It is impossible to ignore . Discussion and debate about and Machine Based (MBL) is everywhere you turn at the moment, with software vendors claiming their status as -powered, and giants of the tech world forming coalitions to fast-track the development of intelligence technologies.
There is no doubt that the evolution of technology for enterprise B2B is set to have a profound impact on the lives of those in commercial teams, but what is it really? And what are the likely ways that it could affect the every-day working practices of B2B professionals both today, and in the longer-term?
Man and Machine Communicating Naturally
Humans interacting directly with software using an intelligent “natural” mechanism. Sounds like something out of science fiction (man and bot communicating and working in unison), but modern algorithms are already smart enough and fast enough to recognise word patterns and extract meaning or “intent”. Such models built into business applications will transform previously manual tasks in the most intuitive and natural ways – ask a question, get an answer; prompt an action get a response. When humans find a way of interacting with machines suddenly everything gets easier. For commercial teams this means frictionless access to intelligence of greatest value – golden nuggets which will improve understanding of the customer or prospect and their drivers at any given moment, delivered in the most human way possible for swift, empathetic, direct action. In time these powered applications may even act as a proxy, communicating with customers directly in human-like ways, answering their questions, delivering content and generating leads.
Predict and Disrupt
As more and more information, opinion and hard-data is generated and held in a myriad of places, the sheer volume and diversity of information can be overwhelming, making keeping up a challenge, let alone predicting future needs. As , mathematical modelling, and machine technologies collide with “big data” it opens up a whole new realm of possibilities in terms of predicting where the customer journey will go next – the actions to be taken that will influence decision making and disrupt the market now, next week, and in the months to come. When we know exactly what to do next we can better predict success and avoid failure, making for more sophisticated sales strategies, campaigns and product/service development. As time goes by and the volume of results data improves precision and predictive capacity, these models will get better still, enabling faster and more accurate predictions of customer needs, pain, market challenges and opportunities, before customers themselves even realise what lies ahead. […]