They are what they eat: Feeding the chatbots

2018 was the year of the chatbot. Organisations of all sizes embraced artificial intelligence to support an increasing array of functions across customer services, sales and marketing and began exploring new opportunities in cybersecurity and financial management systems.

Back in 2011, Gartner predicted that organisations will be capturing 85% of their customer contact using artificial intelligence (AI) technology by the year 2020. We also expect to see chatbot technology being developed in the hands of the organisation, enabling tailoring to specific workplaces and integration across multiple bots that connect automated functions across the business.

For example, in an online retail company, the chatbots that function in front line sales, customer service, purchasing, logistics and all the way through to delivery will seamlessly interact.

Ideally, companies want the chatbot to answer as many questions and handle as many scenarios as accurately as possible from the word go. And therein lies the challenge, because AI is a learning technology not a plug-and-play solution.

Think of AI as an iceberg. The tip above the water is the chatbot front end, happily receiving customer questions and delivering information in response. But 90% of its working is hidden away under the waterline, powered by the capabilities of cloud computing.

AI programmes need to consumer a LOT of data to fill the iceberg that sits below. That data is processed and digested to find patterns, set up algorithms and make predictions to enable fast and accurate output. These patterns and predictions are only as good as the quantity and quality of data that the chatbot has been fed from the outset. And the better that the chatbots connect with live applications in use across the business, the better their frame of reference.

A chatbot can only be as good as the data it is fed.

Cloud technologies have enabled organisations to amass enormous amounts of information. The twin challenges faced are, firstly, integration between enterprise platform and chatbot itself, and secondly, making connections between multiple enterprise applications to feed in all of the information it needs to draw on. Achieving a fully integrated enterprise input is key to supporting a highly effective and intelligent output.

Integrating across multiple data sources that are not necessarily designed to be compatible can create a major headache for organisations, particularly those that are seeking to self-install an AI solution rather than outsource IT management. Besides, developers and companies alike want to be spending their money on polishing the face of the chatbot, not on what it is eating for breakfast.

Also native to the cloud, integration platform as a service (iPaas) can enable compatibility across the wealth of data that the enterprise has collected to feed and educate the chatbot on real-life information. Whether the cloud is on premises or outsourced, iPaaS can connect the chatbot to any data sources within the enterprise system that are relevant to its function – or to other chatbots with which it is designed to work, side by side.

Connecting data sets within the enterprise enables the chatbot to create patterns and predictions based on real information that has optimum relevance to the environment in which it will be expected to function. And with the right iPaaS, any organisation can feed their chatbot using integration built on simple and intuitive dashboards to provide secure, clean data rather than requiring deeply technical coding capabilities.

They are what they eat. So, when inviting a chatbot to join your organisation, don’t let it go hungry!

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