduce repetitive work, shorten waiting times and make services easier to access.

But there is another side to it that I think businesses need to prepare for.

If the simple work is increasingly handled by technology, the work that reaches people is more likely to be the complicated work.

It is the customer who has already tried self-service and got nowhere. The complaint that has been through several people. The unusual request that does not fit the normal process. The technical issue that is difficult to explain. The situation where there is no obvious answer and somebody has to make a judgement call.

Those interactions have always existed, but they start to make up a much larger share of the human workload once the easier contacts disappear.

That changes what good service looks like.

If a customer has already tried the automated route before reaching an employee, they are often starting that conversation with less patience than they would have had at the beginning.

They may have spent ten minutes going through a chatbot. They may have read an article that did not answer the question. They may have followed the instructions exactly and still ended up stuck.

By the time they reach a person, they do not just need the original problem solved. They also need confidence restored.

That puts a lot more weight on the human interaction.

The employee needs to understand the issue, but they also need to understand the customer standing in front of them. They need to be able to explain what happens next, manage expectations, recognise when somebody is frustrated and take ownership without making the customer feel they are being passed around again.

That is where service skills become much more important.

A technically correct answer delivered badly can still damage the relationship. A slightly slower answer, handled with clarity and ownership, can leave the customer feeling far more confident in the service.

In technical service environments, recruitment has traditionally focused heavily on technical competence, which makes sense. You need people who understand the systems, products and services they support.

But if AI is helping with diagnosis, knowledge retrieval and routine requests, the human role starts to lean more heavily towards judgement, communication and problem-solving in situations where the answer is not obvious.

Can somebody explain a technical problem clearly to a non-technical customer?

Can they say that they do not yet know the answer without sounding dismissive?

Can they manage a situation where several teams are involved and the customer just wants somebody to take responsibility?

Can they recognise when a customer has lost confidence?

Can they make a sensible decision when the documented process does not quite fit the situation in front of them?

Those are important capabilities in any service role, but they become even more valuable when the simpler work is being handled elsewhere.

This is one of the reasons I think recruitment for service roles will need to evolve alongside automation.

If the role is going to involve more complex and more emotionally charged situations, it makes sense to assess how people respond to them before they join.

A technical assessment can tell you whether somebody understands the technology. A situational judgement exercise can tell you something about how they think when the situation is less straightforward.

For example, what would they do if a customer had already been let down twice and was now demanding an immediate answer? How would they handle another team failing to complete an action? What would they do if they realised they had given incorrect information? How would they balance following process with the wider customer impact?

You are not looking for somebody who magically knows your organisation’s exact way of working before they have joined.

You are looking for signs of judgement, ownership and communication.

Then, once they join, the business needs to build on that deliberately.

One thing I have always thought is that businesses are usually much better at teaching the technical parts of a role than the service parts.

A new employee may receive detailed training on systems, products, ticketing tools and processes, but far less guidance on what good customer communication actually looks like.

They may be told to “take ownership” or “put the customer first”, but those phrases can mean very different things depending on the person hearing them.

If service interactions are becoming more complex, those expectations need to be much clearer.

What does ownership look like in practice? How often should a customer be updated? What should somebody do if they do not yet have an answer? When should they escalate? How should they respond when a customer is angry? What does good communication look like when the resolution is taking longer than expected?

These are things that can be taught, observed and coached.

And they should be.

If businesses expect employees to handle the hardest parts of the customer journey, they need to give them the best possible chance of doing that well.

There is also an employee side to this that is easy to overlook.

If automation removes a lot of the straightforward work, the remaining workload may become more demanding.

Imagine a service role where most of the routine contacts have disappeared. What is left could be a constant stream of exceptions, complaints, escalations and customers who are already frustrated by the time they reach a person.

That can be exhausting if the service is not designed properly.

Employees need good training, but they also need clear processes, reliable information and sensible escalation routes. They need managers who can coach them through difficult situations and enough authority to solve problems without having to ask for permission at every step.

They also need technology that helps rather than gets in the way.

If somebody takes over from an AI interaction, they should be able to see what the customer has already asked, what information has already been provided and where the journey broke down. Making the customer repeat everything from the beginning is frustrating for the customer and inefficient for the employee.

A good handoff should make the human interaction easier for both sides.

There is a lot of discussion about how much work AI can take away from people.

I think there is just as much value in thinking about what people can do better once some of that repetitive work is removed.

Experienced employees notice things that are easy to miss in a workflow. They recognise when a customer’s frustration is really the result of several previous failures. They know when a process should be challenged. They can explain complexity in a way that makes sense to the person they are speaking to. They can recognise the commercial importance of a situation that might look fairly ordinary in a queue.

They can also rebuild trust.

That is especially important when the customer reaches a person because the automated route has already failed them.

In those moments, the human interaction becomes part of the value of the service.

As businesses introduce more automation, they will naturally measure how much work the technology is handling. They will look at things like automated resolution, reduced handling time and the number of contacts that no longer need a person.

Those are useful measures.

I would also want to understand what is happening to the work that remains.

Are the interactions reaching employees becoming more complex? Are complaints increasing or reducing? Are people spending more time dealing with emotionally difficult situations? Are employees getting the support they need? Are customers more frustrated by the time they reach a person, or is the handoff working well?

Those questions help you understand whether the service as a whole is improving.

Because the aim should not simply be to move as much work as possible away from people.

It should be to use technology where it genuinely helps and make sure the human part of the service is stronger where people still matter most.

As AI takes on more of the simple work, the role of the frontline employee will continue to change. Businesses that prepare for that change properly will need to think just as carefully about recruitment, training, coaching and service standards as they do about the technology itself.