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How to Solve These 3 Common Chatbot Implementation Challenges

How to Solve These 3 Common Chatbot Implementation Challenges

These are your three biggest chatbot implementation challenges – and how to solve them..

The chatbot market – set to be worth $9.4 billion as soon as 2024 – is seeing major growth as adoption grows and organizations realize the tangible business benefits of both AI chatbots and simple keyword chatbots.

But even with the availability of powerful chatbot building platforms, building a chatbot is not without its challenges and common pitfalls.

Here we look at some of the common chatbot implementation challenges (and how to solve them).

Chatbot implementation challenge 1: measuring value

There are compelling business benefits of adding a chatbot to your customer service mix. But all too often, organizations launch a chatbot for the sake of it – without clarifying the business need or objective the chatbot must support, and how the business will measure the chatbot’s success against that objective. This measurable framework needs to be baked in from the start.


Start with what you know. Research which support enquiries your team most commonly handles, and equip your chatbot to deal with these questions.

Doing this allows you to gradually move repetitive tasks from your customer support team on to your chatbot.

This achieves two key benefits – first, your customers will receive instant support, resulting in higher customer satisfaction. Next, your customer support team will receive fewer of the repetitive questions, which goes towards improving their efficiency and job satisfaction.

Once you equip your chatbot to handle low-value, high-volume enquiries, start gradually introducing progressively more complex customer support tasks. Before you know it, your chatbot will be supporting the teams that support your customers – which brings the most value.

You don’t launch a chatbot simply because your competitor did – you launch it to help you drive efficiencies in your customer service and get to your goals faster. Check out our related post on chatbot success metrics to learn more.

Chatbot implementation challenge 2: understanding customer intents

There are two main types of chatbots, based on how users interact with them. These are simpler keyword, and more complex conversational AI chatbots.

Tip: learn more about keyword and intent-based chatbots. 

With simpler keyword bots, it’s not too challenging to figure our user intents (i.e. the goal that user is trying to achieve). You serve them a list of options or keywords, and the user selects from this range of options.

With AI chatbots (or intent-based chatbots), however, things get a lot more challenging.

Getting machines to seamlessly interact with humans using natural language has been a challenge ever since Alan Turing practically invented modern computing.

We’re a little bit closer to that goal than we were in the 1960s with ELIZA, but businesses still face some key challenges with understanding and anticipating user intents.


To program a chatbot to talk to your customers, you need to know what your customers want to talk about. Learn about the most frequently asked questions your customers are asking. Then, research phrase variations of these questions. The expert recommendation is to train chatbots to understand at least fifty phrase variations.

This is no small task, of course – which is why most chatbot platforms have experts on hand to assist clients with building up databases of phrase variations.

Chatbot implementation challenge 3: seamless agent takeover

Chatbots are great, but they don’t have superpowers. While you’ll find they can, in time, handle most of your customer interactions, there are limits. So how do businesses deal with surprise customer questions their bot hasn’t been programmed to handle?


Seamless human agent takeover can save your bot from embarrassment, while providing superior customer service to customers with more complex queries.

Cloud contact centers can come equipped to deal with these situations by switching to a human agent best trained to handle specific customer question types. The interaction is kept on-channel, which preserves conversation continuity and context, resulting in positive customer experience – same channel, full context.

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