Imago Techmedia Ltd is registered in England and Wales under Company No. 04865455. VAT No. GB 843 8456 01
Registered Office: Bedford House, Fulham Green, 69-79 Fulham High Street, London, SW6 3JW, United Kingdom
Business Address: Imago Techmedia, 2C Bedford House, Fulham Green, 69-79 Fulham High Street, London, SW6 3JW, United Kingdom
Imago Techmedia is a subsidiary of Clarion Events Limited
Capitalising on AI and Contextual Communications - Part 2
Thursday 03 May 2018
Rob Pickering, CEO at IPCortex
Delivering a positive customer experience is an important differentiator for many businesses, and AI and machine learning are now poised to transform business communications, introducing automation to make it much more effective. Many businesses are already automating some of their interactions, for example customer service centres are frequently using chatbots or automated assistants to help direct calls or answer the most basic enquiries.
Machine learning and AI in business communications
Where a business can record a conversation or interaction and its outcome, machine learning could be used to help determine whether it was an effective communication or not, and provide ways to make it more effective if needed. This machine-based future isn’t far off, and to prepare, businesses should start capturing, classifying and tagging their business communication data today. Building up a valuable database of different sorts of conversations, interactions and outcomes will dramatically improve the value of machine learning when it is introduced because there are more opportunities for the machine to identify patterns using actionable insights. These thousands of data points will become the basis of automated systems in the future of your own unique business and context.
How does machine learning add value?
There are two ways that machine learning, or automated assistants, will function in the workplace: firstly programmed assistants or chatbots - already being used by some businesses, but increasingly prevalent - that are used for first line customer contact, dealing with the most common queries and making suggestions according to what’s in their database.
Secondly, we have applications where machine learning can learn intelligently, based on real life data, to give usable and consumable outcomes. This is where machines can not only comprehend interactions and provide intelligent responses, but can also understand intonation and sentiment direct from voice recordings, learning even more about what’s going on at the customer end of the transactions.
In 2018 we’ll see more business communication solutions start to weave in context to every interaction, and the companies that are going to gain the most from this trend are those who make context relevant and applicable for businesses as individuals (see more on this in our previous blog post). In our next blog post, we’ll look at how businesses can actually go about combining contextual communications with machine learning and AI.
Once these steps have been put is in place, the possibilities and benefits of machine learning and AI in business communications are significant.