Artificial Intelligence: Roadblocks and Pitfalls

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The Challenges for Implementing AI

In any technology implementation there will be risks of failure: with AI covering a vast amount of territory and with the potential to be misunderstood by business owners, planning and expectations must be managed very careful.

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 strategic step

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

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

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

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

There have been a lot of media scare stories about AI and robots making people unemployed.

It is important to emphasize to agents that any AI implementation is about making their jobs more interesting and effective by allowing AI to handle simple and repetitive requests, as well as providing them with more of the information that they need to serve the customer more effectively. While agents are experts on answering customer queries, it may be too much to ask them to spend significant amounts of their time on contact curation as well. As such, businesses should consider how to incentivize power user experts (both inside and outside the enterprise) to help with knowledge management and problem resolution

In the AI world, knowledge management is not something that is a part-time job or that can be handled by amateurs.

Consider developing more full-time, expert roles to support knowledge bases and to enable understanding of data models and flows across the entire enterprise. AI experts have to understand both data and also the real-life business / customer issues, and this resource can be difficult to find.