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5 Tips for Avoiding AI Contact Centre Implementation Pitfalls

In the long-term, there’s no doubt that AI will be used as a key part of handling customer interactions in most businesses, but the question is: how can we avoid the pitfalls that can come with the implementation of a poorly-understood, heavily hyped technology? 

The use of AI should be focused on use cases where the AI does a better job than a human, whether that’s being quicker, more accurate, improving first contact resolution, being available 24/7, or able to see patterns in data that no person could find. 

AI is going to be easy to get wrong – elevated expectations, limited reference sites, murky data, the risk of customer pushback…the list goes on.

With that in mind, here’re 5 tips on successful AI implementations:

1) AI is not a silver bullet.

Expectations of what the AI implementation can actually achieve must be closely managed. There may be the expectation from senior management that headcount will immediately begin to drop, but in the majority of instances this is not why AI is being implemented. Focusing on a tightly-defined use case would reduce the risk of implementation delays and expecting too much, too soon from AI. However it is important not to see even a relatively modest implementation of AI as being a point solution, rather than a single step in a long-term strategy

2) If it isn't broken, don't fix it.

There are areas of customer interaction where AI cannot come close to matching a human agent. Machines simply are incapable of feeling empathy, and even sophisticated sentiment detection at its best comes close to what an ordinary human being can do naturally. Use cases for AI should be focused upon areas where there is a gap in functionality, rather than trying to replace something that isn’t broken

3) AI skills are few and far between.

AI in the contact centre is relatively new, and with it being so popular, there is a shortage of skills, support and resource within the industry as a whole. In-house technology departments are less likely to have capability, expertise and experience, meaning that the risk of suboptimal deployment and the requirement for third-party assistance may be higher than with other more traditional IT implementations

4) Clean your data.

Businesses' data assets must be in place before implementation of AI, as this is a technology that relies upon having large, clean pools of data that it can be trained on and learn from. Without this in place, it will be virtually impossible for any AI implementation to get close to its potential. The preparation of data will involve having an organized, non-siloed data architecture, a consistent data vocabulary, the means of accessing this data securely and quickly, and the ability to access other pieces of relevant information (e.g. customer-related metadata) in order to include greater context. Without this, it will be difficult for a machine learning process to train itself effectively, or for a chatbot to be able to use all of the relevant data in order to reach a correct conclusion

5) Don't trap customers.

Always have a well-designed and clear path out of the AI-enabled service and onto a human agent. Trapping a frustrated customer in a self-service session runs the risk not only of training them not to use self-service again, but also poisons the well for other companies using AI. This is what happened in the early days of email support – customers would try to communicate with one or two businesses via email, and when they didn’t receive a response for days (or ever), they decided that the whole email support channel was unworthy of their time. It took many years to change this perception and to get them to trust the channel again.

The eventual overall roadmap of the contact centre industry will lead to significant levels of AI involvement in customer contact, but in the foreseeable future this is likely to improve self-service and assisting agents, rather than having seismic effects on headcount. An AI implementation whose success is to be measured mainly by the reduction in HR resource is unlikely to do well.

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