Some exciting innovations in the field of AI (artificial intelligence) are expected this year. Will the individual communication with customers and colleagues fall by the wayside or will they even profit significantly from it in the end?
Artificial Intelligence Trends 2018
This year’s developments focus on deep neural networks (DNS), which imitate the learning behaviour of the human brain. Thus they form an independent process for evaluating data not only in large quantities, but also for formulating solutions adapted to situations and influences. Simply put, the system learns.
So far, AI has been very appealing for many users, but not very comprehensible. Entrepreneurs however are increasingly preparing for the market launch of mature AI modules in order to optimally design and adapt production and marketing-related processes in the future. Mostly, expectations are to obtain more information from collected data, e.g. on customer behaviour, marketing strategies, logistical tasks and internal company structures.
- So-called capsule networks are a subcategory of DNS and mainly process visual information. New in these systems is the recognition of hierarchies, whereby the developers hope for a more exact classification of the analyzed data.
- Software equipped with Deep Reinforcement Learning (DLR) learns by observation, interaction with the environment and reward. This offers decision levels, i.e. strategists, some profitable support in the future.
- General Adversarial Networks (GAN), on the other hand, compete against each other in a digital scenario, creating falsified data and then differentiating between them. In this way, the programs learn independently and are able to better protect against cyber attacks in the future.
Despite the numerous and very promising prospects, AI still has some problems to overcome. One of these is the lack of availability of suitable data necessary for deep learning. At this point it is necessary to communicate with the target group or the information carriers. There is important input, e.g. from SMS surveys, email newsletters, etc. On the one hand this can be controlled via gateway with HTTP APIs. On the other hand the results can be transferred to the own AI sortware via data export and further processed there.
Communication = data acquisition?
Each entry into a search engine, newsletter registration, linking, registration, time stamp etc. provides valuable data. However, this alone does not “feed” the artificially learning networks enough to gain specific analyses, for example about buying behaviour, personal preferences, interests or needs.
Amazon, for example, evaluates customer ratings using artificial intelligence in order to suggest suitable products and to implement dynamic pricing. This means that the Group is dependent on authentic feedback from customers in order to supply the AI software with suitable data.
In contrast to robots or complex systems that perform tasks according to fixed specifications, such AI concepts are subject to the constant influence of new factors. As a result, they must function beyond the scope of dehumanized interactions and not – as is often accused – disrupt communication, but enrich it.
Benefits for companies
How do artificial intelligence and communication ideally influence each other?
Essential information can be obtained from any form of communication, be it by email, SMS, surveys, search queries, account registrations, newsletter registrations, etc. It is important to process and analyze these information in a meaningful way. Thanks to Deep Learning, Artificial Intelligence can collect detailed data and show very characteristic solution approaches for further evaluation. Entrepreneurs only need to know how to use them strategically.
Ideally at this point, the information obtained from the customer should be used to provide the customer with useful information, e.g. on product selection, new publications, taste, size, price segmentation and much, much more.
Internally, on the other hand, employee communication can be significantly improved and capacities can be used according to the needs of the situation. Thanks to AI, the entire workflow becomes even more fluid.