Is model operationalization really necessary? Every single day, companies spend millions of dollars on data scientists, software engineers and AI applications. Most of these companies have no strategy for making the most of their investment. They don’t know how to leverage their technology investment for maximum impact on revenue or profits.

AI is fast becoming critical to business and IT applications and operations. Organizations have been investing in artificial intelligence capabilities for years to stay competitive, are hiring the best data scientist teams and are investing more and more in artificial intelligence and machine learning systems. However, implementing AI / ML models is not easy and the risk of failure is just around the corner. A solid methodology is needed to reduce this risk and enable companies to succeed.

One of our great scientists, Stephen Hawking, said that “The development of full Artificial intelligence could spell the end of the human race”.Artificial Intelligence is a very hot topic actually nowadays. We always wonder what else it can do, how it is a very interesting, strange, and special topic for our generation. Neural Networks are one of the topics in AI, which gives us the same feeling.

Let’s start with the basics. Digital transformation solutions not only bring automation in various processes but also provide actionable insights for making informed decisions. Whatever digital transformation in manufacturing offers, one thing is for sure, it can change the shape of the manufacturing sector. In a PwC survey, out of 2000 manufacturing enterprises, a whopping 86% expect to gain from reduced costs and increased revenue through digitization in the next five years.  

As a society we’re on the cusp of experiencing a proliferation of IoT sensors and devices brimming with intelligence, designed to improve our quality of life in every way imaginable. But with all great opportunities comes significant challenges. For IoT this includes: Security and regulation, Data storage and management, and Bandwidth.

It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services.

These are some of the steps an IT team needs to take to ensure that the post-COVID era is safe without harming the business in terms of security. Let's take a look at the below advice.

Giuliano Liguori highlights Italy as an example and warning that pushing ahead with smart city plans should ensure access for the worst off, on an individual and municipal level, as top priority.