world, with the portals of many hugely popular brands embracing it invisibly (from the customer perspective) to deliver the service many of us have become accustomed to receiving.
Quote Cees Bos, CTO of Marval: “AI is not rocket science, it is
data science”
Artificial Intelligence in this context refers to the capability of a web portal to learn more and more from the data provided to it. So, by way of an example, as I buy more things from an online website, the next time I log in it will suggest to me things I might be interested in buying, and being the pushover I am, there’s a good chance I will, in so doing increasing my satisfaction with the site, but more crucially adding to the revenue of the site in question.
To allay the fears of those who are still unnerved by the concept, it should be emphasised that AI does not think on its own and is totally dependant upon the instructions it receives, telling it how to interpret the data it will interact with. From that point on it will only ever learn from the data it receives (what I have purchased, in the previous example) and will not ever create data of its own.
In contrast to the multitude of business rules that underpin the vast majority
of shift-left undertakings, AI is able to interpret a broad situation based upon
a single algorithm which makes it maintainable, manageable and hence
much more useable. And this is exactly what Marval’s Service Management
system does.
The Importance of Machine Learning (ML)
Much the same as with humans, in the world of AI, intelligence can only be developed and enhanced through training. Once an algorithm is built, it also needs to be trained and this is where machine learning comes in.
Machine learning requires data – lots of data – with the quality and performance of the algorithm typically being influenced by the amount of data fed into the system. More data leads to better training which leads to better AI output is the simple rule of thumb.
Data Challenges within Service Management
Whilst typically being considered to be “data rich”, IT departments regularly face a number of challenges with regards to gathering data for use in a Shift-Left or AI adoption. The usual suspects of siloed data, an overly complex IT landscape, overly bureaucratic process design will be familiar to many.