How to Test AI Technology in your Contact Centre

How to test AI technology in your contact centre – Henry Jinman from EBI.AI looks at the challenges of running proof of concept AI projects in contact centres, and how to extract the maximum business benefits and learning from them.

Getting into the right mindset

The first thing to understand is that piloting new technologies is nothing like conducting business as usual. The number one goal of business as usual is to keep the lights on; and the number one goal of piloting is to learn things.

For this reason, many businesses employ innovation and transformation teams whose sole function is to run pilot projects. While this team may operate alongside business as usual teams – and may even draw members from them – their remit is very different, and so is their approach.

Formulating your hypothesis

Piloting technology projects is a little like the scientific method in that it follows a cycle of hypothesis setting and proving. Learn to think in statements like: We want to prove that we could do this (automate 50% of inbound phone calls, for example).

Of course, you won’t prove that with a single pilot project, so you have to break it down into smaller goals that move you towards the objective incrementally. In this case you might analyse your incoming calls and realise that 10% of them are just routine queries – checking a balance, maybe – that could lend themselves to automation.

Start by proving that you could automate those interactions or transactions, and demonstrate what conditions apply; for example, what it would cost and how long it would take. Part of your pilot is to see how those conditions change with different approaches to the problem.

Start with real business challenges

There is no point spending time and money to automate something unless it A) really causes customers, staff, partners or suppliers a lot of pain, B) is a time sink or costs too much the way it’s done right now, or C) enables some fundamental new way of doing business that just wasn’t possible before.

The history of technology, particularly communications technology, shows that rapid progress on a mass scale really only occurs when a cheap and scalable solution comes along that impacts on at least two but preferably all three of these factors at the same time.

When it comes time to decide exactly what business impact you would like some new technology to have, choose something that really matters and which cannot be fixed in any other way. For example, a major financial services organisation decided to automate balance queries as these made up a significant percentage of its live agent calls. It later turned out that the reason for that was an underlying broken business process. It didn’t need a chatbot to solve the problem after all, so the whole project was based on a false premise.

Identify one or two challenges that technology could potentially help solve and which would make a significant impact if you did solve them. Of course, you cannot improve something unless you can measure it. For this reason, we always suggest picking a short-term goal that is as specific and narrow as possible while still giving a business benefit. It must also be a common enough problem that you can collect data on it quickly.



Henry Jinman is Commercial Director at EBI.AI

Learn some more lessons about AI failures like this with the EBI.AI whitepaper on ‘why AI fails’ to Download Click Here

For additional information on EBI.AI visit their Website or view their Company Profile

error: Content Protected