Inform

Measure and Optimise

Inform

Measure and Optimise

Now that you have implemented AI within your organisation, you can really start to reap the rewards and – most importantly – measure and monitor the impact they are having on your operations. There are many ways that AI can play a role here, either in isolation or in concert with human effort.

AI can breathe relief into a stretched and stressed customer service environment, automating the collation and analysis of the high volumes of data generated each day in order to identify new opportunities for improvement. For example, providing status updates, either proactively or on demand, represents low-hanging fruit for a large number of organisations. Whether this is a relatively simple task, like the status of an order, or something more complex, like the status of an insurance claim, provided the salient information is available and can be translated into an actionable form, a surprisingly large amount of transactional “noise” can be automatically dealt with.

The use of generative AI presents a perfect example of how AI can “help the human”. If all conversations are transcribed in real time, then why not automate the wrap at the end of a call by having the AI generate a summary of the conversation and post it to the Customer Relationship Management (CRM) system? If the advisor must access several diverse data sources to determine an answer to a customer’s question, why not have generative AI collate them? Or if the advisor needs to be quickly appraised of a customer’s case history, why not let generative AI to provide a summary and even recommend an appropriate next step?

With the new breed of AI tools available, real-time operational insight is more powerful than ever. At the start of this whitepaper, we started the AI journey by performing a large-scale machine learning analysis of conversations to establish trends and patterns at a macro level. We can build on this by tracking patterns, trends, and sentiment across unstructured conversations on an ongoing basis. It is even possible to apply alerts in real time, whenever action is required as quickly as possible, e.g. identifying a vulnerable customer, or a potential concern around employee well-being.

We would recommend continuous monitoring of all KPIs that were identified at the start of the AI project, so that a realistic gauge of progress can be established. Moreover, consider creating custom reports that specifically measure how well your AI is performing. In the contact centre industry, we measure every element of human performance, and the same rigour should be applied to machines. What is the percentage of partial and/or full automation obtained across all channels, for example? What is the success rate? How efficient have deflection efforts been, such as in the case of status updates? These should be discussed and agreed in partnership with technology partners and regularly reviewed as joint measures of success.

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