AI Business did an interview with Rulex’s Andrea Ridi about the areas of challenge in implementing AI, and how he believes the professional services industry will change by adopting the technology. Rulex provides revolutionary AI software that enables business and process experts to embed automated real time predictive intelligence in applications, infrastructure, and IoT edge apps.  Rulex’s proprietary machine learning algorithms automatically learn and extract predictive if-then logical rules from raw data with no need for speculative data exploration or iterative scientific experimentation.  Unlike the math-based predictive models produced by conventional machine learning algorithms, Rulex’s logic-based models are compact and efficient, and can be easily used for making predictions on highly distributed systems and low cost, low power IoT devices.  With the Rulex platform, business analysts and solution developers can easily create a new class of advanced applications for automated decision making, self-managing networks, and real-time native prediction on IoT edge devices.

andrearidiAndrea Ridi is a serial entrepreneur that has founded a number of ultra-high-tech start-ups in Italy since 1998, spanning from Internet-of-things to nanotechnologies to machine learning. In 2007 he started the first Italian machine learning company, Impara, recently Rulex, Inc. in Boston. In Rulex, he drives the business and innovation strategies at a global level.Andrea holds a M.S. in Physics from the University of Genoa, Italy.

How do you believe AI will impact business overall and in what ways?


“Much of the focus on AI these days is on it’s potential for eliminating jobs through automation, and that potential is undeniable and already being shown in Manufacturing, Customer Service, and other areas”, Ridi begins.

“However, the real promise of AI is in its potential to enable humans – executives, managers, planners, and workers – to work in a more intelligent fashion, working faster, making fewer mistakes, and applying themselves to the highest value tasks”, Ridi

This will save money and reduce risk, but it can also enable fundamental changes to the business processes for dramatically improved competitiveness, customer satisfaction, and growth in market share, Ridi explains to us.

Looking at areas such as machine learning through to image recognition, what do you consider the main challenges in adopting AI technologies?


Ridi highlights what he deems as the three distinct areas of challenge in adopting AI: input, algorithm, and prediction. “Getting the right learning data, choosing the right way to learn from that data, and using that learning to make the best prediction-based decisions”.

“Many AI projects fail because the company has the data, but is lacking a solid predictive objective tied to a business goal”, Ridi says.

“Others fail because the company knows what they want to predict, but cannot get the data they need to do that”. Furthermore, Ridi mentions that in both cases, many companies compound the problem by trying to use various algorithms either to figure out what to predict or to find the best data for prediction, what Ridi calls an “inside-out” approach.

“The rapid expansion in the relevant technologies themselves only makes the problem worse”, he continues. He mentions how new low-cost database technologies for conventional business data and so-called “unstructured” data, including text, images, and video, have enabled companies to amass huge quantities of Big Data.

“However, they then find themselves saying, “What can that data tell us and how can we get an ROI from it?”, Ridi says, following up with: “And a rapidly growing array of algorithms and data visualisation tools have enabled companies to formulated sophisticated prediction concepts, but then they find themselves saying, “Where is the data we need and how can we get it?”

Ridi finds that most AI successes start with an interdisciplinary team, comprising experts in the business, the applications, and the data, who can work together to identify a valuable, achievable prediction objective, to identify and qualify the appropriate available input data, and to select the best algorithm for learning from the selected data to provide the desired prediction.

Ridi have previously attended our AI Summit, so we were eager to know what his key takeaways from the summit was, and in what ways his experience of the event might impact his approach to business at Rulex Inc.?


“This was one of the better AI conferences we have seen”, Ridi said. “From the presentation from the podium to the coffee and cocktail chatter, there was a lot of substance and actionable insight to be found”.

“The conference [the AI Summit] was very good for helping us get a high definition picture of the emerging marketing and a vision of the best business practices of the future”.

So what part does artificial intelligence have to play at Rulex Inc. in the short- and long- term then?

“Rulex provides a revolutionary AI platform that has been applied to use cases across many industries, from self-correcting ERP data to predictive patient monitoring in health care, so AI is our past, present and future”, Ridi answered.

Looking beyond your own business, how do you think the professional services industry will change by adopting AI?


“Right now, professional services is focused on finding and deploying the data science skills in programming, mathematics, statistics, and visualisation that are needed to get useful results from conventional machine learning technologies”, Ridi says.

“This skills skew is due to the fact that the existing algorithms require speculative data exploration and iterative learning experimentation which can take days months to produce an acceptable result”, Ridi explains, saying: “However, there is a new generation of emerging algorithms, ours included, that make a lot of that unnecessary through more effective automation of the machine learning process”.

“Our platform is designed to be used by business, data, and process experts to implement automated business decision making.  The software is the data scientist. For professional services organisations, this means that they will be able to focus on higher value service offerings like business process optimisation and transformation”.